mirror of
https://github.com/jamiepine/voicebox.git
synced 2026-09-27 06:05:14 -07:00
Compare commits
6
Commits
| Author | SHA1 | Date | |
|---|---|---|---|
 Jamie Pine
|
83906c6c4b | ||
|
Jamie Pine
|
65f132e9c2 | ||
|
Jamie Pine
|
c211e52382 | ||
|
Jamie Pine
|
7c093130c6 | ||
|
Jamie Pine
|
8ffd5bc008 | ||
|
Jamie PineandClaude Sonnet 4.5
|
2542f64e1b |
+1
-1
@@ -1,5 +1,5 @@
|
||||
[bumpversion]
|
||||
current_version = 0.1.13
|
||||
current_version = 0.1.12
|
||||
commit = True
|
||||
tag = True
|
||||
tag_name = v{new_version}
|
||||
|
||||
@@ -1,46 +0,0 @@
|
||||
# Version control
|
||||
.git
|
||||
.github
|
||||
.gitignore
|
||||
|
||||
# Desktop-only (not needed in web container)
|
||||
tauri/
|
||||
landing/
|
||||
docs/
|
||||
mlx-test/
|
||||
scripts/
|
||||
|
||||
# Dependencies & build artifacts (rebuilt in Docker)
|
||||
node_modules/
|
||||
__pycache__/
|
||||
*.pyc
|
||||
*.pyo
|
||||
*.egg-info/
|
||||
dist/
|
||||
build/
|
||||
*.spec
|
||||
|
||||
# Data (will be bind-mounted)
|
||||
data/
|
||||
backend/data/
|
||||
|
||||
# IDE & OS
|
||||
.vscode/
|
||||
.idea/
|
||||
*.swp
|
||||
*.swo
|
||||
.DS_Store
|
||||
Thumbs.db
|
||||
|
||||
# Config files not needed in container
|
||||
biome.json
|
||||
.biomeignore
|
||||
.bumpversion.cfg
|
||||
.npmrc
|
||||
Makefile
|
||||
CHANGELOG.md
|
||||
CONTRIBUTING.md
|
||||
SECURITY.md
|
||||
LICENSE
|
||||
README.md
|
||||
backend/README.md
|
||||
@@ -1,73 +0,0 @@
|
||||
name: Build CUDA Backend
|
||||
|
||||
on:
|
||||
workflow_dispatch:
|
||||
push:
|
||||
tags:
|
||||
- "v*"
|
||||
|
||||
jobs:
|
||||
build-cuda-windows:
|
||||
runs-on: windows-latest
|
||||
permissions:
|
||||
contents: write
|
||||
|
||||
steps:
|
||||
- uses: actions/checkout@v4
|
||||
|
||||
- name: Setup Python
|
||||
uses: actions/setup-python@v5
|
||||
with:
|
||||
python-version: "3.12"
|
||||
cache: "pip"
|
||||
|
||||
- name: Install Python dependencies
|
||||
run: |
|
||||
python -m pip install --upgrade pip
|
||||
pip install pyinstaller
|
||||
pip install -r backend/requirements.txt
|
||||
|
||||
- name: Install PyTorch with CUDA 12.1
|
||||
run: |
|
||||
pip install torch --index-url https://download.pytorch.org/whl/cu121 --force-reinstall --no-deps
|
||||
pip install torchaudio --index-url https://download.pytorch.org/whl/cu121
|
||||
|
||||
- name: Verify CUDA support in torch
|
||||
run: |
|
||||
python -c "import torch; print(f'CUDA available in build: {torch.cuda.is_available()}'); print(f'CUDA version: {torch.version.cuda}')"
|
||||
|
||||
- name: Build CUDA server binary
|
||||
shell: bash
|
||||
working-directory: backend
|
||||
run: python build_binary.py --cuda
|
||||
|
||||
- name: Split binary for GitHub Releases
|
||||
shell: bash
|
||||
run: |
|
||||
python scripts/split_binary.py \
|
||||
backend/dist/voicebox-server-cuda.exe \
|
||||
--output release-assets/
|
||||
|
||||
- name: Upload split parts to GitHub Release
|
||||
if: startsWith(github.ref, 'refs/tags/')
|
||||
uses: softprops/action-gh-release@v1
|
||||
with:
|
||||
files: |
|
||||
release-assets/voicebox-server-cuda.part*.exe
|
||||
release-assets/voicebox-server-cuda.sha256
|
||||
release-assets/voicebox-server-cuda.manifest
|
||||
draft: true
|
||||
env:
|
||||
GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
|
||||
|
||||
- name: Upload binary as workflow artifact (for testing)
|
||||
uses: actions/upload-artifact@v4
|
||||
with:
|
||||
name: voicebox-server-cuda-windows
|
||||
path: backend/dist/voicebox-server-cuda.exe
|
||||
retention-days: 7
|
||||
|
||||
# Linux CUDA build can be added later with:
|
||||
# build-cuda-linux:
|
||||
# runs-on: ubuntu-22.04
|
||||
# ...
|
||||
@@ -1,63 +0,0 @@
|
||||
name: Build Windows
|
||||
|
||||
on:
|
||||
workflow_dispatch:
|
||||
|
||||
jobs:
|
||||
build-windows:
|
||||
permissions:
|
||||
contents: write
|
||||
runs-on: windows-latest
|
||||
|
||||
steps:
|
||||
- uses: actions/checkout@v4
|
||||
|
||||
- name: Setup Python
|
||||
uses: actions/setup-python@v5
|
||||
with:
|
||||
python-version: "3.12"
|
||||
cache: "pip"
|
||||
|
||||
- name: Install Python dependencies
|
||||
run: |
|
||||
python -m pip install --upgrade pip
|
||||
pip install pyinstaller
|
||||
pip install -r backend/requirements.txt
|
||||
|
||||
- name: Build Python server
|
||||
shell: bash
|
||||
run: |
|
||||
cd backend
|
||||
python build_binary.py
|
||||
|
||||
PLATFORM=$(rustc --print host-tuple)
|
||||
mkdir -p ../tauri/src-tauri/binaries
|
||||
cp dist/voicebox-server.exe ../tauri/src-tauri/binaries/voicebox-server-${PLATFORM}.exe
|
||||
echo "Built voicebox-server-${PLATFORM}.exe"
|
||||
|
||||
- name: Setup Bun
|
||||
uses: oven-sh/setup-bun@v2
|
||||
|
||||
- name: Install Rust stable
|
||||
uses: dtolnay/rust-toolchain@stable
|
||||
|
||||
- name: Rust cache
|
||||
uses: swatinem/rust-cache@v2
|
||||
with:
|
||||
workspaces: "./tauri/src-tauri -> target"
|
||||
|
||||
- name: Install dependencies
|
||||
run: bun install
|
||||
|
||||
- uses: tauri-apps/tauri-action@v0
|
||||
env:
|
||||
GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
|
||||
with:
|
||||
projectPath: tauri
|
||||
tagName: v__VERSION__
|
||||
releaseName: "voicebox v__VERSION__ (test build)"
|
||||
releaseBody: "Test build for audio export fix"
|
||||
releaseDraft: true
|
||||
prerelease: true
|
||||
args: ""
|
||||
includeUpdaterJson: false
|
||||
@@ -4,7 +4,7 @@ on:
|
||||
workflow_dispatch:
|
||||
push:
|
||||
tags:
|
||||
- "v*"
|
||||
- 'v*'
|
||||
|
||||
jobs:
|
||||
release:
|
||||
@@ -14,22 +14,22 @@ jobs:
|
||||
fail-fast: false
|
||||
matrix:
|
||||
include:
|
||||
- platform: "macos-latest"
|
||||
args: "--target aarch64-apple-darwin"
|
||||
python-version: "3.12"
|
||||
backend: "mlx"
|
||||
- platform: "macos-15-intel"
|
||||
args: "--target x86_64-apple-darwin"
|
||||
python-version: "3.12"
|
||||
backend: "pytorch"
|
||||
- platform: "ubuntu-22.04"
|
||||
args: ""
|
||||
python-version: "3.12"
|
||||
backend: "pytorch"
|
||||
- platform: "windows-latest"
|
||||
args: ""
|
||||
python-version: "3.12"
|
||||
backend: "pytorch"
|
||||
- platform: 'macos-latest'
|
||||
args: '--target aarch64-apple-darwin'
|
||||
python-version: '3.12'
|
||||
backend: 'mlx'
|
||||
- platform: 'macos-15-intel'
|
||||
args: '--target x86_64-apple-darwin'
|
||||
python-version: '3.12'
|
||||
backend: 'pytorch'
|
||||
# - platform: 'ubuntu-22.04'
|
||||
# args: ''
|
||||
# python-version: '3.12'
|
||||
# backend: 'pytorch'
|
||||
- platform: 'windows-latest'
|
||||
args: ''
|
||||
python-version: '3.12'
|
||||
backend: 'pytorch'
|
||||
|
||||
runs-on: ${{ matrix.platform }}
|
||||
|
||||
@@ -53,7 +53,7 @@ jobs:
|
||||
uses: actions/setup-python@v5
|
||||
with:
|
||||
python-version: ${{ matrix.python-version }}
|
||||
cache: "pip"
|
||||
cache: 'pip'
|
||||
|
||||
- name: Install Python dependencies
|
||||
run: |
|
||||
@@ -66,24 +66,24 @@ jobs:
|
||||
run: |
|
||||
pip install -r backend/requirements-mlx.txt
|
||||
|
||||
# - name: Install PyTorch with CUDA (Windows only)
|
||||
# if: matrix.platform == 'windows-latest'
|
||||
# run: |
|
||||
# pip install torch --index-url https://download.pytorch.org/whl/cu121 --force-reinstall --no-deps
|
||||
# pip install torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121
|
||||
|
||||
- name: Build Python server (Linux/macOS)
|
||||
if: matrix.platform != 'windows-latest'
|
||||
run: |
|
||||
chmod +x scripts/build-server.sh
|
||||
./scripts/build-server.sh
|
||||
|
||||
- name: Build Python server (Windows)
|
||||
- name: Build CPU Python server (Windows)
|
||||
if: matrix.platform == 'windows-latest'
|
||||
shell: bash
|
||||
run: |
|
||||
cd backend
|
||||
python build_binary.py
|
||||
|
||||
echo "Installing CPU-only PyTorch..."
|
||||
pip uninstall -y torch torchvision torchaudio
|
||||
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu
|
||||
|
||||
echo "Building CPU server binary..."
|
||||
python build_binary.py cpu
|
||||
|
||||
# Get platform tuple
|
||||
PLATFORM=$(rustc --print host-tuple)
|
||||
@@ -91,9 +91,31 @@ jobs:
|
||||
# Create binaries directory
|
||||
mkdir -p ../tauri/src-tauri/binaries
|
||||
|
||||
# Copy with platform suffix
|
||||
# Copy CPU version (default for installer)
|
||||
cp dist/voicebox-server.exe ../tauri/src-tauri/binaries/voicebox-server-${PLATFORM}.exe
|
||||
echo "Built voicebox-server-${PLATFORM}.exe"
|
||||
echo "Built CPU server: voicebox-server-${PLATFORM}.exe (~500MB)"
|
||||
|
||||
- name: Build CUDA Python server (Windows)
|
||||
if: matrix.platform == 'windows-latest'
|
||||
shell: bash
|
||||
run: |
|
||||
cd backend
|
||||
|
||||
echo "Installing CUDA PyTorch..."
|
||||
pip uninstall -y torch torchvision torchaudio
|
||||
pip install torch --index-url https://download.pytorch.org/whl/cu121 --force-reinstall --no-deps
|
||||
pip install torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121
|
||||
|
||||
echo "Building CUDA server binary..."
|
||||
python build_binary.py cuda
|
||||
|
||||
# Get platform tuple
|
||||
PLATFORM=$(rustc --print host-tuple)
|
||||
|
||||
# Copy CUDA version for separate upload
|
||||
mkdir -p cuda-release
|
||||
cp dist/voicebox-server-cuda.exe cuda-release/voicebox-server-cuda-${PLATFORM}.exe
|
||||
echo "Built CUDA server: voicebox-server-cuda-${PLATFORM}.exe (~3GB)"
|
||||
|
||||
- name: Setup Bun
|
||||
uses: oven-sh/setup-bun@v2
|
||||
@@ -106,7 +128,7 @@ jobs:
|
||||
- name: Rust cache
|
||||
uses: swatinem/rust-cache@v2
|
||||
with:
|
||||
workspaces: "./tauri/src-tauri -> target"
|
||||
workspaces: './tauri/src-tauri -> target'
|
||||
|
||||
- name: Install dependencies
|
||||
run: bun install
|
||||
@@ -142,7 +164,7 @@ jobs:
|
||||
with:
|
||||
projectPath: tauri
|
||||
tagName: v__VERSION__
|
||||
releaseName: "voicebox v__VERSION__"
|
||||
releaseName: 'voicebox v__VERSION__'
|
||||
releaseBody: |
|
||||
## What's Changed
|
||||
See the assets below to download and install this version.
|
||||
@@ -150,11 +172,41 @@ jobs:
|
||||
### Installation
|
||||
- **macOS (Apple Silicon)**: Download the `aarch64.dmg` file - uses MLX for fast native inference
|
||||
- **macOS (Intel)**: Download the `x64.dmg` file - uses PyTorch
|
||||
- **Windows**: Download the `.msi` installer
|
||||
- **Windows**: Download the `.msi` installer - includes CPU-only inference (~500MB)
|
||||
- **Linux**: Download the `.AppImage` or `.deb` package
|
||||
|
||||
### NVIDIA GPU Acceleration (Windows)
|
||||
Windows users with NVIDIA GPUs can enable CUDA for 4-5x faster inference:
|
||||
1. Install the app normally (CPU version included in installer)
|
||||
2. The app will detect your GPU and offer to download CUDA support automatically
|
||||
3. Or manually download: [voicebox-server-cuda-x86_64-pc-windows-msvc.exe](https://downloads.voicebox.sh/cuda/__VERSION__/voicebox-server-cuda-x86_64-pc-windows-msvc.exe) (~2.4GB)
|
||||
|
||||
The app includes automatic updates - future updates will be installed automatically.
|
||||
releaseDraft: true
|
||||
prerelease: false
|
||||
args: ${{ matrix.args }}
|
||||
includeUpdaterJson: true
|
||||
|
||||
- name: Upload CUDA server to Cloudflare R2 (Windows only)
|
||||
if: matrix.platform == 'windows-latest'
|
||||
env:
|
||||
AWS_ACCESS_KEY_ID: ${{ secrets.R2_ACCESS_KEY_ID }}
|
||||
AWS_SECRET_ACCESS_KEY: ${{ secrets.R2_SECRET_ACCESS_KEY }}
|
||||
R2_ENDPOINT: ${{ secrets.R2_ENDPOINT }}
|
||||
run: |
|
||||
# Install AWS CLI if not available
|
||||
pip install awscli
|
||||
|
||||
# Get version from tag
|
||||
VERSION=${GITHUB_REF#refs/tags/}
|
||||
|
||||
# Get platform tuple
|
||||
PLATFORM=$(rustc --print host-tuple)
|
||||
|
||||
# Upload to R2
|
||||
aws s3 cp backend/cuda-release/voicebox-server-cuda-${PLATFORM}.exe \
|
||||
s3://voicebox/cuda/${VERSION}/voicebox-server-cuda-${PLATFORM}.exe \
|
||||
--endpoint-url $R2_ENDPOINT \
|
||||
--acl public-read
|
||||
|
||||
echo "CUDA binary uploaded to: https://downloads.voicebox.sh/cuda/${VERSION}/voicebox-server-cuda-${PLATFORM}.exe"
|
||||
|
||||
@@ -5,14 +5,6 @@ All notable changes to Voicebox will be documented in this file.
|
||||
The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
|
||||
and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
|
||||
|
||||
## [Unreleased]
|
||||
|
||||
### Fixed
|
||||
- **Profile Name Validation** - Added proper validation to prevent duplicate profile names ([#134](https://github.com/jamiepine/voicebox/issues/134))
|
||||
- Users now receive clear error messages when attempting to create or update profiles with duplicate names
|
||||
- Improved error handling in create and update profile API endpoints
|
||||
- Added comprehensive test suite for duplicate name validation
|
||||
|
||||
## [0.1.0] - 2026-01-25
|
||||
|
||||
### Added
|
||||
@@ -61,10 +53,6 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
|
||||
|
||||
## [Unreleased]
|
||||
|
||||
### Fixed
|
||||
- Audio export failing when Tauri save dialog returns object instead of string path
|
||||
- OpenAPI client generator script now documents the local backend port and avoids an unused loop variable warning
|
||||
|
||||
### Added
|
||||
- **Makefile** - Comprehensive development workflow automation with commands for setup, development, building, testing, and code quality checks
|
||||
- Includes Python version detection and compatibility warnings
|
||||
|
||||
+2
-22
@@ -27,32 +27,12 @@ Thank you for your interest in contributing to Voicebox! This document provides
|
||||
```bash
|
||||
rustc --version # Check if installed
|
||||
```
|
||||
- **[Tauri Prerequisites](https://v2.tauri.app/start/prerequisites)** - Tauri-specific system dependencies (varies by OS).
|
||||
|
||||
- **Git** - Version control
|
||||
|
||||
### Development Setup
|
||||
|
||||
**Using `just` (recommended):**
|
||||
|
||||
Install [just](https://github.com/casey/just) (`brew install just` or `cargo install just`), then:
|
||||
|
||||
```bash
|
||||
just setup # creates venv, installs Python + JS deps
|
||||
just dev # starts backend + desktop app in one terminal
|
||||
```
|
||||
|
||||
Other useful commands:
|
||||
|
||||
```bash
|
||||
just dev-web # backend + web app (no Tauri/Rust build)
|
||||
just dev-backend # backend only
|
||||
just kill # stop all dev processes
|
||||
just clean-all # nuke everything and start fresh
|
||||
just --list # see all available commands
|
||||
```
|
||||
|
||||
**Using the Makefile:** Run `make setup` then `make dev`. See `make help` for all commands.
|
||||
**Using the Makefile (recommended for macOS/Linux):** Run `make setup` to install all dependencies, then `make dev` to start development servers. See `make help` for all available commands.
|
||||
|
||||
**Manual setup (required for Windows):**
|
||||
|
||||
@@ -427,7 +407,7 @@ See [docs/TROUBLESHOOTING.md](docs/TROUBLESHOOTING.md) for common issues and sol
|
||||
|
||||
- **Backend won't start:** Check Python version (3.11+), ensure venv is activated, install dependencies
|
||||
- **Tauri build fails:** Ensure Rust is installed, clean build with `cd tauri/src-tauri && cargo clean`
|
||||
- **OpenAPI client generation fails:** Ensure backend is running, check `curl http://localhost:17493/openapi.json`
|
||||
- **OpenAPI client generation fails:** Ensure backend is running, check `curl http://localhost:8000/openapi.json`
|
||||
|
||||
## Questions?
|
||||
|
||||
|
||||
-79
@@ -1,79 +0,0 @@
|
||||
# ============================================================
|
||||
# Voicebox — Local TTS Server with Web UI (CPU)
|
||||
# 3-stage build: Frontend → Python deps → Runtime
|
||||
# ============================================================
|
||||
|
||||
# === Stage 1: Build frontend ===
|
||||
FROM oven/bun:1 AS frontend
|
||||
|
||||
WORKDIR /build
|
||||
|
||||
# Copy workspace config and frontend source
|
||||
COPY package.json bun.lock ./
|
||||
COPY app/ ./app/
|
||||
COPY web/ ./web/
|
||||
|
||||
# Strip workspaces not needed for web build, and fix trailing comma
|
||||
RUN sed -i '/"tauri"/d; /"landing"/d' package.json && \
|
||||
sed -i -z 's/,\n ]/\n ]/' package.json
|
||||
RUN bun install --no-save
|
||||
# Build frontend (skip tsc — upstream has pre-existing type errors)
|
||||
RUN cd web && bunx --bun vite build
|
||||
|
||||
|
||||
# === Stage 2: Build Python dependencies ===
|
||||
FROM python:3.11-slim AS backend-builder
|
||||
|
||||
WORKDIR /build
|
||||
|
||||
RUN apt-get update && apt-get install -y --no-install-recommends \
|
||||
git \
|
||||
build-essential \
|
||||
&& rm -rf /var/lib/apt/lists/*
|
||||
|
||||
COPY backend/requirements.txt .
|
||||
RUN pip install --no-cache-dir --prefix=/install -r requirements.txt
|
||||
RUN pip install --no-cache-dir --prefix=/install \
|
||||
git+https://github.com/QwenLM/Qwen3-TTS.git
|
||||
|
||||
|
||||
# === Stage 3: Runtime ===
|
||||
FROM python:3.11-slim
|
||||
|
||||
# Create non-root user for security
|
||||
RUN groupadd -r voicebox && \
|
||||
useradd -r -g voicebox -m -s /bin/bash voicebox
|
||||
|
||||
WORKDIR /app
|
||||
|
||||
# Install only runtime system dependencies
|
||||
RUN apt-get update && apt-get install -y --no-install-recommends \
|
||||
ffmpeg \
|
||||
curl \
|
||||
&& rm -rf /var/lib/apt/lists/*
|
||||
|
||||
# Copy installed Python packages from builder stage
|
||||
COPY --from=backend-builder /install /usr/local
|
||||
|
||||
# Copy backend application code
|
||||
COPY --chown=voicebox:voicebox backend/ /app/backend/
|
||||
|
||||
# Copy built frontend from frontend stage
|
||||
COPY --from=frontend --chown=voicebox:voicebox /build/web/dist /app/frontend/
|
||||
|
||||
# Create data directories owned by non-root user
|
||||
RUN mkdir -p /app/data/generations /app/data/profiles /app/data/cache \
|
||||
&& chown -R voicebox:voicebox /app/data
|
||||
|
||||
# Switch to non-root user
|
||||
USER voicebox
|
||||
|
||||
# Expose the API port
|
||||
EXPOSE 17493
|
||||
|
||||
# Health check — auto-restart if the server hangs
|
||||
HEALTHCHECK --interval=30s --timeout=10s --retries=3 --start-period=60s \
|
||||
CMD curl -f http://localhost:17493/health || exit 1
|
||||
|
||||
# Start the FastAPI server
|
||||
CMD ["uvicorn", "backend.main:app", "--host", "0.0.0.0", "--port", "17493"]
|
||||
@@ -48,7 +48,6 @@ setup-python: $(VENV)/bin/activate ## Set up Python virtual environment and depe
|
||||
@echo -e "$(BLUE)Installing Python dependencies...$(NC)"
|
||||
$(PIP) install --upgrade pip
|
||||
$(PIP) install -r $(BACKEND_DIR)/requirements.txt
|
||||
$(PIP) install --no-deps chatterbox-tts
|
||||
@if [ "$$(uname -m)" = "arm64" ] && [ "$$(uname)" = "Darwin" ]; then \
|
||||
echo -e "$(BLUE)Detected Apple Silicon - installing MLX dependencies...$(NC)"; \
|
||||
$(PIP) install -r $(BACKEND_DIR)/requirements-mlx.txt; \
|
||||
@@ -80,11 +79,7 @@ dev: ## Start backend + desktop app (parallel)
|
||||
@echo -e "$(YELLOW)Note: If Tauri fails, run 'make build-server' first or use separate terminals$(NC)"
|
||||
@trap 'kill 0' EXIT; \
|
||||
$(MAKE) dev-backend & \
|
||||
sleep 2 && if [ "$$(uname)" = "Linux" ] && lspci 2>/dev/null | grep -qi nvidia; then \
|
||||
WEBKIT_DISABLE_DMABUF_RENDERER=1 $(MAKE) dev-frontend; \
|
||||
else \
|
||||
$(MAKE) dev-frontend; \
|
||||
fi & \
|
||||
sleep 2 && $(MAKE) dev-frontend & \
|
||||
wait
|
||||
|
||||
dev-backend: ## Start FastAPI backend server
|
||||
|
||||
@@ -1,58 +0,0 @@
|
||||
# Voicebox Offline Mode Fix
|
||||
|
||||
## Problem
|
||||
Voicebox crashes when generating speech if HuggingFace is unreachable, even when models are fully cached locally.
|
||||
|
||||
**Root Cause:**
|
||||
- Voicebox downloads `mlx-community/Qwen3-TTS-12Hz-1.7B-Base-bf16` (MLX optimized version)
|
||||
- But `mlx_audio.tts.load()` tries to fetch `config.json` from original repo `Qwen/Qwen3-TTS-12Hz-1.7B-Base`
|
||||
- This network request fails → server crashes with `RemoteDisconnected`
|
||||
|
||||
**Related Issues:**
|
||||
- Issue #150: "Internet connection required, even though models are downloaded?"
|
||||
- Issue #151: "API Stability Issues: Model Loading Hangs and Server Crashes"
|
||||
|
||||
## Solution
|
||||
Two-part fix:
|
||||
|
||||
### 1. Monkey-patch huggingface_hub (`backend/utils/hf_offline_patch.py`)
|
||||
- Intercepts cache lookup functions
|
||||
- Forces offline mode early (before mlx_audio imports)
|
||||
- Adds debug logging for cache hits/misses
|
||||
|
||||
### 2. Symlink original repo to MLX version (`ensure_original_qwen_config_cached()`)
|
||||
- When original `Qwen/Qwen3-TTS-12Hz-1.7B-Base` cache doesn't exist
|
||||
- But MLX `mlx-community/Qwen3-TTS-12Hz-1.7B-Base-bf16` does exist
|
||||
- Creates a symlink so cache lookups succeed
|
||||
|
||||
## Files Changed
|
||||
- `backend/backends/mlx_backend.py` - Added patch imports at top
|
||||
- `backend/utils/hf_offline_patch.py` - New patch module
|
||||
|
||||
## Testing
|
||||
To test this fix:
|
||||
1. Build Voicebox from source: `make build`
|
||||
2. Disconnect from internet
|
||||
3. Try generating speech
|
||||
4. Should work without network requests
|
||||
|
||||
## Build Instructions
|
||||
|
||||
```bash
|
||||
# Install dependencies
|
||||
pip install -r requirements.txt
|
||||
|
||||
# Build the app
|
||||
make build
|
||||
|
||||
# Or build just the server
|
||||
make build-server
|
||||
```
|
||||
|
||||
## Notes
|
||||
- The patch is applied automatically when `mlx_backend.py` is imported
|
||||
- Set `VOICEBOX_OFFLINE_PATCH=0` to disable the patch
|
||||
- The symlink approach works because the config.json is compatible between versions
|
||||
|
||||
---
|
||||
*Patch contributed by community*
|
||||
@@ -59,7 +59,7 @@
|
||||
|
||||
## What is Voicebox?
|
||||
|
||||
Voicebox is a **local-first voice cloning studio** with DAW-like features for professional voice synthesis. Think of it as a **local, free and open-source alternative to ElevenLabs** — download models, clone voices, and generate speech entirely on your machine.
|
||||
Voicebox is a **local-first voice cloning studio** with DAW-like features for professional voice synthesis. Think of it as the **Ollama for voice** — download models, clone voices, and generate speech entirely on your machine.
|
||||
|
||||
Unlike cloud services that lock your voice data behind subscriptions, Voicebox gives you:
|
||||
|
||||
@@ -80,10 +80,10 @@ Voicebox is available now for macOS and Windows.
|
||||
|
||||
| Platform | Download |
|
||||
|----------|----------|
|
||||
| macOS (Apple Silicon) | [Voicebox_aarch64.app.tar.gz](https://github.com/jamiepine/voicebox/releases/latest/download/Voicebox_aarch64.app.tar.gz) |
|
||||
| macOS (Intel) | [Voicebox_x64.app.tar.gz](https://github.com/jamiepine/voicebox/releases/latest/download/Voicebox_x64.app.tar.gz) |
|
||||
| Windows (MSI) | [Latest Windows MSI](https://github.com/jamiepine/voicebox/releases/latest) |
|
||||
| Windows (Setup) | [Latest Windows Setup](https://github.com/jamiepine/voicebox/releases/latest) |
|
||||
| macOS (Apple Silicon) | [voicebox_aarch64.app.tar.gz](https://github.com/jamiepine/voicebox/releases/download/v0.1.0/voicebox_aarch64.app.tar.gz) |
|
||||
| macOS (Intel) | [voicebox_x64.app.tar.gz](https://github.com/jamiepine/voicebox/releases/download/v0.1.0/voicebox_x64.app.tar.gz) |
|
||||
| Windows (MSI) | [voicebox_0.1.0_x64_en-US.msi](https://github.com/jamiepine/voicebox/releases/download/v0.1.0/voicebox_0.1.0_x64_en-US.msi) |
|
||||
| Windows (Setup) | [voicebox_0.1.0_x64-setup.exe](https://github.com/jamiepine/voicebox/releases/download/v0.1.0/voicebox_0.1.0_x64-setup.exe) |
|
||||
|
||||
> **Linux builds coming soon** — Currently blocked by GitHub runner disk space limitations.
|
||||
|
||||
@@ -98,12 +98,12 @@ Powered by Alibaba's **Qwen3-TTS** — a breakthrough model that achieves near-p
|
||||
- **Instant cloning** — Upload a sample, get a voice profile
|
||||
- **High fidelity** — Natural prosody, emotion, and cadence
|
||||
- **Multi-language** — English, Chinese, and more coming
|
||||
- **Lightning fast on Mac** — MLX backend leverages Apple Silicon's Neural Engine for super-fast generation
|
||||
- **Lightning fast on Mac** — MLX backend leverages Apple Silicon's Neural Engine for super fast generation
|
||||
|
||||
### Voice Profile Management
|
||||
|
||||
- **Create profiles** from audio files or record directly in-app
|
||||
- **Import/Export** profiles to share or back up
|
||||
- **Import/Export** profiles to share or backup
|
||||
- **Multi-sample support** — combine multiple samples for higher quality cloning
|
||||
- **Organize** with descriptions and language tags
|
||||
|
||||
@@ -147,20 +147,17 @@ Create multi-voice narratives, podcasts, and conversations with a timeline-based
|
||||
|
||||
Voicebox exposes a full REST API, so you can integrate voice synthesis into your own apps.
|
||||
|
||||
For the current local app and development workflow, the backend is typically available at `http://localhost:17493`.
|
||||
If you launch the backend manually with a different host or port, use that address instead.
|
||||
|
||||
```bash
|
||||
# Generate speech
|
||||
curl -X POST http://localhost:17493/generate \
|
||||
curl -X POST http://localhost:8000/generate \
|
||||
-H "Content-Type: application/json" \
|
||||
-d '{"text": "Hello world", "profile_id": "abc123", "language": "en"}'
|
||||
|
||||
# List voice profiles
|
||||
curl http://localhost:17493/profiles
|
||||
curl http://localhost:8000/profiles
|
||||
|
||||
# Create a profile
|
||||
curl -X POST http://localhost:17493/profiles \
|
||||
curl -X POST http://localhost:8000/profiles \
|
||||
-H "Content-Type: application/json" \
|
||||
-d '{"name": "My Voice", "language": "en"}'
|
||||
```
|
||||
@@ -173,7 +170,7 @@ curl -X POST http://localhost:17493/profiles \
|
||||
- Voice assistants
|
||||
- Content creation automation
|
||||
|
||||
Full API documentation is available at `http://localhost:17493/docs` in the default local workflow, or at `/docs` on whatever server address you configured.
|
||||
Full API documentation available at `http://localhost:8000/docs` when running.
|
||||
|
||||
---
|
||||
|
||||
@@ -228,21 +225,42 @@ Voicebox aims to be the **one-stop shop for everything voice** — cloning, synt
|
||||
|
||||
See [CONTRIBUTING.md](CONTRIBUTING.md) for detailed setup and contribution guidelines.
|
||||
|
||||
**Using the Makefile (recommended):** Run `make help` to see all available commands for setup, development, building, and testing.
|
||||
|
||||
### Quick Start
|
||||
|
||||
**With Makefile (Unix/macOS/Linux):**
|
||||
|
||||
```bash
|
||||
git clone https://github.com/jamiepine/voicebox.git
|
||||
# Clone the repo
|
||||
git clone https://github.com/voicebox-sh/voicebox.git
|
||||
cd voicebox
|
||||
|
||||
just setup # creates Python venv, installs all deps
|
||||
just dev # starts backend + desktop app
|
||||
# Setup everything
|
||||
make setup
|
||||
|
||||
# Start development
|
||||
make dev
|
||||
```
|
||||
|
||||
Install [just](https://github.com/casey/just): `brew install just` or `cargo install just`. Run `just --list` to see all commands.
|
||||
**Manual setup (all platforms):**
|
||||
|
||||
Also available via Makefile: `make setup && make dev` (run `make help` for all commands).
|
||||
```bash
|
||||
# Clone the repo
|
||||
git clone https://github.com/voicebox-sh/voicebox.git
|
||||
cd voicebox
|
||||
|
||||
**Prerequisites:** [Bun](https://bun.sh), [Rust](https://rustup.rs), [Python 3.11+](https://python.org), [XCode on macOS](https://developer.apple.com/xcode/), [Tauri Prerequisites](https://v2.tauri.app/start/prerequisites/).
|
||||
# Install dependencies
|
||||
bun install
|
||||
|
||||
# Install Python dependencies
|
||||
cd backend && pip install -r requirements.txt && cd ..
|
||||
|
||||
# Start development
|
||||
bun run dev
|
||||
```
|
||||
|
||||
**Prerequisites:** [Bun](https://bun.sh), [Rust](https://rustup.rs), [Python 3.11+](https://python.org).
|
||||
|
||||
**Performance:**
|
||||
- **Apple Silicon (M1/M2/M3)**: Uses MLX backend with native Metal acceleration for 4-5x faster inference
|
||||
|
||||
+1
-1
@@ -1,6 +1,6 @@
|
||||
{
|
||||
"name": "@voicebox/app",
|
||||
"version": "0.1.13",
|
||||
"version": "0.1.12",
|
||||
"private": true,
|
||||
"type": "module",
|
||||
"scripts": {
|
||||
|
||||
+2
-3
@@ -93,11 +93,10 @@ function App() {
|
||||
}
|
||||
|
||||
serverStartingRef.current = true;
|
||||
const isRemote = useServerStore.getState().mode === 'remote';
|
||||
console.log(`Production mode: Starting bundled server... (remote: ${isRemote})`);
|
||||
console.log('Production mode: Starting bundled server...');
|
||||
|
||||
platform.lifecycle
|
||||
.startServer(isRemote)
|
||||
.startServer(false)
|
||||
.then((serverUrl) => {
|
||||
console.log('Server is ready at:', serverUrl);
|
||||
// Update the server URL in the store with the dynamically assigned port
|
||||
|
||||
@@ -1,6 +1,6 @@
|
||||
import { useQuery } from '@tanstack/react-query';
|
||||
import { Pause, Play, Repeat, Volume2, VolumeX, X } from 'lucide-react';
|
||||
import { useEffect, useId, useMemo, useRef, useState } from 'react';
|
||||
import { useEffect, useMemo, useRef, useState } from 'react';
|
||||
import WaveSurfer from 'wavesurfer.js';
|
||||
import { Button } from '@/components/ui/button';
|
||||
import { Slider } from '@/components/ui/slider';
|
||||
@@ -12,7 +12,6 @@ import { usePlatform } from '@/platform/PlatformContext';
|
||||
|
||||
export function AudioPlayer() {
|
||||
const platform = usePlatform();
|
||||
const volumeLabelId = useId();
|
||||
const {
|
||||
audioUrl,
|
||||
audioId,
|
||||
@@ -832,13 +831,6 @@ export function AudioPlayer() {
|
||||
disabled={isLoading || duration === 0}
|
||||
className="shrink-0"
|
||||
title={duration === 0 && !isLoading ? 'Audio not loaded' : ''}
|
||||
aria-label={
|
||||
duration === 0 && !isLoading
|
||||
? 'Audio not loaded'
|
||||
: isPlaying
|
||||
? 'Pause'
|
||||
: 'Play'
|
||||
}
|
||||
>
|
||||
{isPlaying ? <Pause className="h-5 w-5" /> : <Play className="h-5 w-5" />}
|
||||
</Button>
|
||||
@@ -853,8 +845,6 @@ export function AudioPlayer() {
|
||||
max={100}
|
||||
step={0.1}
|
||||
className="w-full"
|
||||
aria-label="Playback position"
|
||||
aria-valuetext={`${formatAudioDuration(currentTime)} of ${formatAudioDuration(duration)}`}
|
||||
/>
|
||||
)}
|
||||
{isLoading && (
|
||||
@@ -882,33 +872,26 @@ export function AudioPlayer() {
|
||||
onClick={toggleLoop}
|
||||
className={isLooping ? 'text-primary' : ''}
|
||||
title="Toggle loop"
|
||||
aria-label={isLooping ? 'Stop looping' : 'Loop'}
|
||||
>
|
||||
<Repeat className="h-4 w-4" />
|
||||
</Button>
|
||||
|
||||
{/* Volume Control */}
|
||||
<div className="flex items-center gap-2 shrink-0 w-[120px]" role="group" aria-label="Volume">
|
||||
<div className="flex items-center gap-2 shrink-0 w-[120px]">
|
||||
<Button
|
||||
variant="ghost"
|
||||
size="icon"
|
||||
onClick={() => setVolume(volume > 0 ? 0 : 1)}
|
||||
className="h-8 w-8"
|
||||
aria-label={volume > 0 ? 'Mute' : 'Unmute'}
|
||||
>
|
||||
{volume > 0 ? <Volume2 className="h-4 w-4" /> : <VolumeX className="h-4 w-4" />}
|
||||
</Button>
|
||||
<span id={volumeLabelId} className="sr-only">
|
||||
Volume level, {Math.round(volume * 100)}%
|
||||
</span>
|
||||
<Slider
|
||||
value={[volume * 100]}
|
||||
onValueChange={handleVolumeChange}
|
||||
max={100}
|
||||
step={1}
|
||||
className="flex-1"
|
||||
aria-labelledby={volumeLabelId}
|
||||
aria-valuetext={`${Math.round(volume * 100)}%`}
|
||||
/>
|
||||
</div>
|
||||
|
||||
@@ -919,7 +902,6 @@ export function AudioPlayer() {
|
||||
onClick={handleClose}
|
||||
className="shrink-0"
|
||||
title="Close player"
|
||||
aria-label="Close player"
|
||||
>
|
||||
<X className="h-5 w-5" />
|
||||
</Button>
|
||||
|
||||
@@ -124,13 +124,6 @@ export function AudioTab() {
|
||||
);
|
||||
}
|
||||
|
||||
const handleChannelDelete = async (e, channelId) => {
|
||||
e.stopPropagation();
|
||||
if (await confirm('Delete this channel?')) {
|
||||
deleteChannel.mutate(channelId);
|
||||
}
|
||||
}
|
||||
|
||||
const allChannels = channels || [];
|
||||
const allDevices = devices || [];
|
||||
const selectedChannel = selectedChannelId
|
||||
@@ -248,7 +241,12 @@ export function AudioTab() {
|
||||
variant="ghost"
|
||||
size="sm"
|
||||
className="h-8 w-8 p-0"
|
||||
onClick={(e) => handleChannelDelete(e, channel.id)}
|
||||
onClick={(e) => {
|
||||
e.stopPropagation();
|
||||
if (confirm('Delete this channel?')) {
|
||||
deleteChannel.mutate(channel.id);
|
||||
}
|
||||
}}
|
||||
>
|
||||
<Trash2 className="h-4 w-4" />
|
||||
</Button>
|
||||
|
||||
@@ -1,6 +1,6 @@
|
||||
import { useMatchRoute } from '@tanstack/react-router';
|
||||
import { AnimatePresence, motion } from 'framer-motion';
|
||||
import { Loader2, SlidersHorizontal, Sparkles } from 'lucide-react';
|
||||
import { Loader2, MessageSquare, Sparkles } from 'lucide-react';
|
||||
import { useEffect, useRef, useState } from 'react';
|
||||
import { Button } from '@/components/ui/button';
|
||||
import { Form, FormControl, FormField, FormItem, FormMessage } from '@/components/ui/form';
|
||||
@@ -13,14 +13,13 @@ import {
|
||||
} from '@/components/ui/select';
|
||||
import { Textarea } from '@/components/ui/textarea';
|
||||
import { useToast } from '@/components/ui/use-toast';
|
||||
import { getLanguageOptionsForEngine, type LanguageCode } from '@/lib/constants/languages';
|
||||
import { LANGUAGE_OPTIONS } from '@/lib/constants/languages';
|
||||
import { useGenerationForm } from '@/lib/hooks/useGenerationForm';
|
||||
import { useProfile, useProfiles } from '@/lib/hooks/useProfiles';
|
||||
import { useAddStoryItem, useStory } from '@/lib/hooks/useStories';
|
||||
import { cn } from '@/lib/utils/cn';
|
||||
import { useStoryStore } from '@/stores/storyStore';
|
||||
import { useUIStore } from '@/stores/uiStore';
|
||||
import { ParalinguisticInput } from './ParalinguisticInput';
|
||||
|
||||
interface FloatingGenerateBoxProps {
|
||||
isPlayerOpen?: boolean;
|
||||
@@ -113,13 +112,6 @@ export function FloatingGenerateBox({
|
||||
}
|
||||
}, [selectedProfileId, profiles, setSelectedProfileId]);
|
||||
|
||||
// Sync generation form language with selected profile's language
|
||||
useEffect(() => {
|
||||
if (selectedProfile?.language) {
|
||||
form.setValue('language', selectedProfile.language as LanguageCode);
|
||||
}
|
||||
}, [selectedProfile, form]);
|
||||
|
||||
// Auto-resize textarea based on content (only when expanded)
|
||||
useEffect(() => {
|
||||
if (!isExpanded) {
|
||||
@@ -195,7 +187,7 @@ export function FloatingGenerateBox({
|
||||
}}
|
||||
>
|
||||
<motion.div
|
||||
className="bg-background/30 backdrop-blur-2xl border border-accent/20 rounded-[2rem] shadow-2xl hover:bg-background/40 hover:border-accent/20 transition-all duration-300 p-3"
|
||||
className="bg-background/30 backdrop-blur-2xl border border-accent/20 rounded-[2rem] shadow-2xl hover:bg-background/40 hover:border-accent/20 transition-all duration-300 overflow-hidden p-3"
|
||||
transition={{ duration: 0.6, ease: 'easeInOut' }}
|
||||
>
|
||||
<Form {...form}>
|
||||
@@ -220,57 +212,34 @@ export function FloatingGenerateBox({
|
||||
transition={{ duration: 0.15, ease: 'easeOut' }}
|
||||
style={{ overflow: 'hidden' }}
|
||||
>
|
||||
{form.watch('engine') === 'chatterbox_turbo' ? (
|
||||
<ParalinguisticInput
|
||||
value={field.value}
|
||||
onChange={field.onChange}
|
||||
placeholder={
|
||||
isStoriesRoute && currentStory
|
||||
? `Generate speech for "${currentStory.name}"... (type / for effects)`
|
||||
: selectedProfile
|
||||
? `Type / for effects like [laugh], [sigh]...`
|
||||
: 'Select a voice profile above...'
|
||||
<Textarea
|
||||
{...field}
|
||||
ref={(node: HTMLTextAreaElement | null) => {
|
||||
// Store ref for auto-resize (only for active field)
|
||||
if (!isInstructMode) {
|
||||
textareaRef.current = node;
|
||||
}
|
||||
className="px-3 py-2 resize-none bg-transparent border-none focus-visible:ring-0 focus-visible:ring-offset-0 focus:outline-none focus:ring-0 outline-none ring-0 rounded-2xl text-sm w-full"
|
||||
style={{
|
||||
minHeight: isExpanded ? '100px' : '32px',
|
||||
maxHeight: '300px',
|
||||
overflowY: 'auto',
|
||||
}}
|
||||
disabled={!selectedProfileId}
|
||||
onClick={() => setIsExpanded(true)}
|
||||
onFocus={() => setIsExpanded(true)}
|
||||
/>
|
||||
) : (
|
||||
<Textarea
|
||||
{...field}
|
||||
ref={(node: HTMLTextAreaElement | null) => {
|
||||
// Store ref for auto-resize (only for active field)
|
||||
if (!isInstructMode) {
|
||||
textareaRef.current = node;
|
||||
}
|
||||
// Forward ref to react-hook-form
|
||||
if (typeof field.ref === 'function') {
|
||||
field.ref(node);
|
||||
}
|
||||
}}
|
||||
placeholder={
|
||||
isStoriesRoute && currentStory
|
||||
? `Generate speech for "${currentStory.name}"...`
|
||||
: selectedProfile
|
||||
? `Generate speech using ${selectedProfile.name}...`
|
||||
: 'Select a voice profile above...'
|
||||
// Forward ref to react-hook-form
|
||||
if (typeof field.ref === 'function') {
|
||||
field.ref(node);
|
||||
}
|
||||
className="resize-none bg-transparent border-none focus-visible:ring-0 focus-visible:ring-offset-0 focus:outline-none focus:ring-0 outline-none ring-0 rounded-2xl text-sm placeholder:text-muted-foreground/60 w-full"
|
||||
style={{
|
||||
minHeight: isExpanded ? '100px' : '32px',
|
||||
maxHeight: '300px',
|
||||
}}
|
||||
disabled={!selectedProfileId}
|
||||
onClick={() => setIsExpanded(true)}
|
||||
onFocus={() => setIsExpanded(true)}
|
||||
/>
|
||||
)}
|
||||
}}
|
||||
placeholder={
|
||||
isStoriesRoute && currentStory
|
||||
? `Generate speech for "${currentStory.name}"...`
|
||||
: selectedProfile
|
||||
? `Generate speech using ${selectedProfile.name}...`
|
||||
: 'Select a voice profile above...'
|
||||
}
|
||||
className="resize-none bg-transparent border-none focus-visible:ring-0 focus-visible:ring-offset-0 focus:outline-none focus:ring-0 outline-none ring-0 rounded-2xl text-sm placeholder:text-muted-foreground/60 w-full"
|
||||
style={{
|
||||
minHeight: isExpanded ? '100px' : '32px',
|
||||
maxHeight: '300px',
|
||||
}}
|
||||
disabled={!selectedProfileId}
|
||||
onClick={() => setIsExpanded(true)}
|
||||
onFocus={() => setIsExpanded(true)}
|
||||
/>
|
||||
</motion.div>
|
||||
</FormControl>
|
||||
<FormMessage className="text-xs" />
|
||||
@@ -305,7 +274,7 @@ export function FloatingGenerateBox({
|
||||
field.ref(node);
|
||||
}
|
||||
}}
|
||||
placeholder="e.g. very happy and excited"
|
||||
placeholder="Add delivery instructions..."
|
||||
className="resize-none bg-transparent border-none focus-visible:ring-0 focus-visible:ring-offset-0 focus:outline-none focus:ring-0 outline-none ring-0 rounded-2xl text-sm placeholder:text-muted-foreground/60 w-full"
|
||||
style={{
|
||||
minHeight: isExpanded ? '100px' : '32px',
|
||||
@@ -325,36 +294,20 @@ export function FloatingGenerateBox({
|
||||
</motion.div>
|
||||
|
||||
<div className="relative shrink-0">
|
||||
<div className="group relative">
|
||||
<Button
|
||||
type="submit"
|
||||
disabled={isPending || !selectedProfileId}
|
||||
className="h-10 w-10 rounded-full bg-accent hover:bg-accent/90 hover:scale-105 text-accent-foreground shadow-lg hover:shadow-accent/50 transition-all duration-200"
|
||||
size="icon"
|
||||
aria-label={
|
||||
isPending
|
||||
? 'Generating...'
|
||||
: !selectedProfileId
|
||||
? 'Select a voice profile first'
|
||||
: 'Generate speech'
|
||||
}
|
||||
>
|
||||
{isPending ? (
|
||||
<Loader2 className="h-4 w-4 animate-spin" />
|
||||
) : (
|
||||
<Sparkles className="h-4 w-4" />
|
||||
)}
|
||||
</Button>
|
||||
<span className="pointer-events-none absolute bottom-full left-1/2 -translate-x-1/2 mb-2 whitespace-nowrap rounded-md bg-popover px-3 py-1.5 text-xs text-popover-foreground border border-border opacity-0 transition-opacity group-hover:opacity-100 z-[9999]">
|
||||
{isPending
|
||||
? 'Generating...'
|
||||
: !selectedProfileId
|
||||
? 'Select a voice profile first'
|
||||
: 'Generate speech'}
|
||||
</span>
|
||||
</div>
|
||||
<Button
|
||||
type="submit"
|
||||
disabled={isPending || !selectedProfileId}
|
||||
className="h-10 w-10 rounded-full bg-accent hover:bg-accent/90 hover:scale-105 text-accent-foreground shadow-lg hover:shadow-accent/50 transition-all duration-200"
|
||||
size="icon"
|
||||
>
|
||||
{isPending ? (
|
||||
<Loader2 className="h-4 w-4 animate-spin" />
|
||||
) : (
|
||||
<Sparkles className="h-4 w-4" />
|
||||
)}
|
||||
</Button>
|
||||
<AnimatePresence>
|
||||
{isExpanded && form.watch('engine') === 'qwen' && (
|
||||
{isExpanded && (
|
||||
<motion.div
|
||||
initial={{ opacity: 0, scale: 0.8 }}
|
||||
animate={{ opacity: 1, scale: 1 }}
|
||||
@@ -362,28 +315,20 @@ export function FloatingGenerateBox({
|
||||
transition={{ duration: 0.2 }}
|
||||
className="absolute top-0 right-[calc(100%+0.5rem)]"
|
||||
>
|
||||
<div className="group relative">
|
||||
<Button
|
||||
type="button"
|
||||
variant="ghost"
|
||||
size="icon"
|
||||
onClick={() => setIsInstructMode(!isInstructMode)}
|
||||
className={cn(
|
||||
'h-10 w-10 rounded-full transition-all duration-200',
|
||||
isInstructMode
|
||||
? 'bg-accent text-accent-foreground border border-accent hover:bg-accent/90'
|
||||
: 'bg-card border border-border hover:bg-background/50',
|
||||
)}
|
||||
aria-label={
|
||||
isInstructMode ? 'Fine tune instructions, on' : 'Fine tune instructions'
|
||||
}
|
||||
>
|
||||
<SlidersHorizontal className="h-4 w-4" />
|
||||
</Button>
|
||||
<span className="pointer-events-none absolute bottom-full left-1/2 -translate-x-1/2 mb-2 whitespace-nowrap rounded-md bg-popover px-3 py-1.5 text-xs text-popover-foreground border border-border opacity-0 transition-opacity group-hover:opacity-100 z-[9999]">
|
||||
Fine tune instructions
|
||||
</span>
|
||||
</div>
|
||||
<Button
|
||||
type="button"
|
||||
variant="ghost"
|
||||
size="icon"
|
||||
onClick={() => setIsInstructMode(!isInstructMode)}
|
||||
className={cn(
|
||||
'h-10 w-10 rounded-full transition-all duration-200',
|
||||
isInstructMode
|
||||
? 'bg-accent text-accent-foreground border border-accent hover:bg-accent/90'
|
||||
: 'bg-card border border-border hover:bg-background/50',
|
||||
)}
|
||||
>
|
||||
<MessageSquare className="h-4 w-4" />
|
||||
</Button>
|
||||
</motion.div>
|
||||
)}
|
||||
</AnimatePresence>
|
||||
@@ -422,86 +367,51 @@ export function FloatingGenerateBox({
|
||||
<FormField
|
||||
control={form.control}
|
||||
name="language"
|
||||
render={({ field }) => {
|
||||
const engineLangs = getLanguageOptionsForEngine(
|
||||
form.watch('engine') || 'qwen',
|
||||
);
|
||||
return (
|
||||
<FormItem className="flex-1 space-y-0">
|
||||
<Select onValueChange={field.onChange} value={field.value}>
|
||||
<FormControl>
|
||||
<SelectTrigger className="h-8 text-xs bg-card border-border rounded-full hover:bg-background/50 transition-all">
|
||||
<SelectValue />
|
||||
</SelectTrigger>
|
||||
</FormControl>
|
||||
<SelectContent>
|
||||
{engineLangs.map((lang) => (
|
||||
<SelectItem key={lang.value} value={lang.value} className="text-xs">
|
||||
{lang.label}
|
||||
</SelectItem>
|
||||
))}
|
||||
</SelectContent>
|
||||
</Select>
|
||||
<FormMessage className="text-xs" />
|
||||
</FormItem>
|
||||
);
|
||||
}}
|
||||
render={({ field }) => (
|
||||
<FormItem className="flex-1 space-y-0">
|
||||
<Select onValueChange={field.onChange} defaultValue={field.value}>
|
||||
<FormControl>
|
||||
<SelectTrigger className="h-8 text-xs bg-card border-border rounded-full hover:bg-background/50 transition-all">
|
||||
<SelectValue />
|
||||
</SelectTrigger>
|
||||
</FormControl>
|
||||
<SelectContent>
|
||||
{LANGUAGE_OPTIONS.map((lang) => (
|
||||
<SelectItem key={lang.value} value={lang.value} className="text-xs">
|
||||
{lang.label}
|
||||
</SelectItem>
|
||||
))}
|
||||
</SelectContent>
|
||||
</Select>
|
||||
<FormMessage className="text-xs" />
|
||||
</FormItem>
|
||||
)}
|
||||
/>
|
||||
|
||||
<FormItem className="flex-1 space-y-0">
|
||||
<Select
|
||||
value={
|
||||
form.watch('engine') === 'luxtts'
|
||||
? 'luxtts'
|
||||
: form.watch('engine') === 'chatterbox'
|
||||
? 'chatterbox'
|
||||
: form.watch('engine') === 'chatterbox_turbo'
|
||||
? 'chatterbox_turbo'
|
||||
: `qwen:${form.watch('modelSize') || '1.7B'}`
|
||||
}
|
||||
onValueChange={(value) => {
|
||||
if (value === 'luxtts') {
|
||||
form.setValue('engine', 'luxtts');
|
||||
form.setValue('language', 'en');
|
||||
} else if (value === 'chatterbox') {
|
||||
form.setValue('engine', 'chatterbox');
|
||||
} else if (value === 'chatterbox_turbo') {
|
||||
form.setValue('engine', 'chatterbox_turbo');
|
||||
form.setValue('language', 'en');
|
||||
} else {
|
||||
const [, modelSize] = value.split(':');
|
||||
form.setValue('engine', 'qwen');
|
||||
form.setValue('modelSize', modelSize as '1.7B' | '0.6B');
|
||||
}
|
||||
}}
|
||||
>
|
||||
<FormControl>
|
||||
<SelectTrigger className="h-8 text-xs bg-card border-border rounded-full hover:bg-background/50 transition-all">
|
||||
<SelectValue />
|
||||
</SelectTrigger>
|
||||
</FormControl>
|
||||
<SelectContent>
|
||||
<SelectItem value="qwen:1.7B" className="text-xs text-muted-foreground">
|
||||
Qwen3-TTS 1.7B
|
||||
</SelectItem>
|
||||
<SelectItem value="qwen:0.6B" className="text-xs text-muted-foreground">
|
||||
Qwen3-TTS 0.6B
|
||||
</SelectItem>
|
||||
<SelectItem value="luxtts" className="text-xs text-muted-foreground">
|
||||
LuxTTS
|
||||
</SelectItem>
|
||||
<SelectItem value="chatterbox" className="text-xs text-muted-foreground">
|
||||
Chatterbox
|
||||
</SelectItem>
|
||||
<SelectItem
|
||||
value="chatterbox_turbo"
|
||||
className="text-xs text-muted-foreground"
|
||||
>
|
||||
Chatterbox Turbo
|
||||
</SelectItem>
|
||||
</SelectContent>
|
||||
</Select>
|
||||
</FormItem>
|
||||
<FormField
|
||||
control={form.control}
|
||||
name="modelSize"
|
||||
render={({ field }) => (
|
||||
<FormItem className="flex-1 space-y-0">
|
||||
<Select onValueChange={field.onChange} defaultValue={field.value}>
|
||||
<FormControl>
|
||||
<SelectTrigger className="h-8 text-xs bg-card border-border rounded-full hover:bg-background/50 transition-all">
|
||||
<SelectValue />
|
||||
</SelectTrigger>
|
||||
</FormControl>
|
||||
<SelectContent>
|
||||
<SelectItem value="1.7B" className="text-xs text-muted-foreground">
|
||||
Qwen3-TTS 1.7B
|
||||
</SelectItem>
|
||||
<SelectItem value="0.6B" className="text-xs text-muted-foreground">
|
||||
Qwen3-TTS 0.6B
|
||||
</SelectItem>
|
||||
</SelectContent>
|
||||
</Select>
|
||||
<FormMessage className="text-xs" />
|
||||
</FormItem>
|
||||
)}
|
||||
/>
|
||||
</div>
|
||||
</motion.div>
|
||||
</AnimatePresence>
|
||||
|
||||
@@ -19,11 +19,10 @@ import {
|
||||
SelectValue,
|
||||
} from '@/components/ui/select';
|
||||
import { Textarea } from '@/components/ui/textarea';
|
||||
import { getLanguageOptionsForEngine } from '@/lib/constants/languages';
|
||||
import { LANGUAGE_OPTIONS } from '@/lib/constants/languages';
|
||||
import { useGenerationForm } from '@/lib/hooks/useGenerationForm';
|
||||
import { useProfile } from '@/lib/hooks/useProfiles';
|
||||
import { useUIStore } from '@/stores/uiStore';
|
||||
import { ParalinguisticInput } from './ParalinguisticInput';
|
||||
|
||||
export function GenerationForm() {
|
||||
const selectedProfileId = useUIStore((state) => state.selectedProfileId);
|
||||
@@ -65,134 +64,87 @@ export function GenerationForm() {
|
||||
<FormItem>
|
||||
<FormLabel>Text to Speak</FormLabel>
|
||||
<FormControl>
|
||||
{form.watch('engine') === 'chatterbox_turbo' ? (
|
||||
<ParalinguisticInput
|
||||
value={field.value}
|
||||
onChange={field.onChange}
|
||||
placeholder="Enter text... type / for effects like [laugh], [sigh]"
|
||||
className="min-h-[150px] rounded-md border border-input bg-background px-3 py-2"
|
||||
/>
|
||||
) : (
|
||||
<Textarea
|
||||
placeholder="Enter the text you want to generate..."
|
||||
className="min-h-[150px]"
|
||||
{...field}
|
||||
/>
|
||||
)}
|
||||
<Textarea
|
||||
placeholder="Enter the text you want to generate..."
|
||||
className="min-h-[150px]"
|
||||
{...field}
|
||||
/>
|
||||
</FormControl>
|
||||
<FormDescription>Max 5000 characters</FormDescription>
|
||||
<FormMessage />
|
||||
</FormItem>
|
||||
)}
|
||||
/>
|
||||
|
||||
<FormField
|
||||
control={form.control}
|
||||
name="instruct"
|
||||
render={({ field }) => (
|
||||
<FormItem>
|
||||
<FormLabel>Delivery Instructions (optional)</FormLabel>
|
||||
<FormControl>
|
||||
<Textarea
|
||||
placeholder="e.g. Speak slowly with emphasis, Warm and friendly tone, Professional and authoritative..."
|
||||
className="min-h-[80px]"
|
||||
{...field}
|
||||
/>
|
||||
</FormControl>
|
||||
<FormDescription>
|
||||
{form.watch('engine') === 'chatterbox_turbo'
|
||||
? 'Max 5000 characters. Type / to insert sound effects.'
|
||||
: 'Max 5000 characters'}
|
||||
Natural language instructions to control speech delivery (tone, emotion, pace).
|
||||
Max 500 characters
|
||||
</FormDescription>
|
||||
<FormMessage />
|
||||
</FormItem>
|
||||
)}
|
||||
/>
|
||||
|
||||
{form.watch('engine') === 'qwen' && (
|
||||
<div className="grid gap-4 md:grid-cols-3">
|
||||
<FormField
|
||||
control={form.control}
|
||||
name="instruct"
|
||||
name="language"
|
||||
render={({ field }) => (
|
||||
<FormItem>
|
||||
<FormLabel>Delivery Instructions (optional)</FormLabel>
|
||||
<FormControl>
|
||||
<Textarea
|
||||
placeholder="e.g. Speak slowly with emphasis, Warm and friendly tone, Professional and authoritative..."
|
||||
className="min-h-[80px]"
|
||||
{...field}
|
||||
/>
|
||||
</FormControl>
|
||||
<FormDescription>
|
||||
Natural language instructions to control speech delivery (tone, emotion,
|
||||
pace). Max 500 characters
|
||||
</FormDescription>
|
||||
<FormLabel>Language</FormLabel>
|
||||
<Select onValueChange={field.onChange} defaultValue={field.value}>
|
||||
<FormControl>
|
||||
<SelectTrigger>
|
||||
<SelectValue />
|
||||
</SelectTrigger>
|
||||
</FormControl>
|
||||
<SelectContent>
|
||||
{LANGUAGE_OPTIONS.map((lang) => (
|
||||
<SelectItem key={lang.value} value={lang.value}>
|
||||
{lang.label}
|
||||
</SelectItem>
|
||||
))}
|
||||
</SelectContent>
|
||||
</Select>
|
||||
<FormMessage />
|
||||
</FormItem>
|
||||
)}
|
||||
/>
|
||||
)}
|
||||
|
||||
<div className="grid gap-4 md:grid-cols-3">
|
||||
<FormItem>
|
||||
<FormLabel>Model</FormLabel>
|
||||
<Select
|
||||
value={
|
||||
form.watch('engine') === 'luxtts'
|
||||
? 'luxtts'
|
||||
: form.watch('engine') === 'chatterbox'
|
||||
? 'chatterbox'
|
||||
: form.watch('engine') === 'chatterbox_turbo'
|
||||
? 'chatterbox_turbo'
|
||||
: `qwen:${form.watch('modelSize') || '1.7B'}`
|
||||
}
|
||||
onValueChange={(value) => {
|
||||
if (value === 'luxtts') {
|
||||
form.setValue('engine', 'luxtts');
|
||||
form.setValue('language', 'en');
|
||||
} else if (value === 'chatterbox') {
|
||||
form.setValue('engine', 'chatterbox');
|
||||
} else if (value === 'chatterbox_turbo') {
|
||||
form.setValue('engine', 'chatterbox_turbo');
|
||||
form.setValue('language', 'en');
|
||||
} else {
|
||||
const [, modelSize] = value.split(':');
|
||||
form.setValue('engine', 'qwen');
|
||||
form.setValue('modelSize', modelSize as '1.7B' | '0.6B');
|
||||
}
|
||||
}}
|
||||
>
|
||||
<FormControl>
|
||||
<SelectTrigger>
|
||||
<SelectValue />
|
||||
</SelectTrigger>
|
||||
</FormControl>
|
||||
<SelectContent>
|
||||
<SelectItem value="qwen:1.7B">Qwen3-TTS 1.7B</SelectItem>
|
||||
<SelectItem value="qwen:0.6B">Qwen3-TTS 0.6B</SelectItem>
|
||||
<SelectItem value="luxtts">LuxTTS</SelectItem>
|
||||
<SelectItem value="chatterbox">Chatterbox</SelectItem>
|
||||
<SelectItem value="chatterbox_turbo">Chatterbox Turbo</SelectItem>
|
||||
</SelectContent>
|
||||
</Select>
|
||||
<FormDescription>
|
||||
{form.watch('engine') === 'luxtts'
|
||||
? 'Fast, English-focused'
|
||||
: form.watch('engine') === 'chatterbox'
|
||||
? '23 languages, incl. Hebrew'
|
||||
: form.watch('engine') === 'chatterbox_turbo'
|
||||
? 'English, [laugh] [cough] tags'
|
||||
: 'Multi-language, two sizes'}
|
||||
</FormDescription>
|
||||
</FormItem>
|
||||
|
||||
<FormField
|
||||
control={form.control}
|
||||
name="language"
|
||||
render={({ field }) => {
|
||||
const engineLangs = getLanguageOptionsForEngine(form.watch('engine') || 'qwen');
|
||||
return (
|
||||
<FormItem>
|
||||
<FormLabel>Language</FormLabel>
|
||||
<Select onValueChange={field.onChange} value={field.value}>
|
||||
<FormControl>
|
||||
<SelectTrigger>
|
||||
<SelectValue />
|
||||
</SelectTrigger>
|
||||
</FormControl>
|
||||
<SelectContent>
|
||||
{engineLangs.map((lang) => (
|
||||
<SelectItem key={lang.value} value={lang.value}>
|
||||
{lang.label}
|
||||
</SelectItem>
|
||||
))}
|
||||
</SelectContent>
|
||||
</Select>
|
||||
<FormMessage />
|
||||
</FormItem>
|
||||
);
|
||||
}}
|
||||
name="modelSize"
|
||||
render={({ field }) => (
|
||||
<FormItem>
|
||||
<FormLabel>Model Size</FormLabel>
|
||||
<Select onValueChange={field.onChange} defaultValue={field.value}>
|
||||
<FormControl>
|
||||
<SelectTrigger>
|
||||
<SelectValue />
|
||||
</SelectTrigger>
|
||||
</FormControl>
|
||||
<SelectContent>
|
||||
<SelectItem value="1.7B">Qwen TTS 1.7B (Higher Quality)</SelectItem>
|
||||
<SelectItem value="0.6B">Qwen TTS 0.6B (Faster)</SelectItem>
|
||||
</SelectContent>
|
||||
</Select>
|
||||
<FormDescription>Larger models produce better quality</FormDescription>
|
||||
<FormMessage />
|
||||
</FormItem>
|
||||
)}
|
||||
/>
|
||||
|
||||
<FormField
|
||||
@@ -218,7 +170,11 @@ export function GenerationForm() {
|
||||
/>
|
||||
</div>
|
||||
|
||||
<Button type="submit" className="w-full" disabled={isPending || !selectedProfileId}>
|
||||
<Button
|
||||
type="submit"
|
||||
className="w-full"
|
||||
disabled={isPending || !selectedProfileId}
|
||||
>
|
||||
{isPending ? (
|
||||
<>
|
||||
<Loader2 className="mr-2 h-4 w-4 animate-spin" />
|
||||
|
||||
@@ -1,422 +0,0 @@
|
||||
/**
|
||||
* ParalinguisticInput — a contentEditable rich text input that renders
|
||||
* Chatterbox Turbo paralinguistic tags (e.g. [laugh]) as inline badges.
|
||||
*
|
||||
* Trigger: typing "/" opens an autocomplete dropdown.
|
||||
* Paste: pasting text with [tag] patterns auto-converts to badges.
|
||||
* Output: serializes badges back to plain [tag] text for the API.
|
||||
*/
|
||||
|
||||
import { AnimatePresence, motion } from 'framer-motion';
|
||||
import { forwardRef, useCallback, useEffect, useImperativeHandle, useRef, useState } from 'react';
|
||||
import { createPortal } from 'react-dom';
|
||||
import { cn } from '@/lib/utils/cn';
|
||||
|
||||
// ── Tag definitions ─────────────────────────────────────────────────
|
||||
const PARALINGUISTIC_TAGS = [
|
||||
{ tag: '[laugh]', label: 'laugh', emoji: '\u{1F602}' },
|
||||
{ tag: '[chuckle]', label: 'chuckle', emoji: '\u{1F60F}' },
|
||||
{ tag: '[gasp]', label: 'gasp', emoji: '\u{1F62E}' },
|
||||
{ tag: '[cough]', label: 'cough', emoji: '\u{1F637}' },
|
||||
{ tag: '[sigh]', label: 'sigh', emoji: '\u{1F614}' },
|
||||
{ tag: '[groan]', label: 'groan', emoji: '\u{1F629}' },
|
||||
{ tag: '[sniff]', label: 'sniff', emoji: '\u{1F443}' },
|
||||
{ tag: '[shush]', label: 'shush', emoji: '\u{1F92B}' },
|
||||
{ tag: '[clear throat]', label: 'clear throat', emoji: '\u{1F64A}' },
|
||||
] as const;
|
||||
|
||||
const TAG_REGEX = /\[(laugh|chuckle|gasp|cough|sigh|groan|sniff|shush|clear throat)\]/gi;
|
||||
|
||||
// Data attribute used to identify badge spans in the DOM
|
||||
const BADGE_ATTR = 'data-ptag';
|
||||
|
||||
// ── Helpers ─────────────────────────────────────────────────────────
|
||||
|
||||
/** Build an inline badge <span> for a tag. */
|
||||
function makeBadgeHTML(tag: string): string {
|
||||
const entry = PARALINGUISTIC_TAGS.find((t) => t.tag.toLowerCase() === tag.toLowerCase());
|
||||
const label = entry?.label ?? tag.replace(/[[\]]/g, '');
|
||||
const emoji = entry?.emoji ?? '';
|
||||
// Non-editable inline badge. Zero-width spaces around it let the
|
||||
// caret sit on either side so the user can type before/after.
|
||||
return `\u200B<span ${BADGE_ATTR}="${tag}" contenteditable="false" class="ptag-badge">${emoji ? `${emoji}\u00A0` : ''}${label}</span>\u200B`;
|
||||
}
|
||||
|
||||
/** Convert plain text with [tag] patterns into HTML with badge spans. */
|
||||
function textToHTML(text: string): string {
|
||||
// Escape HTML entities first
|
||||
const escaped = text.replace(/&/g, '&').replace(/</g, '<').replace(/>/g, '>');
|
||||
// Replace tag patterns with badge HTML
|
||||
return escaped.replace(TAG_REGEX, (match) => makeBadgeHTML(match));
|
||||
}
|
||||
|
||||
/** Serialize the contentEditable innerHTML back to plain text with [tag] syntax. */
|
||||
function htmlToText(container: HTMLElement): string {
|
||||
let result = '';
|
||||
for (const node of container.childNodes) {
|
||||
if (node.nodeType === Node.TEXT_NODE) {
|
||||
// Strip zero-width spaces we added around badges
|
||||
result += (node.textContent ?? '').replace(/\u200B/g, '');
|
||||
} else if (node.nodeType === Node.ELEMENT_NODE) {
|
||||
const el = node as HTMLElement;
|
||||
if (el.hasAttribute(BADGE_ATTR)) {
|
||||
result += el.getAttribute(BADGE_ATTR) ?? '';
|
||||
} else if (el.tagName === 'BR') {
|
||||
result += '\n';
|
||||
} else {
|
||||
// Recurse for nested elements (e.g. spans from paste)
|
||||
result += htmlToText(el);
|
||||
}
|
||||
}
|
||||
}
|
||||
return result;
|
||||
}
|
||||
|
||||
/** Get the text content from the current caret position back to the last
|
||||
* whitespace or start of container, to detect the "/" trigger. */
|
||||
function getWordBeforeCaret(_container: HTMLElement): { word: string; range: Range | null } {
|
||||
const sel = window.getSelection();
|
||||
if (!sel || sel.rangeCount === 0) return { word: '', range: null };
|
||||
const range = sel.getRangeAt(0).cloneRange();
|
||||
range.collapse(true);
|
||||
|
||||
// Walk backwards from caret through the text node
|
||||
const textNode = range.startContainer;
|
||||
if (textNode.nodeType !== Node.TEXT_NODE) return { word: '', range: null };
|
||||
const text = textNode.textContent ?? '';
|
||||
const offset = range.startOffset;
|
||||
|
||||
let start = offset;
|
||||
while (
|
||||
start > 0 &&
|
||||
text[start - 1] !== ' ' &&
|
||||
text[start - 1] !== '\n' &&
|
||||
text[start - 1] !== '\u00A0'
|
||||
) {
|
||||
start--;
|
||||
}
|
||||
|
||||
const word = text.slice(start, offset);
|
||||
const wordRange = document.createRange();
|
||||
wordRange.setStart(textNode, start);
|
||||
wordRange.setEnd(textNode, offset);
|
||||
|
||||
return { word, range: wordRange };
|
||||
}
|
||||
|
||||
// ── Component ───────────────────────────────────────────────────────
|
||||
|
||||
export interface ParalinguisticInputProps {
|
||||
value?: string;
|
||||
onChange?: (value: string) => void;
|
||||
placeholder?: string;
|
||||
disabled?: boolean;
|
||||
className?: string;
|
||||
style?: React.CSSProperties;
|
||||
onClick?: () => void;
|
||||
onFocus?: () => void;
|
||||
}
|
||||
|
||||
export interface ParalinguisticInputRef {
|
||||
focus: () => void;
|
||||
element: HTMLDivElement | null;
|
||||
}
|
||||
|
||||
export const ParalinguisticInput = forwardRef<ParalinguisticInputRef, ParalinguisticInputProps>(
|
||||
function ParalinguisticInput(
|
||||
{ value, onChange, placeholder, disabled, className, style, onClick, onFocus },
|
||||
ref,
|
||||
) {
|
||||
const editorRef = useRef<HTMLDivElement>(null);
|
||||
const [showMenu, setShowMenu] = useState(false);
|
||||
const [menuFilter, setMenuFilter] = useState('');
|
||||
const [menuIndex, setMenuIndex] = useState(0);
|
||||
const [menuPosition, setMenuPosition] = useState<{ bottom: number; left: number }>({
|
||||
bottom: 0,
|
||||
left: 0,
|
||||
});
|
||||
const triggerRangeRef = useRef<Range | null>(null);
|
||||
const lastSerializedRef = useRef<string>('');
|
||||
const isComposingRef = useRef(false);
|
||||
|
||||
useImperativeHandle(ref, () => ({
|
||||
focus: () => editorRef.current?.focus(),
|
||||
element: editorRef.current,
|
||||
}));
|
||||
|
||||
// Filtered tag list for the autocomplete menu
|
||||
const filteredTags = PARALINGUISTIC_TAGS.filter((t) =>
|
||||
t.label.toLowerCase().includes(menuFilter.toLowerCase()),
|
||||
);
|
||||
|
||||
// ── Sync external value → editor ──────────────────────────────
|
||||
useEffect(() => {
|
||||
const el = editorRef.current;
|
||||
if (!el) return;
|
||||
// Only update DOM if the external value differs from what we last emitted
|
||||
if (value !== undefined && value !== lastSerializedRef.current) {
|
||||
lastSerializedRef.current = value;
|
||||
el.innerHTML = value ? textToHTML(value) : '';
|
||||
}
|
||||
}, [value]);
|
||||
|
||||
// ── Emit plain-text value on input ────────────────────────────
|
||||
const emitChange = useCallback(() => {
|
||||
const el = editorRef.current;
|
||||
if (!el || !onChange) return;
|
||||
const text = htmlToText(el);
|
||||
lastSerializedRef.current = text;
|
||||
onChange(text);
|
||||
}, [onChange]);
|
||||
|
||||
// ── Insert a tag badge at the caret ───────────────────────────
|
||||
const insertTag = useCallback(
|
||||
(tag: string) => {
|
||||
const el = editorRef.current;
|
||||
if (!el) return;
|
||||
|
||||
// Delete the /filter text
|
||||
const wordRange = triggerRangeRef.current;
|
||||
if (wordRange) {
|
||||
wordRange.deleteContents();
|
||||
}
|
||||
|
||||
// Insert badge HTML
|
||||
const temp = document.createElement('span');
|
||||
temp.innerHTML = makeBadgeHTML(tag);
|
||||
const frag = document.createDocumentFragment();
|
||||
let lastNode: Node | null = null;
|
||||
while (temp.firstChild) {
|
||||
lastNode = frag.appendChild(temp.firstChild);
|
||||
}
|
||||
|
||||
const sel = window.getSelection();
|
||||
if (sel && sel.rangeCount > 0) {
|
||||
const range = sel.getRangeAt(0);
|
||||
range.deleteContents();
|
||||
range.insertNode(frag);
|
||||
|
||||
// Move caret after the badge
|
||||
if (lastNode) {
|
||||
const newRange = document.createRange();
|
||||
newRange.setStartAfter(lastNode);
|
||||
newRange.collapse(true);
|
||||
sel.removeAllRanges();
|
||||
sel.addRange(newRange);
|
||||
}
|
||||
}
|
||||
|
||||
setShowMenu(false);
|
||||
setMenuFilter('');
|
||||
emitChange();
|
||||
el.focus();
|
||||
},
|
||||
[emitChange],
|
||||
);
|
||||
|
||||
// ── Handle keydown for autocomplete navigation ────────────────
|
||||
const handleKeyDown = useCallback(
|
||||
(e: React.KeyboardEvent) => {
|
||||
if (showMenu) {
|
||||
if (filteredTags.length === 0) {
|
||||
if (e.key === 'Escape') {
|
||||
e.preventDefault();
|
||||
setShowMenu(false);
|
||||
}
|
||||
return;
|
||||
}
|
||||
if (e.key === 'ArrowDown') {
|
||||
e.preventDefault();
|
||||
setMenuIndex((i) => (i + 1) % filteredTags.length);
|
||||
} else if (e.key === 'ArrowUp') {
|
||||
e.preventDefault();
|
||||
setMenuIndex((i) => (i - 1 + filteredTags.length) % filteredTags.length);
|
||||
} else if (e.key === 'Enter' || e.key === 'Tab') {
|
||||
e.preventDefault();
|
||||
if (filteredTags[menuIndex]) {
|
||||
insertTag(filteredTags[menuIndex].tag);
|
||||
}
|
||||
} else if (e.key === 'Escape') {
|
||||
e.preventDefault();
|
||||
setShowMenu(false);
|
||||
}
|
||||
} else {
|
||||
// Prevent Enter from creating <div> blocks in contentEditable
|
||||
if (e.key === 'Enter' && !e.shiftKey) {
|
||||
// Let the form handle submit
|
||||
}
|
||||
}
|
||||
},
|
||||
[showMenu, filteredTags, menuIndex, insertTag],
|
||||
);
|
||||
|
||||
// ── Handle input (check for / trigger) ────────────────────────
|
||||
const handleInput = useCallback(() => {
|
||||
if (isComposingRef.current) return;
|
||||
const el = editorRef.current;
|
||||
if (!el) return;
|
||||
|
||||
const { word, range } = getWordBeforeCaret(el);
|
||||
|
||||
if (word.startsWith('/')) {
|
||||
const filter = word.slice(1); // strip the /
|
||||
setMenuFilter(filter);
|
||||
setMenuIndex(0);
|
||||
triggerRangeRef.current = range;
|
||||
|
||||
// Position the menu above the caret using viewport coords (portalled)
|
||||
const sel = window.getSelection();
|
||||
if (sel && sel.rangeCount > 0) {
|
||||
const rect = sel.getRangeAt(0).getBoundingClientRect();
|
||||
setMenuPosition({
|
||||
bottom: window.innerHeight - rect.top + 4,
|
||||
left: rect.left,
|
||||
});
|
||||
}
|
||||
|
||||
setShowMenu(true);
|
||||
} else {
|
||||
setShowMenu(false);
|
||||
}
|
||||
|
||||
emitChange();
|
||||
}, [emitChange]);
|
||||
|
||||
// ── Handle paste — convert [tag] patterns to badges ───────────
|
||||
const handlePaste = useCallback(
|
||||
(e: React.ClipboardEvent) => {
|
||||
e.preventDefault();
|
||||
const text = e.clipboardData.getData('text/plain');
|
||||
if (!text) return;
|
||||
|
||||
const el = editorRef.current;
|
||||
if (!el) return;
|
||||
|
||||
const html = textToHTML(text);
|
||||
|
||||
// Insert at caret
|
||||
const sel = window.getSelection();
|
||||
if (sel && sel.rangeCount > 0) {
|
||||
const range = sel.getRangeAt(0);
|
||||
range.deleteContents();
|
||||
const temp = document.createElement('div');
|
||||
temp.innerHTML = html;
|
||||
const frag = document.createDocumentFragment();
|
||||
let lastNode: Node | null = null;
|
||||
while (temp.firstChild) {
|
||||
lastNode = frag.appendChild(temp.firstChild);
|
||||
}
|
||||
range.insertNode(frag);
|
||||
if (lastNode) {
|
||||
const newRange = document.createRange();
|
||||
newRange.setStartAfter(lastNode);
|
||||
newRange.collapse(true);
|
||||
sel.removeAllRanges();
|
||||
sel.addRange(newRange);
|
||||
}
|
||||
}
|
||||
|
||||
emitChange();
|
||||
},
|
||||
[emitChange],
|
||||
);
|
||||
|
||||
// ── Show placeholder ──────────────────────────────────────────
|
||||
const isEmpty = !value || value.trim() === '';
|
||||
|
||||
return (
|
||||
<div className="relative">
|
||||
{/* Placeholder */}
|
||||
{isEmpty && placeholder && (
|
||||
<div
|
||||
className="pointer-events-none absolute inset-0 text-sm text-muted-foreground/60 px-3 py-2 select-none"
|
||||
aria-hidden
|
||||
>
|
||||
{placeholder}
|
||||
</div>
|
||||
)}
|
||||
|
||||
{/* Editable area */}
|
||||
<div
|
||||
ref={editorRef}
|
||||
contentEditable={!disabled}
|
||||
suppressContentEditableWarning
|
||||
role={disabled ? undefined : 'textbox'}
|
||||
aria-multiline={disabled ? undefined : true}
|
||||
aria-placeholder={placeholder}
|
||||
aria-disabled={disabled}
|
||||
tabIndex={disabled ? -1 : 0}
|
||||
className={cn(
|
||||
'min-h-[32px] text-sm whitespace-pre-wrap break-words outline-none',
|
||||
'[&_.ptag-badge]:inline-flex [&_.ptag-badge]:items-center [&_.ptag-badge]:rounded-full',
|
||||
'[&_.ptag-badge]:bg-accent/20 [&_.ptag-badge]:text-accent [&_.ptag-badge]:border [&_.ptag-badge]:border-accent/30',
|
||||
'[&_.ptag-badge]:px-2 [&_.ptag-badge]:py-0 [&_.ptag-badge]:text-xs [&_.ptag-badge]:font-medium',
|
||||
'[&_.ptag-badge]:mx-0.5 [&_.ptag-badge]:select-none [&_.ptag-badge]:cursor-default',
|
||||
'[&_.ptag-badge]:align-baseline',
|
||||
disabled && 'opacity-50 cursor-not-allowed',
|
||||
className,
|
||||
)}
|
||||
style={style}
|
||||
onInput={!disabled ? handleInput : undefined}
|
||||
onKeyDown={!disabled ? handleKeyDown : undefined}
|
||||
onPaste={!disabled ? handlePaste : undefined}
|
||||
onClick={!disabled ? onClick : undefined}
|
||||
onFocus={!disabled ? onFocus : undefined}
|
||||
onBlur={() => {
|
||||
setShowMenu(false);
|
||||
triggerRangeRef.current = null;
|
||||
}}
|
||||
onCompositionStart={() => {
|
||||
isComposingRef.current = true;
|
||||
}}
|
||||
onCompositionEnd={() => {
|
||||
isComposingRef.current = false;
|
||||
handleInput();
|
||||
}}
|
||||
/>
|
||||
|
||||
{/* Autocomplete dropdown — portalled to body, positioned above the caret */}
|
||||
{showMenu &&
|
||||
filteredTags.length > 0 &&
|
||||
createPortal(
|
||||
<AnimatePresence>
|
||||
<motion.div
|
||||
initial={{ opacity: 0, y: 4 }}
|
||||
animate={{ opacity: 1, y: 0 }}
|
||||
exit={{ opacity: 0, y: 4 }}
|
||||
transition={{ duration: 0.12 }}
|
||||
className="fixed z-[9999] min-w-[200px] max-h-[280px] overflow-y-auto rounded-lg border border-border bg-popover shadow-lg"
|
||||
style={{
|
||||
bottom: menuPosition.bottom,
|
||||
left: menuPosition.left,
|
||||
}}
|
||||
>
|
||||
{filteredTags.map((t, i) => (
|
||||
<button
|
||||
key={t.tag}
|
||||
type="button"
|
||||
className={cn(
|
||||
'flex items-center gap-2 w-full px-3 py-1.5 text-sm text-left transition-colors',
|
||||
i === menuIndex
|
||||
? 'bg-accent/20 text-accent-foreground'
|
||||
: 'text-popover-foreground hover:bg-muted/50',
|
||||
)}
|
||||
onMouseDown={(e) => {
|
||||
e.preventDefault(); // Keep focus in editor
|
||||
insertTag(t.tag);
|
||||
}}
|
||||
onMouseEnter={() => setMenuIndex(i)}
|
||||
>
|
||||
<span className="text-base leading-none">{t.emoji}</span>
|
||||
<span>{t.label}</span>
|
||||
<span className="ml-auto text-xs text-muted-foreground font-mono">{t.tag}</span>
|
||||
</button>
|
||||
))}
|
||||
</motion.div>
|
||||
</AnimatePresence>,
|
||||
document.body,
|
||||
)}
|
||||
</div>
|
||||
);
|
||||
},
|
||||
);
|
||||
@@ -253,17 +253,10 @@ export function HistoryTable() {
|
||||
return (
|
||||
<div
|
||||
key={gen.id}
|
||||
role="button"
|
||||
tabIndex={0}
|
||||
className={cn(
|
||||
'flex items-stretch gap-4 h-26 border rounded-md p-3 bg-card hover:bg-muted/70 transition-colors text-left w-full',
|
||||
isCurrentlyPlaying && 'bg-muted/70',
|
||||
)}
|
||||
aria-label={
|
||||
isCurrentlyPlaying
|
||||
? `Sample from ${gen.profile_name}, ${formatDuration(gen.duration)}, ${formatDate(gen.created_at)}. Playing. Press Enter to restart.`
|
||||
: `Sample from ${gen.profile_name}, ${formatDuration(gen.duration)}, ${formatDate(gen.created_at)}. Press Enter to play.`
|
||||
}
|
||||
onMouseDown={(e) => {
|
||||
// Don't trigger play if clicking on textarea or if text is selected
|
||||
const target = e.target as HTMLElement;
|
||||
@@ -272,14 +265,6 @@ export function HistoryTable() {
|
||||
}
|
||||
handlePlay(gen.id, gen.text, gen.profile_id);
|
||||
}}
|
||||
onKeyDown={(e) => {
|
||||
const target = e.target as HTMLElement;
|
||||
if (target.closest('textarea') || target.closest('button')) return;
|
||||
if (e.key === 'Enter' || e.key === ' ') {
|
||||
e.preventDefault();
|
||||
handlePlay(gen.id, gen.text, gen.profile_id);
|
||||
}
|
||||
}}
|
||||
>
|
||||
{/* Waveform icon */}
|
||||
<div className="flex items-center shrink-0">
|
||||
@@ -308,7 +293,6 @@ export function HistoryTable() {
|
||||
value={gen.text}
|
||||
className="flex-1 resize-none text-sm text-muted-foreground select-text"
|
||||
readOnly
|
||||
aria-label={`Transcript for sample from ${gen.profile_name}, ${formatDuration(gen.duration)}`}
|
||||
/>
|
||||
</div>
|
||||
|
||||
|
||||
@@ -2,7 +2,7 @@ import { ModelManagement } from '@/components/ServerSettings/ModelManagement';
|
||||
|
||||
export function ModelsTab() {
|
||||
return (
|
||||
<div className="h-full flex flex-col p-4">
|
||||
<div className="space-y-4 overflow-y-auto flex flex-col">
|
||||
<ModelManagement />
|
||||
</div>
|
||||
);
|
||||
|
||||
@@ -1,12 +1,9 @@
|
||||
import { zodResolver } from '@hookform/resolvers/zod';
|
||||
import { Loader2, XCircle } from 'lucide-react';
|
||||
import { useEffect } from 'react';
|
||||
import { useForm } from 'react-hook-form';
|
||||
import * as z from 'zod';
|
||||
import { Badge } from '@/components/ui/badge';
|
||||
import { Button } from '@/components/ui/button';
|
||||
import { Card, CardContent, CardHeader, CardTitle } from '@/components/ui/card';
|
||||
import { Checkbox } from '@/components/ui/checkbox';
|
||||
import {
|
||||
Form,
|
||||
FormControl,
|
||||
@@ -17,10 +14,10 @@ import {
|
||||
FormMessage,
|
||||
} from '@/components/ui/form';
|
||||
import { Input } from '@/components/ui/input';
|
||||
import { Checkbox } from '@/components/ui/checkbox';
|
||||
import { useToast } from '@/components/ui/use-toast';
|
||||
import { useServerHealth } from '@/lib/hooks/useServer';
|
||||
import { usePlatform } from '@/platform/PlatformContext';
|
||||
import { useServerStore } from '@/stores/serverStore';
|
||||
import { usePlatform } from '@/platform/PlatformContext';
|
||||
|
||||
const connectionSchema = z.object({
|
||||
serverUrl: z.string().url('Please enter a valid URL'),
|
||||
@@ -34,10 +31,7 @@ export function ConnectionForm() {
|
||||
const setServerUrl = useServerStore((state) => state.setServerUrl);
|
||||
const keepServerRunningOnClose = useServerStore((state) => state.keepServerRunningOnClose);
|
||||
const setKeepServerRunningOnClose = useServerStore((state) => state.setKeepServerRunningOnClose);
|
||||
const mode = useServerStore((state) => state.mode);
|
||||
const setMode = useServerStore((state) => state.setMode);
|
||||
const { toast } = useToast();
|
||||
const { data: health, isLoading, error: healthError } = useServerHealth();
|
||||
|
||||
const form = useForm<ConnectionFormValues>({
|
||||
resolver: zodResolver(connectionSchema),
|
||||
@@ -55,7 +49,7 @@ export function ConnectionForm() {
|
||||
|
||||
function onSubmit(data: ConnectionFormValues) {
|
||||
setServerUrl(data.serverUrl);
|
||||
form.reset(data);
|
||||
form.reset(data); // Reset form state after successful submission
|
||||
toast({
|
||||
title: 'Server URL updated',
|
||||
description: `Connected to ${data.serverUrl}`,
|
||||
@@ -63,7 +57,7 @@ export function ConnectionForm() {
|
||||
}
|
||||
|
||||
return (
|
||||
<Card role="region" aria-label="Server Connection" tabIndex={0}>
|
||||
<Card>
|
||||
<CardHeader>
|
||||
<CardTitle>Server Connection</CardTitle>
|
||||
</CardHeader>
|
||||
@@ -89,37 +83,6 @@ export function ConnectionForm() {
|
||||
</form>
|
||||
</Form>
|
||||
|
||||
{/* Connection status */}
|
||||
<div className="mt-4">
|
||||
{isLoading ? (
|
||||
<div className="flex items-center gap-2">
|
||||
<Loader2 className="h-4 w-4 animate-spin" />
|
||||
<span className="text-sm text-muted-foreground">Checking connection...</span>
|
||||
</div>
|
||||
) : healthError ? (
|
||||
<div className="flex items-center gap-2">
|
||||
<XCircle className="h-4 w-4 text-destructive" />
|
||||
<span className="text-sm text-destructive">
|
||||
Connection failed: {healthError.message}
|
||||
</span>
|
||||
</div>
|
||||
) : health ? (
|
||||
<div className="flex flex-wrap gap-2">
|
||||
<Badge
|
||||
variant={health.model_loaded || health.model_downloaded ? 'default' : 'secondary'}
|
||||
>
|
||||
{health.model_loaded || health.model_downloaded ? 'Model Ready' : 'No Model'}
|
||||
</Badge>
|
||||
<Badge variant={health.gpu_available ? 'default' : 'secondary'}>
|
||||
GPU: {health.gpu_available ? 'Available' : 'Not Available'}
|
||||
</Badge>
|
||||
{health.vram_used_mb && (
|
||||
<Badge variant="outline">VRAM: {health.vram_used_mb.toFixed(0)} MB</Badge>
|
||||
)}
|
||||
</div>
|
||||
) : null}
|
||||
</div>
|
||||
|
||||
<div className="mt-6 pt-6 border-t">
|
||||
<div className="flex items-start space-x-3">
|
||||
<Checkbox
|
||||
@@ -152,38 +115,6 @@ export function ConnectionForm() {
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
{platform.metadata.isTauri && (
|
||||
<div className="mt-6 pt-6 border-t">
|
||||
<div className="flex items-start space-x-3">
|
||||
<Checkbox
|
||||
id="allowNetworkAccess"
|
||||
checked={mode === 'remote'}
|
||||
onCheckedChange={(checked: boolean) => {
|
||||
setMode(checked ? 'remote' : 'local');
|
||||
toast({
|
||||
title: 'Setting updated',
|
||||
description: checked
|
||||
? 'Network access enabled. Restart the app to apply.'
|
||||
: 'Network access disabled. Restart the app to apply.',
|
||||
});
|
||||
}}
|
||||
/>
|
||||
<div className="space-y-1">
|
||||
<label
|
||||
htmlFor="allowNetworkAccess"
|
||||
className="text-sm font-medium leading-none peer-disabled:cursor-not-allowed peer-disabled:opacity-70 cursor-pointer"
|
||||
>
|
||||
Allow network access
|
||||
</label>
|
||||
<p className="text-sm text-muted-foreground">
|
||||
Makes the server accessible from other devices on your network. Restart the app
|
||||
after changing this setting.
|
||||
</p>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
)}
|
||||
</CardContent>
|
||||
</Card>
|
||||
);
|
||||
|
||||
@@ -1,71 +0,0 @@
|
||||
import { Card, CardContent, CardDescription, CardHeader, CardTitle } from '@/components/ui/card';
|
||||
import { Slider } from '@/components/ui/slider';
|
||||
import { useServerStore } from '@/stores/serverStore';
|
||||
|
||||
export function GenerationSettings() {
|
||||
const maxChunkChars = useServerStore((state) => state.maxChunkChars);
|
||||
const setMaxChunkChars = useServerStore((state) => state.setMaxChunkChars);
|
||||
const crossfadeMs = useServerStore((state) => state.crossfadeMs);
|
||||
const setCrossfadeMs = useServerStore((state) => state.setCrossfadeMs);
|
||||
|
||||
return (
|
||||
<Card role="region" aria-label="Generation Settings" tabIndex={0}>
|
||||
<CardHeader>
|
||||
<CardTitle>Generation Settings</CardTitle>
|
||||
<CardDescription>
|
||||
Controls for long text generation. These settings apply to all engines.
|
||||
</CardDescription>
|
||||
</CardHeader>
|
||||
<CardContent>
|
||||
<div className="space-y-6">
|
||||
<div className="space-y-3">
|
||||
<div className="flex items-center justify-between">
|
||||
<label htmlFor="maxChunkChars" className="text-sm font-medium leading-none">
|
||||
Auto-chunking limit
|
||||
</label>
|
||||
<span className="text-sm tabular-nums text-muted-foreground">
|
||||
{maxChunkChars} chars
|
||||
</span>
|
||||
</div>
|
||||
<Slider
|
||||
id="maxChunkChars"
|
||||
value={[maxChunkChars]}
|
||||
onValueChange={([value]) => setMaxChunkChars(value)}
|
||||
min={100}
|
||||
max={2000}
|
||||
step={50}
|
||||
aria-label="Auto-chunking character limit"
|
||||
/>
|
||||
<p className="text-sm text-muted-foreground">
|
||||
Long text is split into chunks at sentence boundaries before generating. Lower values
|
||||
can improve quality for long outputs.
|
||||
</p>
|
||||
</div>
|
||||
|
||||
<div className="space-y-3">
|
||||
<div className="flex items-center justify-between">
|
||||
<label htmlFor="crossfadeMs" className="text-sm font-medium leading-none">
|
||||
Chunk crossfade
|
||||
</label>
|
||||
<span className="text-sm tabular-nums text-muted-foreground">
|
||||
{crossfadeMs === 0 ? 'Cut' : `${crossfadeMs}ms`}
|
||||
</span>
|
||||
</div>
|
||||
<Slider
|
||||
id="crossfadeMs"
|
||||
value={[crossfadeMs]}
|
||||
onValueChange={([value]) => setCrossfadeMs(value)}
|
||||
min={0}
|
||||
max={200}
|
||||
step={10}
|
||||
aria-label="Chunk crossfade duration"
|
||||
/>
|
||||
<p className="text-sm text-muted-foreground">
|
||||
Blends audio between chunks to smooth transitions. Set to 0 for a hard cut.
|
||||
</p>
|
||||
</div>
|
||||
</div>
|
||||
</CardContent>
|
||||
</Card>
|
||||
);
|
||||
}
|
||||
@@ -1,366 +0,0 @@
|
||||
import { useQuery, useQueryClient } from '@tanstack/react-query';
|
||||
import { AlertCircle, Download, Loader2, RotateCw, Trash2 } from 'lucide-react';
|
||||
import { useCallback, useEffect, useRef, useState } from 'react';
|
||||
import { Button } from '@/components/ui/button';
|
||||
import { Card, CardContent, CardHeader, CardTitle } from '@/components/ui/card';
|
||||
import { Progress } from '@/components/ui/progress';
|
||||
import { apiClient } from '@/lib/api/client';
|
||||
import type { CudaDownloadProgress } from '@/lib/api/types';
|
||||
import { useServerHealth } from '@/lib/hooks/useServer';
|
||||
import { usePlatform } from '@/platform/PlatformContext';
|
||||
import { useServerStore } from '@/stores/serverStore';
|
||||
|
||||
type RestartPhase = 'idle' | 'stopping' | 'waiting' | 'ready';
|
||||
|
||||
export function GpuAcceleration() {
|
||||
const platform = usePlatform();
|
||||
const queryClient = useQueryClient();
|
||||
const serverUrl = useServerStore((state) => state.serverUrl);
|
||||
const { data: health } = useServerHealth();
|
||||
|
||||
const [restartPhase, setRestartPhase] = useState<RestartPhase>('idle');
|
||||
const [error, setError] = useState<string | null>(null);
|
||||
const [downloadProgress, setDownloadProgress] = useState<CudaDownloadProgress | null>(null);
|
||||
const healthPollRef = useRef<ReturnType<typeof setInterval> | null>(null);
|
||||
|
||||
// Query CUDA backend status
|
||||
const {
|
||||
data: cudaStatus,
|
||||
isLoading: cudaStatusLoading,
|
||||
refetch: refetchCudaStatus,
|
||||
} = useQuery({
|
||||
queryKey: ['cuda-status', serverUrl],
|
||||
queryFn: () => apiClient.getCudaStatus(),
|
||||
refetchInterval: (query) => (query.state.status === 'pending' ? false : 10000),
|
||||
retry: 1,
|
||||
enabled: !!health, // Only fetch when backend is reachable
|
||||
});
|
||||
|
||||
// Derived state
|
||||
const isCurrentlyCuda = health?.backend_variant === 'cuda';
|
||||
const cudaAvailable = cudaStatus?.available ?? false;
|
||||
const cudaDownloading = cudaStatus?.downloading ?? false;
|
||||
|
||||
// Clean up health poll on unmount
|
||||
useEffect(() => {
|
||||
return () => {
|
||||
if (healthPollRef.current) {
|
||||
clearInterval(healthPollRef.current);
|
||||
healthPollRef.current = null;
|
||||
}
|
||||
};
|
||||
}, []);
|
||||
|
||||
// SSE progress tracking during download
|
||||
useEffect(() => {
|
||||
if (!cudaDownloading || !serverUrl) {
|
||||
return;
|
||||
}
|
||||
|
||||
const eventSource = new EventSource(`${serverUrl}/backend/cuda-progress`);
|
||||
|
||||
eventSource.onmessage = (event) => {
|
||||
try {
|
||||
const data = JSON.parse(event.data) as CudaDownloadProgress;
|
||||
setDownloadProgress(data);
|
||||
|
||||
if (data.status === 'complete') {
|
||||
eventSource.close();
|
||||
setDownloadProgress(null);
|
||||
refetchCudaStatus();
|
||||
} else if (data.status === 'error') {
|
||||
eventSource.close();
|
||||
setError(data.error || 'Download failed');
|
||||
setDownloadProgress(null);
|
||||
refetchCudaStatus();
|
||||
}
|
||||
} catch (e) {
|
||||
console.error('Error parsing CUDA progress event:', e);
|
||||
}
|
||||
};
|
||||
|
||||
eventSource.onerror = () => {
|
||||
eventSource.close();
|
||||
};
|
||||
|
||||
return () => {
|
||||
eventSource.close();
|
||||
};
|
||||
}, [cudaDownloading, serverUrl, refetchCudaStatus]);
|
||||
|
||||
// Start aggressive health polling during restart
|
||||
const startHealthPolling = useCallback(() => {
|
||||
if (healthPollRef.current) return;
|
||||
|
||||
healthPollRef.current = setInterval(async () => {
|
||||
try {
|
||||
const result = await apiClient.getHealth();
|
||||
if (result.status === 'healthy') {
|
||||
// Server is back up
|
||||
if (healthPollRef.current) {
|
||||
clearInterval(healthPollRef.current);
|
||||
healthPollRef.current = null;
|
||||
}
|
||||
setRestartPhase('ready');
|
||||
// Invalidate all queries to refresh UI
|
||||
queryClient.invalidateQueries();
|
||||
// Reset after a moment
|
||||
setTimeout(() => setRestartPhase('idle'), 2000);
|
||||
}
|
||||
} catch {
|
||||
// Server still down, keep polling
|
||||
}
|
||||
}, 1000);
|
||||
}, [queryClient]);
|
||||
|
||||
const handleDownload = async () => {
|
||||
setError(null);
|
||||
try {
|
||||
await apiClient.downloadCudaBackend();
|
||||
refetchCudaStatus();
|
||||
} catch (e: unknown) {
|
||||
const msg = e instanceof Error ? e.message : 'Failed to start download';
|
||||
if (msg.includes('already downloaded')) {
|
||||
refetchCudaStatus();
|
||||
} else {
|
||||
setError(msg);
|
||||
}
|
||||
}
|
||||
};
|
||||
|
||||
const handleRestart = async () => {
|
||||
setError(null);
|
||||
setRestartPhase('stopping');
|
||||
|
||||
try {
|
||||
setRestartPhase('waiting');
|
||||
startHealthPolling();
|
||||
await platform.lifecycle.restartServer();
|
||||
// Invoke resolved — server is likely ready. Stop polling and refresh.
|
||||
if (healthPollRef.current) {
|
||||
clearInterval(healthPollRef.current);
|
||||
healthPollRef.current = null;
|
||||
}
|
||||
setRestartPhase('ready');
|
||||
queryClient.invalidateQueries();
|
||||
setTimeout(() => setRestartPhase('idle'), 2000);
|
||||
} catch (e: unknown) {
|
||||
setRestartPhase('idle');
|
||||
if (healthPollRef.current) {
|
||||
clearInterval(healthPollRef.current);
|
||||
healthPollRef.current = null;
|
||||
}
|
||||
setError(e instanceof Error ? e.message : 'Restart failed');
|
||||
}
|
||||
};
|
||||
|
||||
const handleSwitchToCpu = async () => {
|
||||
// To switch to CPU: delete the CUDA binary, then restart.
|
||||
// start_server always prefers CUDA if present, so we must remove it first.
|
||||
setError(null);
|
||||
setRestartPhase('stopping');
|
||||
|
||||
try {
|
||||
await apiClient.deleteCudaBackend();
|
||||
setRestartPhase('waiting');
|
||||
startHealthPolling();
|
||||
await platform.lifecycle.restartServer();
|
||||
// Invoke resolved — server is likely ready
|
||||
if (healthPollRef.current) {
|
||||
clearInterval(healthPollRef.current);
|
||||
healthPollRef.current = null;
|
||||
}
|
||||
setRestartPhase('ready');
|
||||
queryClient.invalidateQueries();
|
||||
setTimeout(() => setRestartPhase('idle'), 2000);
|
||||
} catch (e: unknown) {
|
||||
setRestartPhase('idle');
|
||||
if (healthPollRef.current) {
|
||||
clearInterval(healthPollRef.current);
|
||||
healthPollRef.current = null;
|
||||
}
|
||||
setError(e instanceof Error ? e.message : 'Failed to switch to CPU');
|
||||
refetchCudaStatus();
|
||||
}
|
||||
};
|
||||
|
||||
const handleDelete = async () => {
|
||||
setError(null);
|
||||
try {
|
||||
await apiClient.deleteCudaBackend();
|
||||
refetchCudaStatus();
|
||||
} catch (e: unknown) {
|
||||
setError(e instanceof Error ? e.message : 'Failed to delete CUDA backend');
|
||||
}
|
||||
};
|
||||
|
||||
const formatBytes = (bytes: number): string => {
|
||||
if (bytes === 0) return '0 B';
|
||||
const k = 1024;
|
||||
const sizes = ['B', 'KB', 'MB', 'GB'];
|
||||
const i = Math.floor(Math.log(bytes) / Math.log(k));
|
||||
return `${(bytes / k ** i).toFixed(1)} ${sizes[i]}`;
|
||||
};
|
||||
|
||||
// Don't render until health data is available
|
||||
if (!health) return null;
|
||||
|
||||
// If the system already has native GPU (MPS, etc.), only show info - no CUDA needed
|
||||
const hasNativeGpu =
|
||||
health.gpu_available &&
|
||||
!isCurrentlyCuda &&
|
||||
health.gpu_type &&
|
||||
!health.gpu_type.includes('CUDA');
|
||||
|
||||
return (
|
||||
<Card>
|
||||
<CardHeader>
|
||||
<CardTitle>GPU Acceleration</CardTitle>
|
||||
</CardHeader>
|
||||
<CardContent className="space-y-4">
|
||||
{/* Current status */}
|
||||
<div className="space-y-1">
|
||||
<div className="text-sm font-medium">Backend</div>
|
||||
<div className="text-sm text-muted-foreground">
|
||||
{isCurrentlyCuda
|
||||
? 'CUDA (GPU accelerated)'
|
||||
: hasNativeGpu
|
||||
? `${health.backend_type === 'mlx' ? 'MLX' : 'PyTorch'} (GPU accelerated)`
|
||||
: 'CPU'}
|
||||
</div>
|
||||
</div>
|
||||
|
||||
{/* GPU info from health */}
|
||||
{health.gpu_type && (
|
||||
<div className="space-y-1">
|
||||
<div className="text-sm font-medium">GPU</div>
|
||||
<div className="text-sm text-muted-foreground">{health.gpu_type}</div>
|
||||
{health.vram_used_mb != null && (
|
||||
<div className="text-xs text-muted-foreground">
|
||||
VRAM: {health.vram_used_mb.toFixed(0)} MB used
|
||||
</div>
|
||||
)}
|
||||
</div>
|
||||
)}
|
||||
|
||||
{/* Native GPU detected - no CUDA download needed */}
|
||||
|
||||
{/* CUDA download section - only show when native GPU is NOT detected (i.e., Windows/Linux NVIDIA users) */}
|
||||
{!hasNativeGpu && (
|
||||
<>
|
||||
{/* Download progress */}
|
||||
{cudaDownloading && downloadProgress && (
|
||||
<div className="space-y-2">
|
||||
<div className="flex items-center justify-between text-sm">
|
||||
<div className="flex items-center gap-2">
|
||||
<Loader2 className="h-4 w-4 animate-spin" />
|
||||
<span>{downloadProgress.filename || 'Downloading CUDA backend...'}</span>
|
||||
</div>
|
||||
{downloadProgress.total > 0 && (
|
||||
<span className="text-muted-foreground">
|
||||
{downloadProgress.progress.toFixed(1)}%
|
||||
</span>
|
||||
)}
|
||||
</div>
|
||||
{downloadProgress.total > 0 && (
|
||||
<>
|
||||
<Progress value={downloadProgress.progress} className="h-2" />
|
||||
<div className="text-xs text-muted-foreground">
|
||||
{formatBytes(downloadProgress.current)} /{' '}
|
||||
{formatBytes(downloadProgress.total)}
|
||||
</div>
|
||||
</>
|
||||
)}
|
||||
</div>
|
||||
)}
|
||||
|
||||
{/* Restart in progress */}
|
||||
{restartPhase !== 'idle' && (
|
||||
<div className="flex items-center gap-2 p-3 rounded-lg bg-primary/5 border">
|
||||
<Loader2 className="h-4 w-4 animate-spin" />
|
||||
<span className="text-sm">
|
||||
{restartPhase === 'stopping' && 'Stopping server...'}
|
||||
{restartPhase === 'waiting' && 'Restarting server...'}
|
||||
{restartPhase === 'ready' && 'Server restarted successfully!'}
|
||||
</span>
|
||||
</div>
|
||||
)}
|
||||
|
||||
{/* Error display */}
|
||||
{error && (
|
||||
<div className="flex items-center gap-2 text-sm text-destructive">
|
||||
<AlertCircle className="h-4 w-4 shrink-0" />
|
||||
<span>{error}</span>
|
||||
</div>
|
||||
)}
|
||||
|
||||
{/* Actions */}
|
||||
{restartPhase === 'idle' && !cudaDownloading && (
|
||||
<div className="space-y-2">
|
||||
{/* Not downloaded yet - show download button */}
|
||||
{!cudaAvailable && (
|
||||
<div className="space-y-3">
|
||||
<p className="text-sm text-muted-foreground">
|
||||
Download the CUDA backend (~2.4 GB) for NVIDIA GPU acceleration. Requires an
|
||||
NVIDIA GPU with CUDA support.
|
||||
</p>
|
||||
<Button onClick={handleDownload} className="w-full" size="sm">
|
||||
<Download className="h-4 w-4 mr-2" />
|
||||
Download CUDA Backend
|
||||
</Button>
|
||||
</div>
|
||||
)}
|
||||
|
||||
{/* Downloaded but not active - show switch button */}
|
||||
{cudaAvailable && !isCurrentlyCuda && platform.metadata.isTauri && (
|
||||
<div className="space-y-3">
|
||||
<p className="text-sm text-muted-foreground">
|
||||
CUDA backend is downloaded and ready. Restart the server to enable GPU
|
||||
acceleration.
|
||||
</p>
|
||||
<Button onClick={handleRestart} className="w-full" size="sm">
|
||||
<RotateCw className="h-4 w-4 mr-2" />
|
||||
Switch to CUDA Backend
|
||||
</Button>
|
||||
</div>
|
||||
)}
|
||||
|
||||
{/* Currently active - show switch back to CPU */}
|
||||
{isCurrentlyCuda && platform.metadata.isTauri && (
|
||||
<div className="space-y-3">
|
||||
<p className="text-sm text-muted-foreground">
|
||||
Running with CUDA GPU acceleration. Switch back to CPU if needed (you can
|
||||
re-download later).
|
||||
</p>
|
||||
<Button
|
||||
onClick={handleSwitchToCpu}
|
||||
variant="outline"
|
||||
className="w-full"
|
||||
size="sm"
|
||||
>
|
||||
<RotateCw className="h-4 w-4 mr-2" />
|
||||
Switch to CPU Backend
|
||||
</Button>
|
||||
</div>
|
||||
)}
|
||||
|
||||
{/* Delete option when downloaded (and not active) */}
|
||||
{cudaAvailable && !isCurrentlyCuda && (
|
||||
<Button
|
||||
onClick={handleDelete}
|
||||
variant="ghost"
|
||||
className="w-full text-muted-foreground hover:text-destructive"
|
||||
size="sm"
|
||||
>
|
||||
<Trash2 className="h-4 w-4 mr-2" />
|
||||
Remove CUDA Backend
|
||||
</Button>
|
||||
)}
|
||||
</div>
|
||||
)}
|
||||
</>
|
||||
)}
|
||||
</CardContent>
|
||||
</Card>
|
||||
);
|
||||
}
|
||||
@@ -1,22 +1,6 @@
|
||||
import { useMutation, useQuery, useQueryClient } from '@tanstack/react-query';
|
||||
import {
|
||||
ChevronDown,
|
||||
ChevronRight,
|
||||
ChevronUp,
|
||||
CircleCheck,
|
||||
CircleX,
|
||||
Download,
|
||||
ExternalLink,
|
||||
HardDrive,
|
||||
Heart,
|
||||
Loader2,
|
||||
RotateCcw,
|
||||
Scale,
|
||||
Trash2,
|
||||
Unplug,
|
||||
X,
|
||||
} from 'lucide-react';
|
||||
import { useCallback, useMemo, useState } from 'react';
|
||||
import { Download, Loader2, Trash2 } from 'lucide-react';
|
||||
import { useCallback, useState } from 'react';
|
||||
import {
|
||||
AlertDialog,
|
||||
AlertDialogAction,
|
||||
@@ -29,156 +13,43 @@ import {
|
||||
} from '@/components/ui/alert-dialog';
|
||||
import { Badge } from '@/components/ui/badge';
|
||||
import { Button } from '@/components/ui/button';
|
||||
import {
|
||||
Dialog,
|
||||
DialogContent,
|
||||
DialogDescription,
|
||||
DialogHeader,
|
||||
DialogTitle,
|
||||
} from '@/components/ui/dialog';
|
||||
import { Progress } from '@/components/ui/progress';
|
||||
import { Card, CardContent, CardDescription, CardHeader, CardTitle } from '@/components/ui/card';
|
||||
import { useToast } from '@/components/ui/use-toast';
|
||||
import { apiClient } from '@/lib/api/client';
|
||||
import type { ActiveDownloadTask, HuggingFaceModelInfo, ModelStatus } from '@/lib/api/types';
|
||||
import { useModelDownloadToast } from '@/lib/hooks/useModelDownloadToast';
|
||||
|
||||
async function fetchHuggingFaceModelInfo(repoId: string): Promise<HuggingFaceModelInfo> {
|
||||
const response = await fetch(`https://huggingface.co/api/models/${repoId}`);
|
||||
if (!response.ok) throw new Error(`Failed to fetch model info: ${response.status}`);
|
||||
return response.json();
|
||||
}
|
||||
|
||||
function formatDownloads(n: number): string {
|
||||
if (n >= 1_000_000) return `${(n / 1_000_000).toFixed(1)}M`;
|
||||
if (n >= 1_000) return `${(n / 1_000).toFixed(1)}k`;
|
||||
return n.toString();
|
||||
}
|
||||
|
||||
function formatLicense(license: string): string {
|
||||
const map: Record<string, string> = {
|
||||
'apache-2.0': 'Apache 2.0',
|
||||
mit: 'MIT',
|
||||
'cc-by-4.0': 'CC BY 4.0',
|
||||
'cc-by-sa-4.0': 'CC BY-SA 4.0',
|
||||
'cc-by-nc-4.0': 'CC BY-NC 4.0',
|
||||
'openrail++': 'OpenRAIL++',
|
||||
openrail: 'OpenRAIL',
|
||||
};
|
||||
return map[license] || license;
|
||||
}
|
||||
|
||||
function formatPipelineTag(tag: string): string {
|
||||
return tag
|
||||
.split('-')
|
||||
.map((w) => w.charAt(0).toUpperCase() + w.slice(1))
|
||||
.join(' ');
|
||||
}
|
||||
|
||||
function formatBytes(bytes: number): string {
|
||||
if (bytes === 0) return '0 B';
|
||||
const k = 1024;
|
||||
const sizes = ['B', 'KB', 'MB', 'GB'];
|
||||
const i = Math.floor(Math.log(bytes) / Math.log(k));
|
||||
return `${(bytes / k ** i).toFixed(1)} ${sizes[i]}`;
|
||||
}
|
||||
|
||||
export function ModelManagement() {
|
||||
const { toast } = useToast();
|
||||
const queryClient = useQueryClient();
|
||||
const [downloadingModel, setDownloadingModel] = useState<string | null>(null);
|
||||
const [downloadingDisplayName, setDownloadingDisplayName] = useState<string | null>(null);
|
||||
const [consoleOpen, setConsoleOpen] = useState(false);
|
||||
const [dismissedErrors, setDismissedErrors] = useState<Set<string>>(new Set());
|
||||
const [localErrors, setLocalErrors] = useState<Map<string, string>>(new Map());
|
||||
|
||||
// Modal state
|
||||
const [selectedModel, setSelectedModel] = useState<ModelStatus | null>(null);
|
||||
const [detailOpen, setDetailOpen] = useState(false);
|
||||
|
||||
const { data: modelStatus, isLoading } = useQuery({
|
||||
queryKey: ['modelStatus'],
|
||||
queryFn: async () => {
|
||||
console.log('[Query] Fetching model status');
|
||||
const result = await apiClient.getModelStatus();
|
||||
console.log('[Query] Model status fetched:', result);
|
||||
return result;
|
||||
},
|
||||
refetchInterval: 5000,
|
||||
refetchInterval: 5000, // Refresh every 5 seconds
|
||||
});
|
||||
|
||||
const { data: activeTasks } = useQuery({
|
||||
queryKey: ['activeTasks'],
|
||||
queryFn: () => apiClient.getActiveTasks(),
|
||||
refetchInterval: (query) => {
|
||||
const data = query.state.data;
|
||||
const hasActive = data?.downloads.some((d) => d.status === 'downloading');
|
||||
return hasActive ? 1000 : 5000;
|
||||
},
|
||||
});
|
||||
|
||||
// HuggingFace model card query - only fetches when modal is open and model has a repo ID
|
||||
const { data: hfModelInfo, isLoading: hfLoading } = useQuery({
|
||||
queryKey: ['hfModelInfo', selectedModel?.hf_repo_id],
|
||||
queryFn: () => fetchHuggingFaceModelInfo(selectedModel!.hf_repo_id!),
|
||||
enabled: detailOpen && !!selectedModel?.hf_repo_id,
|
||||
staleTime: 1000 * 60 * 30, // Cache for 30 minutes
|
||||
retry: 1,
|
||||
});
|
||||
|
||||
// Build a map of errored downloads for quick lookup, excluding dismissed ones
|
||||
const erroredDownloads = new Map<string, ActiveDownloadTask>();
|
||||
if (activeTasks?.downloads) {
|
||||
for (const dl of activeTasks.downloads) {
|
||||
if (dl.status === 'error' && !dismissedErrors.has(dl.model_name)) {
|
||||
const localErr = localErrors.get(dl.model_name);
|
||||
erroredDownloads.set(dl.model_name, localErr ? { ...dl, error: localErr } : dl);
|
||||
}
|
||||
}
|
||||
}
|
||||
for (const [modelName, error] of localErrors) {
|
||||
if (!erroredDownloads.has(modelName) && !dismissedErrors.has(modelName)) {
|
||||
erroredDownloads.set(modelName, {
|
||||
model_name: modelName,
|
||||
status: 'error',
|
||||
started_at: new Date().toISOString(),
|
||||
error,
|
||||
});
|
||||
}
|
||||
}
|
||||
|
||||
const errorCount = erroredDownloads.size;
|
||||
|
||||
// Build progress map from active tasks for inline display
|
||||
const downloadProgressMap = useMemo(() => {
|
||||
const map = new Map<string, ActiveDownloadTask>();
|
||||
if (activeTasks?.downloads) {
|
||||
for (const dl of activeTasks.downloads) {
|
||||
if (dl.status === 'downloading') {
|
||||
map.set(dl.model_name, dl);
|
||||
}
|
||||
}
|
||||
}
|
||||
return map;
|
||||
}, [activeTasks]);
|
||||
|
||||
// Callbacks for download completion
|
||||
const handleDownloadComplete = useCallback(() => {
|
||||
console.log('[ModelManagement] Download complete, clearing state');
|
||||
setDownloadingModel(null);
|
||||
setDownloadingDisplayName(null);
|
||||
queryClient.invalidateQueries({ queryKey: ['modelStatus'] });
|
||||
queryClient.invalidateQueries({ queryKey: ['activeTasks'] });
|
||||
}, [queryClient]);
|
||||
|
||||
const handleDownloadError = useCallback(
|
||||
(error: string) => {
|
||||
if (downloadingModel) {
|
||||
setLocalErrors((prev) => new Map(prev).set(downloadingModel, error));
|
||||
setConsoleOpen(true);
|
||||
}
|
||||
setDownloadingModel(null);
|
||||
setDownloadingDisplayName(null);
|
||||
queryClient.invalidateQueries({ queryKey: ['activeTasks'] });
|
||||
},
|
||||
[queryClient, downloadingModel],
|
||||
);
|
||||
const handleDownloadError = useCallback(() => {
|
||||
console.log('[ModelManagement] Download error, clearing state');
|
||||
setDownloadingModel(null);
|
||||
setDownloadingDisplayName(null);
|
||||
}, []);
|
||||
|
||||
// Use progress toast hook for the downloading model
|
||||
useModelDownloadToast({
|
||||
modelName: downloadingModel || '',
|
||||
displayName: downloadingDisplayName || '',
|
||||
@@ -195,24 +66,29 @@ export function ModelManagement() {
|
||||
} | null>(null);
|
||||
|
||||
const handleDownload = async (modelName: string) => {
|
||||
setDismissedErrors((prev) => {
|
||||
const next = new Set(prev);
|
||||
next.delete(modelName);
|
||||
return next;
|
||||
});
|
||||
|
||||
console.log('[Download] Button clicked for:', modelName, 'at', new Date().toISOString());
|
||||
|
||||
// Find display name
|
||||
const model = modelStatus?.models.find((m) => m.model_name === modelName);
|
||||
const displayName = model?.display_name || modelName;
|
||||
|
||||
|
||||
try {
|
||||
await apiClient.triggerModelDownload(modelName);
|
||||
|
||||
// IMPORTANT: Call the API FIRST before setting state
|
||||
// Setting state enables the SSE EventSource in useModelDownloadToast,
|
||||
// which can block/delay the download fetch due to HTTP/1.1 connection limits
|
||||
console.log('[Download] Calling download API for:', modelName);
|
||||
const result = await apiClient.triggerModelDownload(modelName);
|
||||
console.log('[Download] Download API responded:', result);
|
||||
|
||||
// NOW set state to enable SSE tracking (after download has started on backend)
|
||||
setDownloadingModel(modelName);
|
||||
setDownloadingDisplayName(displayName);
|
||||
|
||||
|
||||
// Download initiated successfully - state will be cleared when SSE reports completion
|
||||
// or by the polling interval detecting the model is downloaded
|
||||
queryClient.invalidateQueries({ queryKey: ['modelStatus'] });
|
||||
queryClient.invalidateQueries({ queryKey: ['activeTasks'] });
|
||||
} catch (error) {
|
||||
console.error('[Download] Download failed:', error);
|
||||
setDownloadingModel(null);
|
||||
setDownloadingDisplayName(null);
|
||||
toast({
|
||||
@@ -223,76 +99,35 @@ export function ModelManagement() {
|
||||
}
|
||||
};
|
||||
|
||||
const cancelMutation = useMutation({
|
||||
mutationFn: (modelName: string) => apiClient.cancelDownload(modelName),
|
||||
onSuccess: async () => {
|
||||
await queryClient.invalidateQueries({ queryKey: ['modelStatus'], refetchType: 'all' });
|
||||
await queryClient.invalidateQueries({ queryKey: ['activeTasks'], refetchType: 'all' });
|
||||
},
|
||||
});
|
||||
|
||||
const handleCancel = (modelName: string) => {
|
||||
const prevDismissed = dismissedErrors;
|
||||
const prevLocalErrors = localErrors;
|
||||
const prevDownloadingModel = downloadingModel;
|
||||
const prevDownloadingDisplayName = downloadingDisplayName;
|
||||
|
||||
setDismissedErrors((prev) => new Set(prev).add(modelName));
|
||||
setLocalErrors((prev) => {
|
||||
const next = new Map(prev);
|
||||
next.delete(modelName);
|
||||
return next;
|
||||
});
|
||||
if (downloadingModel === modelName) {
|
||||
setDownloadingModel(null);
|
||||
setDownloadingDisplayName(null);
|
||||
}
|
||||
|
||||
cancelMutation.mutate(modelName, {
|
||||
onError: () => {
|
||||
setDismissedErrors(prevDismissed);
|
||||
setLocalErrors(prevLocalErrors);
|
||||
setDownloadingModel(prevDownloadingModel);
|
||||
setDownloadingDisplayName(prevDownloadingDisplayName);
|
||||
toast({
|
||||
title: 'Cancel failed',
|
||||
description: 'Could not cancel the download task.',
|
||||
variant: 'destructive',
|
||||
});
|
||||
},
|
||||
});
|
||||
};
|
||||
|
||||
const clearAllMutation = useMutation({
|
||||
mutationFn: () => apiClient.clearAllTasks(),
|
||||
onSuccess: async () => {
|
||||
setDismissedErrors(new Set());
|
||||
setLocalErrors(new Map());
|
||||
setDownloadingModel(null);
|
||||
setDownloadingDisplayName(null);
|
||||
await queryClient.invalidateQueries({ queryKey: ['modelStatus'], refetchType: 'all' });
|
||||
await queryClient.invalidateQueries({ queryKey: ['activeTasks'], refetchType: 'all' });
|
||||
},
|
||||
});
|
||||
|
||||
const deleteMutation = useMutation({
|
||||
mutationFn: async (modelName: string) => {
|
||||
console.log('[Delete] Deleting model:', modelName);
|
||||
const result = await apiClient.deleteModel(modelName);
|
||||
console.log('[Delete] Model deleted successfully:', modelName);
|
||||
return result;
|
||||
},
|
||||
onSuccess: async () => {
|
||||
onSuccess: async (_data, _modelName) => {
|
||||
console.log('[Delete] onSuccess - showing toast and invalidating queries');
|
||||
toast({
|
||||
title: 'Model deleted',
|
||||
description: `${modelToDelete?.displayName || 'Model'} has been deleted successfully.`,
|
||||
});
|
||||
setDeleteDialogOpen(false);
|
||||
setModelToDelete(null);
|
||||
setDetailOpen(false);
|
||||
setSelectedModel(null);
|
||||
await queryClient.invalidateQueries({ queryKey: ['modelStatus'], refetchType: 'all' });
|
||||
// Invalidate AND explicitly refetch to ensure UI updates
|
||||
// Using refetchType: 'all' ensures we refetch even if the query is stale
|
||||
console.log('[Delete] Invalidating modelStatus query');
|
||||
await queryClient.invalidateQueries({
|
||||
queryKey: ['modelStatus'],
|
||||
refetchType: 'all',
|
||||
});
|
||||
// Also explicitly refetch to guarantee fresh data
|
||||
console.log('[Delete] Explicitly refetching modelStatus query');
|
||||
await queryClient.refetchQueries({ queryKey: ['modelStatus'] });
|
||||
console.log('[Delete] Query refetched');
|
||||
},
|
||||
onError: (error: Error) => {
|
||||
console.log('[Delete] onError:', error);
|
||||
toast({
|
||||
title: 'Delete failed',
|
||||
description: error.message,
|
||||
@@ -301,475 +136,86 @@ export function ModelManagement() {
|
||||
},
|
||||
});
|
||||
|
||||
const unloadMutation = useMutation({
|
||||
mutationFn: async (modelName: string) => {
|
||||
return await apiClient.unloadModel(modelName);
|
||||
},
|
||||
onSuccess: async (_data, modelName) => {
|
||||
toast({
|
||||
title: 'Model unloaded',
|
||||
description: `${modelName} has been unloaded from memory.`,
|
||||
});
|
||||
await queryClient.invalidateQueries({ queryKey: ['modelStatus'], refetchType: 'all' });
|
||||
await queryClient.refetchQueries({ queryKey: ['modelStatus'] });
|
||||
},
|
||||
onError: (error: Error) => {
|
||||
toast({
|
||||
title: 'Unload failed',
|
||||
description: error.message,
|
||||
variant: 'destructive',
|
||||
});
|
||||
},
|
||||
});
|
||||
|
||||
const formatSize = (sizeMb?: number): string => {
|
||||
if (!sizeMb) return 'Unknown size';
|
||||
if (!sizeMb) return 'Unknown';
|
||||
if (sizeMb < 1024) return `${sizeMb.toFixed(1)} MB`;
|
||||
return `${(sizeMb / 1024).toFixed(2)} GB`;
|
||||
};
|
||||
|
||||
const getModelState = (model: ModelStatus) => {
|
||||
const isDownloading =
|
||||
(model.downloading || downloadingModel === model.model_name) &&
|
||||
!erroredDownloads.has(model.model_name) &&
|
||||
!dismissedErrors.has(model.model_name);
|
||||
const hasError = erroredDownloads.has(model.model_name);
|
||||
return { isDownloading, hasError };
|
||||
};
|
||||
|
||||
const openModelDetail = (model: ModelStatus) => {
|
||||
setSelectedModel(model);
|
||||
setDetailOpen(true);
|
||||
};
|
||||
|
||||
const voiceModels =
|
||||
modelStatus?.models.filter(
|
||||
(m) =>
|
||||
m.model_name.startsWith('qwen-tts') ||
|
||||
m.model_name.startsWith('luxtts') ||
|
||||
m.model_name.startsWith('chatterbox'),
|
||||
) ?? [];
|
||||
const whisperModels = modelStatus?.models.filter((m) => m.model_name.startsWith('whisper')) ?? [];
|
||||
|
||||
// Build sections
|
||||
const sections: { label: string; models: ModelStatus[] }[] = [
|
||||
{ label: 'Voice Generation', models: voiceModels },
|
||||
{ label: 'Transcription', models: whisperModels },
|
||||
];
|
||||
|
||||
// Get detail modal state for selected model
|
||||
const selectedState = selectedModel ? getModelState(selectedModel) : null;
|
||||
const selectedError = selectedModel ? erroredDownloads.get(selectedModel.model_name) : undefined;
|
||||
|
||||
// Keep selectedModel data fresh from query results
|
||||
const freshSelectedModel =
|
||||
selectedModel && modelStatus
|
||||
? modelStatus.models.find((m) => m.model_name === selectedModel.model_name) || selectedModel
|
||||
: selectedModel;
|
||||
|
||||
// Derive license from HF data
|
||||
const license =
|
||||
hfModelInfo?.cardData?.license ||
|
||||
hfModelInfo?.tags?.find((t) => t.startsWith('license:'))?.replace('license:', '');
|
||||
|
||||
return (
|
||||
<div className="flex flex-col h-full">
|
||||
{/* Header */}
|
||||
<div className="shrink-0 pb-4">
|
||||
<h1 className="text-lg font-semibold">Models</h1>
|
||||
<p className="text-sm text-muted-foreground">
|
||||
<Card>
|
||||
<CardHeader>
|
||||
<CardTitle>Model Management</CardTitle>
|
||||
<CardDescription>
|
||||
Download and manage AI models for voice generation and transcription
|
||||
</p>
|
||||
</div>
|
||||
|
||||
{/* Model list */}
|
||||
{isLoading ? (
|
||||
<div className="flex items-center justify-center py-16">
|
||||
<Loader2 className="h-5 w-5 animate-spin text-muted-foreground" />
|
||||
</div>
|
||||
) : modelStatus ? (
|
||||
<div className="flex-1 min-h-0 overflow-y-auto space-y-6">
|
||||
{sections.map((section) => (
|
||||
<div key={section.label}>
|
||||
<h2 className="text-xs font-medium text-muted-foreground uppercase tracking-wider mb-1 px-1">
|
||||
{section.label}
|
||||
</h2>
|
||||
<div className="border rounded-lg divide-y overflow-hidden">
|
||||
{section.models.map((model) => {
|
||||
const { isDownloading, hasError } = getModelState(model);
|
||||
return (
|
||||
<button
|
||||
</CardDescription>
|
||||
</CardHeader>
|
||||
<CardContent className="space-y-4">
|
||||
{isLoading ? (
|
||||
<div className="flex items-center justify-center py-8">
|
||||
<Loader2 className="h-6 w-6 animate-spin text-muted-foreground" />
|
||||
</div>
|
||||
) : modelStatus ? (
|
||||
<div className="space-y-4">
|
||||
{/* TTS Models */}
|
||||
<div>
|
||||
<h3 className="text-sm font-semibold mb-3 text-muted-foreground">
|
||||
Voice Generation Models
|
||||
</h3>
|
||||
<div className="space-y-2">
|
||||
{modelStatus.models
|
||||
.filter((m) => m.model_name.startsWith('qwen-tts'))
|
||||
.map((model) => (
|
||||
<ModelItem
|
||||
key={model.model_name}
|
||||
type="button"
|
||||
onClick={() => openModelDetail(model)}
|
||||
className="w-full flex items-center gap-3 px-3 py-2.5 text-left hover:bg-muted/50 transition-colors group"
|
||||
>
|
||||
{/* Status indicator */}
|
||||
<div className="shrink-0">
|
||||
{hasError ? (
|
||||
<CircleX className="h-4 w-4 text-destructive" />
|
||||
) : isDownloading ? (
|
||||
<Loader2 className="h-4 w-4 animate-spin text-muted-foreground" />
|
||||
) : model.loaded ? (
|
||||
<CircleCheck className="h-4 w-4 text-accent" />
|
||||
) : model.downloaded ? (
|
||||
<CircleCheck className="h-4 w-4 text-emerald-500" />
|
||||
) : (
|
||||
<Download className="h-4 w-4 text-muted-foreground/50" />
|
||||
)}
|
||||
</div>
|
||||
|
||||
{/* Name + inline progress */}
|
||||
<div className="flex-1 min-w-0">
|
||||
<span className="text-sm font-medium">{model.display_name}</span>
|
||||
{isDownloading &&
|
||||
(() => {
|
||||
const dl = downloadProgressMap.get(model.model_name);
|
||||
const pct = dl?.progress ?? 0;
|
||||
const hasProgress = dl && dl.total && dl.total > 0;
|
||||
return (
|
||||
<div className="mt-1 space-y-0.5">
|
||||
<Progress value={hasProgress ? pct : undefined} className="h-1" />
|
||||
<div className="text-[10px] text-muted-foreground truncate">
|
||||
{hasProgress
|
||||
? `${formatBytes(dl.current ?? 0)} / ${formatBytes(dl.total!)} (${pct.toFixed(0)}%)`
|
||||
: dl?.filename || 'Connecting...'}
|
||||
</div>
|
||||
</div>
|
||||
);
|
||||
})()}
|
||||
</div>
|
||||
|
||||
{/* Right side info */}
|
||||
<div className="shrink-0 flex items-center gap-2">
|
||||
{hasError && (
|
||||
<Badge variant="destructive" className="text-[10px] h-5">
|
||||
Error
|
||||
</Badge>
|
||||
)}
|
||||
{model.loaded && (
|
||||
<Badge className="text-[10px] h-5 bg-accent/15 text-accent border-accent/30 hover:bg-accent/15">
|
||||
Loaded
|
||||
</Badge>
|
||||
)}
|
||||
{model.downloaded && !isDownloading && !hasError && (
|
||||
<span className="text-xs text-muted-foreground">
|
||||
{formatSize(model.size_mb)}
|
||||
</span>
|
||||
)}
|
||||
{!model.downloaded && !isDownloading && !hasError && (
|
||||
<span className="text-xs text-muted-foreground/60">Not downloaded</span>
|
||||
)}
|
||||
<ChevronRight className="h-4 w-4 text-muted-foreground/40 group-hover:text-muted-foreground transition-colors" />
|
||||
</div>
|
||||
</button>
|
||||
);
|
||||
})}
|
||||
</div>
|
||||
</div>
|
||||
))}
|
||||
|
||||
{/* Error console */}
|
||||
{errorCount > 0 && (
|
||||
<div className="border rounded-lg overflow-hidden">
|
||||
<div className="flex items-center justify-between px-3 py-1.5 bg-muted/50 text-xs font-medium text-muted-foreground">
|
||||
<button
|
||||
type="button"
|
||||
onClick={() => setConsoleOpen((v) => !v)}
|
||||
className="flex items-center gap-2 hover:text-foreground transition-colors"
|
||||
>
|
||||
{consoleOpen ? (
|
||||
<ChevronUp className="h-3.5 w-3.5" />
|
||||
) : (
|
||||
<ChevronDown className="h-3.5 w-3.5" />
|
||||
)}
|
||||
<span>Problems</span>
|
||||
<Badge variant="destructive" className="text-[10px] h-4 px-1.5 rounded-full">
|
||||
{errorCount}
|
||||
</Badge>
|
||||
</button>
|
||||
<Button
|
||||
size="sm"
|
||||
variant="ghost"
|
||||
className="h-6 px-2 text-xs text-muted-foreground hover:text-foreground"
|
||||
onClick={() => clearAllMutation.mutate()}
|
||||
disabled={clearAllMutation.isPending}
|
||||
>
|
||||
<RotateCcw className="h-3 w-3 mr-1" />
|
||||
Clear All
|
||||
</Button>
|
||||
</div>
|
||||
{consoleOpen && (
|
||||
<div className="bg-[#1e1e1e] text-[#d4d4d4] p-3 max-h-48 overflow-auto font-mono text-xs leading-relaxed">
|
||||
{Array.from(erroredDownloads.entries()).map(([modelName, dl]) => (
|
||||
<div key={modelName} className="mb-2 last:mb-0">
|
||||
<span className="text-[#f44747]">[error]</span>{' '}
|
||||
<span className="text-[#569cd6]">{modelName}</span>
|
||||
{dl.error ? (
|
||||
<>
|
||||
{': '}
|
||||
<span className="text-[#ce9178] whitespace-pre-wrap break-all">
|
||||
{dl.error}
|
||||
</span>
|
||||
</>
|
||||
) : (
|
||||
<>
|
||||
{': '}
|
||||
<span className="text-[#808080]">
|
||||
No error details available. Try downloading again.
|
||||
</span>
|
||||
</>
|
||||
)}
|
||||
<div className="text-[#6a9955] mt-0.5">
|
||||
started at {new Date(dl.started_at).toLocaleString()}
|
||||
</div>
|
||||
</div>
|
||||
model={model}
|
||||
onDownload={() => handleDownload(model.model_name)}
|
||||
onDelete={() => {
|
||||
setModelToDelete({
|
||||
name: model.model_name,
|
||||
displayName: model.display_name,
|
||||
sizeMb: model.size_mb,
|
||||
});
|
||||
setDeleteDialogOpen(true);
|
||||
}}
|
||||
isDownloading={downloadingModel === model.model_name}
|
||||
formatSize={formatSize}
|
||||
/>
|
||||
))}
|
||||
</div>
|
||||
)}
|
||||
</div>
|
||||
)}
|
||||
</div>
|
||||
) : null}
|
||||
|
||||
{/* Model Detail Modal */}
|
||||
<Dialog open={detailOpen} onOpenChange={setDetailOpen}>
|
||||
<DialogContent className="sm:max-w-md">
|
||||
{freshSelectedModel && (
|
||||
<>
|
||||
<DialogHeader>
|
||||
<DialogTitle>{freshSelectedModel.display_name}</DialogTitle>
|
||||
<DialogDescription className="flex items-center gap-1.5">
|
||||
{freshSelectedModel.hf_repo_id ? (
|
||||
<a
|
||||
href={`https://huggingface.co/${freshSelectedModel.hf_repo_id}`}
|
||||
target="_blank"
|
||||
rel="noopener noreferrer"
|
||||
className="inline-flex items-center gap-1 hover:underline"
|
||||
>
|
||||
{freshSelectedModel.hf_repo_id}
|
||||
<ExternalLink className="h-3 w-3" />
|
||||
</a>
|
||||
) : (
|
||||
freshSelectedModel.model_name
|
||||
)}
|
||||
</DialogDescription>
|
||||
</DialogHeader>
|
||||
|
||||
<div className="space-y-4 pt-2">
|
||||
{/* Status badges */}
|
||||
<div className="flex items-center gap-2 flex-wrap">
|
||||
{freshSelectedModel.loaded && (
|
||||
<Badge className="text-xs bg-accent/15 text-accent border-accent/30 hover:bg-accent/15">
|
||||
<CircleCheck className="h-3 w-3 mr-1" />
|
||||
Loaded
|
||||
</Badge>
|
||||
)}
|
||||
{selectedState?.hasError && (
|
||||
<Badge variant="destructive" className="text-xs">
|
||||
<CircleX className="h-3 w-3 mr-1" />
|
||||
Error
|
||||
</Badge>
|
||||
)}
|
||||
{!freshSelectedModel.downloaded &&
|
||||
!selectedState?.isDownloading &&
|
||||
!selectedState?.hasError && (
|
||||
<Badge variant="outline" className="text-xs text-muted-foreground">
|
||||
Not downloaded
|
||||
</Badge>
|
||||
)}
|
||||
</div>
|
||||
|
||||
{/* HuggingFace model card info */}
|
||||
{hfLoading && freshSelectedModel.hf_repo_id && (
|
||||
<div className="flex items-center gap-2 text-xs text-muted-foreground py-2">
|
||||
<Loader2 className="h-3 w-3 animate-spin" />
|
||||
Loading model info...
|
||||
</div>
|
||||
)}
|
||||
|
||||
{hfModelInfo && (
|
||||
<div className="space-y-3">
|
||||
{/* Pipeline tag + author */}
|
||||
<div className="flex flex-wrap gap-1.5">
|
||||
{hfModelInfo.pipeline_tag && (
|
||||
<Badge variant="outline" className="text-[10px]">
|
||||
{formatPipelineTag(hfModelInfo.pipeline_tag)}
|
||||
</Badge>
|
||||
)}
|
||||
{hfModelInfo.library_name && (
|
||||
<Badge variant="outline" className="text-[10px]">
|
||||
{hfModelInfo.library_name}
|
||||
</Badge>
|
||||
)}
|
||||
{hfModelInfo.author && (
|
||||
<Badge variant="outline" className="text-[10px]">
|
||||
by {hfModelInfo.author}
|
||||
</Badge>
|
||||
)}
|
||||
</div>
|
||||
|
||||
{/* Stats row */}
|
||||
<div className="flex items-center gap-4 text-xs text-muted-foreground">
|
||||
<span className="flex items-center gap-1" title="Downloads">
|
||||
<Download className="h-3.5 w-3.5" />
|
||||
{formatDownloads(hfModelInfo.downloads)}
|
||||
</span>
|
||||
<span className="flex items-center gap-1" title="Likes">
|
||||
<Heart className="h-3.5 w-3.5" />
|
||||
{formatDownloads(hfModelInfo.likes)}
|
||||
</span>
|
||||
{license && (
|
||||
<span className="flex items-center gap-1" title="License">
|
||||
<Scale className="h-3.5 w-3.5" />
|
||||
{formatLicense(license)}
|
||||
</span>
|
||||
)}
|
||||
</div>
|
||||
|
||||
{/* Languages */}
|
||||
{hfModelInfo.cardData?.language && hfModelInfo.cardData.language.length > 0 && (
|
||||
<div>
|
||||
<span className="text-xs text-muted-foreground">
|
||||
{hfModelInfo.cardData.language.length > 10
|
||||
? `${hfModelInfo.cardData.language.length} languages supported`
|
||||
: `Languages: ${hfModelInfo.cardData.language.join(', ')}`}
|
||||
</span>
|
||||
</div>
|
||||
)}
|
||||
</div>
|
||||
)}
|
||||
|
||||
{/* Disk size */}
|
||||
{freshSelectedModel.downloaded && freshSelectedModel.size_mb && (
|
||||
<div className="flex items-center gap-2 text-xs text-muted-foreground">
|
||||
<HardDrive className="h-3.5 w-3.5" />
|
||||
<span>{formatSize(freshSelectedModel.size_mb)} on disk</span>
|
||||
</div>
|
||||
)}
|
||||
|
||||
{/* Error detail */}
|
||||
{selectedError?.error && (
|
||||
<div className="rounded-md bg-destructive/10 border border-destructive/20 p-3 text-xs text-destructive">
|
||||
{selectedError.error}
|
||||
</div>
|
||||
)}
|
||||
|
||||
{/* Actions */}
|
||||
<div className="flex items-center gap-2 pt-2">
|
||||
{selectedState?.hasError ? (
|
||||
<>
|
||||
<Button
|
||||
size="sm"
|
||||
onClick={() => handleDownload(freshSelectedModel.model_name)}
|
||||
variant="outline"
|
||||
className="flex-1"
|
||||
>
|
||||
<Download className="h-4 w-4 mr-2" />
|
||||
Retry Download
|
||||
</Button>
|
||||
<Button
|
||||
size="sm"
|
||||
onClick={() => handleCancel(freshSelectedModel.model_name)}
|
||||
variant="ghost"
|
||||
disabled={
|
||||
cancelMutation.isPending &&
|
||||
cancelMutation.variables === freshSelectedModel.model_name
|
||||
}
|
||||
>
|
||||
<X className="h-4 w-4" />
|
||||
</Button>
|
||||
</>
|
||||
) : selectedState?.isDownloading ? (
|
||||
<>
|
||||
<div className="flex-1 space-y-2">
|
||||
{(() => {
|
||||
const dl = freshSelectedModel
|
||||
? downloadProgressMap.get(freshSelectedModel.model_name)
|
||||
: undefined;
|
||||
const pct = dl?.progress ?? 0;
|
||||
const hasProgress = dl && dl.total && dl.total > 0;
|
||||
return (
|
||||
<>
|
||||
<Progress value={hasProgress ? pct : undefined} className="h-2" />
|
||||
<div className="text-xs text-muted-foreground">
|
||||
{hasProgress
|
||||
? `${formatBytes(dl.current ?? 0)} / ${formatBytes(dl.total!)} (${pct.toFixed(1)}%)`
|
||||
: dl?.filename || 'Connecting to HuggingFace...'}
|
||||
</div>
|
||||
</>
|
||||
);
|
||||
})()}
|
||||
</div>
|
||||
<Button
|
||||
size="sm"
|
||||
onClick={() => handleCancel(freshSelectedModel.model_name)}
|
||||
variant="ghost"
|
||||
disabled={
|
||||
cancelMutation.isPending &&
|
||||
cancelMutation.variables === freshSelectedModel.model_name
|
||||
}
|
||||
>
|
||||
<X className="h-4 w-4" />
|
||||
</Button>
|
||||
</>
|
||||
) : freshSelectedModel.downloaded ? (
|
||||
<div className="flex gap-2 flex-1">
|
||||
{freshSelectedModel.loaded && (
|
||||
<Button
|
||||
size="sm"
|
||||
onClick={() => unloadMutation.mutate(freshSelectedModel.model_name)}
|
||||
variant="outline"
|
||||
disabled={unloadMutation.isPending}
|
||||
className="flex-1"
|
||||
>
|
||||
{unloadMutation.isPending ? (
|
||||
<Loader2 className="h-4 w-4 mr-2 animate-spin" />
|
||||
) : (
|
||||
<Unplug className="h-4 w-4 mr-2" />
|
||||
)}
|
||||
{unloadMutation.isPending ? 'Unloading...' : 'Unload'}
|
||||
</Button>
|
||||
)}
|
||||
<Button
|
||||
size="sm"
|
||||
onClick={() => {
|
||||
setModelToDelete({
|
||||
name: freshSelectedModel.model_name,
|
||||
displayName: freshSelectedModel.display_name,
|
||||
sizeMb: freshSelectedModel.size_mb,
|
||||
});
|
||||
setDeleteDialogOpen(true);
|
||||
}}
|
||||
variant="outline"
|
||||
disabled={freshSelectedModel.loaded}
|
||||
title={
|
||||
freshSelectedModel.loaded
|
||||
? 'Unload model before deleting'
|
||||
: 'Delete model'
|
||||
}
|
||||
className="flex-1"
|
||||
>
|
||||
<Trash2 className="h-4 w-4 mr-2" />
|
||||
Delete Model
|
||||
</Button>
|
||||
</div>
|
||||
) : (
|
||||
<Button
|
||||
size="sm"
|
||||
onClick={() => handleDownload(freshSelectedModel.model_name)}
|
||||
className="flex-1"
|
||||
>
|
||||
<Download className="h-4 w-4 mr-2" />
|
||||
Download
|
||||
</Button>
|
||||
)}
|
||||
</div>
|
||||
</div>
|
||||
</>
|
||||
)}
|
||||
</DialogContent>
|
||||
</Dialog>
|
||||
</div>
|
||||
|
||||
{/* Whisper Models */}
|
||||
<div>
|
||||
<h3 className="text-sm font-semibold mb-3 text-muted-foreground">
|
||||
Transcription Models
|
||||
</h3>
|
||||
<div className="space-y-2">
|
||||
{modelStatus.models
|
||||
.filter((m) => m.model_name.startsWith('whisper'))
|
||||
.map((model) => (
|
||||
<ModelItem
|
||||
key={model.model_name}
|
||||
model={model}
|
||||
onDownload={() => handleDownload(model.model_name)}
|
||||
onDelete={() => {
|
||||
setModelToDelete({
|
||||
name: model.model_name,
|
||||
displayName: model.display_name,
|
||||
sizeMb: model.size_mb,
|
||||
});
|
||||
setDeleteDialogOpen(true);
|
||||
}}
|
||||
isDownloading={downloadingModel === model.model_name}
|
||||
formatSize={formatSize}
|
||||
/>
|
||||
))}
|
||||
</div>
|
||||
</div>
|
||||
|
||||
</div>
|
||||
) : null}
|
||||
</CardContent>
|
||||
|
||||
{/* Delete Confirmation Dialog */}
|
||||
<AlertDialog open={deleteDialogOpen} onOpenChange={setDeleteDialogOpen}>
|
||||
@@ -810,7 +256,7 @@ export function ModelManagement() {
|
||||
</AlertDialogFooter>
|
||||
</AlertDialogContent>
|
||||
</AlertDialog>
|
||||
</div>
|
||||
</Card>
|
||||
);
|
||||
}
|
||||
|
||||
@@ -819,38 +265,22 @@ interface ModelItemProps {
|
||||
model_name: string;
|
||||
display_name: string;
|
||||
downloaded: boolean;
|
||||
downloading?: boolean; // From server - true if download in progress
|
||||
downloading?: boolean; // From server - true if download in progress
|
||||
size_mb?: number;
|
||||
loaded: boolean;
|
||||
};
|
||||
onDownload: () => void;
|
||||
onDelete: () => void;
|
||||
isDownloading: boolean; // Local state - true if user just clicked download
|
||||
isDownloading: boolean; // Local state - true if user just clicked download
|
||||
formatSize: (sizeMb?: number) => string;
|
||||
}
|
||||
|
||||
function ModelItem({ model, onDownload, onDelete, isDownloading, formatSize }: ModelItemProps) {
|
||||
// Use server's downloading state OR local state (for immediate feedback before server updates)
|
||||
const showDownloading = model.downloading || isDownloading;
|
||||
|
||||
const statusText = model.loaded
|
||||
? 'Loaded'
|
||||
: showDownloading
|
||||
? 'Downloading'
|
||||
: model.downloaded
|
||||
? 'Downloaded'
|
||||
: 'Not downloaded';
|
||||
const sizeText =
|
||||
model.downloaded && model.size_mb && !showDownloading ? `, ${formatSize(model.size_mb)}` : '';
|
||||
const rowLabel = `${model.display_name}, ${statusText}${sizeText}. Use Tab to reach Download or Delete.`;
|
||||
|
||||
|
||||
return (
|
||||
<div
|
||||
className="flex items-center justify-between p-3 border rounded-lg"
|
||||
role="group"
|
||||
tabIndex={0}
|
||||
aria-label={rowLabel}
|
||||
>
|
||||
<div className="flex items-center justify-between p-3 border rounded-lg">
|
||||
<div className="flex-1">
|
||||
<div className="flex items-center gap-2">
|
||||
<span className="font-medium text-sm">{model.display_name}</span>
|
||||
@@ -884,30 +314,17 @@ function ModelItem({ model, onDownload, onDelete, isDownloading, formatSize }: M
|
||||
variant="outline"
|
||||
disabled={model.loaded}
|
||||
title={model.loaded ? 'Unload model before deleting' : 'Delete model'}
|
||||
aria-label={
|
||||
model.loaded ? 'Unload model before deleting' : `Delete ${model.display_name}`
|
||||
}
|
||||
>
|
||||
<Trash2 className="h-4 w-4" />
|
||||
</Button>
|
||||
</div>
|
||||
) : showDownloading ? (
|
||||
<Button
|
||||
size="sm"
|
||||
variant="outline"
|
||||
disabled
|
||||
aria-label={`${model.display_name} downloading`}
|
||||
>
|
||||
<Button size="sm" variant="outline" disabled>
|
||||
<Loader2 className="h-4 w-4 mr-2 animate-spin" />
|
||||
Downloading...
|
||||
</Button>
|
||||
) : (
|
||||
<Button
|
||||
size="sm"
|
||||
onClick={onDownload}
|
||||
variant="outline"
|
||||
aria-label={`Download ${model.display_name}`}
|
||||
>
|
||||
<Button size="sm" onClick={onDownload} variant="outline">
|
||||
<Download className="h-4 w-4 mr-2" />
|
||||
Download
|
||||
</Button>
|
||||
|
||||
@@ -3,13 +3,14 @@ import { Badge } from '@/components/ui/badge';
|
||||
import { Card, CardContent, CardHeader, CardTitle } from '@/components/ui/card';
|
||||
import { useServerHealth } from '@/lib/hooks/useServer';
|
||||
import { useServerStore } from '@/stores/serverStore';
|
||||
import { ModelProgress } from './ModelProgress';
|
||||
|
||||
export function ServerStatus() {
|
||||
const { data: health, isLoading, error } = useServerHealth();
|
||||
const serverUrl = useServerStore((state) => state.serverUrl);
|
||||
|
||||
return (
|
||||
<Card role="region" aria-label="Server Status" tabIndex={0}>
|
||||
<Card>
|
||||
<CardHeader>
|
||||
<CardTitle>Server Status</CardTitle>
|
||||
</CardHeader>
|
||||
@@ -19,6 +20,16 @@ export function ServerStatus() {
|
||||
<div className="font-mono text-sm">{serverUrl}</div>
|
||||
</div>
|
||||
|
||||
{/* Model download progress */}
|
||||
<div className="space-y-2">
|
||||
<ModelProgress modelName="qwen-tts-1.7B" displayName="Qwen TTS 1.7B" />
|
||||
<ModelProgress modelName="qwen-tts-0.6B" displayName="Qwen TTS 0.6B" />
|
||||
<ModelProgress modelName="whisper-base" displayName="Whisper Base" />
|
||||
<ModelProgress modelName="whisper-small" displayName="Whisper Small" />
|
||||
<ModelProgress modelName="whisper-medium" displayName="Whisper Medium" />
|
||||
<ModelProgress modelName="whisper-large" displayName="Whisper Large" />
|
||||
</div>
|
||||
|
||||
{isLoading ? (
|
||||
<div className="flex items-center gap-2">
|
||||
<Loader2 className="h-4 w-4 animate-spin" />
|
||||
|
||||
@@ -20,11 +20,7 @@ export function UpdateStatus() {
|
||||
}, [platform]);
|
||||
|
||||
return (
|
||||
<Card
|
||||
role="region"
|
||||
aria-label="App Updates"
|
||||
tabIndex={0}
|
||||
>
|
||||
<Card>
|
||||
<CardHeader>
|
||||
<CardTitle>App Updates</CardTitle>
|
||||
</CardHeader>
|
||||
|
||||
@@ -1,19 +1,17 @@
|
||||
import { ConnectionForm } from '@/components/ServerSettings/ConnectionForm';
|
||||
import { GenerationSettings } from '@/components/ServerSettings/GenerationSettings';
|
||||
import { GpuAcceleration } from '@/components/ServerSettings/GpuAcceleration';
|
||||
import { ServerStatus } from '@/components/ServerSettings/ServerStatus';
|
||||
import { UpdateStatus } from '@/components/ServerSettings/UpdateStatus';
|
||||
import { usePlatform } from '@/platform/PlatformContext';
|
||||
|
||||
export function ServerTab() {
|
||||
const platform = usePlatform();
|
||||
return (
|
||||
<div className="overflow-y-auto flex flex-col">
|
||||
<div className="space-y-4 overflow-y-auto flex flex-col">
|
||||
<div className="grid gap-4 md:grid-cols-2">
|
||||
<ConnectionForm />
|
||||
<GenerationSettings />
|
||||
{platform.metadata.isTauri && <GpuAcceleration />}
|
||||
{platform.metadata.isTauri && <UpdateStatus />}
|
||||
<ServerStatus />
|
||||
</div>
|
||||
{platform.metadata.isTauri && <UpdateStatus />}
|
||||
<div className="py-8 text-center text-sm text-muted-foreground">
|
||||
Created by{' '}
|
||||
<a
|
||||
|
||||
@@ -1,11 +1,8 @@
|
||||
import { FloatingGenerateBox } from '@/components/Generation/FloatingGenerateBox';
|
||||
import { usePlayerStore } from '@/stores/playerStore';
|
||||
import { StoryContent } from './StoryContent';
|
||||
import { StoryList } from './StoryList';
|
||||
|
||||
export function StoriesTab() {
|
||||
const audioUrl = usePlayerStore((state) => state.audioUrl);
|
||||
|
||||
return (
|
||||
<div className="flex flex-col h-full min-h-0 overflow-hidden">
|
||||
{/* Main content area */}
|
||||
@@ -21,7 +18,7 @@ export function StoriesTab() {
|
||||
</div>
|
||||
|
||||
{/* Floating Generate Box - position is managed via storyStore.trackEditorHeight */}
|
||||
<FloatingGenerateBox showVoiceSelector isPlayerOpen={!!audioUrl} />
|
||||
<FloatingGenerateBox showVoiceSelector />
|
||||
</div>
|
||||
</div>
|
||||
);
|
||||
|
||||
@@ -194,29 +194,17 @@ export function StoryList() {
|
||||
storyList.map((story) => (
|
||||
<div
|
||||
key={story.id}
|
||||
role="button"
|
||||
tabIndex={0}
|
||||
className={cn(
|
||||
'h-24 p-4 border rounded-2xl transition-colors group flex items-center cursor-pointer',
|
||||
'h-24 p-4 border rounded-2xl transition-colors group flex items-center',
|
||||
selectedStoryId === story.id && 'bg-muted border-primary',
|
||||
)}
|
||||
aria-label={
|
||||
selectedStoryId === story.id
|
||||
? `Story ${story.name}, ${story.item_count} ${story.item_count === 1 ? 'item' : 'items'}, ${formatDate(story.updated_at)}. Selected. Press Enter to select.`
|
||||
: `Story ${story.name}, ${story.item_count} ${story.item_count === 1 ? 'item' : 'items'}, ${formatDate(story.updated_at)}. Press Enter to select.`
|
||||
}
|
||||
aria-pressed={selectedStoryId === story.id}
|
||||
onClick={() => setSelectedStoryId(story.id)}
|
||||
onKeyDown={(e) => {
|
||||
if (e.target !== e.currentTarget) return;
|
||||
if (e.key === 'Enter' || e.key === ' ') {
|
||||
e.preventDefault();
|
||||
setSelectedStoryId(story.id);
|
||||
}
|
||||
}}
|
||||
>
|
||||
<div className="flex items-start justify-between gap-2 w-full min-w-0">
|
||||
<div className="flex-1 min-w-0 text-left overflow-hidden">
|
||||
<button
|
||||
type="button"
|
||||
className="flex-1 min-w-0 text-left cursor-pointer overflow-hidden"
|
||||
onClick={() => setSelectedStoryId(story.id)}
|
||||
>
|
||||
<h3 className="font-medium truncate">{story.name}</h3>
|
||||
{story.description && (
|
||||
<p className="text-sm text-muted-foreground mt-1 truncate">
|
||||
@@ -230,7 +218,7 @@ export function StoryList() {
|
||||
<span>•</span>
|
||||
<span>{formatDate(story.updated_at)}</span>
|
||||
</div>
|
||||
</div>
|
||||
</button>
|
||||
<DropdownMenu>
|
||||
<DropdownMenuTrigger asChild>
|
||||
<Button
|
||||
@@ -238,7 +226,6 @@ export function StoryList() {
|
||||
size="icon"
|
||||
className="h-8 w-8 opacity-0 group-hover:opacity-100 transition-opacity"
|
||||
onClick={(e) => e.stopPropagation()}
|
||||
aria-label={`Actions for ${story.name}`}
|
||||
>
|
||||
<MoreHorizontal className="h-4 w-4" />
|
||||
</Button>
|
||||
|
||||
@@ -736,7 +736,6 @@ export function StoryTrackEditor({ storyId, items }: StoryTrackEditorProps) {
|
||||
className="h-7 w-7"
|
||||
onClick={handlePlayPause}
|
||||
title="Play/Pause (Space)"
|
||||
aria-label={isCurrentlyPlaying ? 'Pause' : 'Play'}
|
||||
>
|
||||
{isCurrentlyPlaying ? <Pause className="h-4 w-4" /> : <Play className="h-4 w-4" />}
|
||||
</Button>
|
||||
@@ -746,7 +745,6 @@ export function StoryTrackEditor({ storyId, items }: StoryTrackEditorProps) {
|
||||
className="h-7 w-7"
|
||||
onClick={handleStop}
|
||||
disabled={!isCurrentlyPlaying}
|
||||
aria-label="Stop"
|
||||
>
|
||||
<Square className="h-3 w-3" />
|
||||
</Button>
|
||||
@@ -764,7 +762,6 @@ export function StoryTrackEditor({ storyId, items }: StoryTrackEditorProps) {
|
||||
className="h-7 w-7"
|
||||
onClick={handleSplit}
|
||||
title="Split at playhead (S)"
|
||||
aria-label="Split at playhead"
|
||||
>
|
||||
<Scissors className="h-4 w-4" />
|
||||
</Button>
|
||||
@@ -774,7 +771,6 @@ export function StoryTrackEditor({ storyId, items }: StoryTrackEditorProps) {
|
||||
className="h-7 w-7"
|
||||
onClick={handleDuplicate}
|
||||
title="Duplicate (Cmd/Ctrl+D)"
|
||||
aria-label="Duplicate clip"
|
||||
>
|
||||
<Copy className="h-4 w-4" />
|
||||
</Button>
|
||||
@@ -784,7 +780,6 @@ export function StoryTrackEditor({ storyId, items }: StoryTrackEditorProps) {
|
||||
className="h-7 w-7"
|
||||
onClick={handleDelete}
|
||||
title="Delete (Delete/Backspace)"
|
||||
aria-label="Delete clip"
|
||||
>
|
||||
<Trash2 className="h-4 w-4" />
|
||||
</Button>
|
||||
@@ -794,22 +789,10 @@ export function StoryTrackEditor({ storyId, items }: StoryTrackEditorProps) {
|
||||
{/* Zoom controls - right side */}
|
||||
<div className="flex items-center gap-2">
|
||||
<span className="text-xs text-muted-foreground">Zoom:</span>
|
||||
<Button
|
||||
variant="ghost"
|
||||
size="icon"
|
||||
className="h-6 w-6"
|
||||
onClick={handleZoomOut}
|
||||
aria-label="Zoom out"
|
||||
>
|
||||
<Button variant="ghost" size="icon" className="h-6 w-6" onClick={handleZoomOut}>
|
||||
<Minus className="h-3 w-3" />
|
||||
</Button>
|
||||
<Button
|
||||
variant="ghost"
|
||||
size="icon"
|
||||
className="h-6 w-6"
|
||||
onClick={handleZoomIn}
|
||||
aria-label="Zoom in"
|
||||
>
|
||||
<Button variant="ghost" size="icon" className="h-6 w-6" onClick={handleZoomIn}>
|
||||
<Plus className="h-3 w-3" />
|
||||
</Button>
|
||||
</div>
|
||||
|
||||
@@ -58,7 +58,6 @@ export function AudioSampleRecording({
|
||||
// Request microphone access when component mounts
|
||||
useEffect(() => {
|
||||
if (!showWaveform) return;
|
||||
if (!navigator.mediaDevices || !navigator.mediaDevices.getUserMedia) return;
|
||||
|
||||
let stream: MediaStream | null = null;
|
||||
|
||||
@@ -140,13 +139,7 @@ export function AudioSampleRecording({
|
||||
</div>
|
||||
<p className="text-sm text-muted-foreground text-center">File: {file.name}</p>
|
||||
<div className="flex gap-2">
|
||||
<Button
|
||||
type="button"
|
||||
size="icon"
|
||||
variant="outline"
|
||||
onClick={onPlayPause}
|
||||
aria-label={isPlaying ? 'Pause' : 'Play'}
|
||||
>
|
||||
<Button type="button" size="icon" variant="outline" onClick={onPlayPause}>
|
||||
{isPlaying ? <Pause className="h-4 w-4" /> : <Play className="h-4 w-4" />}
|
||||
</Button>
|
||||
<Button
|
||||
|
||||
@@ -77,13 +77,7 @@ export function AudioSampleSystem({
|
||||
</div>
|
||||
<p className="text-sm text-muted-foreground text-center">File: {file.name}</p>
|
||||
<div className="flex gap-2">
|
||||
<Button
|
||||
type="button"
|
||||
size="icon"
|
||||
variant="outline"
|
||||
onClick={onPlayPause}
|
||||
aria-label={isPlaying ? 'Pause' : 'Play'}
|
||||
>
|
||||
<Button type="button" size="icon" variant="outline" onClick={onPlayPause}>
|
||||
{isPlaying ? <Pause className="h-4 w-4" /> : <Play className="h-4 w-4" />}
|
||||
</Button>
|
||||
<Button
|
||||
|
||||
@@ -110,7 +110,6 @@ export function AudioSampleUpload({
|
||||
variant="outline"
|
||||
onClick={onPlayPause}
|
||||
disabled={isValidating}
|
||||
aria-label={isPlaying ? 'Pause' : 'Play'}
|
||||
>
|
||||
{isPlaying ? <Pause className="h-4 w-4" /> : <Play className="h-4 w-4" />}
|
||||
</Button>
|
||||
|
||||
@@ -61,19 +61,6 @@ export function ProfileCard({ profile }: ProfileCardProps) {
|
||||
exportProfile.mutate(profile.id);
|
||||
};
|
||||
|
||||
const handleKeyDown = (e: React.KeyboardEvent) => {
|
||||
const target = e.target as HTMLElement;
|
||||
if (target.closest('button')) return;
|
||||
if (e.key === 'Enter' || e.key === ' ') {
|
||||
e.preventDefault();
|
||||
handleSelect();
|
||||
}
|
||||
};
|
||||
|
||||
const selectLabel = isSelected
|
||||
? `${profile.name}, ${profile.language}. Selected as voice for generation.`
|
||||
: `${profile.name}, ${profile.language}. Select as voice for generation.`;
|
||||
|
||||
return (
|
||||
<>
|
||||
<Card
|
||||
@@ -82,11 +69,6 @@ export function ProfileCard({ profile }: ProfileCardProps) {
|
||||
isSelected && 'ring-2 ring-primary shadow-md',
|
||||
)}
|
||||
onClick={handleSelect}
|
||||
tabIndex={0}
|
||||
role="button"
|
||||
aria-label={selectLabel}
|
||||
aria-pressed={isSelected}
|
||||
onKeyDown={handleKeyDown}
|
||||
>
|
||||
<CardHeader className="p-3 pb-2">
|
||||
<CardTitle className="flex items-center gap-1.5 text-base font-medium">
|
||||
|
||||
@@ -43,7 +43,7 @@ import {
|
||||
} from '@/lib/hooks/useProfiles';
|
||||
import { useSystemAudioCapture } from '@/lib/hooks/useSystemAudioCapture';
|
||||
import { useTranscription } from '@/lib/hooks/useTranscription';
|
||||
import { convertToWav, formatAudioDuration, getAudioDuration } from '@/lib/utils/audio';
|
||||
import { formatAudioDuration, getAudioDuration } from '@/lib/utils/audio';
|
||||
import { usePlatform } from '@/platform/PlatformContext';
|
||||
import { useServerStore } from '@/stores/serverStore';
|
||||
import { type ProfileFormDraft, useUIStore } from '@/stores/uiStore';
|
||||
@@ -505,23 +505,10 @@ export function ProfileForm() {
|
||||
language: data.language,
|
||||
});
|
||||
|
||||
// Convert non-WAV uploads to WAV so the backend can always use soundfile.
|
||||
// Recorded audio is already WAV (from useAudioRecording's convertToWav call).
|
||||
let fileToUpload: File = sampleFile;
|
||||
if (!sampleFile.type.includes('wav') && !sampleFile.name.toLowerCase().endsWith('.wav')) {
|
||||
try {
|
||||
const wavBlob = await convertToWav(sampleFile);
|
||||
const wavName = sampleFile.name.replace(/\.[^.]+$/, '.wav');
|
||||
fileToUpload = new File([wavBlob], wavName, { type: 'audio/wav' });
|
||||
} catch {
|
||||
// If browser can't decode the format, send the original and let the backend try.
|
||||
}
|
||||
}
|
||||
|
||||
try {
|
||||
await addSample.mutateAsync({
|
||||
profileId: profile.id,
|
||||
file: fileToUpload,
|
||||
file: sampleFile,
|
||||
referenceText: referenceText,
|
||||
});
|
||||
|
||||
|
||||
@@ -102,7 +102,6 @@ function MiniSamplePlayer({ audioUrl }: MiniSamplePlayerProps) {
|
||||
className="h-7 w-7 shrink-0"
|
||||
onClick={handlePlayPause}
|
||||
disabled={isLoading}
|
||||
aria-label={isPlaying ? 'Pause sample' : 'Play sample'}
|
||||
>
|
||||
{isPlaying ? <Pause className="h-3.5 w-3.5" /> : <Play className="h-3.5 w-3.5 ml-0.5" />}
|
||||
</Button>
|
||||
@@ -114,8 +113,6 @@ function MiniSamplePlayer({ audioUrl }: MiniSamplePlayerProps) {
|
||||
max={100}
|
||||
step={0.1}
|
||||
className="flex-1"
|
||||
aria-label="Sample playback position"
|
||||
aria-valuetext={`${formatAudioDuration(currentTime)} of ${formatAudioDuration(duration)}`}
|
||||
/>
|
||||
<div className="flex items-center gap-1 text-xs text-muted-foreground shrink-0 min-w-[70px]">
|
||||
<span className="font-mono">{formatAudioDuration(currentTime)}</span>
|
||||
@@ -131,7 +128,6 @@ function MiniSamplePlayer({ audioUrl }: MiniSamplePlayerProps) {
|
||||
className="h-7 w-7 shrink-0"
|
||||
onClick={handleStop}
|
||||
title="Stop"
|
||||
aria-label="Stop playback"
|
||||
>
|
||||
<X className="h-3.5 w-3.5" />
|
||||
</Button>
|
||||
|
||||
@@ -79,8 +79,8 @@ export function VoicesTab() {
|
||||
setDialogOpen(true);
|
||||
};
|
||||
|
||||
const handleProfileDelete = async (profileId: string) => {
|
||||
if (await confirm('Are you sure you want to delete this profile?')) {
|
||||
const handleDelete = (profileId: string) => {
|
||||
if (confirm('Are you sure you want to delete this profile?')) {
|
||||
deleteProfile.mutate(profileId);
|
||||
}
|
||||
};
|
||||
@@ -147,7 +147,7 @@ export function VoicesTab() {
|
||||
channels={channels || []}
|
||||
onChannelChange={(channelIds) => handleChannelChange(profile.id, channelIds)}
|
||||
onEdit={() => handleEdit(profile.id)}
|
||||
onDelete={() => handleProfileDelete(profile.id)}
|
||||
onDelete={() => handleDelete(profile.id)}
|
||||
/>
|
||||
))}
|
||||
</TableBody>
|
||||
@@ -179,36 +179,25 @@ function VoiceRow({
|
||||
onDelete,
|
||||
}: VoiceRowProps) {
|
||||
const { data: samples } = useProfileSamples(profile.id);
|
||||
const sampleCount = samples?.length || 0;
|
||||
|
||||
const rowLabel = `${profile.name}, ${profile.language}, ${generationCount} generations, ${sampleCount} samples. Press Enter to edit.`;
|
||||
|
||||
return (
|
||||
<TableRow className="cursor-pointer" onClick={onEdit}>
|
||||
<TableCell>
|
||||
<button
|
||||
type="button"
|
||||
className="flex w-full min-w-0 items-center gap-2 text-left focus:outline-none focus-visible:ring-2 focus-visible:ring-ring focus-visible:ring-offset-2 rounded"
|
||||
aria-label={rowLabel}
|
||||
onClick={(e) => {
|
||||
e.stopPropagation();
|
||||
onEdit();
|
||||
}}
|
||||
>
|
||||
<div className="flex items-center gap-2">
|
||||
<div className="h-8 w-8 rounded-lg bg-muted flex items-center justify-center shrink-0">
|
||||
<Mic className="h-4 w-4 text-muted-foreground" />
|
||||
</div>
|
||||
<div className="min-w-0">
|
||||
<div className="font-medium truncate">{profile.name}</div>
|
||||
<div>
|
||||
<div className="font-medium">{profile.name}</div>
|
||||
{profile.description && (
|
||||
<div className="text-sm text-muted-foreground truncate">{profile.description}</div>
|
||||
<div className="text-sm text-muted-foreground">{profile.description}</div>
|
||||
)}
|
||||
</div>
|
||||
</button>
|
||||
</div>
|
||||
</TableCell>
|
||||
<TableCell onClick={(e) => e.stopPropagation()}>{profile.language}</TableCell>
|
||||
<TableCell onClick={(e) => e.stopPropagation()}>{generationCount}</TableCell>
|
||||
<TableCell onClick={(e) => e.stopPropagation()}>{sampleCount}</TableCell>
|
||||
<TableCell onClick={(e) => e.stopPropagation()}>{samples?.length || 0}</TableCell>
|
||||
<TableCell onClick={(e) => e.stopPropagation()}>
|
||||
<MultiSelect
|
||||
options={channels.map((ch) => ({
|
||||
@@ -224,7 +213,7 @@ function VoiceRow({
|
||||
<TableCell onClick={(e) => e.stopPropagation()}>
|
||||
<DropdownMenu>
|
||||
<DropdownMenuTrigger asChild>
|
||||
<Button variant="ghost" size="icon" aria-label={`Actions for ${profile.name}`}>
|
||||
<Button variant="ghost" size="icon">
|
||||
<MoreHorizontal className="h-4 w-4" />
|
||||
</Button>
|
||||
</DropdownMenuTrigger>
|
||||
|
||||
+39
-88
@@ -1,30 +1,29 @@
|
||||
import type { LanguageCode } from '@/lib/constants/languages';
|
||||
import { useServerStore } from '@/stores/serverStore';
|
||||
import type { LanguageCode } from '@/lib/constants/languages';
|
||||
import type {
|
||||
ActiveTasksResponse,
|
||||
CudaStatus,
|
||||
GenerationRequest,
|
||||
GenerationResponse,
|
||||
HealthResponse,
|
||||
HistoryListResponse,
|
||||
HistoryQuery,
|
||||
HistoryResponse,
|
||||
ModelDownloadRequest,
|
||||
ModelStatusListResponse,
|
||||
ProfileSampleResponse,
|
||||
StoryCreate,
|
||||
StoryDetailResponse,
|
||||
StoryItemBatchUpdate,
|
||||
StoryItemCreate,
|
||||
StoryItemDetail,
|
||||
StoryItemMove,
|
||||
StoryItemReorder,
|
||||
StoryItemSplit,
|
||||
StoryItemTrim,
|
||||
StoryResponse,
|
||||
TranscriptionResponse,
|
||||
VoiceProfileCreate,
|
||||
VoiceProfileResponse,
|
||||
ProfileSampleResponse,
|
||||
GenerationRequest,
|
||||
GenerationResponse,
|
||||
HistoryQuery,
|
||||
HistoryListResponse,
|
||||
HistoryResponse,
|
||||
TranscriptionResponse,
|
||||
HealthResponse,
|
||||
ModelStatusListResponse,
|
||||
ModelDownloadRequest,
|
||||
ActiveTasksResponse,
|
||||
StoryCreate,
|
||||
StoryResponse,
|
||||
StoryDetailResponse,
|
||||
StoryItemCreate,
|
||||
StoryItemDetail,
|
||||
StoryItemBatchUpdate,
|
||||
StoryItemReorder,
|
||||
StoryItemMove,
|
||||
StoryItemTrim,
|
||||
StoryItemSplit,
|
||||
} from './types';
|
||||
|
||||
class ApiClient {
|
||||
@@ -252,13 +251,7 @@ class ApiClient {
|
||||
return response.blob();
|
||||
}
|
||||
|
||||
async importGeneration(file: File): Promise<{
|
||||
id: string;
|
||||
profile_id: string;
|
||||
profile_name: string;
|
||||
text: string;
|
||||
message: string;
|
||||
}> {
|
||||
async importGeneration(file: File): Promise<{ id: string; profile_id: string; profile_name: string; text: string; message: string }> {
|
||||
const url = `${this.getBaseUrl()}/history/import`;
|
||||
const formData = new FormData();
|
||||
formData.append('file', file);
|
||||
@@ -317,12 +310,7 @@ class ApiClient {
|
||||
}
|
||||
|
||||
async triggerModelDownload(modelName: string): Promise<{ message: string }> {
|
||||
console.log(
|
||||
'[API] triggerModelDownload called for:',
|
||||
modelName,
|
||||
'at',
|
||||
new Date().toISOString(),
|
||||
);
|
||||
console.log('[API] triggerModelDownload called for:', modelName, 'at', new Date().toISOString());
|
||||
const result = await this.request<{ message: string }>('/models/download', {
|
||||
method: 'POST',
|
||||
body: JSON.stringify({ model_name: modelName } as ModelDownloadRequest),
|
||||
@@ -337,28 +325,11 @@ class ApiClient {
|
||||
});
|
||||
}
|
||||
|
||||
async unloadModel(modelName: string): Promise<{ message: string }> {
|
||||
return this.request<{ message: string }>(`/models/${modelName}/unload`, {
|
||||
method: 'POST',
|
||||
});
|
||||
}
|
||||
|
||||
async cancelDownload(modelName: string): Promise<{ message: string }> {
|
||||
return this.request<{ message: string }>('/models/download/cancel', {
|
||||
method: 'POST',
|
||||
body: JSON.stringify({ model_name: modelName } as ModelDownloadRequest),
|
||||
});
|
||||
}
|
||||
|
||||
// Task Management
|
||||
async getActiveTasks(): Promise<ActiveTasksResponse> {
|
||||
return this.request<ActiveTasksResponse>('/tasks/active');
|
||||
}
|
||||
|
||||
async clearAllTasks(): Promise<{ message: string }> {
|
||||
return this.request<{ message: string }>('/tasks/clear', { method: 'POST' });
|
||||
}
|
||||
|
||||
// Audio Channels
|
||||
async listChannels(): Promise<
|
||||
Array<{
|
||||
@@ -372,7 +343,10 @@ class ApiClient {
|
||||
return this.request('/channels');
|
||||
}
|
||||
|
||||
async createChannel(data: { name: string; device_ids: string[] }): Promise<{
|
||||
async createChannel(data: {
|
||||
name: string;
|
||||
device_ids: string[];
|
||||
}): Promise<{
|
||||
id: string;
|
||||
name: string;
|
||||
is_default: boolean;
|
||||
@@ -414,7 +388,10 @@ class ApiClient {
|
||||
return this.request(`/channels/${channelId}/voices`);
|
||||
}
|
||||
|
||||
async setChannelVoices(channelId: string, profileIds: string[]): Promise<{ message: string }> {
|
||||
async setChannelVoices(
|
||||
channelId: string,
|
||||
profileIds: string[],
|
||||
): Promise<{ message: string }> {
|
||||
return this.request(`/channels/${channelId}/voices`, {
|
||||
method: 'PUT',
|
||||
body: JSON.stringify({ profile_ids: profileIds }),
|
||||
@@ -425,30 +402,16 @@ class ApiClient {
|
||||
return this.request(`/profiles/${profileId}/channels`);
|
||||
}
|
||||
|
||||
async setProfileChannels(profileId: string, channelIds: string[]): Promise<{ message: string }> {
|
||||
async setProfileChannels(
|
||||
profileId: string,
|
||||
channelIds: string[],
|
||||
): Promise<{ message: string }> {
|
||||
return this.request(`/profiles/${profileId}/channels`, {
|
||||
method: 'PUT',
|
||||
body: JSON.stringify({ channel_ids: channelIds }),
|
||||
});
|
||||
}
|
||||
|
||||
// CUDA Backend Management
|
||||
async getCudaStatus(): Promise<CudaStatus> {
|
||||
return this.request<CudaStatus>('/backend/cuda-status');
|
||||
}
|
||||
|
||||
async downloadCudaBackend(): Promise<{ message: string; progress_key: string }> {
|
||||
return this.request<{ message: string; progress_key: string }>('/backend/download-cuda', {
|
||||
method: 'POST',
|
||||
});
|
||||
}
|
||||
|
||||
async deleteCudaBackend(): Promise<{ message: string }> {
|
||||
return this.request<{ message: string }>('/backend/cuda', {
|
||||
method: 'DELETE',
|
||||
});
|
||||
}
|
||||
|
||||
// Stories
|
||||
async listStories(): Promise<StoryResponse[]> {
|
||||
return this.request<StoryResponse[]>('/stories');
|
||||
@@ -505,33 +468,21 @@ class ApiClient {
|
||||
});
|
||||
}
|
||||
|
||||
async moveStoryItem(
|
||||
storyId: string,
|
||||
itemId: string,
|
||||
data: StoryItemMove,
|
||||
): Promise<StoryItemDetail> {
|
||||
async moveStoryItem(storyId: string, itemId: string, data: StoryItemMove): Promise<StoryItemDetail> {
|
||||
return this.request<StoryItemDetail>(`/stories/${storyId}/items/${itemId}/move`, {
|
||||
method: 'PUT',
|
||||
body: JSON.stringify(data),
|
||||
});
|
||||
}
|
||||
|
||||
async trimStoryItem(
|
||||
storyId: string,
|
||||
itemId: string,
|
||||
data: StoryItemTrim,
|
||||
): Promise<StoryItemDetail> {
|
||||
async trimStoryItem(storyId: string, itemId: string, data: StoryItemTrim): Promise<StoryItemDetail> {
|
||||
return this.request<StoryItemDetail>(`/stories/${storyId}/items/${itemId}/trim`, {
|
||||
method: 'PUT',
|
||||
body: JSON.stringify(data),
|
||||
});
|
||||
}
|
||||
|
||||
async splitStoryItem(
|
||||
storyId: string,
|
||||
itemId: string,
|
||||
data: StoryItemSplit,
|
||||
): Promise<StoryItemDetail[]> {
|
||||
async splitStoryItem(storyId: string, itemId: string, data: StoryItemSplit): Promise<StoryItemDetail[]> {
|
||||
return this.request<StoryItemDetail[]>(`/stories/${storyId}/items/${itemId}/split`, {
|
||||
method: 'POST',
|
||||
body: JSON.stringify(data),
|
||||
|
||||
@@ -34,10 +34,6 @@ export interface GenerationRequest {
|
||||
language: LanguageCode;
|
||||
seed?: number;
|
||||
model_size?: '1.7B' | '0.6B';
|
||||
engine?: 'qwen' | 'luxtts' | 'chatterbox' | 'chatterbox_turbo';
|
||||
instruct?: string;
|
||||
max_chunk_chars?: number;
|
||||
crossfade_ms?: number;
|
||||
}
|
||||
|
||||
export interface GenerationResponse {
|
||||
@@ -82,29 +78,7 @@ export interface HealthResponse {
|
||||
model_downloaded?: boolean;
|
||||
model_size?: string;
|
||||
gpu_available: boolean;
|
||||
gpu_type?: string;
|
||||
vram_used_mb?: number;
|
||||
backend_type?: string;
|
||||
backend_variant?: string; // "cpu" or "cuda"
|
||||
}
|
||||
|
||||
export interface CudaDownloadProgress {
|
||||
model_name: string;
|
||||
current: number;
|
||||
total: number;
|
||||
progress: number;
|
||||
filename?: string;
|
||||
status: 'downloading' | 'extracting' | 'complete' | 'error';
|
||||
timestamp: string;
|
||||
error?: string;
|
||||
}
|
||||
|
||||
export interface CudaStatus {
|
||||
available: boolean; // CUDA binary exists on disk
|
||||
active: boolean; // Currently running the CUDA binary
|
||||
binary_path?: string;
|
||||
downloading: boolean; // Download in progress
|
||||
download_progress?: CudaDownloadProgress;
|
||||
}
|
||||
|
||||
export interface ModelProgress {
|
||||
@@ -121,29 +95,12 @@ export interface ModelProgress {
|
||||
export interface ModelStatus {
|
||||
model_name: string;
|
||||
display_name: string;
|
||||
hf_repo_id?: string; // HuggingFace repository ID
|
||||
downloaded: boolean;
|
||||
downloading: boolean; // True if download is in progress
|
||||
downloading: boolean; // True if download is in progress
|
||||
size_mb?: number;
|
||||
loaded: boolean;
|
||||
}
|
||||
|
||||
export interface HuggingFaceModelInfo {
|
||||
id: string;
|
||||
author: string;
|
||||
lastModified: string;
|
||||
pipeline_tag?: string;
|
||||
library_name?: string;
|
||||
downloads: number;
|
||||
likes: number;
|
||||
tags: string[];
|
||||
cardData?: {
|
||||
license?: string;
|
||||
language?: string[];
|
||||
pipeline_tag?: string;
|
||||
};
|
||||
}
|
||||
|
||||
export interface ModelStatusListResponse {
|
||||
models: ModelStatus[];
|
||||
}
|
||||
@@ -156,11 +113,6 @@ export interface ActiveDownloadTask {
|
||||
model_name: string;
|
||||
status: string;
|
||||
started_at: string;
|
||||
error?: string;
|
||||
progress?: number; // 0-100 percentage
|
||||
current?: number; // bytes downloaded
|
||||
total?: number; // total bytes
|
||||
filename?: string; // current file being downloaded
|
||||
}
|
||||
|
||||
export interface ActiveGenerationTask {
|
||||
|
||||
@@ -1,86 +1,26 @@
|
||||
/**
|
||||
* Supported languages for voice generation, per engine.
|
||||
*
|
||||
* Qwen3-TTS supports 10 languages.
|
||||
* LuxTTS is English-only.
|
||||
* Chatterbox Multilingual supports 23 languages.
|
||||
* Chatterbox Turbo is English-only.
|
||||
* Supported languages for Qwen3-TTS
|
||||
* Based on: https://github.com/QwenLM/Qwen3-TTS
|
||||
*/
|
||||
|
||||
/** All languages that any engine supports. */
|
||||
export const ALL_LANGUAGES = {
|
||||
ar: 'Arabic',
|
||||
da: 'Danish',
|
||||
de: 'German',
|
||||
el: 'Greek',
|
||||
export const SUPPORTED_LANGUAGES = {
|
||||
zh: 'Chinese',
|
||||
en: 'English',
|
||||
es: 'Spanish',
|
||||
fi: 'Finnish',
|
||||
fr: 'French',
|
||||
he: 'Hebrew',
|
||||
hi: 'Hindi',
|
||||
it: 'Italian',
|
||||
ja: 'Japanese',
|
||||
ko: 'Korean',
|
||||
ms: 'Malay',
|
||||
nl: 'Dutch',
|
||||
no: 'Norwegian',
|
||||
pl: 'Polish',
|
||||
pt: 'Portuguese',
|
||||
de: 'German',
|
||||
fr: 'French',
|
||||
ru: 'Russian',
|
||||
sv: 'Swedish',
|
||||
sw: 'Swahili',
|
||||
tr: 'Turkish',
|
||||
zh: 'Chinese',
|
||||
pt: 'Portuguese',
|
||||
es: 'Spanish',
|
||||
it: 'Italian',
|
||||
} as const;
|
||||
|
||||
export type LanguageCode = keyof typeof ALL_LANGUAGES;
|
||||
export type LanguageCode = keyof typeof SUPPORTED_LANGUAGES;
|
||||
|
||||
/** Per-engine supported language codes. */
|
||||
export const ENGINE_LANGUAGES: Record<string, readonly LanguageCode[]> = {
|
||||
qwen: ['zh', 'en', 'ja', 'ko', 'de', 'fr', 'ru', 'pt', 'es', 'it'],
|
||||
luxtts: ['en'],
|
||||
chatterbox: [
|
||||
'ar',
|
||||
'da',
|
||||
'de',
|
||||
'el',
|
||||
'en',
|
||||
'es',
|
||||
'fi',
|
||||
'fr',
|
||||
'he',
|
||||
'hi',
|
||||
'it',
|
||||
'ja',
|
||||
'ko',
|
||||
'ms',
|
||||
'nl',
|
||||
'no',
|
||||
'pl',
|
||||
'pt',
|
||||
'ru',
|
||||
'sv',
|
||||
'sw',
|
||||
'tr',
|
||||
'zh',
|
||||
],
|
||||
chatterbox_turbo: ['en'],
|
||||
} as const;
|
||||
export const LANGUAGE_CODES = Object.keys(SUPPORTED_LANGUAGES) as LanguageCode[];
|
||||
|
||||
/** Helper: get language options for a given engine. */
|
||||
export function getLanguageOptionsForEngine(engine: string) {
|
||||
const codes = ENGINE_LANGUAGES[engine] ?? ENGINE_LANGUAGES.qwen;
|
||||
return codes.map((code) => ({
|
||||
value: code,
|
||||
label: ALL_LANGUAGES[code],
|
||||
}));
|
||||
}
|
||||
|
||||
// ── Backwards-compatible exports used elsewhere ──────────────────────
|
||||
export const SUPPORTED_LANGUAGES = ALL_LANGUAGES;
|
||||
export const LANGUAGE_CODES = Object.keys(ALL_LANGUAGES) as LanguageCode[];
|
||||
export const LANGUAGE_OPTIONS = LANGUAGE_CODES.map((code) => ({
|
||||
value: code,
|
||||
label: ALL_LANGUAGES[code],
|
||||
label: SUPPORTED_LANGUAGES[code],
|
||||
}));
|
||||
|
||||
@@ -20,13 +20,11 @@ export function useAudioRecording({
|
||||
const streamRef = useRef<MediaStream | null>(null);
|
||||
const timerRef = useRef<number | null>(null);
|
||||
const startTimeRef = useRef<number | null>(null);
|
||||
const cancelledRef = useRef<boolean>(false);
|
||||
|
||||
const startRecording = useCallback(async () => {
|
||||
try {
|
||||
setError(null);
|
||||
chunksRef.current = [];
|
||||
cancelledRef.current = false;
|
||||
setDuration(0);
|
||||
|
||||
// Check if getUserMedia is available
|
||||
@@ -89,34 +87,31 @@ export function useAudioRecording({
|
||||
};
|
||||
|
||||
mediaRecorder.onstop = async () => {
|
||||
// Snapshot the cancellation flag and recorded duration immediately —
|
||||
// cancelRecording() clears chunks and sets cancelledRef synchronously
|
||||
// before this async handler runs, so we must check it first.
|
||||
const wasCancelled = cancelledRef.current;
|
||||
const recordedDuration = startTimeRef.current
|
||||
? (Date.now() - startTimeRef.current) / 1000
|
||||
: undefined;
|
||||
|
||||
const webmBlob = new Blob(chunksRef.current, { type: 'audio/webm' });
|
||||
|
||||
// Stop all tracks now that we have the data
|
||||
streamRef.current?.getTracks().forEach((track) => {
|
||||
track.stop();
|
||||
});
|
||||
streamRef.current = null;
|
||||
|
||||
// Don't fire completion callback if the recording was cancelled
|
||||
if (wasCancelled) return;
|
||||
|
||||
// Convert to WAV format to avoid needing ffmpeg on backend
|
||||
try {
|
||||
const wavBlob = await convertToWav(webmBlob);
|
||||
|
||||
// Pass the actual recorded duration
|
||||
const recordedDuration = startTimeRef.current
|
||||
? (Date.now() - startTimeRef.current) / 1000
|
||||
: undefined;
|
||||
onRecordingComplete?.(wavBlob, recordedDuration);
|
||||
} catch (err) {
|
||||
console.error('Error converting audio to WAV:', err);
|
||||
// Fallback to original blob if conversion fails
|
||||
const recordedDuration = startTimeRef.current
|
||||
? (Date.now() - startTimeRef.current) / 1000
|
||||
: undefined;
|
||||
onRecordingComplete?.(webmBlob, recordedDuration);
|
||||
}
|
||||
|
||||
// Stop all tracks
|
||||
streamRef.current?.getTracks().forEach((track) => {
|
||||
track.stop();
|
||||
});
|
||||
streamRef.current = null;
|
||||
};
|
||||
|
||||
mediaRecorder.onerror = (event) => {
|
||||
@@ -172,10 +167,9 @@ export function useAudioRecording({
|
||||
|
||||
const cancelRecording = useCallback(() => {
|
||||
if (mediaRecorderRef.current) {
|
||||
cancelledRef.current = true; // Must be set before stop() triggers onstop
|
||||
chunksRef.current = [];
|
||||
mediaRecorderRef.current.stop();
|
||||
setIsRecording(false);
|
||||
chunksRef.current = [];
|
||||
setDuration(0);
|
||||
}
|
||||
|
||||
|
||||
@@ -9,15 +9,13 @@ import { useGeneration } from '@/lib/hooks/useGeneration';
|
||||
import { useModelDownloadToast } from '@/lib/hooks/useModelDownloadToast';
|
||||
import { useGenerationStore } from '@/stores/generationStore';
|
||||
import { usePlayerStore } from '@/stores/playerStore';
|
||||
import { useServerStore } from '@/stores/serverStore';
|
||||
|
||||
const generationSchema = z.object({
|
||||
text: z.string().min(1, 'Text is required').max(50000),
|
||||
text: z.string().min(1, 'Text is required').max(5000),
|
||||
language: z.enum(LANGUAGE_CODES as [LanguageCode, ...LanguageCode[]]),
|
||||
seed: z.number().int().optional(),
|
||||
modelSize: z.enum(['1.7B', '0.6B']).optional(),
|
||||
instruct: z.string().max(500).optional(),
|
||||
engine: z.enum(['qwen', 'luxtts', 'chatterbox', 'chatterbox_turbo']).optional(),
|
||||
});
|
||||
|
||||
export type GenerationFormValues = z.infer<typeof generationSchema>;
|
||||
@@ -32,8 +30,6 @@ export function useGenerationForm(options: UseGenerationFormOptions = {}) {
|
||||
const generation = useGeneration();
|
||||
const setAudioWithAutoPlay = usePlayerStore((state) => state.setAudioWithAutoPlay);
|
||||
const setIsGenerating = useGenerationStore((state) => state.setIsGenerating);
|
||||
const maxChunkChars = useServerStore((state) => state.maxChunkChars);
|
||||
const crossfadeMs = useServerStore((state) => state.crossfadeMs);
|
||||
const [downloadingModelName, setDownloadingModelName] = useState<string | null>(null);
|
||||
const [downloadingDisplayName, setDownloadingDisplayName] = useState<string | null>(null);
|
||||
|
||||
@@ -51,7 +47,6 @@ export function useGenerationForm(options: UseGenerationFormOptions = {}) {
|
||||
seed: undefined,
|
||||
modelSize: '1.7B',
|
||||
instruct: '',
|
||||
engine: 'qwen',
|
||||
...options.defaultValues,
|
||||
},
|
||||
});
|
||||
@@ -72,25 +67,8 @@ export function useGenerationForm(options: UseGenerationFormOptions = {}) {
|
||||
try {
|
||||
setIsGenerating(true);
|
||||
|
||||
const engine = data.engine || 'qwen';
|
||||
const modelName =
|
||||
engine === 'luxtts'
|
||||
? 'luxtts'
|
||||
: engine === 'chatterbox'
|
||||
? 'chatterbox-tts'
|
||||
: engine === 'chatterbox_turbo'
|
||||
? 'chatterbox-turbo'
|
||||
: `qwen-tts-${data.modelSize}`;
|
||||
const displayName =
|
||||
engine === 'luxtts'
|
||||
? 'LuxTTS'
|
||||
: engine === 'chatterbox'
|
||||
? 'Chatterbox TTS'
|
||||
: engine === 'chatterbox_turbo'
|
||||
? 'Chatterbox Turbo'
|
||||
: data.modelSize === '1.7B'
|
||||
? 'Qwen TTS 1.7B'
|
||||
: 'Qwen TTS 0.6B';
|
||||
const modelName = `qwen-tts-${data.modelSize}`;
|
||||
const displayName = data.modelSize === '1.7B' ? 'Qwen TTS 1.7B' : 'Qwen TTS 0.6B';
|
||||
|
||||
try {
|
||||
const modelStatus = await apiClient.getModelStatus();
|
||||
@@ -104,17 +82,13 @@ export function useGenerationForm(options: UseGenerationFormOptions = {}) {
|
||||
console.error('Failed to check model status:', error);
|
||||
}
|
||||
|
||||
const isQwen = engine === 'qwen';
|
||||
const result = await generation.mutateAsync({
|
||||
profile_id: selectedProfileId,
|
||||
text: data.text,
|
||||
language: data.language,
|
||||
seed: data.seed,
|
||||
model_size: isQwen ? data.modelSize : undefined,
|
||||
engine,
|
||||
instruct: isQwen ? data.instruct || undefined : undefined,
|
||||
max_chunk_chars: maxChunkChars,
|
||||
crossfade_ms: crossfadeMs,
|
||||
model_size: data.modelSize,
|
||||
instruct: data.instruct || undefined,
|
||||
});
|
||||
|
||||
toast({
|
||||
@@ -125,14 +99,7 @@ export function useGenerationForm(options: UseGenerationFormOptions = {}) {
|
||||
const audioUrl = apiClient.getAudioUrl(result.id);
|
||||
setAudioWithAutoPlay(audioUrl, result.id, selectedProfileId, data.text.substring(0, 50));
|
||||
|
||||
form.reset({
|
||||
text: '',
|
||||
language: data.language,
|
||||
seed: undefined,
|
||||
modelSize: data.modelSize,
|
||||
instruct: '',
|
||||
engine: data.engine,
|
||||
});
|
||||
form.reset();
|
||||
options.onSuccess?.(result.id);
|
||||
} catch (error) {
|
||||
toast({
|
||||
|
||||
@@ -10,7 +10,7 @@ interface UseModelDownloadToastOptions {
|
||||
displayName: string;
|
||||
enabled?: boolean;
|
||||
onComplete?: () => void;
|
||||
onError?: (error: string) => void;
|
||||
onError?: () => void;
|
||||
}
|
||||
|
||||
/**
|
||||
@@ -101,7 +101,7 @@ export function useModelDownloadToast({
|
||||
break;
|
||||
case 'error':
|
||||
statusIcon = <XCircle className="h-4 w-4 text-destructive" />;
|
||||
statusText = 'Download failed. See Problems panel for details.';
|
||||
statusText = `Error: ${progress.error || 'Unknown error'}`;
|
||||
break;
|
||||
case 'downloading':
|
||||
statusIcon = <Loader2 className="h-4 w-4 animate-spin" />;
|
||||
@@ -131,7 +131,8 @@ export function useModelDownloadToast({
|
||||
)}
|
||||
</div>
|
||||
),
|
||||
duration: progress.status === 'complete' || progress.status === 'error' ? 5000 : Infinity,
|
||||
duration: progress.status === 'complete' ? 5000 : Infinity,
|
||||
variant: progress.status === 'error' ? 'destructive' : 'default',
|
||||
});
|
||||
|
||||
// Close connection and dismiss toast on completion or error
|
||||
@@ -168,7 +169,7 @@ export function useModelDownloadToast({
|
||||
onComplete();
|
||||
} else if (isError && onError) {
|
||||
console.log('[useModelDownloadToast] Download error, calling onError callback');
|
||||
onError(progress.error || 'Unknown error');
|
||||
onError();
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
+18
-36
@@ -22,11 +22,6 @@ export function formatAudioDuration(seconds: number): string {
|
||||
* If the file has a recordedDuration property (from recording hooks),
|
||||
* use that instead of trying to read metadata. This fixes issues on Windows
|
||||
* where WebM files from MediaRecorder don't have proper duration metadata.
|
||||
*
|
||||
* For uploaded files we use AudioContext.decodeAudioData which fully decodes
|
||||
* the audio and returns the exact duration. This is more reliable than
|
||||
* HTMLMediaElement.duration which can return incorrect large values for VBR
|
||||
* MP3 files that lack a proper XING/VBRI header.
|
||||
*/
|
||||
export async function getAudioDuration(
|
||||
file: File & { recordedDuration?: number },
|
||||
@@ -35,39 +30,26 @@ export async function getAudioDuration(
|
||||
return file.recordedDuration;
|
||||
}
|
||||
|
||||
// Use Web Audio API for accurate duration — avoids VBR MP3 metadata issues.
|
||||
try {
|
||||
const audioContext = new AudioContext();
|
||||
try {
|
||||
const arrayBuffer = await file.arrayBuffer();
|
||||
const audioBuffer = await audioContext.decodeAudioData(arrayBuffer);
|
||||
return audioBuffer.duration;
|
||||
} finally {
|
||||
await audioContext.close();
|
||||
}
|
||||
} catch {
|
||||
// Fallback: read duration from the media element (less accurate but works for WAV).
|
||||
return new Promise((resolve, reject) => {
|
||||
const audio = new Audio();
|
||||
const url = URL.createObjectURL(file);
|
||||
return new Promise((resolve, reject) => {
|
||||
const audio = new Audio();
|
||||
const url = URL.createObjectURL(file);
|
||||
|
||||
audio.addEventListener('loadedmetadata', () => {
|
||||
URL.revokeObjectURL(url);
|
||||
if (Number.isFinite(audio.duration) && audio.duration > 0) {
|
||||
resolve(audio.duration);
|
||||
} else {
|
||||
reject(new Error('Audio file has invalid duration metadata'));
|
||||
}
|
||||
});
|
||||
|
||||
audio.addEventListener('error', () => {
|
||||
URL.revokeObjectURL(url);
|
||||
reject(new Error('Failed to load audio file'));
|
||||
});
|
||||
|
||||
audio.src = url;
|
||||
audio.addEventListener('loadedmetadata', () => {
|
||||
URL.revokeObjectURL(url);
|
||||
if (Number.isFinite(audio.duration) && audio.duration > 0) {
|
||||
resolve(audio.duration);
|
||||
} else {
|
||||
reject(new Error('Audio file has invalid duration metadata'));
|
||||
}
|
||||
});
|
||||
}
|
||||
|
||||
audio.addEventListener('error', () => {
|
||||
URL.revokeObjectURL(url);
|
||||
reject(new Error('Failed to load audio file'));
|
||||
});
|
||||
|
||||
audio.src = url;
|
||||
});
|
||||
}
|
||||
|
||||
/**
|
||||
|
||||
@@ -51,7 +51,6 @@ export interface PlatformAudio {
|
||||
export interface PlatformLifecycle {
|
||||
startServer(remote?: boolean): Promise<string>;
|
||||
stopServer(): Promise<void>;
|
||||
restartServer(): Promise<string>;
|
||||
setKeepServerRunning(keep: boolean): Promise<void>;
|
||||
setupWindowCloseHandler(): Promise<void>;
|
||||
onServerReady?: () => void;
|
||||
|
||||
@@ -13,12 +13,6 @@ interface ServerStore {
|
||||
|
||||
keepServerRunningOnClose: boolean;
|
||||
setKeepServerRunningOnClose: (keepRunning: boolean) => void;
|
||||
|
||||
maxChunkChars: number;
|
||||
setMaxChunkChars: (value: number) => void;
|
||||
|
||||
crossfadeMs: number;
|
||||
setCrossfadeMs: (value: number) => void;
|
||||
}
|
||||
|
||||
export const useServerStore = create<ServerStore>()(
|
||||
@@ -35,12 +29,6 @@ export const useServerStore = create<ServerStore>()(
|
||||
|
||||
keepServerRunningOnClose: false,
|
||||
setKeepServerRunningOnClose: (keepRunning) => set({ keepServerRunningOnClose: keepRunning }),
|
||||
|
||||
maxChunkChars: 800,
|
||||
setMaxChunkChars: (value) => set({ maxChunkChars: value }),
|
||||
|
||||
crossfadeMs: 50,
|
||||
setCrossfadeMs: (value) => set({ crossfadeMs: value }),
|
||||
}),
|
||||
{
|
||||
name: 'voicebox-server',
|
||||
|
||||
+9
-12
@@ -334,21 +334,18 @@ python -m backend.main --host 0.0.0.0 --port 8000
|
||||
|
||||
## Usage Examples
|
||||
|
||||
The desktop app, web client, and current development workflow use `http://localhost:17493` by default.
|
||||
If you launch the backend manually with a different host or port, substitute that address in the examples below.
|
||||
|
||||
### Creating a Voice Profile
|
||||
|
||||
```bash
|
||||
# 1. Create profile
|
||||
curl -X POST http://localhost:17493/profiles \
|
||||
curl -X POST http://localhost:8000/profiles \
|
||||
-H "Content-Type: application/json" \
|
||||
-d '{"name": "My Voice", "language": "en"}'
|
||||
|
||||
# Response: {"id": "abc-123", ...}
|
||||
|
||||
# 2. Add sample
|
||||
curl -X POST http://localhost:17493/profiles/abc-123/samples \
|
||||
curl -X POST http://localhost:8000/profiles/abc-123/samples \
|
||||
-F "[email protected]" \
|
||||
-F "reference_text=This is my voice sample"
|
||||
```
|
||||
@@ -356,7 +353,7 @@ curl -X POST http://localhost:17493/profiles/abc-123/samples \
|
||||
### Generating Speech
|
||||
|
||||
```bash
|
||||
curl -X POST http://localhost:17493/generate \
|
||||
curl -X POST http://localhost:8000/generate \
|
||||
-H "Content-Type: application/json" \
|
||||
-d '{
|
||||
"profile_id": "abc-123",
|
||||
@@ -368,13 +365,13 @@ curl -X POST http://localhost:17493/generate \
|
||||
# Response: {"id": "gen-456", "audio_path": "/path/to/audio.wav", ...}
|
||||
|
||||
# Download audio
|
||||
curl http://localhost:17493/audio/gen-456 -o output.wav
|
||||
curl http://localhost:8000/audio/gen-456 -o output.wav
|
||||
```
|
||||
|
||||
### Transcribing Audio
|
||||
|
||||
```bash
|
||||
curl -X POST http://localhost:17493/transcribe \
|
||||
curl -X POST http://localhost:8000/transcribe \
|
||||
-F "[email protected]" \
|
||||
-F "language=en"
|
||||
|
||||
@@ -389,12 +386,12 @@ Add multiple samples to a profile for better quality:
|
||||
|
||||
```bash
|
||||
# Add first sample
|
||||
curl -X POST http://localhost:17493/profiles/abc-123/samples \
|
||||
curl -X POST http://localhost:8000/profiles/abc-123/samples \
|
||||
-F "[email protected]" \
|
||||
-F "reference_text=First sample"
|
||||
|
||||
# Add second sample
|
||||
curl -X POST http://localhost:17493/profiles/abc-123/samples \
|
||||
curl -X POST http://localhost:8000/profiles/abc-123/samples \
|
||||
-F "[email protected]" \
|
||||
-F "reference_text=Second sample"
|
||||
|
||||
@@ -415,10 +412,10 @@ Models are lazy-loaded and can be manually unloaded:
|
||||
|
||||
```bash
|
||||
# Unload TTS model
|
||||
curl -X POST http://localhost:17493/models/unload
|
||||
curl -X POST http://localhost:8000/models/unload
|
||||
|
||||
# Load specific model size
|
||||
curl -X POST "http://localhost:17493/models/load?model_size=0.6B"
|
||||
curl -X POST "http://localhost:8000/models/load?model_size=0.6B"
|
||||
```
|
||||
|
||||
## Error Handling
|
||||
|
||||
+1
-1
@@ -1,3 +1,3 @@
|
||||
# Backend package
|
||||
|
||||
__version__ = "0.1.13"
|
||||
__version__ = "0.1.12"
|
||||
|
||||
@@ -4,7 +4,6 @@ Backend abstraction layer for TTS and STT.
|
||||
Provides a unified interface for MLX and PyTorch backends.
|
||||
"""
|
||||
|
||||
import threading
|
||||
from typing import Protocol, Optional, Tuple, List
|
||||
from typing_extensions import runtime_checkable
|
||||
import numpy as np
|
||||
@@ -113,73 +112,29 @@ class STTBackend(Protocol):
|
||||
|
||||
# Global backend instances
|
||||
_tts_backend: Optional[TTSBackend] = None
|
||||
_tts_backends: dict[str, TTSBackend] = {}
|
||||
_tts_backends_lock = threading.Lock()
|
||||
_stt_backend: Optional[STTBackend] = None
|
||||
|
||||
# Supported TTS engines
|
||||
TTS_ENGINES = {
|
||||
"qwen": "Qwen TTS",
|
||||
"luxtts": "LuxTTS",
|
||||
"chatterbox": "Chatterbox TTS",
|
||||
"chatterbox_turbo": "Chatterbox Turbo",
|
||||
}
|
||||
|
||||
|
||||
def get_tts_backend() -> TTSBackend:
|
||||
"""
|
||||
Get or create the default (Qwen) TTS backend instance based on platform.
|
||||
Get or create TTS backend instance based on platform.
|
||||
|
||||
Returns:
|
||||
TTS backend instance (MLX or PyTorch)
|
||||
"""
|
||||
return get_tts_backend_for_engine("qwen")
|
||||
|
||||
|
||||
def get_tts_backend_for_engine(engine: str) -> TTSBackend:
|
||||
"""
|
||||
Get or create a TTS backend for the given engine.
|
||||
global _tts_backend
|
||||
|
||||
Args:
|
||||
engine: Engine name ("qwen" or "luxtts")
|
||||
|
||||
Returns:
|
||||
TTS backend instance
|
||||
"""
|
||||
global _tts_backends
|
||||
|
||||
# Fast path: check without lock
|
||||
if engine in _tts_backends:
|
||||
return _tts_backends[engine]
|
||||
|
||||
# Slow path: create with lock to avoid duplicate instantiation
|
||||
with _tts_backends_lock:
|
||||
# Double-check after acquiring lock
|
||||
if engine in _tts_backends:
|
||||
return _tts_backends[engine]
|
||||
if _tts_backend is None:
|
||||
backend_type = get_backend_type()
|
||||
|
||||
if engine == "qwen":
|
||||
backend_type = get_backend_type()
|
||||
if backend_type == "mlx":
|
||||
from .mlx_backend import MLXTTSBackend
|
||||
backend = MLXTTSBackend()
|
||||
else:
|
||||
from .pytorch_backend import PyTorchTTSBackend
|
||||
backend = PyTorchTTSBackend()
|
||||
elif engine == "luxtts":
|
||||
from .luxtts_backend import LuxTTSBackend
|
||||
backend = LuxTTSBackend()
|
||||
elif engine == "chatterbox":
|
||||
from .chatterbox_backend import ChatterboxTTSBackend
|
||||
backend = ChatterboxTTSBackend()
|
||||
elif engine == "chatterbox_turbo":
|
||||
from .chatterbox_turbo_backend import ChatterboxTurboTTSBackend
|
||||
backend = ChatterboxTurboTTSBackend()
|
||||
if backend_type == "mlx":
|
||||
from .mlx_backend import MLXTTSBackend
|
||||
_tts_backend = MLXTTSBackend()
|
||||
else:
|
||||
raise ValueError(f"Unknown TTS engine: {engine}. Supported: {list(TTS_ENGINES.keys())}")
|
||||
|
||||
_tts_backends[engine] = backend
|
||||
return backend
|
||||
from .pytorch_backend import PyTorchTTSBackend
|
||||
_tts_backend = PyTorchTTSBackend()
|
||||
|
||||
return _tts_backend
|
||||
|
||||
|
||||
def get_stt_backend() -> STTBackend:
|
||||
@@ -206,7 +161,6 @@ def get_stt_backend() -> STTBackend:
|
||||
|
||||
def reset_backends():
|
||||
"""Reset backend instances (useful for testing)."""
|
||||
global _tts_backend, _tts_backends, _stt_backend
|
||||
global _tts_backend, _stt_backend
|
||||
_tts_backend = None
|
||||
_tts_backends.clear()
|
||||
_stt_backend = None
|
||||
|
||||
@@ -1,360 +0,0 @@
|
||||
"""
|
||||
Chatterbox TTS backend implementation.
|
||||
|
||||
Wraps ChatterboxMultilingualTTS from chatterbox-tts for zero-shot
|
||||
voice cloning. Supports 23 languages including Hebrew. Forces CPU
|
||||
on macOS due to known MPS tensor issues.
|
||||
"""
|
||||
|
||||
import asyncio
|
||||
import logging
|
||||
import platform
|
||||
import threading
|
||||
from pathlib import Path
|
||||
from typing import ClassVar, List, Optional, Tuple
|
||||
|
||||
import numpy as np
|
||||
|
||||
from . import TTSBackend
|
||||
from ..utils.audio import normalize_audio, load_audio
|
||||
from ..utils.progress import get_progress_manager
|
||||
from ..utils.tasks import get_task_manager
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
CHATTERBOX_HF_REPO = "ResembleAI/chatterbox"
|
||||
|
||||
# Files that must be present for the multilingual model
|
||||
_MTL_WEIGHT_FILES = [
|
||||
"t3_mtl23ls_v2.safetensors",
|
||||
"s3gen.pt",
|
||||
"ve.pt",
|
||||
]
|
||||
|
||||
|
||||
class ChatterboxTTSBackend:
|
||||
"""Chatterbox Multilingual TTS backend for voice cloning."""
|
||||
|
||||
# Class-level lock for torch.load monkey-patching
|
||||
_load_lock: ClassVar[threading.Lock] = threading.Lock()
|
||||
|
||||
def __init__(self):
|
||||
self.model = None
|
||||
self.model_size = "default"
|
||||
self._device = None
|
||||
self._model_load_lock = asyncio.Lock()
|
||||
|
||||
def _get_device(self) -> str:
|
||||
"""Get the best available device. Forces CPU on macOS (MPS issue)."""
|
||||
if platform.system() == "Darwin":
|
||||
return "cpu"
|
||||
try:
|
||||
import torch
|
||||
|
||||
if torch.cuda.is_available():
|
||||
return "cuda"
|
||||
except ImportError:
|
||||
pass
|
||||
return "cpu"
|
||||
|
||||
def is_loaded(self) -> bool:
|
||||
return self.model is not None
|
||||
|
||||
def _get_model_path(self, model_size: str = "default") -> str:
|
||||
return CHATTERBOX_HF_REPO
|
||||
|
||||
def _is_model_cached(self, model_size: str = "default") -> bool:
|
||||
"""Check if the Chatterbox multilingual model is cached locally."""
|
||||
try:
|
||||
from huggingface_hub import constants as hf_constants
|
||||
|
||||
repo_cache = Path(hf_constants.HF_HUB_CACHE) / (
|
||||
"models--" + CHATTERBOX_HF_REPO.replace("/", "--")
|
||||
)
|
||||
|
||||
if not repo_cache.exists():
|
||||
return False
|
||||
|
||||
blobs_dir = repo_cache / "blobs"
|
||||
if blobs_dir.exists() and any(blobs_dir.glob("*.incomplete")):
|
||||
return False
|
||||
|
||||
# Check for multilingual weight files
|
||||
snapshots_dir = repo_cache / "snapshots"
|
||||
if snapshots_dir.exists():
|
||||
for fname in _MTL_WEIGHT_FILES:
|
||||
if not any(snapshots_dir.rglob(fname)):
|
||||
return False
|
||||
return True
|
||||
|
||||
return False
|
||||
except Exception as e:
|
||||
logger.warning(f"Error checking Chatterbox cache: {e}")
|
||||
return False
|
||||
|
||||
async def load_model(self, model_size: str = "default") -> None:
|
||||
"""Load the Chatterbox multilingual model."""
|
||||
if self.model is not None:
|
||||
return
|
||||
async with self._model_load_lock:
|
||||
if self.model is not None:
|
||||
return
|
||||
await asyncio.to_thread(self._load_model_sync)
|
||||
|
||||
def _load_model_sync(self):
|
||||
"""Synchronous model loading."""
|
||||
from ..utils.hf_progress import HFProgressTracker, create_hf_progress_callback
|
||||
|
||||
progress_manager = get_progress_manager()
|
||||
task_manager = get_task_manager()
|
||||
model_name = "chatterbox-tts"
|
||||
|
||||
is_cached = self._is_model_cached()
|
||||
|
||||
# Set up HF progress tracking (intercepts tqdm for file-level progress)
|
||||
progress_callback = create_hf_progress_callback(model_name, progress_manager)
|
||||
tracker = HFProgressTracker(progress_callback, filter_non_downloads=is_cached)
|
||||
tracker_context = tracker.patch_download()
|
||||
tracker_context.__enter__()
|
||||
|
||||
if not is_cached:
|
||||
task_manager.start_download(model_name)
|
||||
progress_manager.update_progress(
|
||||
model_name=model_name,
|
||||
current=0,
|
||||
total=0,
|
||||
filename="Connecting to HuggingFace...",
|
||||
status="downloading",
|
||||
)
|
||||
|
||||
try:
|
||||
device = self._get_device()
|
||||
self._device = device
|
||||
|
||||
logger.info(f"Loading Chatterbox Multilingual TTS on {device}...")
|
||||
|
||||
import torch
|
||||
from chatterbox.mtl_tts import ChatterboxMultilingualTTS
|
||||
|
||||
# Load into a local variable first, apply all patches, then
|
||||
# assign to self.model. This avoids leaving a half-initialised
|
||||
# model on self.model if any patch step raises an exception.
|
||||
#
|
||||
# Monkey-patch torch.load for CPU loading. The model's .pt files
|
||||
# were saved on CUDA; from_pretrained() doesn't pass map_location
|
||||
# so loading on CPU fails without this.
|
||||
try:
|
||||
if device == "cpu":
|
||||
_orig_torch_load = torch.load
|
||||
|
||||
def _patched_load(*args, **kwargs):
|
||||
kwargs.setdefault("map_location", "cpu")
|
||||
return _orig_torch_load(*args, **kwargs)
|
||||
|
||||
with ChatterboxTTSBackend._load_lock:
|
||||
torch.load = _patched_load
|
||||
try:
|
||||
model = ChatterboxMultilingualTTS.from_pretrained(
|
||||
device=device,
|
||||
)
|
||||
finally:
|
||||
torch.load = _orig_torch_load
|
||||
else:
|
||||
model = ChatterboxMultilingualTTS.from_pretrained(
|
||||
device=device,
|
||||
)
|
||||
finally:
|
||||
tracker_context.__exit__(None, None, None)
|
||||
|
||||
# Fix: transformers >= 4.36 defaults LlamaModel to sdpa attention
|
||||
# which doesn't support output_attentions=True (needed by
|
||||
# Chatterbox's AlignmentStreamAnalyzer). Force eager attention.
|
||||
t3_tfmr = model.t3.tfmr
|
||||
if hasattr(t3_tfmr, "config") and hasattr(
|
||||
t3_tfmr.config, "_attn_implementation"
|
||||
):
|
||||
t3_tfmr.config._attn_implementation = "eager"
|
||||
for layer in getattr(t3_tfmr, "layers", []):
|
||||
if hasattr(layer, "self_attn"):
|
||||
layer.self_attn._attn_implementation = "eager"
|
||||
|
||||
if not is_cached:
|
||||
progress_manager.mark_complete(model_name)
|
||||
task_manager.complete_download(model_name)
|
||||
|
||||
# Patch float64 → float32 dtype mismatches in upstream chatterbox.
|
||||
# librosa.load returns float64 numpy; multiple upstream code paths
|
||||
# convert it to a torch tensor via torch.from_numpy() without
|
||||
# casting, then matmul it against float32 model weights.
|
||||
import types
|
||||
|
||||
# Patch S3Tokenizer (used by s3gen.tokenizer)
|
||||
_tokzr = model.s3gen.tokenizer
|
||||
_orig_log_mel = _tokzr.log_mel_spectrogram.__func__
|
||||
|
||||
def _f32_log_mel(self_tokzr, audio, padding=0):
|
||||
import torch as _torch
|
||||
if _torch.is_tensor(audio):
|
||||
audio = audio.float()
|
||||
return _orig_log_mel(self_tokzr, audio, padding)
|
||||
|
||||
_tokzr.log_mel_spectrogram = types.MethodType(_f32_log_mel, _tokzr)
|
||||
|
||||
# Patch VoiceEncoder
|
||||
_ve = model.ve
|
||||
_orig_ve_forward = _ve.forward.__func__
|
||||
|
||||
def _f32_ve_forward(self_ve, mels):
|
||||
return _orig_ve_forward(self_ve, mels.float())
|
||||
|
||||
_ve.forward = types.MethodType(_f32_ve_forward, _ve)
|
||||
|
||||
# All patches applied successfully — publish the model
|
||||
self.model = model
|
||||
|
||||
logger.info("Chatterbox Multilingual TTS loaded successfully")
|
||||
|
||||
except ImportError as e:
|
||||
logger.error(
|
||||
"chatterbox-tts package not found. "
|
||||
"Install with: pip install chatterbox-tts"
|
||||
)
|
||||
if not is_cached:
|
||||
progress_manager.mark_error(model_name, str(e))
|
||||
task_manager.error_download(model_name, str(e))
|
||||
raise
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to load Chatterbox: {e}")
|
||||
if not is_cached:
|
||||
progress_manager.mark_error(model_name, str(e))
|
||||
task_manager.error_download(model_name, str(e))
|
||||
raise
|
||||
|
||||
def unload_model(self) -> None:
|
||||
"""Unload model to free memory."""
|
||||
if self.model is not None:
|
||||
device = self._device
|
||||
del self.model
|
||||
self.model = None
|
||||
self._device = None
|
||||
if device == "cuda":
|
||||
import torch
|
||||
|
||||
torch.cuda.empty_cache()
|
||||
logger.info("Chatterbox unloaded")
|
||||
|
||||
async def create_voice_prompt(
|
||||
self,
|
||||
audio_path: str,
|
||||
reference_text: str,
|
||||
use_cache: bool = True,
|
||||
) -> Tuple[dict, bool]:
|
||||
"""
|
||||
Create voice prompt from reference audio.
|
||||
|
||||
Chatterbox processes reference audio at generation time, so the
|
||||
prompt just stores the file path. The actual audio is loaded by
|
||||
model.generate() via audio_prompt_path.
|
||||
"""
|
||||
voice_prompt = {
|
||||
"ref_audio": str(audio_path),
|
||||
"ref_text": reference_text,
|
||||
}
|
||||
return voice_prompt, False
|
||||
|
||||
async def combine_voice_prompts(
|
||||
self,
|
||||
audio_paths: List[str],
|
||||
reference_texts: List[str],
|
||||
) -> Tuple[np.ndarray, str]:
|
||||
"""Combine multiple reference samples."""
|
||||
combined_audio = []
|
||||
for path in audio_paths:
|
||||
audio, _sr = load_audio(path)
|
||||
audio = normalize_audio(audio)
|
||||
combined_audio.append(audio)
|
||||
|
||||
mixed = np.concatenate(combined_audio)
|
||||
mixed = normalize_audio(mixed)
|
||||
combined_text = " ".join(reference_texts)
|
||||
return mixed, combined_text
|
||||
|
||||
# Per-language generation defaults. Lower temp + higher cfg = clearer speech.
|
||||
_LANG_DEFAULTS: ClassVar[dict] = {
|
||||
"he": {
|
||||
"exaggeration": 0.4,
|
||||
"cfg_weight": 0.7,
|
||||
"temperature": 0.65,
|
||||
"repetition_penalty": 2.5,
|
||||
},
|
||||
}
|
||||
_GLOBAL_DEFAULTS: ClassVar[dict] = {
|
||||
"exaggeration": 0.5,
|
||||
"cfg_weight": 0.5,
|
||||
"temperature": 0.8,
|
||||
"repetition_penalty": 2.0,
|
||||
}
|
||||
|
||||
async def generate(
|
||||
self,
|
||||
text: str,
|
||||
voice_prompt: dict,
|
||||
language: str = "en",
|
||||
seed: Optional[int] = None,
|
||||
instruct: Optional[str] = None,
|
||||
) -> Tuple[np.ndarray, int]:
|
||||
"""
|
||||
Generate audio using Chatterbox Multilingual TTS.
|
||||
|
||||
Args:
|
||||
text: Text to synthesize
|
||||
voice_prompt: Dict with ref_audio path
|
||||
language: BCP-47 language code
|
||||
seed: Random seed for reproducibility
|
||||
instruct: Unused (protocol compatibility)
|
||||
|
||||
Returns:
|
||||
Tuple of (audio_array, sample_rate)
|
||||
"""
|
||||
await self.load_model()
|
||||
|
||||
ref_audio = voice_prompt.get("ref_audio")
|
||||
if ref_audio and not Path(ref_audio).exists():
|
||||
logger.warning(f"Reference audio not found: {ref_audio}")
|
||||
ref_audio = None
|
||||
|
||||
# Merge language-specific defaults with global defaults
|
||||
lang_defaults = self._LANG_DEFAULTS.get(language, self._GLOBAL_DEFAULTS)
|
||||
|
||||
def _generate_sync():
|
||||
import torch
|
||||
|
||||
if seed is not None:
|
||||
torch.manual_seed(seed)
|
||||
|
||||
logger.info(f"[Chatterbox] Generating: lang={language}")
|
||||
|
||||
wav = self.model.generate(
|
||||
text,
|
||||
language_id=language,
|
||||
audio_prompt_path=ref_audio,
|
||||
exaggeration=lang_defaults["exaggeration"],
|
||||
cfg_weight=lang_defaults["cfg_weight"],
|
||||
temperature=lang_defaults["temperature"],
|
||||
repetition_penalty=lang_defaults["repetition_penalty"],
|
||||
)
|
||||
|
||||
# Convert tensor -> numpy
|
||||
if isinstance(wav, torch.Tensor):
|
||||
audio = wav.squeeze().cpu().numpy().astype(np.float32)
|
||||
else:
|
||||
audio = np.asarray(wav, dtype=np.float32)
|
||||
|
||||
sample_rate = (
|
||||
getattr(self.model, "sr", None)
|
||||
or getattr(self.model, "sample_rate", 24000)
|
||||
)
|
||||
|
||||
return audio, sample_rate
|
||||
|
||||
return await asyncio.to_thread(_generate_sync)
|
||||
@@ -1,345 +0,0 @@
|
||||
"""
|
||||
Chatterbox Turbo TTS backend implementation.
|
||||
|
||||
Wraps ChatterboxTurboTTS from chatterbox-tts for fast, English-only
|
||||
voice cloning with paralinguistic tag support ([laugh], [cough], etc.).
|
||||
Forces CPU on macOS due to known MPS tensor issues.
|
||||
"""
|
||||
|
||||
import asyncio
|
||||
import logging
|
||||
import platform
|
||||
import threading
|
||||
from pathlib import Path
|
||||
from typing import ClassVar, List, Optional, Tuple
|
||||
|
||||
import numpy as np
|
||||
|
||||
from . import TTSBackend
|
||||
from ..utils.audio import normalize_audio, load_audio
|
||||
from ..utils.progress import get_progress_manager
|
||||
from ..utils.tasks import get_task_manager
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
CHATTERBOX_TURBO_HF_REPO = "ResembleAI/chatterbox-turbo"
|
||||
|
||||
# Files that must be present for the turbo model
|
||||
_TURBO_WEIGHT_FILES = [
|
||||
"t3_turbo_v1.safetensors",
|
||||
"s3gen_meanflow.safetensors",
|
||||
"ve.safetensors",
|
||||
]
|
||||
|
||||
|
||||
class ChatterboxTurboTTSBackend:
|
||||
"""Chatterbox Turbo TTS backend — fast, English-only, with paralinguistic tags."""
|
||||
|
||||
# Class-level lock for torch.load monkey-patching
|
||||
_load_lock: ClassVar[threading.Lock] = threading.Lock()
|
||||
|
||||
def __init__(self):
|
||||
self.model = None
|
||||
self.model_size = "default"
|
||||
self._device = None
|
||||
self._model_load_lock = asyncio.Lock()
|
||||
|
||||
def _get_device(self) -> str:
|
||||
"""Get the best available device. Forces CPU on macOS (MPS issue)."""
|
||||
if platform.system() == "Darwin":
|
||||
return "cpu"
|
||||
try:
|
||||
import torch
|
||||
|
||||
if torch.cuda.is_available():
|
||||
return "cuda"
|
||||
except ImportError:
|
||||
pass
|
||||
return "cpu"
|
||||
|
||||
def is_loaded(self) -> bool:
|
||||
return self.model is not None
|
||||
|
||||
def _get_model_path(self, model_size: str = "default") -> str:
|
||||
return CHATTERBOX_TURBO_HF_REPO
|
||||
|
||||
def _is_model_cached(self, model_size: str = "default") -> bool:
|
||||
"""Check if the Chatterbox Turbo model is cached locally."""
|
||||
try:
|
||||
from huggingface_hub import constants as hf_constants
|
||||
|
||||
repo_cache = Path(hf_constants.HF_HUB_CACHE) / (
|
||||
"models--" + CHATTERBOX_TURBO_HF_REPO.replace("/", "--")
|
||||
)
|
||||
|
||||
if not repo_cache.exists():
|
||||
return False
|
||||
|
||||
blobs_dir = repo_cache / "blobs"
|
||||
if blobs_dir.exists() and any(blobs_dir.glob("*.incomplete")):
|
||||
return False
|
||||
|
||||
# Check for turbo weight files
|
||||
snapshots_dir = repo_cache / "snapshots"
|
||||
if snapshots_dir.exists():
|
||||
for fname in _TURBO_WEIGHT_FILES:
|
||||
if not any(snapshots_dir.rglob(fname)):
|
||||
return False
|
||||
return True
|
||||
|
||||
return False
|
||||
except Exception as e:
|
||||
logger.warning(f"Error checking Chatterbox Turbo cache: {e}")
|
||||
return False
|
||||
|
||||
async def load_model(self, model_size: str = "default") -> None:
|
||||
"""Load the Chatterbox Turbo model."""
|
||||
if self.model is not None:
|
||||
return
|
||||
async with self._model_load_lock:
|
||||
if self.model is not None:
|
||||
return
|
||||
await asyncio.to_thread(self._load_model_sync)
|
||||
|
||||
def _load_model_sync(self):
|
||||
"""Synchronous model loading."""
|
||||
from ..utils.hf_progress import HFProgressTracker, create_hf_progress_callback
|
||||
|
||||
progress_manager = get_progress_manager()
|
||||
task_manager = get_task_manager()
|
||||
model_name = "chatterbox-turbo"
|
||||
|
||||
is_cached = self._is_model_cached()
|
||||
|
||||
# Set up HF progress tracking (intercepts tqdm for file-level progress)
|
||||
progress_callback = create_hf_progress_callback(model_name, progress_manager)
|
||||
tracker = HFProgressTracker(progress_callback, filter_non_downloads=is_cached)
|
||||
tracker_context = tracker.patch_download()
|
||||
tracker_context.__enter__()
|
||||
|
||||
if not is_cached:
|
||||
task_manager.start_download(model_name)
|
||||
progress_manager.update_progress(
|
||||
model_name=model_name,
|
||||
current=0,
|
||||
total=0,
|
||||
filename="Connecting to HuggingFace...",
|
||||
status="downloading",
|
||||
)
|
||||
|
||||
try:
|
||||
device = self._get_device()
|
||||
self._device = device
|
||||
|
||||
logger.info(f"Loading Chatterbox Turbo TTS on {device}...")
|
||||
|
||||
import torch
|
||||
from huggingface_hub import snapshot_download
|
||||
from chatterbox.tts_turbo import ChatterboxTurboTTS
|
||||
|
||||
# Download model files ourselves so we can pass token=None
|
||||
# (upstream from_pretrained passes token=True which requires
|
||||
# a stored HF token even though the repo is public).
|
||||
try:
|
||||
local_path = snapshot_download(
|
||||
repo_id=CHATTERBOX_TURBO_HF_REPO,
|
||||
token=None,
|
||||
allow_patterns=[
|
||||
"*.safetensors", "*.json", "*.txt", "*.pt", "*.model",
|
||||
],
|
||||
)
|
||||
finally:
|
||||
tracker_context.__exit__(None, None, None)
|
||||
|
||||
# Monkey-patch torch.load for CPU loading. The model's .pt files
|
||||
# were saved on CUDA; from_local() doesn't pass map_location
|
||||
# so loading on CPU fails without this.
|
||||
# Load into a local var, apply patches, then publish to
|
||||
# self.model so a failed patch doesn't leave us half-initialised.
|
||||
if device == "cpu":
|
||||
_orig_torch_load = torch.load
|
||||
|
||||
def _patched_load(*args, **kwargs):
|
||||
kwargs.setdefault("map_location", "cpu")
|
||||
return _orig_torch_load(*args, **kwargs)
|
||||
|
||||
with ChatterboxTurboTTSBackend._load_lock:
|
||||
torch.load = _patched_load
|
||||
try:
|
||||
model = ChatterboxTurboTTS.from_local(
|
||||
local_path, device,
|
||||
)
|
||||
finally:
|
||||
torch.load = _orig_torch_load
|
||||
else:
|
||||
model = ChatterboxTurboTTS.from_local(
|
||||
local_path, device,
|
||||
)
|
||||
|
||||
if not is_cached:
|
||||
progress_manager.mark_complete(model_name)
|
||||
task_manager.complete_download(model_name)
|
||||
|
||||
# Patch float64 → float32 dtype mismatches in upstream chatterbox.
|
||||
# librosa.load returns float64 numpy; multiple upstream code paths
|
||||
# convert it to a torch tensor via torch.from_numpy() without
|
||||
# casting, then matmul it against float32 model weights.
|
||||
# We patch the two known entry points:
|
||||
#
|
||||
# 1. S3Tokenizer.log_mel_spectrogram — the audio tensor from
|
||||
# librosa hits _mel_filters (float32) in a matmul.
|
||||
# 2. VoiceEncoder.forward — float64 mel spectrograms hit the
|
||||
# float32 LSTM weights.
|
||||
import types
|
||||
|
||||
# Patch S3Tokenizer (used by s3gen.tokenizer)
|
||||
_tokzr = model.s3gen.tokenizer
|
||||
_orig_log_mel = _tokzr.log_mel_spectrogram.__func__
|
||||
|
||||
def _f32_log_mel(self_tokzr, audio, padding=0):
|
||||
import torch as _torch
|
||||
if _torch.is_tensor(audio):
|
||||
audio = audio.float()
|
||||
return _orig_log_mel(self_tokzr, audio, padding)
|
||||
|
||||
_tokzr.log_mel_spectrogram = types.MethodType(_f32_log_mel, _tokzr)
|
||||
|
||||
# Patch VoiceEncoder
|
||||
_ve = model.ve
|
||||
_orig_ve_forward = _ve.forward.__func__
|
||||
|
||||
def _f32_ve_forward(self_ve, mels):
|
||||
return _orig_ve_forward(self_ve, mels.float())
|
||||
|
||||
_ve.forward = types.MethodType(_f32_ve_forward, _ve)
|
||||
|
||||
# Only publish after all patches succeed
|
||||
self.model = model
|
||||
|
||||
logger.info("Chatterbox Turbo TTS loaded successfully")
|
||||
|
||||
except ImportError as e:
|
||||
logger.error(
|
||||
"chatterbox-tts package not found. "
|
||||
"Install with: pip install chatterbox-tts"
|
||||
)
|
||||
if not is_cached:
|
||||
progress_manager.mark_error(model_name, str(e))
|
||||
task_manager.error_download(model_name, str(e))
|
||||
raise
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to load Chatterbox Turbo: {e}")
|
||||
if not is_cached:
|
||||
progress_manager.mark_error(model_name, str(e))
|
||||
task_manager.error_download(model_name, str(e))
|
||||
raise
|
||||
|
||||
def unload_model(self) -> None:
|
||||
"""Unload model to free memory."""
|
||||
if self.model is not None:
|
||||
device = self._device
|
||||
del self.model
|
||||
self.model = None
|
||||
self._device = None
|
||||
if device == "cuda":
|
||||
import torch
|
||||
|
||||
torch.cuda.empty_cache()
|
||||
logger.info("Chatterbox Turbo unloaded")
|
||||
|
||||
async def create_voice_prompt(
|
||||
self,
|
||||
audio_path: str,
|
||||
reference_text: str,
|
||||
use_cache: bool = True,
|
||||
) -> Tuple[dict, bool]:
|
||||
"""
|
||||
Create voice prompt from reference audio.
|
||||
|
||||
Chatterbox Turbo processes reference audio at generation time, so the
|
||||
prompt just stores the file path.
|
||||
"""
|
||||
voice_prompt = {
|
||||
"ref_audio": str(audio_path),
|
||||
"ref_text": reference_text,
|
||||
}
|
||||
return voice_prompt, False
|
||||
|
||||
async def combine_voice_prompts(
|
||||
self,
|
||||
audio_paths: List[str],
|
||||
reference_texts: List[str],
|
||||
) -> Tuple[np.ndarray, str]:
|
||||
"""Combine multiple reference samples."""
|
||||
combined_audio = []
|
||||
for path in audio_paths:
|
||||
audio, _sr = load_audio(path)
|
||||
audio = normalize_audio(audio)
|
||||
combined_audio.append(audio)
|
||||
|
||||
mixed = np.concatenate(combined_audio)
|
||||
mixed = normalize_audio(mixed)
|
||||
combined_text = " ".join(reference_texts)
|
||||
return mixed, combined_text
|
||||
|
||||
async def generate(
|
||||
self,
|
||||
text: str,
|
||||
voice_prompt: dict,
|
||||
language: str = "en",
|
||||
seed: Optional[int] = None,
|
||||
instruct: Optional[str] = None,
|
||||
) -> Tuple[np.ndarray, int]:
|
||||
"""
|
||||
Generate audio using Chatterbox Turbo TTS.
|
||||
|
||||
Supports paralinguistic tags in text: [laugh], [cough], [chuckle], etc.
|
||||
|
||||
Args:
|
||||
text: Text to synthesize (may include paralinguistic tags)
|
||||
voice_prompt: Dict with ref_audio path
|
||||
language: Ignored (Turbo is English-only)
|
||||
seed: Random seed for reproducibility
|
||||
instruct: Unused (protocol compatibility)
|
||||
|
||||
Returns:
|
||||
Tuple of (audio_array, sample_rate)
|
||||
"""
|
||||
await self.load_model()
|
||||
|
||||
ref_audio = voice_prompt.get("ref_audio")
|
||||
if ref_audio and not Path(ref_audio).exists():
|
||||
logger.warning(f"Reference audio not found: {ref_audio}")
|
||||
ref_audio = None
|
||||
|
||||
def _generate_sync():
|
||||
import torch
|
||||
|
||||
if seed is not None:
|
||||
torch.manual_seed(seed)
|
||||
|
||||
logger.info("[Chatterbox Turbo] Generating (English)")
|
||||
|
||||
wav = self.model.generate(
|
||||
text,
|
||||
audio_prompt_path=ref_audio,
|
||||
temperature=0.8,
|
||||
top_k=1000,
|
||||
top_p=0.95,
|
||||
repetition_penalty=1.2,
|
||||
)
|
||||
|
||||
# Convert tensor -> numpy
|
||||
if isinstance(wav, torch.Tensor):
|
||||
audio = wav.squeeze().cpu().numpy().astype(np.float32)
|
||||
else:
|
||||
audio = np.asarray(wav, dtype=np.float32)
|
||||
|
||||
sample_rate = (
|
||||
getattr(self.model, "sr", None)
|
||||
or getattr(self.model, "sample_rate", 24000)
|
||||
)
|
||||
|
||||
return audio, sample_rate
|
||||
|
||||
return await asyncio.to_thread(_generate_sync)
|
||||
@@ -1,275 +0,0 @@
|
||||
"""
|
||||
LuxTTS backend implementation.
|
||||
|
||||
Wraps the LuxTTS (ZipVoice) model for zero-shot voice cloning.
|
||||
~1GB VRAM, 48kHz output, 150x realtime on CPU.
|
||||
"""
|
||||
|
||||
import asyncio
|
||||
import logging
|
||||
from pathlib import Path
|
||||
from typing import List, Optional, Tuple
|
||||
|
||||
import numpy as np
|
||||
|
||||
from . import TTSBackend
|
||||
from ..utils.audio import normalize_audio, load_audio
|
||||
from ..utils.cache import get_cache_key, get_cached_voice_prompt, cache_voice_prompt
|
||||
from ..utils.progress import get_progress_manager
|
||||
from ..utils.tasks import get_task_manager
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
# HuggingFace repo for model weight detection
|
||||
LUXTTS_HF_REPO = "YatharthS/LuxTTS"
|
||||
|
||||
|
||||
class LuxTTSBackend:
|
||||
"""LuxTTS backend for zero-shot voice cloning."""
|
||||
|
||||
def __init__(self):
|
||||
self.model = None
|
||||
self.model_size = "default" # LuxTTS has only one model size
|
||||
self._device = None
|
||||
|
||||
def _get_device(self) -> str:
|
||||
"""Get the best available device."""
|
||||
import torch
|
||||
|
||||
if torch.cuda.is_available():
|
||||
return "cuda"
|
||||
if hasattr(torch.backends, "mps") and torch.backends.mps.is_available():
|
||||
return "mps"
|
||||
return "cpu"
|
||||
|
||||
def is_loaded(self) -> bool:
|
||||
return self.model is not None
|
||||
|
||||
@property
|
||||
def device(self) -> str:
|
||||
if self._device is None:
|
||||
self._device = self._get_device()
|
||||
return self._device
|
||||
|
||||
def _get_model_path(self, model_size: str) -> str:
|
||||
return LUXTTS_HF_REPO
|
||||
|
||||
def _is_model_cached(self, model_size: str = "default") -> bool:
|
||||
"""Check if LuxTTS model weights are cached locally."""
|
||||
try:
|
||||
from huggingface_hub import constants as hf_constants
|
||||
|
||||
repo_cache = (
|
||||
Path(hf_constants.HF_HUB_CACHE)
|
||||
/ ("models--" + LUXTTS_HF_REPO.replace("/", "--"))
|
||||
)
|
||||
|
||||
if not repo_cache.exists():
|
||||
return False
|
||||
|
||||
blobs_dir = repo_cache / "blobs"
|
||||
if blobs_dir.exists() and any(blobs_dir.glob("*.incomplete")):
|
||||
return False
|
||||
|
||||
snapshots_dir = repo_cache / "snapshots"
|
||||
if snapshots_dir.exists():
|
||||
has_weights = any(snapshots_dir.rglob("*.pt")) or any(
|
||||
snapshots_dir.rglob("*.safetensors")
|
||||
) or any(snapshots_dir.rglob("*.onnx")) or any(
|
||||
snapshots_dir.rglob("*.bin")
|
||||
)
|
||||
return has_weights
|
||||
|
||||
return False
|
||||
except Exception as e:
|
||||
logger.warning(f"Error checking LuxTTS cache: {e}")
|
||||
return False
|
||||
|
||||
async def load_model(self, model_size: str = "default") -> None:
|
||||
"""Load the LuxTTS model."""
|
||||
if self.model is not None:
|
||||
return
|
||||
|
||||
await asyncio.to_thread(self._load_model_sync)
|
||||
|
||||
def _load_model_sync(self):
|
||||
"""Synchronous model loading."""
|
||||
from ..utils.hf_progress import HFProgressTracker, create_hf_progress_callback
|
||||
|
||||
progress_manager = get_progress_manager()
|
||||
task_manager = get_task_manager()
|
||||
model_name = "luxtts"
|
||||
|
||||
is_cached = self._is_model_cached()
|
||||
|
||||
# Set up HF progress tracking (intercepts tqdm for file-level progress)
|
||||
progress_callback = create_hf_progress_callback(model_name, progress_manager)
|
||||
tracker = HFProgressTracker(progress_callback, filter_non_downloads=is_cached)
|
||||
tracker_context = tracker.patch_download()
|
||||
tracker_context.__enter__()
|
||||
|
||||
if not is_cached:
|
||||
task_manager.start_download(model_name)
|
||||
progress_manager.update_progress(
|
||||
model_name=model_name,
|
||||
current=0,
|
||||
total=0,
|
||||
filename="Connecting to HuggingFace...",
|
||||
status="downloading",
|
||||
)
|
||||
|
||||
try:
|
||||
from zipvoice.luxvoice import LuxTTS
|
||||
|
||||
device = self.device
|
||||
logger.info(f"Loading LuxTTS on {device}...")
|
||||
|
||||
# LuxTTS constructor downloads model and loads everything
|
||||
try:
|
||||
if device == "cpu":
|
||||
import os
|
||||
threads = os.cpu_count() or 4
|
||||
self.model = LuxTTS(
|
||||
model_path=LUXTTS_HF_REPO,
|
||||
device="cpu",
|
||||
threads=min(threads, 8),
|
||||
)
|
||||
else:
|
||||
self.model = LuxTTS(
|
||||
model_path=LUXTTS_HF_REPO,
|
||||
device=device,
|
||||
)
|
||||
finally:
|
||||
tracker_context.__exit__(None, None, None)
|
||||
|
||||
if not is_cached:
|
||||
progress_manager.mark_complete(model_name)
|
||||
task_manager.complete_download(model_name)
|
||||
|
||||
logger.info("LuxTTS loaded successfully")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to load LuxTTS: {e}")
|
||||
if not is_cached:
|
||||
progress_manager.mark_error(model_name, str(e))
|
||||
task_manager.error_download(model_name, str(e))
|
||||
raise
|
||||
|
||||
def unload_model(self) -> None:
|
||||
"""Unload model to free memory."""
|
||||
if self.model is not None:
|
||||
del self.model
|
||||
self.model = None
|
||||
|
||||
import torch
|
||||
if torch.cuda.is_available():
|
||||
torch.cuda.empty_cache()
|
||||
|
||||
logger.info("LuxTTS unloaded")
|
||||
|
||||
async def create_voice_prompt(
|
||||
self,
|
||||
audio_path: str,
|
||||
reference_text: str,
|
||||
use_cache: bool = True,
|
||||
) -> Tuple[dict, bool]:
|
||||
"""
|
||||
Create voice prompt from reference audio.
|
||||
|
||||
LuxTTS uses its own encode_prompt() which runs Whisper ASR internally
|
||||
to transcribe the reference. The reference_text parameter is not used
|
||||
by LuxTTS itself, but we include it in the cache key for consistency.
|
||||
"""
|
||||
await self.load_model()
|
||||
|
||||
# Compute cache key once for both lookup and storage
|
||||
cache_key = ("luxtts_" + get_cache_key(audio_path, reference_text)) if use_cache else None
|
||||
|
||||
if cache_key:
|
||||
cached = get_cached_voice_prompt(cache_key)
|
||||
if cached is not None and isinstance(cached, dict):
|
||||
return cached, True
|
||||
|
||||
def _encode_sync():
|
||||
return self.model.encode_prompt(
|
||||
prompt_audio=str(audio_path),
|
||||
duration=5,
|
||||
rms=0.01,
|
||||
)
|
||||
|
||||
encoded = await asyncio.to_thread(_encode_sync)
|
||||
|
||||
if cache_key:
|
||||
cache_voice_prompt(cache_key, encoded)
|
||||
|
||||
return encoded, False
|
||||
|
||||
async def combine_voice_prompts(
|
||||
self,
|
||||
audio_paths: List[str],
|
||||
reference_texts: List[str],
|
||||
) -> Tuple[np.ndarray, str]:
|
||||
"""
|
||||
Combine multiple reference samples.
|
||||
|
||||
LuxTTS doesn't have native multi-prompt support, so we concatenate
|
||||
the audio and let encode_prompt handle the combined clip.
|
||||
"""
|
||||
combined_audio = []
|
||||
for path in audio_paths:
|
||||
audio, _sr = load_audio(path, sample_rate=24000)
|
||||
audio = normalize_audio(audio)
|
||||
combined_audio.append(audio)
|
||||
|
||||
mixed = np.concatenate(combined_audio)
|
||||
mixed = normalize_audio(mixed)
|
||||
combined_text = " ".join(reference_texts)
|
||||
|
||||
return mixed, combined_text
|
||||
|
||||
async def generate(
|
||||
self,
|
||||
text: str,
|
||||
voice_prompt: dict,
|
||||
language: str = "en",
|
||||
seed: Optional[int] = None,
|
||||
instruct: Optional[str] = None,
|
||||
) -> Tuple[np.ndarray, int]:
|
||||
"""
|
||||
Generate audio from text using LuxTTS.
|
||||
|
||||
Args:
|
||||
text: Text to synthesize
|
||||
voice_prompt: Encoded prompt dict from encode_prompt()
|
||||
language: Language code (LuxTTS is English-focused)
|
||||
seed: Random seed for reproducibility
|
||||
instruct: Not supported by LuxTTS (ignored)
|
||||
|
||||
Returns:
|
||||
Tuple of (audio_array, sample_rate)
|
||||
"""
|
||||
await self.load_model()
|
||||
|
||||
def _generate_sync():
|
||||
import torch
|
||||
|
||||
if seed is not None:
|
||||
torch.manual_seed(seed)
|
||||
if torch.cuda.is_available():
|
||||
torch.cuda.manual_seed(seed)
|
||||
|
||||
wav = self.model.generate_speech(
|
||||
text=text,
|
||||
encode_dict=voice_prompt,
|
||||
num_steps=4,
|
||||
guidance_scale=3.0,
|
||||
t_shift=0.5,
|
||||
speed=1.0,
|
||||
return_smooth=False, # 48kHz output
|
||||
)
|
||||
|
||||
# LuxTTS returns a tensor (may be on GPU/MPS), move to CPU first
|
||||
audio = wav.detach().cpu().numpy().squeeze()
|
||||
return audio, 48000
|
||||
|
||||
return await asyncio.to_thread(_generate_sync)
|
||||
@@ -5,15 +5,8 @@ MLX backend implementation for TTS and STT using mlx-audio.
|
||||
from typing import Optional, List, Tuple
|
||||
import asyncio
|
||||
import numpy as np
|
||||
import os
|
||||
from pathlib import Path
|
||||
|
||||
# PATCH: Import and apply offline patch BEFORE any huggingface_hub usage
|
||||
# This prevents mlx_audio from making network requests when models are cached
|
||||
from ..utils.hf_offline_patch import patch_huggingface_hub_offline, ensure_original_qwen_config_cached
|
||||
patch_huggingface_hub_offline()
|
||||
ensure_original_qwen_config_cached()
|
||||
|
||||
from . import TTSBackend, STTBackend
|
||||
from ..utils.cache import get_cache_key, get_cached_voice_prompt, cache_voice_prompt
|
||||
from ..utils.audio import normalize_audio, load_audio
|
||||
@@ -21,12 +14,6 @@ from ..utils.progress import get_progress_manager
|
||||
from ..utils.hf_progress import HFProgressTracker, create_hf_progress_callback
|
||||
from ..utils.tasks import get_task_manager
|
||||
|
||||
LANGUAGE_CODE_TO_NAME = {
|
||||
"zh": "chinese", "en": "english", "ja": "japanese", "ko": "korean",
|
||||
"de": "german", "fr": "french", "ru": "russian", "pt": "portuguese",
|
||||
"es": "spanish", "it": "italian",
|
||||
}
|
||||
|
||||
|
||||
class MLXTTSBackend:
|
||||
"""MLX-based TTS backend using mlx-audio."""
|
||||
@@ -172,35 +159,15 @@ class MLXTTSBackend:
|
||||
tracker_context = tracker.patch_download()
|
||||
tracker_context.__enter__()
|
||||
|
||||
# PATCH: Force offline mode when model is already cached
|
||||
# This prevents crashes when HuggingFace is unreachable
|
||||
original_hf_hub_offline = os.environ.get("HF_HUB_OFFLINE")
|
||||
if is_cached:
|
||||
os.environ["HF_HUB_OFFLINE"] = "1"
|
||||
print(f"[PATCH] Model {model_size} is cached, forcing HF_HUB_OFFLINE=1 to avoid network requests")
|
||||
|
||||
# Import mlx_audio AFTER patching tqdm
|
||||
from mlx_audio.tts import load
|
||||
|
||||
# Load MLX model (downloads automatically)
|
||||
try:
|
||||
self.model = load(model_path)
|
||||
except Exception as load_error:
|
||||
# If offline mode failed, try with network enabled as fallback
|
||||
if is_cached and "offline" in str(load_error).lower():
|
||||
print(f"[PATCH] Offline load failed, trying with network: {load_error}")
|
||||
os.environ.pop("HF_HUB_OFFLINE", None)
|
||||
self.model = load(model_path)
|
||||
else:
|
||||
raise
|
||||
finally:
|
||||
# Exit the patch context
|
||||
tracker_context.__exit__(None, None, None)
|
||||
# Restore original HF_HUB_OFFLINE setting
|
||||
if original_hf_hub_offline is not None:
|
||||
os.environ["HF_HUB_OFFLINE"] = original_hf_hub_offline
|
||||
else:
|
||||
os.environ.pop("HF_HUB_OFFLINE", None)
|
||||
|
||||
# Only mark download as complete if we were tracking it
|
||||
if not is_cached:
|
||||
@@ -349,8 +316,7 @@ class MLXTTSBackend:
|
||||
# MLX generate() returns a generator yielding GenerationResult objects
|
||||
audio_chunks = []
|
||||
sample_rate = 24000
|
||||
lang = LANGUAGE_CODE_TO_NAME.get(language, "auto")
|
||||
|
||||
|
||||
# Set seed if provided (MLX uses numpy random)
|
||||
if seed is not None:
|
||||
import mlx.core as mx
|
||||
@@ -378,23 +344,23 @@ class MLXTTSBackend:
|
||||
sig = inspect.signature(self.model.generate)
|
||||
if "ref_audio" in sig.parameters:
|
||||
# Generate with voice cloning
|
||||
for result in self.model.generate(text, ref_audio=ref_audio, ref_text=ref_text, lang_code=lang):
|
||||
for result in self.model.generate(text, ref_audio=ref_audio, ref_text=ref_text):
|
||||
audio_chunks.append(np.array(result.audio))
|
||||
sample_rate = result.sample_rate
|
||||
else:
|
||||
# Fallback: generate without voice cloning
|
||||
for result in self.model.generate(text, lang_code=lang):
|
||||
for result in self.model.generate(text):
|
||||
audio_chunks.append(np.array(result.audio))
|
||||
sample_rate = result.sample_rate
|
||||
else:
|
||||
# No voice prompt, generate normally
|
||||
for result in self.model.generate(text, lang_code=lang):
|
||||
for result in self.model.generate(text):
|
||||
audio_chunks.append(np.array(result.audio))
|
||||
sample_rate = result.sample_rate
|
||||
except Exception as e:
|
||||
# If voice cloning fails, try without it
|
||||
print(f"Warning: Voice cloning failed, generating without voice prompt: {e}")
|
||||
for result in self.model.generate(text, lang_code=lang):
|
||||
for result in self.model.generate(text):
|
||||
audio_chunks.append(np.array(result.audio))
|
||||
sample_rate = result.sample_rate
|
||||
|
||||
@@ -413,17 +379,9 @@ class MLXTTSBackend:
|
||||
return audio, sample_rate
|
||||
|
||||
|
||||
WHISPER_HF_REPOS = {
|
||||
"base": "openai/whisper-base",
|
||||
"small": "openai/whisper-small",
|
||||
"medium": "openai/whisper-medium",
|
||||
"large": "openai/whisper-large-v3",
|
||||
}
|
||||
|
||||
|
||||
class MLXSTTBackend:
|
||||
"""MLX-based STT backend using mlx-audio Whisper."""
|
||||
|
||||
|
||||
def __init__(self, model_size: str = "base"):
|
||||
self.model = None
|
||||
self.model_size = model_size
|
||||
@@ -444,8 +402,8 @@ class MLXSTTBackend:
|
||||
"""
|
||||
try:
|
||||
from huggingface_hub import constants as hf_constants
|
||||
hf_repo = WHISPER_HF_REPOS.get(model_size, f"openai/whisper-{model_size}")
|
||||
repo_cache = Path(hf_constants.HF_HUB_CACHE) / ("models--" + hf_repo.replace("/", "--"))
|
||||
model_name = f"openai/whisper-{model_size}"
|
||||
repo_cache = Path(hf_constants.HF_HUB_CACHE) / ("models--" + model_name.replace("/", "--"))
|
||||
|
||||
if not repo_cache.exists():
|
||||
return False
|
||||
@@ -516,7 +474,7 @@ class MLXSTTBackend:
|
||||
from mlx_audio.stt import load
|
||||
|
||||
# MLX Whisper uses the standard OpenAI models
|
||||
model_name = WHISPER_HF_REPOS.get(model_size, f"openai/whisper-{model_size}")
|
||||
model_name = f"openai/whisper-{model_size}"
|
||||
|
||||
print(f"Loading MLX Whisper model {model_size}...")
|
||||
|
||||
|
||||
@@ -15,12 +15,6 @@ from ..utils.progress import get_progress_manager
|
||||
from ..utils.hf_progress import HFProgressTracker, create_hf_progress_callback
|
||||
from ..utils.tasks import get_task_manager
|
||||
|
||||
LANGUAGE_CODE_TO_NAME = {
|
||||
"zh": "chinese", "en": "english", "ja": "japanese", "ko": "korean",
|
||||
"de": "german", "fr": "french", "ru": "russian", "pt": "portuguese",
|
||||
"es": "spanish", "it": "italian",
|
||||
}
|
||||
|
||||
|
||||
class PyTorchTTSBackend:
|
||||
"""PyTorch-based TTS backend using Qwen3-TTS."""
|
||||
@@ -35,23 +29,9 @@ class PyTorchTTSBackend:
|
||||
"""Get the best available device."""
|
||||
if torch.cuda.is_available():
|
||||
return "cuda"
|
||||
# Intel Arc / Intel Xe GPU via intel-extension-for-pytorch (IPEX)
|
||||
try:
|
||||
import intel_extension_for_pytorch # noqa: F401
|
||||
if hasattr(torch, 'xpu') and torch.xpu.is_available():
|
||||
return "xpu"
|
||||
except ImportError:
|
||||
pass
|
||||
# Any GPU on Windows via DirectML (torch-directml)
|
||||
try:
|
||||
import torch_directml
|
||||
if torch_directml.device_count() > 0:
|
||||
return torch_directml.device(0)
|
||||
except ImportError:
|
||||
pass
|
||||
# MPS (Apple Silicon) — kept for completeness but MLX backend is preferred
|
||||
if hasattr(torch.backends, 'mps') and torch.backends.mps.is_available():
|
||||
return "cpu" # MPS disabled for stability; MLX backend handles Apple Silicon
|
||||
elif hasattr(torch.backends, 'mps') and torch.backends.mps.is_available():
|
||||
# MPS can have issues, use CPU for stability
|
||||
return "cpu"
|
||||
return "cpu"
|
||||
|
||||
def is_loaded(self) -> bool:
|
||||
@@ -186,21 +166,11 @@ class PyTorchTTSBackend:
|
||||
|
||||
# Load the model (tqdm is patched, but filters out non-download progress)
|
||||
try:
|
||||
# Don't pass device_map on CPU: accelerate's meta-tensor mechanism
|
||||
# causes "Cannot copy out of meta tensor" when moving to CPU.
|
||||
# Instead load directly then call .to(device) if needed.
|
||||
if self.device == "cpu":
|
||||
self.model = Qwen3TTSModel.from_pretrained(
|
||||
model_path,
|
||||
torch_dtype=torch.float32,
|
||||
low_cpu_mem_usage=False,
|
||||
)
|
||||
else:
|
||||
self.model = Qwen3TTSModel.from_pretrained(
|
||||
model_path,
|
||||
device_map=self.device,
|
||||
torch_dtype=torch.bfloat16,
|
||||
)
|
||||
self.model = Qwen3TTSModel.from_pretrained(
|
||||
model_path,
|
||||
device_map=self.device,
|
||||
torch_dtype=torch.float32 if self.device == "cpu" else torch.bfloat16,
|
||||
)
|
||||
finally:
|
||||
# Exit the patch context
|
||||
tracker_context.__exit__(None, None, None)
|
||||
@@ -365,7 +335,6 @@ class PyTorchTTSBackend:
|
||||
wavs, sample_rate = self.model.generate_voice_clone(
|
||||
text=text,
|
||||
voice_clone_prompt=voice_prompt,
|
||||
language=LANGUAGE_CODE_TO_NAME.get(language, "auto"),
|
||||
instruct=instruct,
|
||||
)
|
||||
return wavs[0], sample_rate
|
||||
@@ -376,18 +345,9 @@ class PyTorchTTSBackend:
|
||||
return audio, sample_rate
|
||||
|
||||
|
||||
WHISPER_HF_REPOS = {
|
||||
"base": "openai/whisper-base",
|
||||
"small": "openai/whisper-small",
|
||||
"medium": "openai/whisper-medium",
|
||||
"large": "openai/whisper-large-v3",
|
||||
"turbo": "openai/whisper-large-v3-turbo",
|
||||
}
|
||||
|
||||
|
||||
class PyTorchSTTBackend:
|
||||
"""PyTorch-based STT backend using Whisper."""
|
||||
|
||||
|
||||
def __init__(self, model_size: str = "base"):
|
||||
self.model = None
|
||||
self.processor = None
|
||||
@@ -398,22 +358,9 @@ class PyTorchSTTBackend:
|
||||
"""Get the best available device."""
|
||||
if torch.cuda.is_available():
|
||||
return "cuda"
|
||||
# Intel Arc / Intel Xe GPU via intel-extension-for-pytorch (IPEX)
|
||||
try:
|
||||
import intel_extension_for_pytorch # noqa: F401
|
||||
if hasattr(torch, 'xpu') and torch.xpu.is_available():
|
||||
return "xpu"
|
||||
except ImportError:
|
||||
pass
|
||||
# Any GPU on Windows via DirectML (torch-directml)
|
||||
try:
|
||||
import torch_directml
|
||||
if torch_directml.device_count() > 0:
|
||||
return torch_directml.device(0)
|
||||
except ImportError:
|
||||
pass
|
||||
if hasattr(torch.backends, 'mps') and torch.backends.mps.is_available():
|
||||
return "cpu" # MPS disabled for stability
|
||||
elif hasattr(torch.backends, 'mps') and torch.backends.mps.is_available():
|
||||
# MPS support for Whisper
|
||||
return "cpu" # Use CPU for stability
|
||||
return "cpu"
|
||||
|
||||
def is_loaded(self) -> bool:
|
||||
@@ -432,18 +379,18 @@ class PyTorchSTTBackend:
|
||||
"""
|
||||
try:
|
||||
from huggingface_hub import constants as hf_constants
|
||||
hf_repo = WHISPER_HF_REPOS.get(model_size, f"openai/whisper-{model_size}")
|
||||
repo_cache = Path(hf_constants.HF_HUB_CACHE) / ("models--" + hf_repo.replace("/", "--"))
|
||||
|
||||
model_name = f"openai/whisper-{model_size}"
|
||||
repo_cache = Path(hf_constants.HF_HUB_CACHE) / ("models--" + model_name.replace("/", "--"))
|
||||
|
||||
if not repo_cache.exists():
|
||||
return False
|
||||
|
||||
|
||||
# Check for .incomplete files - if any exist, download is still in progress
|
||||
blobs_dir = repo_cache / "blobs"
|
||||
if blobs_dir.exists() and any(blobs_dir.glob("*.incomplete")):
|
||||
print(f"[_is_model_cached] Found .incomplete files for whisper-{model_size}, treating as not cached")
|
||||
return False
|
||||
|
||||
|
||||
# Check that actual model weight files exist in snapshots
|
||||
snapshots_dir = repo_cache / "snapshots"
|
||||
if snapshots_dir.exists():
|
||||
@@ -454,12 +401,12 @@ class PyTorchSTTBackend:
|
||||
if not has_weights:
|
||||
print(f"[_is_model_cached] No model weights found for whisper-{model_size}, treating as not cached")
|
||||
return False
|
||||
|
||||
|
||||
return True
|
||||
except Exception as e:
|
||||
print(f"[_is_model_cached] Error checking cache for whisper-{model_size}: {e}")
|
||||
return False
|
||||
|
||||
|
||||
async def load_model_async(self, model_size: Optional[str] = None):
|
||||
"""
|
||||
Lazy load the Whisper model.
|
||||
@@ -510,7 +457,7 @@ class PyTorchSTTBackend:
|
||||
# Import transformers
|
||||
from transformers import WhisperProcessor, WhisperForConditionalGeneration
|
||||
|
||||
model_name = WHISPER_HF_REPOS.get(model_size, f"openai/whisper-{model_size}")
|
||||
model_name = f"openai/whisper-{model_size}"
|
||||
print(f"[DEBUG] Model name: {model_name}")
|
||||
|
||||
print(f"Loading Whisper model {model_size} on {self.device}...")
|
||||
@@ -599,20 +546,21 @@ class PyTorchSTTBackend:
|
||||
)
|
||||
inputs = inputs.to(self.device)
|
||||
|
||||
# Generate transcription
|
||||
# If language is provided, force it; otherwise let Whisper auto-detect
|
||||
generate_kwargs = {}
|
||||
# Set language if provided
|
||||
forced_decoder_ids = None
|
||||
if language:
|
||||
# Support all languages from frontend: en, zh, ja, ko, de, fr, ru, pt, es, it
|
||||
# Whisper supports these and many more
|
||||
forced_decoder_ids = self.processor.get_decoder_prompt_ids(
|
||||
language=language,
|
||||
task="transcribe",
|
||||
)
|
||||
generate_kwargs["forced_decoder_ids"] = forced_decoder_ids
|
||||
|
||||
# Generate transcription
|
||||
with torch.no_grad():
|
||||
predicted_ids = self.model.generate(
|
||||
inputs["input_features"],
|
||||
**generate_kwargs,
|
||||
forced_decoder_ids=forced_decoder_ids,
|
||||
)
|
||||
|
||||
# Decode
|
||||
|
||||
+24
-38
@@ -1,15 +1,11 @@
|
||||
"""
|
||||
PyInstaller build script for creating standalone Python server binary.
|
||||
|
||||
Usage:
|
||||
python build_binary.py # Build default (CPU) server binary
|
||||
python build_binary.py --cuda # Build CUDA-enabled server binary
|
||||
"""
|
||||
|
||||
import PyInstaller.__main__
|
||||
import argparse
|
||||
import os
|
||||
import platform
|
||||
import sys
|
||||
from pathlib import Path
|
||||
|
||||
|
||||
@@ -18,16 +14,21 @@ def is_apple_silicon():
|
||||
return platform.system() == "Darwin" and platform.machine() == "arm64"
|
||||
|
||||
|
||||
def build_server(cuda=False):
|
||||
def build_server(variant="cpu"):
|
||||
"""Build Python server as standalone binary.
|
||||
|
||||
Args:
|
||||
cuda: If True, build with CUDA support and name the binary
|
||||
voicebox-server-cuda instead of voicebox-server.
|
||||
variant: 'cpu' for CPU-only build (~500MB) or 'cuda' for CUDA build (~3GB)
|
||||
"""
|
||||
backend_dir = Path(__file__).parent
|
||||
|
||||
binary_name = 'voicebox-server-cuda' if cuda else 'voicebox-server'
|
||||
if variant not in ['cpu', 'cuda']:
|
||||
raise ValueError(f"Invalid variant: {variant}. Must be 'cpu' or 'cuda'")
|
||||
|
||||
# Set binary name based on variant
|
||||
binary_name = f'voicebox-server-{variant}' if variant == 'cuda' else 'voicebox-server'
|
||||
|
||||
print(f"Building {variant.upper()} variant: {binary_name}")
|
||||
|
||||
# PyInstaller arguments
|
||||
args = [
|
||||
@@ -61,7 +62,6 @@ def build_server(cuda=False):
|
||||
'--hidden-import', 'backend.utils.progress',
|
||||
'--hidden-import', 'backend.utils.hf_progress',
|
||||
'--hidden-import', 'backend.utils.validation',
|
||||
'--hidden-import', 'backend.cuda_download',
|
||||
'--hidden-import', 'torch',
|
||||
'--hidden-import', 'transformers',
|
||||
'--hidden-import', 'fastapi',
|
||||
@@ -83,16 +83,8 @@ def build_server(cuda=False):
|
||||
'--collect-submodules', 'jaraco',
|
||||
])
|
||||
|
||||
# Add CUDA-specific hidden imports
|
||||
if cuda:
|
||||
print("Building with CUDA support")
|
||||
args.extend([
|
||||
'--hidden-import', 'torch.cuda',
|
||||
'--hidden-import', 'torch.backends.cudnn',
|
||||
])
|
||||
|
||||
# Add MLX-specific imports if building on Apple Silicon (never for CUDA builds)
|
||||
if is_apple_silicon() and not cuda:
|
||||
# Add MLX-specific imports if building on Apple Silicon
|
||||
if is_apple_silicon():
|
||||
print("Building for Apple Silicon - including MLX dependencies")
|
||||
args.extend([
|
||||
'--hidden-import', 'backend.backends.mlx_backend',
|
||||
@@ -104,15 +96,11 @@ def build_server(cuda=False):
|
||||
'--hidden-import', 'mlx_audio.stt',
|
||||
'--collect-submodules', 'mlx',
|
||||
'--collect-submodules', 'mlx_audio',
|
||||
# Use --collect-all so PyInstaller bundles both data files AND
|
||||
# native shared libraries (.dylib, .metallib) for MLX.
|
||||
# Previously only --collect-data was used, which caused MLX to
|
||||
# raise OSError at runtime inside the bundled binary because
|
||||
# the Metal shader libraries were missing.
|
||||
'--collect-all', 'mlx',
|
||||
'--collect-all', 'mlx_audio',
|
||||
# Collect MLX data files including Metal shader libraries (.metallib)
|
||||
'--collect-data', 'mlx',
|
||||
'--collect-data', 'mlx_audio',
|
||||
])
|
||||
elif not cuda:
|
||||
else:
|
||||
print("Building for non-Apple Silicon platform - PyTorch only")
|
||||
|
||||
args.extend([
|
||||
@@ -125,16 +113,14 @@ def build_server(cuda=False):
|
||||
|
||||
# Run PyInstaller
|
||||
PyInstaller.__main__.run(args)
|
||||
|
||||
print(f"Binary built in {backend_dir / 'dist' / binary_name}")
|
||||
|
||||
print(f"\n{'='*60}")
|
||||
print(f"Build complete: {variant.upper()} variant")
|
||||
print(f"Binary: {backend_dir / 'dist' / binary_name}")
|
||||
print(f"{'='*60}\n")
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
parser = argparse.ArgumentParser(description="Build voicebox-server binary")
|
||||
parser.add_argument(
|
||||
'--cuda',
|
||||
action='store_true',
|
||||
help="Build CUDA-enabled binary (voicebox-server-cuda)",
|
||||
)
|
||||
cli_args = parser.parse_args()
|
||||
build_server(cuda=cli_args.cuda)
|
||||
# Accept variant as command line argument
|
||||
variant = sys.argv[1] if len(sys.argv) > 1 else 'cpu'
|
||||
build_server(variant)
|
||||
|
||||
@@ -0,0 +1,30 @@
|
||||
@echo off
|
||||
REM Build both CPU and CUDA server binaries for Windows
|
||||
|
||||
echo ============================================================
|
||||
echo Building BOTH server binaries (CPU + CUDA)
|
||||
echo This will take a while...
|
||||
echo ============================================================
|
||||
|
||||
call build_cpu.bat
|
||||
if errorlevel 1 (
|
||||
echo CPU build failed!
|
||||
exit /b 1
|
||||
)
|
||||
|
||||
echo.
|
||||
echo.
|
||||
|
||||
call build_cuda.bat
|
||||
if errorlevel 1 (
|
||||
echo CUDA build failed!
|
||||
exit /b 1
|
||||
)
|
||||
|
||||
echo.
|
||||
echo ============================================================
|
||||
echo Both binaries built successfully!
|
||||
echo ============================================================
|
||||
echo CPU binary: dist\voicebox-server.exe (~500MB)
|
||||
echo CUDA binary: dist\voicebox-server-cuda.exe (~3GB)
|
||||
echo ============================================================
|
||||
@@ -0,0 +1,28 @@
|
||||
@echo off
|
||||
REM Build CPU-only server binary for Windows
|
||||
REM This creates a ~500MB binary without CUDA support
|
||||
|
||||
echo ============================================================
|
||||
echo Building CPU-only server binary
|
||||
echo ============================================================
|
||||
|
||||
echo.
|
||||
echo Step 1: Installing CPU-only PyTorch...
|
||||
pip uninstall -y torch torchvision torchaudio
|
||||
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu
|
||||
|
||||
echo.
|
||||
echo Step 2: Building binary with PyInstaller...
|
||||
python build_binary.py cpu
|
||||
|
||||
echo.
|
||||
echo Step 3: Restoring CUDA PyTorch for development...
|
||||
pip uninstall -y torch torchvision torchaudio
|
||||
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121
|
||||
|
||||
echo.
|
||||
echo ============================================================
|
||||
echo CPU binary built successfully!
|
||||
echo Location: dist\voicebox-server.exe
|
||||
echo Size: ~500MB
|
||||
echo ============================================================
|
||||
@@ -0,0 +1,30 @@
|
||||
#!/bin/bash
|
||||
# Build CPU-only server binary
|
||||
# This creates a ~500MB binary without CUDA support
|
||||
|
||||
set -e
|
||||
|
||||
echo "============================================================"
|
||||
echo "Building CPU-only server binary"
|
||||
echo "============================================================"
|
||||
|
||||
echo ""
|
||||
echo "Step 1: Installing CPU-only PyTorch..."
|
||||
pip uninstall -y torch torchvision torchaudio || true
|
||||
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu
|
||||
|
||||
echo ""
|
||||
echo "Step 2: Building binary with PyInstaller..."
|
||||
python build_binary.py cpu
|
||||
|
||||
echo ""
|
||||
echo "Step 3: Restoring CUDA PyTorch for development..."
|
||||
pip uninstall -y torch torchvision torchaudio || true
|
||||
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121
|
||||
|
||||
echo ""
|
||||
echo "============================================================"
|
||||
echo "CPU binary built successfully!"
|
||||
echo "Location: dist/voicebox-server"
|
||||
echo "Size: ~500MB"
|
||||
echo "============================================================"
|
||||
@@ -0,0 +1,22 @@
|
||||
@echo off
|
||||
REM Build CUDA server binary for Windows
|
||||
REM This creates a ~3GB binary with CUDA support
|
||||
|
||||
echo ============================================================
|
||||
echo Building CUDA server binary
|
||||
echo ============================================================
|
||||
|
||||
echo.
|
||||
echo Step 1: Ensuring CUDA PyTorch is installed...
|
||||
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121 --upgrade
|
||||
|
||||
echo.
|
||||
echo Step 2: Building binary with PyInstaller...
|
||||
python build_binary.py cuda
|
||||
|
||||
echo.
|
||||
echo ============================================================
|
||||
echo CUDA binary built successfully!
|
||||
echo Location: dist\voicebox-server-cuda.exe
|
||||
echo Size: ~3GB
|
||||
echo ============================================================
|
||||
@@ -4,17 +4,8 @@ Configuration module for voicebox backend.
|
||||
Handles data directory configuration for production bundling.
|
||||
"""
|
||||
|
||||
import os
|
||||
from pathlib import Path
|
||||
|
||||
# Allow users to override the HuggingFace model download directory.
|
||||
# Set VOICEBOX_MODELS_DIR to an absolute path before starting the server.
|
||||
# This sets HF_HUB_CACHE so all huggingface_hub downloads go to that path.
|
||||
_custom_models_dir = os.environ.get("VOICEBOX_MODELS_DIR")
|
||||
if _custom_models_dir:
|
||||
os.environ["HF_HUB_CACHE"] = _custom_models_dir
|
||||
print(f"[config] Model download path set to: {_custom_models_dir}")
|
||||
|
||||
# Default data directory (used in development)
|
||||
_data_dir = Path("data")
|
||||
|
||||
|
||||
@@ -1,198 +0,0 @@
|
||||
"""
|
||||
CUDA backend binary download, assembly, and verification.
|
||||
|
||||
Downloads split parts of the CUDA-enabled voicebox-server binary from
|
||||
GitHub Releases, reassembles them, verifies integrity via SHA-256,
|
||||
and places the binary in the app's data directory for use on next
|
||||
backend restart.
|
||||
"""
|
||||
|
||||
import hashlib
|
||||
import logging
|
||||
import os
|
||||
import sys
|
||||
from pathlib import Path
|
||||
from typing import Optional
|
||||
|
||||
from .config import get_data_dir
|
||||
from .utils.progress import get_progress_manager
|
||||
from . import __version__
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
GITHUB_RELEASES_URL = "https://github.com/jamiepine/voicebox/releases/download"
|
||||
|
||||
PROGRESS_KEY = "cuda-backend"
|
||||
|
||||
|
||||
def get_backends_dir() -> Path:
|
||||
"""Directory where downloaded backend binaries are stored."""
|
||||
d = get_data_dir() / "backends"
|
||||
d.mkdir(parents=True, exist_ok=True)
|
||||
return d
|
||||
|
||||
|
||||
def get_cuda_binary_name() -> str:
|
||||
"""Platform-specific CUDA binary filename."""
|
||||
if sys.platform == "win32":
|
||||
return "voicebox-server-cuda.exe"
|
||||
return "voicebox-server-cuda"
|
||||
|
||||
|
||||
def get_cuda_binary_path() -> Optional[Path]:
|
||||
"""Return path to CUDA binary if it exists."""
|
||||
p = get_backends_dir() / get_cuda_binary_name()
|
||||
if p.exists():
|
||||
return p
|
||||
return None
|
||||
|
||||
|
||||
def is_cuda_active() -> bool:
|
||||
"""Check if the current process is the CUDA binary.
|
||||
|
||||
The CUDA binary sets this env var on startup (see server.py).
|
||||
"""
|
||||
return os.environ.get("VOICEBOX_BACKEND_VARIANT") == "cuda"
|
||||
|
||||
|
||||
def get_cuda_status() -> dict:
|
||||
"""Get current CUDA backend status for the API."""
|
||||
progress_manager = get_progress_manager()
|
||||
cuda_path = get_cuda_binary_path()
|
||||
progress = progress_manager.get_progress(PROGRESS_KEY)
|
||||
|
||||
return {
|
||||
"available": cuda_path is not None,
|
||||
"active": is_cuda_active(),
|
||||
"binary_path": str(cuda_path) if cuda_path else None,
|
||||
"downloading": progress is not None and progress.get("status") == "downloading",
|
||||
"download_progress": progress,
|
||||
}
|
||||
|
||||
|
||||
async def download_cuda_binary(version: Optional[str] = None):
|
||||
"""Download the CUDA backend binary from GitHub Releases.
|
||||
|
||||
Downloads split parts listed in a manifest file, concatenates them,
|
||||
and verifies the SHA-256 checksum for integrity. Atomic write
|
||||
(temp file -> rename).
|
||||
|
||||
Args:
|
||||
version: Version tag (e.g. "v0.2.0"). Defaults to current app version.
|
||||
"""
|
||||
import httpx
|
||||
|
||||
if version is None:
|
||||
version = f"v{__version__}"
|
||||
|
||||
progress = get_progress_manager()
|
||||
binary_name = get_cuda_binary_name()
|
||||
dest_dir = get_backends_dir()
|
||||
final_path = dest_dir / binary_name
|
||||
temp_path = dest_dir / f"{binary_name}.download"
|
||||
|
||||
# Clean up any leftover partial download
|
||||
if temp_path.exists():
|
||||
temp_path.unlink()
|
||||
|
||||
logger.info(f"Starting CUDA backend download for {version}")
|
||||
progress.update_progress(
|
||||
PROGRESS_KEY, current=0, total=0,
|
||||
filename="Fetching manifest...", status="downloading",
|
||||
)
|
||||
|
||||
base_url = f"{GITHUB_RELEASES_URL}/{version}"
|
||||
stem = Path(binary_name).stem # voicebox-server-cuda
|
||||
|
||||
try:
|
||||
async with httpx.AsyncClient(follow_redirects=True, timeout=30.0) as client:
|
||||
# Fetch the manifest (list of split part filenames)
|
||||
manifest_url = f"{base_url}/{stem}.manifest"
|
||||
manifest_resp = await client.get(manifest_url)
|
||||
manifest_resp.raise_for_status()
|
||||
parts = [p.strip() for p in manifest_resp.text.strip().splitlines() if p.strip()]
|
||||
|
||||
if not parts:
|
||||
raise ValueError("Empty manifest — no split parts found")
|
||||
|
||||
logger.info(f"Found {len(parts)} split parts to download")
|
||||
|
||||
# Fetch expected checksum (optional — for integrity verification)
|
||||
expected_sha = None
|
||||
try:
|
||||
sha_url = f"{base_url}/{stem}.sha256"
|
||||
sha_resp = await client.get(sha_url)
|
||||
if sha_resp.status_code == 200:
|
||||
# Format: "sha256hex filename\n"
|
||||
expected_sha = sha_resp.text.strip().split()[0]
|
||||
logger.info(f"Expected SHA-256: {expected_sha[:16]}...")
|
||||
except Exception as e:
|
||||
logger.warning(f"Could not fetch checksum file — skipping verification: {e}")
|
||||
|
||||
# Download and concatenate parts
|
||||
total_downloaded = 0
|
||||
with open(temp_path, "wb") as f:
|
||||
for i, part_name in enumerate(parts):
|
||||
part_url = f"{base_url}/{part_name}"
|
||||
logger.info(f"Downloading part {i + 1}/{len(parts)}: {part_name}")
|
||||
|
||||
async with client.stream("GET", part_url) as response:
|
||||
response.raise_for_status()
|
||||
async for chunk in response.aiter_bytes(chunk_size=1024 * 1024):
|
||||
f.write(chunk)
|
||||
total_downloaded += len(chunk)
|
||||
progress.update_progress(
|
||||
PROGRESS_KEY, current=total_downloaded, total=0,
|
||||
filename=f"Part {i + 1}/{len(parts)}",
|
||||
status="downloading",
|
||||
)
|
||||
|
||||
# Verify integrity if checksum was available
|
||||
if expected_sha:
|
||||
progress.update_progress(
|
||||
PROGRESS_KEY, current=total_downloaded, total=total_downloaded,
|
||||
filename="Verifying integrity...", status="downloading",
|
||||
)
|
||||
sha256 = hashlib.sha256()
|
||||
with open(temp_path, "rb") as f:
|
||||
while True:
|
||||
chunk = f.read(1024 * 1024)
|
||||
if not chunk:
|
||||
break
|
||||
sha256.update(chunk)
|
||||
|
||||
actual = sha256.hexdigest()
|
||||
if actual != expected_sha:
|
||||
raise ValueError(
|
||||
f"Integrity check failed: expected {expected_sha[:16]}..., "
|
||||
f"got {actual[:16]}..."
|
||||
)
|
||||
logger.info(f"Integrity verified: {actual[:16]}...")
|
||||
|
||||
# Atomic move into place (replace handles existing target on all platforms)
|
||||
temp_path.replace(final_path)
|
||||
|
||||
# Make executable on Unix
|
||||
if sys.platform != "win32":
|
||||
final_path.chmod(0o755)
|
||||
|
||||
logger.info(f"CUDA backend downloaded to {final_path}")
|
||||
progress.mark_complete(PROGRESS_KEY)
|
||||
|
||||
except Exception as e:
|
||||
# Clean up on failure
|
||||
if temp_path.exists():
|
||||
temp_path.unlink()
|
||||
logger.error(f"CUDA backend download failed: {e}")
|
||||
progress.mark_error(PROGRESS_KEY, str(e))
|
||||
raise
|
||||
|
||||
|
||||
async def delete_cuda_binary() -> bool:
|
||||
"""Delete the downloaded CUDA binary. Returns True if deleted."""
|
||||
path = get_cuda_binary_path()
|
||||
if path and path.exists():
|
||||
path.unlink()
|
||||
logger.info(f"Deleted CUDA binary: {path}")
|
||||
return True
|
||||
return False
|
||||
+53
-737
File diff suppressed because it is too large
Load Diff
+3
-29
@@ -11,7 +11,7 @@ class VoiceProfileCreate(BaseModel):
|
||||
"""Request model for creating a voice profile."""
|
||||
name: str = Field(..., min_length=1, max_length=100)
|
||||
description: Optional[str] = Field(None, max_length=500)
|
||||
language: str = Field(default="en", pattern="^(zh|en|ja|ko|de|fr|ru|pt|es|it|he|ar|da|el|fi|hi|ms|nl|no|pl|sv|sw|tr)$")
|
||||
language: str = Field(default="en", pattern="^(zh|en|ja|ko|de|fr|ru|pt|es|it)$")
|
||||
|
||||
|
||||
class VoiceProfileResponse(BaseModel):
|
||||
@@ -52,14 +52,11 @@ class ProfileSampleResponse(BaseModel):
|
||||
class GenerationRequest(BaseModel):
|
||||
"""Request model for voice generation."""
|
||||
profile_id: str
|
||||
text: str = Field(..., min_length=1, max_length=50000)
|
||||
language: str = Field(default="en", pattern="^(zh|en|ja|ko|de|fr|ru|pt|es|it|he)$")
|
||||
text: str = Field(..., min_length=1, max_length=5000)
|
||||
language: str = Field(default="en", pattern="^(zh|en|ja|ko|de|fr|ru|pt|es|it)$")
|
||||
seed: Optional[int] = Field(None, ge=0)
|
||||
model_size: Optional[str] = Field(default="1.7B", pattern="^(1\\.7B|0\\.6B)$")
|
||||
instruct: Optional[str] = Field(None, max_length=500)
|
||||
engine: Optional[str] = Field(default="qwen", pattern="^(qwen|luxtts|chatterbox|chatterbox_turbo)$")
|
||||
max_chunk_chars: int = Field(default=800, ge=100, le=5000, description="Max characters per chunk for long text splitting")
|
||||
crossfade_ms: int = Field(default=50, ge=0, le=500, description="Crossfade duration in ms between chunks (0 for hard cut)")
|
||||
|
||||
|
||||
class GenerationResponse(BaseModel):
|
||||
@@ -130,30 +127,12 @@ class HealthResponse(BaseModel):
|
||||
gpu_type: Optional[str] = None # GPU type (CUDA, MPS, or None)
|
||||
vram_used_mb: Optional[float] = None
|
||||
backend_type: Optional[str] = None # Backend type (mlx or pytorch)
|
||||
backend_variant: Optional[str] = None # Binary variant (cpu or cuda)
|
||||
|
||||
|
||||
class DirectoryCheck(BaseModel):
|
||||
"""Health status for a single directory."""
|
||||
path: str
|
||||
exists: bool
|
||||
writable: bool
|
||||
error: Optional[str] = None
|
||||
|
||||
|
||||
class FilesystemHealthResponse(BaseModel):
|
||||
"""Response model for filesystem health check."""
|
||||
healthy: bool
|
||||
disk_free_mb: Optional[float] = None
|
||||
disk_total_mb: Optional[float] = None
|
||||
directories: List[DirectoryCheck]
|
||||
|
||||
|
||||
class ModelStatus(BaseModel):
|
||||
"""Response model for model status."""
|
||||
model_name: str
|
||||
display_name: str
|
||||
hf_repo_id: Optional[str] = None # HuggingFace repository ID
|
||||
downloaded: bool
|
||||
downloading: bool = False # True if download is in progress
|
||||
size_mb: Optional[float] = None
|
||||
@@ -175,11 +154,6 @@ class ActiveDownloadTask(BaseModel):
|
||||
model_name: str
|
||||
status: str
|
||||
started_at: datetime
|
||||
error: Optional[str] = None
|
||||
progress: Optional[float] = None # 0-100 percentage
|
||||
current: Optional[int] = None # bytes downloaded
|
||||
total: Optional[int] = None # total bytes
|
||||
filename: Optional[str] = None # current file being downloaded
|
||||
|
||||
|
||||
class ActiveGenerationTask(BaseModel):
|
||||
|
||||
@@ -19,17 +19,15 @@ def is_apple_silicon() -> bool:
|
||||
def get_backend_type() -> Literal["mlx", "pytorch"]:
|
||||
"""
|
||||
Detect the best backend for the current platform.
|
||||
|
||||
|
||||
Returns:
|
||||
"mlx" on Apple Silicon (if MLX is available and functional), "pytorch" otherwise
|
||||
"mlx" on Apple Silicon (if MLX is available), "pytorch" otherwise
|
||||
"""
|
||||
if is_apple_silicon():
|
||||
try:
|
||||
import mlx.core # noqa: F401 — triggers native lib loading
|
||||
import mlx
|
||||
return "mlx"
|
||||
except (ImportError, OSError, RuntimeError):
|
||||
# MLX not installed, or native libraries failed to load inside a
|
||||
# PyInstaller bundle (OSError on missing .dylib / .metallib).
|
||||
# Fall through to PyTorch.
|
||||
except ImportError:
|
||||
# MLX not installed, fallback to PyTorch
|
||||
return "pytorch"
|
||||
return "pytorch"
|
||||
|
||||
+11
-32
@@ -38,22 +38,14 @@ async def create_profile(
|
||||
) -> VoiceProfileResponse:
|
||||
"""
|
||||
Create a new voice profile.
|
||||
|
||||
|
||||
Args:
|
||||
data: Profile creation data
|
||||
db: Database session
|
||||
|
||||
|
||||
Returns:
|
||||
Created profile
|
||||
|
||||
Raises:
|
||||
ValueError: If a profile with the same name already exists
|
||||
"""
|
||||
# Check if profile name already exists
|
||||
existing_profile = db.query(DBVoiceProfile).filter_by(name=data.name).first()
|
||||
if existing_profile:
|
||||
raise ValueError(f"A profile with the name '{data.name}' already exists. Please choose a different name.")
|
||||
|
||||
# Create profile in database
|
||||
db_profile = DBVoiceProfile(
|
||||
id=str(uuid.uuid4()),
|
||||
@@ -63,15 +55,15 @@ async def create_profile(
|
||||
created_at=datetime.utcnow(),
|
||||
updated_at=datetime.utcnow(),
|
||||
)
|
||||
|
||||
|
||||
db.add(db_profile)
|
||||
db.commit()
|
||||
db.refresh(db_profile)
|
||||
|
||||
|
||||
# Create profile directory
|
||||
profile_dir = _get_profiles_dir() / db_profile.id
|
||||
profile_dir.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
|
||||
return VoiceProfileResponse.model_validate(db_profile)
|
||||
|
||||
|
||||
@@ -199,37 +191,28 @@ async def update_profile(
|
||||
) -> Optional[VoiceProfileResponse]:
|
||||
"""
|
||||
Update a voice profile.
|
||||
|
||||
|
||||
Args:
|
||||
profile_id: Profile ID
|
||||
data: Updated profile data
|
||||
db: Database session
|
||||
|
||||
|
||||
Returns:
|
||||
Updated profile or None if not found
|
||||
|
||||
Raises:
|
||||
ValueError: If a profile with the same name already exists (different profile)
|
||||
"""
|
||||
profile = db.query(DBVoiceProfile).filter_by(id=profile_id).first()
|
||||
if not profile:
|
||||
return None
|
||||
|
||||
# Check if the new name conflicts with another profile
|
||||
if profile.name != data.name:
|
||||
existing_profile = db.query(DBVoiceProfile).filter_by(name=data.name).first()
|
||||
if existing_profile:
|
||||
raise ValueError(f"A profile with the name '{data.name}' already exists. Please choose a different name.")
|
||||
|
||||
|
||||
# Update fields
|
||||
profile.name = data.name
|
||||
profile.description = data.description
|
||||
profile.language = data.language
|
||||
profile.updated_at = datetime.utcnow()
|
||||
|
||||
|
||||
db.commit()
|
||||
db.refresh(profile)
|
||||
|
||||
|
||||
return VoiceProfileResponse.model_validate(profile)
|
||||
|
||||
|
||||
@@ -344,7 +327,6 @@ async def create_voice_prompt_for_profile(
|
||||
profile_id: str,
|
||||
db: Session,
|
||||
use_cache: bool = True,
|
||||
engine: str = "qwen",
|
||||
) -> dict:
|
||||
"""
|
||||
Create a combined voice prompt from all samples in a profile.
|
||||
@@ -353,20 +335,17 @@ async def create_voice_prompt_for_profile(
|
||||
profile_id: Profile ID
|
||||
db: Database session
|
||||
use_cache: Whether to use cached prompts
|
||||
engine: TTS engine to create prompt for ("qwen" or "luxtts")
|
||||
|
||||
Returns:
|
||||
Voice prompt dictionary
|
||||
"""
|
||||
from .backends import get_tts_backend_for_engine
|
||||
|
||||
# Get all samples for profile
|
||||
samples = db.query(DBProfileSample).filter_by(profile_id=profile_id).all()
|
||||
|
||||
if not samples:
|
||||
raise ValueError(f"No samples found for profile {profile_id}")
|
||||
|
||||
tts_model = get_tts_backend_for_engine(engine)
|
||||
tts_model = get_tts_model()
|
||||
|
||||
if len(samples) == 1:
|
||||
# Single sample - use directly
|
||||
|
||||
@@ -9,38 +9,15 @@ alembic>=1.13.0
|
||||
|
||||
# ML models
|
||||
torch>=2.1.0
|
||||
transformers>=4.36.0,<=4.57.6
|
||||
transformers>=4.36.0
|
||||
accelerate>=0.26.0
|
||||
huggingface_hub>=0.20.0
|
||||
qwen-tts>=0.0.5
|
||||
|
||||
# LuxTTS (voice cloning engine)
|
||||
# piper-phonemize needs custom index (no PyPI wheels)
|
||||
--find-links https://k2-fsa.github.io/icefall/piper_phonemize.html
|
||||
# linacodec is a git-only dep of Zipvoice (uv-only source, pip can't resolve it)
|
||||
linacodec @ git+https://github.com/ysharma3501/LinaCodec.git
|
||||
Zipvoice @ git+https://github.com/ysharma3501/LuxTTS.git
|
||||
|
||||
# Chatterbox TTS sub-dependencies (chatterbox-tts itself is installed
|
||||
# --no-deps in the setup script because it pins numpy<1.26 / torch==2.6
|
||||
# which are incompatible with Python 3.12+)
|
||||
conformer>=0.3.2
|
||||
diffusers>=0.29.0
|
||||
omegaconf
|
||||
pykakasi
|
||||
resemble-perth>=1.0.1
|
||||
s3tokenizer
|
||||
spacy-pkuseg
|
||||
pyloudnorm
|
||||
|
||||
# Audio processing
|
||||
librosa>=0.10.0
|
||||
soundfile>=0.12.0
|
||||
numpy>=1.24.0
|
||||
numba>=0.60.0,<0.61.0
|
||||
|
||||
# HTTP client (for CUDA backend download)
|
||||
httpx>=0.27.0
|
||||
|
||||
# Utilities
|
||||
python-multipart>=0.0.6
|
||||
|
||||
@@ -64,29 +64,7 @@ if __name__ == "__main__":
|
||||
default=None,
|
||||
help="Data directory for database, profiles, and generated audio",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--version",
|
||||
action="store_true",
|
||||
help="Print version and exit",
|
||||
)
|
||||
args = parser.parse_args()
|
||||
|
||||
if args.version:
|
||||
from backend import __version__
|
||||
print(f"voicebox-server {__version__}")
|
||||
sys.exit(0)
|
||||
|
||||
# Detect backend variant from binary name
|
||||
# voicebox-server-cuda → sets VOICEBOX_BACKEND_VARIANT=cuda
|
||||
import os
|
||||
binary_name = os.path.basename(sys.executable).lower()
|
||||
if "cuda" in binary_name:
|
||||
os.environ["VOICEBOX_BACKEND_VARIANT"] = "cuda"
|
||||
logger.info("Backend variant: CUDA")
|
||||
else:
|
||||
os.environ["VOICEBOX_BACKEND_VARIANT"] = "cpu"
|
||||
logger.info("Backend variant: CPU")
|
||||
|
||||
logger.info(f"Parsed arguments: host={args.host}, port={args.port}, data_dir={args.data_dir}")
|
||||
|
||||
# Set data directory if provided
|
||||
|
||||
@@ -0,0 +1,137 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Test CUDA binary compression to verify it fits under GitHub's 2GB release asset limit.
|
||||
|
||||
Usage:
|
||||
python test_cuda_compression.py [path/to/voicebox-server-cuda.exe]
|
||||
|
||||
If no path provided, looks for the binary in ./dist/
|
||||
"""
|
||||
|
||||
import os
|
||||
import sys
|
||||
import subprocess
|
||||
from pathlib import Path
|
||||
|
||||
|
||||
def format_size(bytes_size):
|
||||
"""Format bytes into human-readable size."""
|
||||
for unit in ['B', 'KB', 'MB', 'GB']:
|
||||
if bytes_size < 1024.0:
|
||||
return f"{bytes_size:.2f} {unit}"
|
||||
bytes_size /= 1024.0
|
||||
return f"{bytes_size:.2f} TB"
|
||||
|
||||
|
||||
def get_file_size(filepath):
|
||||
"""Get file size in bytes."""
|
||||
return os.path.getsize(filepath)
|
||||
|
||||
|
||||
def compress_with_7z(input_file, output_file):
|
||||
"""Compress file using 7z with maximum compression."""
|
||||
print(f"\nCompressing with 7z (maximum compression)...")
|
||||
print(f"This may take several minutes for a ~2.5GB file...\n")
|
||||
|
||||
cmd = [
|
||||
'7z', 'a',
|
||||
'-t7z', # 7z format
|
||||
'-m0=lzma2', # LZMA2 compression
|
||||
'-mx=9', # Maximum compression
|
||||
'-mfb=64', # Fast bytes
|
||||
'-md=32m', # Dictionary size
|
||||
'-ms=on', # Solid archive
|
||||
output_file,
|
||||
input_file
|
||||
]
|
||||
|
||||
try:
|
||||
subprocess.run(cmd, check=True, capture_output=True, text=True)
|
||||
return True
|
||||
except subprocess.CalledProcessError as e:
|
||||
print(f"Error during compression: {e}")
|
||||
print(f"stderr: {e.stderr}")
|
||||
return False
|
||||
except FileNotFoundError:
|
||||
print("ERROR: 7z not found. Please install 7-Zip:")
|
||||
print(" Windows: https://www.7-zip.org/download.html")
|
||||
print(" macOS: brew install p7zip")
|
||||
print(" Linux: apt-get install p7zip-full")
|
||||
return False
|
||||
|
||||
|
||||
def main():
|
||||
# Find CUDA binary
|
||||
if len(sys.argv) > 1:
|
||||
cuda_binary = Path(sys.argv[1])
|
||||
else:
|
||||
# Look in dist directory
|
||||
dist_dir = Path(__file__).parent / 'dist'
|
||||
candidates = list(dist_dir.glob('voicebox-server-cuda*.exe'))
|
||||
|
||||
if not candidates:
|
||||
print("ERROR: CUDA binary not found in ./dist/")
|
||||
print("Please provide the path as an argument:")
|
||||
print(" python test_cuda_compression.py path/to/voicebox-server-cuda.exe")
|
||||
sys.exit(1)
|
||||
|
||||
cuda_binary = candidates[0]
|
||||
|
||||
if not cuda_binary.exists():
|
||||
print(f"ERROR: File not found: {cuda_binary}")
|
||||
sys.exit(1)
|
||||
|
||||
print("=" * 70)
|
||||
print("CUDA Binary Compression Test")
|
||||
print("=" * 70)
|
||||
|
||||
# Get original size
|
||||
original_size = get_file_size(cuda_binary)
|
||||
print(f"\nOriginal file: {cuda_binary.name}")
|
||||
print(f"Original size: {format_size(original_size)} ({original_size:,} bytes)")
|
||||
|
||||
# Check if already over 2GB
|
||||
github_limit = 2 * 1024 * 1024 * 1024 # 2GB in bytes
|
||||
print(f"GitHub limit: {format_size(github_limit)} ({github_limit:,} bytes)")
|
||||
|
||||
if original_size > github_limit:
|
||||
print(f"\n[WARNING] Original file exceeds GitHub limit by {format_size(original_size - github_limit)}")
|
||||
else:
|
||||
print(f"\n[OK] Original file is under GitHub limit")
|
||||
|
||||
# Compress
|
||||
output_file = cuda_binary.parent / f"{cuda_binary.stem}.7z"
|
||||
if output_file.exists():
|
||||
print(f"\nRemoving existing compressed file: {output_file.name}")
|
||||
output_file.unlink()
|
||||
|
||||
success = compress_with_7z(cuda_binary, output_file)
|
||||
|
||||
if not success:
|
||||
sys.exit(1)
|
||||
|
||||
# Check compressed size
|
||||
compressed_size = get_file_size(output_file)
|
||||
compression_ratio = (1 - compressed_size / original_size) * 100
|
||||
|
||||
print("\n" + "=" * 70)
|
||||
print("Compression Results")
|
||||
print("=" * 70)
|
||||
print(f"\nCompressed file: {output_file.name}")
|
||||
print(f"Compressed size: {format_size(compressed_size)} ({compressed_size:,} bytes)")
|
||||
print(f"Compression ratio: {compression_ratio:.1f}%")
|
||||
print(f"Space saved: {format_size(original_size - compressed_size)}")
|
||||
|
||||
if compressed_size <= github_limit:
|
||||
print(f"\n[SUCCESS] Compressed file fits under GitHub's 2GB limit!")
|
||||
print(f" Margin: {format_size(github_limit - compressed_size)} remaining")
|
||||
else:
|
||||
print(f"\n[FAILED] Compressed file still exceeds GitHub limit")
|
||||
print(f" Over by: {format_size(compressed_size - github_limit)}")
|
||||
print(f"\n Alternative: Host on external storage (S3, Azure Blob, etc.)")
|
||||
|
||||
print("\n" + "=" * 70)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
@@ -0,0 +1,86 @@
|
||||
#!/bin/bash
|
||||
# Test R2 upload locally before running in CI
|
||||
|
||||
set -e
|
||||
|
||||
echo "============================================================"
|
||||
echo "Cloudflare R2 Upload Test"
|
||||
echo "============================================================"
|
||||
|
||||
# Check for required environment variables
|
||||
if [ -z "$AWS_ACCESS_KEY_ID" ] || [ -z "$AWS_SECRET_ACCESS_KEY" ] || [ -z "$R2_ENDPOINT" ]; then
|
||||
echo "ERROR: Missing required environment variables"
|
||||
echo ""
|
||||
echo "Please set:"
|
||||
echo " export AWS_ACCESS_KEY_ID='your-r2-access-key-id'"
|
||||
echo " export AWS_SECRET_ACCESS_KEY='your-r2-secret-access-key'"
|
||||
echo " export R2_ENDPOINT='https://your-account-id.r2.cloudflarestorage.com'"
|
||||
echo ""
|
||||
exit 1
|
||||
fi
|
||||
|
||||
# Check for AWS CLI
|
||||
if ! command -v aws &> /dev/null; then
|
||||
echo "Installing AWS CLI..."
|
||||
pip install awscli
|
||||
fi
|
||||
|
||||
# Find CUDA binary
|
||||
CUDA_BINARY=$(ls dist/voicebox-server-cuda*.exe 2>/dev/null | head -1)
|
||||
|
||||
if [ -z "$CUDA_BINARY" ]; then
|
||||
echo "ERROR: CUDA binary not found in dist/"
|
||||
echo "Run: bash build_cuda.bat"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
echo ""
|
||||
echo "Found CUDA binary: $CUDA_BINARY"
|
||||
echo "Size: $(du -h "$CUDA_BINARY" | cut -f1)"
|
||||
echo ""
|
||||
|
||||
# Test version
|
||||
VERSION="v0.1.12-test"
|
||||
PLATFORM="x86_64-pc-windows-msvc"
|
||||
FILENAME="voicebox-server-cuda-${PLATFORM}.exe"
|
||||
|
||||
echo "Test upload configuration:"
|
||||
echo " Version: $VERSION"
|
||||
echo " Platform: $PLATFORM"
|
||||
echo " Endpoint: $R2_ENDPOINT"
|
||||
echo " Bucket: voicebox"
|
||||
echo " Path: cuda/$VERSION/$FILENAME"
|
||||
echo ""
|
||||
|
||||
read -p "Proceed with upload? (y/n) " -n 1 -r
|
||||
echo
|
||||
if [[ ! $REPLY =~ ^[Yy]$ ]]; then
|
||||
echo "Aborted."
|
||||
exit 1
|
||||
fi
|
||||
|
||||
echo ""
|
||||
echo "Uploading to R2..."
|
||||
|
||||
aws s3 cp "$CUDA_BINARY" \
|
||||
"s3://voicebox/cuda/${VERSION}/${FILENAME}" \
|
||||
--endpoint-url "$R2_ENDPOINT" \
|
||||
--acl public-read
|
||||
|
||||
if [ $? -eq 0 ]; then
|
||||
echo ""
|
||||
echo "============================================================"
|
||||
echo "Upload successful!"
|
||||
echo "============================================================"
|
||||
echo ""
|
||||
echo "Download URL:"
|
||||
echo "https://downloads.voicebox.sh/cuda/${VERSION}/${FILENAME}"
|
||||
echo ""
|
||||
echo "Test with:"
|
||||
echo "curl -I https://downloads.voicebox.sh/cuda/${VERSION}/${FILENAME}"
|
||||
echo ""
|
||||
else
|
||||
echo ""
|
||||
echo "Upload failed!"
|
||||
exit 1
|
||||
fi
|
||||
@@ -1,162 +0,0 @@
|
||||
"""
|
||||
Tests for CORS origin restrictions.
|
||||
|
||||
Validates that the CORS middleware only allows known local origins
|
||||
and respects the VOICEBOX_CORS_ORIGINS environment variable.
|
||||
|
||||
Uses a minimal FastAPI app that mirrors the exact CORS configuration
|
||||
from backend/main.py, so tests run without heavy ML dependencies.
|
||||
|
||||
Usage:
|
||||
pip install httpx pytest fastapi starlette
|
||||
python -m pytest backend/tests/test_cors.py -v
|
||||
"""
|
||||
|
||||
import os
|
||||
import pytest
|
||||
from unittest.mock import patch
|
||||
from fastapi import FastAPI
|
||||
from fastapi.middleware.cors import CORSMiddleware
|
||||
from starlette.testclient import TestClient
|
||||
|
||||
|
||||
def _build_app(env_origins: str = "") -> FastAPI:
|
||||
"""
|
||||
Build a minimal FastAPI app with the same CORS logic as backend/main.py.
|
||||
|
||||
This mirrors the exact code in main.py so the test validates the real
|
||||
configuration without needing torch/numpy/transformers installed.
|
||||
"""
|
||||
app = FastAPI()
|
||||
|
||||
_default_origins = [
|
||||
"http://localhost:5173",
|
||||
"http://127.0.0.1:5173",
|
||||
"http://localhost:17493",
|
||||
"http://127.0.0.1:17493",
|
||||
"tauri://localhost",
|
||||
"https://tauri.localhost",
|
||||
]
|
||||
_cors_origins = _default_origins + [o.strip() for o in env_origins.split(",") if o.strip()]
|
||||
|
||||
app.add_middleware(
|
||||
CORSMiddleware,
|
||||
allow_origins=_cors_origins,
|
||||
allow_credentials=True,
|
||||
allow_methods=["*"],
|
||||
allow_headers=["*"],
|
||||
)
|
||||
|
||||
@app.get("/health")
|
||||
async def health():
|
||||
return {"status": "ok"}
|
||||
|
||||
return app
|
||||
|
||||
|
||||
@pytest.fixture()
|
||||
def client():
|
||||
return TestClient(_build_app())
|
||||
|
||||
|
||||
@pytest.fixture()
|
||||
def client_with_custom_origins():
|
||||
return TestClient(_build_app("https://custom.example.com,https://other.example.com"))
|
||||
|
||||
|
||||
def _get_with_origin(client: TestClient, origin: str) -> dict:
|
||||
"""Send a GET with Origin header, return response headers."""
|
||||
response = client.get("/health", headers={"Origin": origin})
|
||||
return dict(response.headers)
|
||||
|
||||
|
||||
def _preflight(client: TestClient, origin: str) -> dict:
|
||||
"""Send CORS preflight OPTIONS request, return response headers."""
|
||||
response = client.options(
|
||||
"/health",
|
||||
headers={
|
||||
"Origin": origin,
|
||||
"Access-Control-Request-Method": "GET",
|
||||
},
|
||||
)
|
||||
return dict(response.headers)
|
||||
|
||||
|
||||
class TestCORSDefaultOrigins:
|
||||
"""CORS should allow known local origins and block everything else."""
|
||||
|
||||
@pytest.mark.parametrize("origin", [
|
||||
"http://localhost:5173",
|
||||
"http://127.0.0.1:5173",
|
||||
"http://localhost:17493",
|
||||
"http://127.0.0.1:17493",
|
||||
"tauri://localhost",
|
||||
"https://tauri.localhost",
|
||||
])
|
||||
def test_allowed_origins(self, client, origin):
|
||||
headers = _get_with_origin(client, origin)
|
||||
assert headers.get("access-control-allow-origin") == origin
|
||||
|
||||
@pytest.mark.parametrize("origin", [
|
||||
"http://evil.com",
|
||||
"http://localhost:9999",
|
||||
"https://attacker.example.com",
|
||||
"null",
|
||||
])
|
||||
def test_blocked_origins(self, client, origin):
|
||||
headers = _get_with_origin(client, origin)
|
||||
assert "access-control-allow-origin" not in headers
|
||||
|
||||
def test_preflight_allowed(self, client):
|
||||
headers = _preflight(client, "http://localhost:5173")
|
||||
assert headers.get("access-control-allow-origin") == "http://localhost:5173"
|
||||
|
||||
def test_preflight_blocked(self, client):
|
||||
headers = _preflight(client, "http://evil.com")
|
||||
assert "access-control-allow-origin" not in headers
|
||||
|
||||
def test_credentials_header_present(self, client):
|
||||
headers = _get_with_origin(client, "http://localhost:5173")
|
||||
assert headers.get("access-control-allow-credentials") == "true"
|
||||
|
||||
|
||||
class TestCORSCustomOrigins:
|
||||
"""VOICEBOX_CORS_ORIGINS env var should extend the allowlist."""
|
||||
|
||||
def test_custom_origin_allowed(self, client_with_custom_origins):
|
||||
headers = _get_with_origin(client_with_custom_origins, "https://custom.example.com")
|
||||
assert headers.get("access-control-allow-origin") == "https://custom.example.com"
|
||||
|
||||
def test_other_custom_origin_allowed(self, client_with_custom_origins):
|
||||
headers = _get_with_origin(client_with_custom_origins, "https://other.example.com")
|
||||
assert headers.get("access-control-allow-origin") == "https://other.example.com"
|
||||
|
||||
def test_default_origins_still_work(self, client_with_custom_origins):
|
||||
headers = _get_with_origin(client_with_custom_origins, "http://localhost:5173")
|
||||
assert headers.get("access-control-allow-origin") == "http://localhost:5173"
|
||||
|
||||
def test_unlisted_origin_still_blocked(self, client_with_custom_origins):
|
||||
headers = _get_with_origin(client_with_custom_origins, "http://evil.com")
|
||||
assert "access-control-allow-origin" not in headers
|
||||
|
||||
|
||||
class TestCORSEnvVarParsing:
|
||||
"""Edge cases for VOICEBOX_CORS_ORIGINS parsing."""
|
||||
|
||||
def test_empty_env_var(self):
|
||||
app = _build_app("")
|
||||
client = TestClient(app)
|
||||
headers = _get_with_origin(client, "http://evil.com")
|
||||
assert "access-control-allow-origin" not in headers
|
||||
|
||||
def test_whitespace_trimmed(self):
|
||||
app = _build_app(" https://spaced.example.com ")
|
||||
client = TestClient(app)
|
||||
headers = _get_with_origin(client, "https://spaced.example.com")
|
||||
assert headers.get("access-control-allow-origin") == "https://spaced.example.com"
|
||||
|
||||
def test_trailing_comma_ignored(self):
|
||||
app = _build_app("https://one.example.com,")
|
||||
client = TestClient(app)
|
||||
headers = _get_with_origin(client, "https://one.example.com")
|
||||
assert headers.get("access-control-allow-origin") == "https://one.example.com"
|
||||
@@ -1,217 +0,0 @@
|
||||
"""
|
||||
Tests for profile duplicate name validation.
|
||||
|
||||
This test suite verifies that the application correctly handles
|
||||
duplicate profile names and provides user-friendly error messages.
|
||||
"""
|
||||
|
||||
import pytest
|
||||
import tempfile
|
||||
import shutil
|
||||
from pathlib import Path
|
||||
from sqlalchemy import create_engine
|
||||
from sqlalchemy.orm import sessionmaker
|
||||
|
||||
# Add parent directory to path to import backend modules
|
||||
import sys
|
||||
sys.path.insert(0, str(Path(__file__).parent.parent))
|
||||
|
||||
from database import Base, VoiceProfile as DBVoiceProfile
|
||||
from models import VoiceProfileCreate
|
||||
from profiles import create_profile, update_profile
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def test_db():
|
||||
"""Create a temporary test database."""
|
||||
# Create temporary directory for test database
|
||||
temp_dir = tempfile.mkdtemp()
|
||||
db_path = Path(temp_dir) / "test.db"
|
||||
|
||||
# Create engine and session
|
||||
engine = create_engine(f"sqlite:///{db_path}")
|
||||
Base.metadata.create_all(bind=engine)
|
||||
SessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=engine)
|
||||
|
||||
db = SessionLocal()
|
||||
|
||||
yield db
|
||||
|
||||
# Cleanup
|
||||
db.close()
|
||||
shutil.rmtree(temp_dir)
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def mock_profiles_dir(monkeypatch, tmp_path):
|
||||
"""Mock the profiles directory to use a temporary path."""
|
||||
import profiles
|
||||
monkeypatch.setattr(profiles, '_get_profiles_dir', lambda: tmp_path)
|
||||
return tmp_path
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_create_profile_duplicate_name_raises_error(test_db, mock_profiles_dir):
|
||||
"""Test that creating a profile with a duplicate name raises a ValueError."""
|
||||
# Create first profile
|
||||
profile_data_1 = VoiceProfileCreate(
|
||||
name="Test Profile",
|
||||
description="First profile",
|
||||
language="en"
|
||||
)
|
||||
|
||||
profile_1 = await create_profile(profile_data_1, test_db)
|
||||
assert profile_1.name == "Test Profile"
|
||||
|
||||
# Try to create second profile with same name
|
||||
profile_data_2 = VoiceProfileCreate(
|
||||
name="Test Profile",
|
||||
description="Second profile",
|
||||
language="en"
|
||||
)
|
||||
|
||||
with pytest.raises(ValueError) as exc_info:
|
||||
await create_profile(profile_data_2, test_db)
|
||||
|
||||
# Verify error message is user-friendly
|
||||
assert "already exists" in str(exc_info.value)
|
||||
assert "Test Profile" in str(exc_info.value)
|
||||
assert "choose a different name" in str(exc_info.value).lower()
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_create_profile_different_names_succeeds(test_db, mock_profiles_dir):
|
||||
"""Test that creating profiles with different names succeeds."""
|
||||
# Create first profile
|
||||
profile_data_1 = VoiceProfileCreate(
|
||||
name="Profile One",
|
||||
description="First profile",
|
||||
language="en"
|
||||
)
|
||||
|
||||
profile_1 = await create_profile(profile_data_1, test_db)
|
||||
assert profile_1.name == "Profile One"
|
||||
|
||||
# Create second profile with different name
|
||||
profile_data_2 = VoiceProfileCreate(
|
||||
name="Profile Two",
|
||||
description="Second profile",
|
||||
language="en"
|
||||
)
|
||||
|
||||
profile_2 = await create_profile(profile_data_2, test_db)
|
||||
assert profile_2.name == "Profile Two"
|
||||
|
||||
# Verify both profiles exist
|
||||
assert profile_1.id != profile_2.id
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_update_profile_to_duplicate_name_raises_error(test_db, mock_profiles_dir):
|
||||
"""Test that updating a profile to a duplicate name raises a ValueError."""
|
||||
# Create two profiles with different names
|
||||
profile_data_1 = VoiceProfileCreate(
|
||||
name="Profile A",
|
||||
description="First profile",
|
||||
language="en"
|
||||
)
|
||||
profile_1 = await create_profile(profile_data_1, test_db)
|
||||
|
||||
profile_data_2 = VoiceProfileCreate(
|
||||
name="Profile B",
|
||||
description="Second profile",
|
||||
language="en"
|
||||
)
|
||||
profile_2 = await create_profile(profile_data_2, test_db)
|
||||
|
||||
# Try to update profile_2 to use profile_1's name
|
||||
update_data = VoiceProfileCreate(
|
||||
name="Profile A", # Duplicate name
|
||||
description="Updated description",
|
||||
language="en"
|
||||
)
|
||||
|
||||
with pytest.raises(ValueError) as exc_info:
|
||||
await update_profile(profile_2.id, update_data, test_db)
|
||||
|
||||
# Verify error message is user-friendly
|
||||
assert "already exists" in str(exc_info.value)
|
||||
assert "Profile A" in str(exc_info.value)
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_update_profile_keep_same_name_succeeds(test_db, mock_profiles_dir):
|
||||
"""Test that updating a profile while keeping the same name succeeds."""
|
||||
# Create profile
|
||||
profile_data = VoiceProfileCreate(
|
||||
name="My Profile",
|
||||
description="Original description",
|
||||
language="en"
|
||||
)
|
||||
profile = await create_profile(profile_data, test_db)
|
||||
|
||||
# Update profile with same name but different description
|
||||
update_data = VoiceProfileCreate(
|
||||
name="My Profile", # Same name
|
||||
description="Updated description",
|
||||
language="en"
|
||||
)
|
||||
|
||||
updated_profile = await update_profile(profile.id, update_data, test_db)
|
||||
|
||||
# Verify update succeeded
|
||||
assert updated_profile is not None
|
||||
assert updated_profile.id == profile.id
|
||||
assert updated_profile.name == "My Profile"
|
||||
assert updated_profile.description == "Updated description"
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_update_profile_to_new_unique_name_succeeds(test_db, mock_profiles_dir):
|
||||
"""Test that updating a profile to a new unique name succeeds."""
|
||||
# Create profile
|
||||
profile_data = VoiceProfileCreate(
|
||||
name="Original Name",
|
||||
description="Profile description",
|
||||
language="en"
|
||||
)
|
||||
profile = await create_profile(profile_data, test_db)
|
||||
|
||||
# Update profile with new unique name
|
||||
update_data = VoiceProfileCreate(
|
||||
name="New Unique Name",
|
||||
description="Updated description",
|
||||
language="en"
|
||||
)
|
||||
|
||||
updated_profile = await update_profile(profile.id, update_data, test_db)
|
||||
|
||||
# Verify update succeeded
|
||||
assert updated_profile is not None
|
||||
assert updated_profile.id == profile.id
|
||||
assert updated_profile.name == "New Unique Name"
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_case_sensitive_names_allowed(test_db, mock_profiles_dir):
|
||||
"""Test that profile names are case-sensitive (e.g., 'Test' and 'test' are different)."""
|
||||
# Create profile with lowercase name
|
||||
profile_data_1 = VoiceProfileCreate(
|
||||
name="test profile",
|
||||
description="Lowercase",
|
||||
language="en"
|
||||
)
|
||||
profile_1 = await create_profile(profile_data_1, test_db)
|
||||
|
||||
# Create profile with different case
|
||||
profile_data_2 = VoiceProfileCreate(
|
||||
name="Test Profile",
|
||||
description="Title case",
|
||||
language="en"
|
||||
)
|
||||
profile_2 = await create_profile(profile_data_2, test_db)
|
||||
|
||||
# Both should succeed since SQLite unique constraint is case-sensitive by default
|
||||
assert profile_1.name == "test profile"
|
||||
assert profile_2.name == "Test Profile"
|
||||
assert profile_1.id != profile_2.id
|
||||
@@ -32,3 +32,11 @@ def audio_to_wav_bytes(audio: np.ndarray, sample_rate: int) -> bytes:
|
||||
sf.write(buffer, audio, sample_rate, format="WAV")
|
||||
buffer.seek(0)
|
||||
return buffer.read()
|
||||
|
||||
|
||||
def audio_to_wav_bytes(audio: np.ndarray, sample_rate: int) -> bytes:
|
||||
"""Convert audio array to WAV bytes."""
|
||||
buffer = io.BytesIO()
|
||||
sf.write(buffer, audio, sample_rate, format="WAV")
|
||||
buffer.seek(0)
|
||||
return buffer.read()
|
||||
|
||||
+3
-122
@@ -70,133 +70,14 @@ def save_audio(
|
||||
sample_rate: int = 24000,
|
||||
) -> None:
|
||||
"""
|
||||
Save audio file with atomic write and error handling.
|
||||
|
||||
Writes to a temporary file first, then atomically renames to the
|
||||
target path. This prevents corrupted/partial WAV files if the
|
||||
process is interrupted mid-write.
|
||||
|
||||
Save audio file.
|
||||
|
||||
Args:
|
||||
audio: Audio array
|
||||
path: Output path
|
||||
sample_rate: Sample rate
|
||||
|
||||
Raises:
|
||||
OSError: If file cannot be written
|
||||
"""
|
||||
from pathlib import Path
|
||||
import os
|
||||
|
||||
temp_path = f"{path}.tmp"
|
||||
try:
|
||||
# Ensure parent directory exists
|
||||
Path(path).parent.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
# Write to temporary file first (explicit format since .tmp
|
||||
# extension is not recognised by soundfile)
|
||||
sf.write(temp_path, audio, sample_rate, format='WAV')
|
||||
|
||||
# Atomic rename to final path
|
||||
os.replace(temp_path, path)
|
||||
|
||||
except Exception as e:
|
||||
# Clean up temp file on failure
|
||||
try:
|
||||
if Path(temp_path).exists():
|
||||
Path(temp_path).unlink()
|
||||
except Exception:
|
||||
pass # Best effort cleanup
|
||||
|
||||
raise OSError(f"Failed to save audio to {path}: {e}") from e
|
||||
|
||||
|
||||
def trim_tts_output(
|
||||
audio: np.ndarray,
|
||||
sample_rate: int = 24000,
|
||||
frame_ms: int = 20,
|
||||
silence_threshold_db: float = -40.0,
|
||||
min_silence_ms: int = 200,
|
||||
max_internal_silence_ms: int = 1000,
|
||||
fade_ms: int = 30,
|
||||
) -> np.ndarray:
|
||||
"""
|
||||
Trim trailing silence and post-silence hallucination from TTS output.
|
||||
|
||||
Chatterbox sometimes produces ``[speech][silence][hallucinated noise]``.
|
||||
This detects internal silence gaps longer than *max_internal_silence_ms*
|
||||
and cuts the audio at that boundary, then trims trailing silence and
|
||||
applies a short cosine fade-out.
|
||||
|
||||
Args:
|
||||
audio: Input audio array (mono float32)
|
||||
sample_rate: Sample rate in Hz
|
||||
frame_ms: Frame size for RMS energy calculation
|
||||
silence_threshold_db: dB threshold below which a frame is silence
|
||||
min_silence_ms: Minimum trailing silence to keep
|
||||
max_internal_silence_ms: Cut after any silence gap longer than this
|
||||
fade_ms: Cosine fade-out duration in ms
|
||||
|
||||
Returns:
|
||||
Trimmed audio array
|
||||
"""
|
||||
frame_len = int(sample_rate * frame_ms / 1000)
|
||||
if frame_len == 0 or len(audio) < frame_len:
|
||||
return audio
|
||||
|
||||
n_frames = len(audio) // frame_len
|
||||
threshold_linear = 10 ** (silence_threshold_db / 20)
|
||||
|
||||
# Compute per-frame RMS
|
||||
rms = np.array(
|
||||
[
|
||||
np.sqrt(np.mean(audio[i * frame_len : (i + 1) * frame_len] ** 2))
|
||||
for i in range(n_frames)
|
||||
]
|
||||
)
|
||||
is_speech = rms >= threshold_linear
|
||||
|
||||
# Find first speech frame
|
||||
first_speech = 0
|
||||
for i, s in enumerate(is_speech):
|
||||
if s:
|
||||
first_speech = max(0, i - 1) # keep 1 frame padding
|
||||
break
|
||||
|
||||
# Walk forward from first speech; cut at long internal silence gaps
|
||||
max_silence_frames = int(max_internal_silence_ms / frame_ms)
|
||||
consecutive_silence = 0
|
||||
cut_frame = n_frames
|
||||
|
||||
for i in range(first_speech, n_frames):
|
||||
if is_speech[i]:
|
||||
consecutive_silence = 0
|
||||
else:
|
||||
consecutive_silence += 1
|
||||
if consecutive_silence >= max_silence_frames:
|
||||
cut_frame = i - consecutive_silence + 1
|
||||
break
|
||||
|
||||
# Trim trailing silence from the cut point
|
||||
min_silence_frames = int(min_silence_ms / frame_ms)
|
||||
end_frame = cut_frame
|
||||
while end_frame > first_speech and not is_speech[end_frame - 1]:
|
||||
end_frame -= 1
|
||||
# Keep a short tail
|
||||
end_frame = min(end_frame + min_silence_frames, cut_frame)
|
||||
|
||||
# Convert frames back to samples
|
||||
start_sample = first_speech * frame_len
|
||||
end_sample = min(end_frame * frame_len, len(audio))
|
||||
|
||||
trimmed = audio[start_sample:end_sample].copy()
|
||||
|
||||
# Cosine fade-out
|
||||
fade_samples = int(sample_rate * fade_ms / 1000)
|
||||
if fade_samples > 0 and len(trimmed) > fade_samples:
|
||||
fade = np.cos(np.linspace(0, np.pi / 2, fade_samples)) ** 2
|
||||
trimmed[-fade_samples:] *= fade
|
||||
|
||||
return trimmed
|
||||
sf.write(path, audio, sample_rate)
|
||||
|
||||
|
||||
def validate_reference_audio(
|
||||
|
||||
@@ -1,302 +0,0 @@
|
||||
"""
|
||||
Chunked TTS generation utilities.
|
||||
|
||||
Splits long text into sentence-boundary chunks, generates audio per-chunk
|
||||
via any TTSBackend, and concatenates with crossfade. All logic is
|
||||
engine-agnostic — it wraps the standard ``TTSBackend.generate()`` interface.
|
||||
|
||||
Short text (≤ max_chunk_chars) uses the single-shot fast path with zero
|
||||
overhead.
|
||||
"""
|
||||
|
||||
import logging
|
||||
import re
|
||||
from typing import List, Tuple
|
||||
|
||||
import numpy as np
|
||||
|
||||
logger = logging.getLogger("voicebox.chunked-tts")
|
||||
|
||||
# Default chunk size in characters. Can be overridden per-request via
|
||||
# the ``max_chunk_chars`` field on GenerationRequest.
|
||||
DEFAULT_MAX_CHUNK_CHARS = 800
|
||||
|
||||
# Common abbreviations that should NOT be treated as sentence endings.
|
||||
# Lowercase for case-insensitive matching.
|
||||
_ABBREVIATIONS = frozenset(
|
||||
{
|
||||
"mr",
|
||||
"mrs",
|
||||
"ms",
|
||||
"dr",
|
||||
"prof",
|
||||
"sr",
|
||||
"jr",
|
||||
"st",
|
||||
"ave",
|
||||
"blvd",
|
||||
"inc",
|
||||
"ltd",
|
||||
"corp",
|
||||
"dept",
|
||||
"est",
|
||||
"approx",
|
||||
"vs",
|
||||
"etc",
|
||||
"e.g",
|
||||
"i.e",
|
||||
"a.m",
|
||||
"p.m",
|
||||
"u.s",
|
||||
"u.s.a",
|
||||
"u.k",
|
||||
}
|
||||
)
|
||||
|
||||
# Paralinguistic tags used by Chatterbox Turbo. The splitter must never
|
||||
# cut inside one of these.
|
||||
_PARA_TAG_RE = re.compile(r"\[[^\]]*\]")
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Text splitting
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
def split_text_into_chunks(text: str, max_chars: int = DEFAULT_MAX_CHUNK_CHARS) -> List[str]:
|
||||
"""Split *text* at natural boundaries into chunks of at most *max_chars*.
|
||||
|
||||
Priority: sentence-end (``.!?`` not preceded by an abbreviation and not
|
||||
inside brackets) → clause boundary (``;:,—``) → whitespace → hard cut.
|
||||
|
||||
Paralinguistic tags like ``[laugh]`` are treated as atomic and will not
|
||||
be split across chunks.
|
||||
"""
|
||||
text = text.strip()
|
||||
if not text:
|
||||
return []
|
||||
if len(text) <= max_chars:
|
||||
return [text]
|
||||
|
||||
chunks: List[str] = []
|
||||
remaining = text
|
||||
|
||||
while remaining:
|
||||
remaining = remaining.lstrip()
|
||||
if not remaining:
|
||||
break
|
||||
if len(remaining) <= max_chars:
|
||||
chunks.append(remaining)
|
||||
break
|
||||
|
||||
segment = remaining[:max_chars]
|
||||
|
||||
# Try to split at the last real sentence ending
|
||||
split_pos = _find_last_sentence_end(segment)
|
||||
if split_pos == -1:
|
||||
split_pos = _find_last_clause_boundary(segment)
|
||||
if split_pos == -1:
|
||||
split_pos = segment.rfind(" ")
|
||||
if split_pos == -1:
|
||||
# Absolute fallback: hard cut but avoid splitting inside a tag
|
||||
split_pos = _safe_hard_cut(segment, max_chars)
|
||||
|
||||
chunk = remaining[: split_pos + 1].strip()
|
||||
if chunk:
|
||||
chunks.append(chunk)
|
||||
remaining = remaining[split_pos + 1 :]
|
||||
|
||||
return chunks
|
||||
|
||||
|
||||
def _find_last_sentence_end(text: str) -> int:
|
||||
"""Return the index of the last sentence-ending punctuation in *text*.
|
||||
|
||||
Skips periods that follow common abbreviations (``Dr.``, ``Mr.``, etc.)
|
||||
and periods inside bracket tags (``[laugh]``). Also handles CJK
|
||||
sentence-ending punctuation (``。！？``).
|
||||
"""
|
||||
best = -1
|
||||
# ASCII sentence ends
|
||||
for m in re.finditer(r"[.!?](?:\s|$)", text):
|
||||
pos = m.start()
|
||||
char = text[pos]
|
||||
# Skip periods after abbreviations
|
||||
if char == ".":
|
||||
# Walk backwards to find the preceding word
|
||||
word_start = pos - 1
|
||||
while word_start >= 0 and text[word_start].isalpha():
|
||||
word_start -= 1
|
||||
word = text[word_start + 1 : pos].lower()
|
||||
if word in _ABBREVIATIONS:
|
||||
continue
|
||||
# Skip decimal numbers (digit immediately before the period)
|
||||
if word_start >= 0 and text[word_start].isdigit():
|
||||
continue
|
||||
# Skip if we're inside a bracket tag
|
||||
if _inside_bracket_tag(text, pos):
|
||||
continue
|
||||
best = pos
|
||||
# CJK sentence-ending punctuation
|
||||
for m in re.finditer(r"[\u3002\uff01\uff1f]", text):
|
||||
if m.start() > best:
|
||||
best = m.start()
|
||||
return best
|
||||
|
||||
|
||||
def _find_last_clause_boundary(text: str) -> int:
|
||||
"""Return the index of the last clause-boundary punctuation."""
|
||||
best = -1
|
||||
for m in re.finditer(r"[;:,\u2014](?:\s|$)", text):
|
||||
pos = m.start()
|
||||
# Skip if inside a bracket tag
|
||||
if _inside_bracket_tag(text, pos):
|
||||
continue
|
||||
best = pos
|
||||
return best
|
||||
|
||||
|
||||
def _inside_bracket_tag(text: str, pos: int) -> bool:
|
||||
"""Return True if *pos* falls inside a ``[...]`` tag."""
|
||||
for m in _PARA_TAG_RE.finditer(text):
|
||||
if m.start() < pos < m.end():
|
||||
return True
|
||||
return False
|
||||
|
||||
|
||||
def _safe_hard_cut(segment: str, max_chars: int) -> int:
|
||||
"""Find a hard-cut position that doesn't split a ``[tag]``."""
|
||||
cut = max_chars - 1
|
||||
# Check if the cut falls inside a bracket tag; if so, move before it
|
||||
for m in _PARA_TAG_RE.finditer(segment):
|
||||
if m.start() < cut < m.end():
|
||||
return m.start() - 1 if m.start() > 0 else cut
|
||||
return cut
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Audio concatenation
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
def concatenate_audio_chunks(
|
||||
chunks: List[np.ndarray],
|
||||
sample_rate: int,
|
||||
crossfade_ms: int = 50,
|
||||
) -> np.ndarray:
|
||||
"""Concatenate audio arrays with a short crossfade to eliminate clicks.
|
||||
|
||||
Each chunk is expected to be a 1-D float32 ndarray at *sample_rate* Hz.
|
||||
"""
|
||||
if not chunks:
|
||||
return np.array([], dtype=np.float32)
|
||||
if len(chunks) == 1:
|
||||
return chunks[0]
|
||||
|
||||
crossfade_samples = int(sample_rate * crossfade_ms / 1000)
|
||||
result = np.array(chunks[0], dtype=np.float32, copy=True)
|
||||
|
||||
for chunk in chunks[1:]:
|
||||
if len(chunk) == 0:
|
||||
continue
|
||||
overlap = min(crossfade_samples, len(result), len(chunk))
|
||||
if overlap > 0:
|
||||
fade_out = np.linspace(1.0, 0.0, overlap, dtype=np.float32)
|
||||
fade_in = np.linspace(0.0, 1.0, overlap, dtype=np.float32)
|
||||
result[-overlap:] = result[-overlap:] * fade_out + chunk[:overlap] * fade_in
|
||||
result = np.concatenate([result, chunk[overlap:]])
|
||||
else:
|
||||
result = np.concatenate([result, chunk])
|
||||
|
||||
return result
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Engine-agnostic chunked generation
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
async def generate_chunked(
|
||||
backend,
|
||||
text: str,
|
||||
voice_prompt: dict,
|
||||
language: str = "en",
|
||||
seed: int | None = None,
|
||||
instruct: str | None = None,
|
||||
max_chunk_chars: int = DEFAULT_MAX_CHUNK_CHARS,
|
||||
crossfade_ms: int = 50,
|
||||
trim_fn=None,
|
||||
) -> Tuple[np.ndarray, int]:
|
||||
"""Generate audio with automatic chunking for long text.
|
||||
|
||||
For text shorter than *max_chunk_chars* this is a thin wrapper around
|
||||
``backend.generate()`` with zero overhead.
|
||||
|
||||
For longer text the input is split at natural sentence boundaries,
|
||||
each chunk is generated independently, optionally trimmed (useful for
|
||||
Chatterbox engines that hallucinate trailing noise), and the results
|
||||
are concatenated with a crossfade (or hard cut if *crossfade_ms* is 0).
|
||||
|
||||
Parameters
|
||||
----------
|
||||
backend : TTSBackend
|
||||
Any backend implementing the ``generate()`` protocol.
|
||||
text : str
|
||||
Input text (may be arbitrarily long).
|
||||
voice_prompt, language, seed, instruct
|
||||
Forwarded to ``backend.generate()`` verbatim.
|
||||
max_chunk_chars : int
|
||||
Maximum characters per chunk (default 800).
|
||||
crossfade_ms : int
|
||||
Crossfade duration in milliseconds between chunks. 0 for a hard
|
||||
cut with no overlap (default 50).
|
||||
trim_fn : callable | None
|
||||
Optional ``(audio, sample_rate) -> audio`` post-processing
|
||||
function applied to each chunk before concatenation (e.g.
|
||||
``trim_tts_output`` for Chatterbox engines).
|
||||
|
||||
Returns
|
||||
-------
|
||||
(audio, sample_rate) : Tuple[np.ndarray, int]
|
||||
"""
|
||||
chunks = split_text_into_chunks(text, max_chunk_chars)
|
||||
|
||||
if len(chunks) <= 1:
|
||||
# Short text — single-shot fast path
|
||||
audio, sample_rate = await backend.generate(
|
||||
text, voice_prompt, language, seed, instruct,
|
||||
)
|
||||
if trim_fn is not None:
|
||||
audio = trim_fn(audio, sample_rate)
|
||||
return audio, sample_rate
|
||||
|
||||
# Long text — chunked generation
|
||||
logger.info(
|
||||
"Splitting %d chars into %d chunks (max %d chars each)",
|
||||
len(text), len(chunks), max_chunk_chars,
|
||||
)
|
||||
audio_chunks: List[np.ndarray] = []
|
||||
sample_rate: int | None = None
|
||||
|
||||
for i, chunk_text in enumerate(chunks):
|
||||
logger.info(
|
||||
"Generating chunk %d/%d (%d chars)",
|
||||
i + 1, len(chunks), len(chunk_text),
|
||||
)
|
||||
# Vary the seed per chunk to avoid correlated RNG artefacts,
|
||||
# but keep it deterministic so the same (text, seed) pair
|
||||
# always produces the same output.
|
||||
chunk_seed = (seed + i) if seed is not None else None
|
||||
|
||||
chunk_audio, chunk_sr = await backend.generate(
|
||||
chunk_text, voice_prompt, language, chunk_seed, instruct,
|
||||
)
|
||||
if trim_fn is not None:
|
||||
chunk_audio = trim_fn(chunk_audio, chunk_sr)
|
||||
|
||||
audio_chunks.append(np.asarray(chunk_audio, dtype=np.float32))
|
||||
if sample_rate is None:
|
||||
sample_rate = chunk_sr
|
||||
|
||||
audio = concatenate_audio_chunks(audio_chunks, sample_rate, crossfade_ms=crossfade_ms)
|
||||
return audio, sample_rate
|
||||
@@ -1,100 +0,0 @@
|
||||
"""
|
||||
Monkey patch for huggingface_hub to force offline mode with cached models.
|
||||
This prevents mlx_audio from making network requests when models are already downloaded.
|
||||
"""
|
||||
|
||||
import os
|
||||
from pathlib import Path
|
||||
from typing import Optional, Union
|
||||
|
||||
|
||||
def patch_huggingface_hub_offline():
|
||||
"""
|
||||
Monkey-patch huggingface_hub to force offline mode.
|
||||
This must be called BEFORE importing mlx_audio.
|
||||
"""
|
||||
try:
|
||||
import huggingface_hub
|
||||
from huggingface_hub import constants as hf_constants
|
||||
from huggingface_hub.file_download import _try_to_load_from_cache
|
||||
|
||||
# Store original function
|
||||
original_try_load = _try_to_load_from_cache
|
||||
|
||||
def _patched_try_to_load_from_cache(
|
||||
repo_id: str,
|
||||
filename: str,
|
||||
cache_dir: Union[str, Path, None] = None,
|
||||
revision: Optional[str] = None,
|
||||
repo_type: Optional[str] = None,
|
||||
):
|
||||
"""
|
||||
Patched version that forces offline mode.
|
||||
Returns None if not cached (instead of making network request).
|
||||
"""
|
||||
# Always use the original function, but we're already in HF_HUB_OFFLINE mode
|
||||
result = original_try_load(
|
||||
repo_id=repo_id,
|
||||
filename=filename,
|
||||
cache_dir=cache_dir,
|
||||
revision=revision,
|
||||
repo_type=repo_type,
|
||||
)
|
||||
|
||||
if result is None:
|
||||
# File not in cache - log this for debugging
|
||||
cache_path = Path(hf_constants.HF_HUB_CACHE) / f"models--{repo_id.replace('/', '--')}"
|
||||
print(f"[HF_PATCH] File not cached: {repo_id}/{filename}")
|
||||
print(f"[HF_PATCH] Expected at: {cache_path}")
|
||||
else:
|
||||
print(f"[HF_PATCH] Cache hit: {repo_id}/{filename}")
|
||||
|
||||
return result
|
||||
|
||||
# Replace the function
|
||||
import huggingface_hub.file_download as fd
|
||||
fd._try_to_load_from_cache = _patched_try_to_load_from_cache
|
||||
|
||||
print("[HF_PATCH] huggingface_hub patched for offline mode")
|
||||
|
||||
except ImportError:
|
||||
print("[HF_PATCH] huggingface_hub not found, skipping patch")
|
||||
except Exception as e:
|
||||
print(f"[HF_PATCH] Error patching huggingface_hub: {e}")
|
||||
|
||||
|
||||
def ensure_original_qwen_config_cached():
|
||||
"""
|
||||
The MLX community model is based on the original Qwen model.
|
||||
mlx_audio may try to fetch config from the original repo.
|
||||
We need to ensure that config is available in the cache.
|
||||
"""
|
||||
from huggingface_hub import constants as hf_constants
|
||||
|
||||
# Original Qwen model that mlx_audio might reference
|
||||
original_repo = "Qwen/Qwen3-TTS-12Hz-1.7B-Base"
|
||||
mlx_repo = "mlx-community/Qwen3-TTS-12Hz-1.7B-Base-bf16"
|
||||
|
||||
cache_dir = Path(hf_constants.HF_HUB_CACHE)
|
||||
|
||||
original_path = cache_dir / f"models--{original_repo.replace('/', '--')}"
|
||||
mlx_path = cache_dir / f"models--{mlx_repo.replace('/', '--')}"
|
||||
|
||||
# If original repo cache doesn't exist but MLX does, create a symlink or copy config
|
||||
if not original_path.exists() and mlx_path.exists():
|
||||
print(f"[HF_PATCH] Original repo not cached, but MLX version is")
|
||||
print(f"[HF_PATCH] Creating symlink from {original_repo} -> {mlx_repo}")
|
||||
|
||||
try:
|
||||
# Create a symlink so the cache lookup succeeds
|
||||
original_path.parent.mkdir(parents=True, exist_ok=True)
|
||||
original_path.symlink_to(mlx_path, target_is_directory=True)
|
||||
print(f"[HF_PATCH] Symlink created successfully")
|
||||
except Exception as e:
|
||||
print(f"[HF_PATCH] Could not create symlink: {e}")
|
||||
|
||||
|
||||
# Auto-apply patch when module is imported
|
||||
if os.environ.get("VOICEBOX_OFFLINE_PATCH", "1") != "0":
|
||||
patch_huggingface_hub_offline()
|
||||
ensure_original_qwen_config_cached()
|
||||
@@ -72,15 +72,6 @@ class TaskManager:
|
||||
"""Get all active generations."""
|
||||
return list(self._active_generations.values())
|
||||
|
||||
def cancel_download(self, model_name: str) -> bool:
|
||||
"""Cancel/dismiss a download task (removes it from active list)."""
|
||||
return self._active_downloads.pop(model_name, None) is not None
|
||||
|
||||
def clear_all(self) -> None:
|
||||
"""Clear all download and generation tasks."""
|
||||
self._active_downloads.clear()
|
||||
self._active_generations.clear()
|
||||
|
||||
def is_download_active(self, model_name: str) -> bool:
|
||||
"""Check if a download is active."""
|
||||
return model_name in self._active_downloads
|
||||
|
||||
@@ -0,0 +1,48 @@
|
||||
# -*- mode: python ; coding: utf-8 -*-
|
||||
from PyInstaller.utils.hooks import collect_data_files
|
||||
from PyInstaller.utils.hooks import collect_submodules
|
||||
from PyInstaller.utils.hooks import copy_metadata
|
||||
|
||||
datas = []
|
||||
hiddenimports = ['backend', 'backend.main', 'backend.config', 'backend.database', 'backend.models', 'backend.profiles', 'backend.history', 'backend.tts', 'backend.transcribe', 'backend.platform_detect', 'backend.backends', 'backend.backends.pytorch_backend', 'backend.utils.audio', 'backend.utils.cache', 'backend.utils.progress', 'backend.utils.hf_progress', 'backend.utils.validation', 'torch', 'transformers', 'fastapi', 'uvicorn', 'sqlalchemy', 'librosa', 'soundfile', 'qwen_tts', 'qwen_tts.inference', 'qwen_tts.inference.qwen3_tts_model', 'qwen_tts.inference.qwen3_tts_tokenizer', 'qwen_tts.core', 'qwen_tts.cli', 'pkg_resources.extern']
|
||||
datas += collect_data_files('qwen_tts')
|
||||
datas += copy_metadata('qwen-tts')
|
||||
hiddenimports += collect_submodules('qwen_tts')
|
||||
hiddenimports += collect_submodules('jaraco')
|
||||
|
||||
|
||||
a = Analysis(
|
||||
['server.py'],
|
||||
pathex=[],
|
||||
binaries=[],
|
||||
datas=datas,
|
||||
hiddenimports=hiddenimports,
|
||||
hookspath=[],
|
||||
hooksconfig={},
|
||||
runtime_hooks=[],
|
||||
excludes=[],
|
||||
noarchive=False,
|
||||
optimize=0,
|
||||
)
|
||||
pyz = PYZ(a.pure)
|
||||
|
||||
exe = EXE(
|
||||
pyz,
|
||||
a.scripts,
|
||||
a.binaries,
|
||||
a.datas,
|
||||
[],
|
||||
name='voicebox-server-cuda',
|
||||
debug=False,
|
||||
bootloader_ignore_signals=False,
|
||||
strip=False,
|
||||
upx=True,
|
||||
upx_exclude=[],
|
||||
runtime_tmpdir=None,
|
||||
console=True,
|
||||
disable_windowed_traceback=False,
|
||||
argv_emulation=False,
|
||||
target_arch=None,
|
||||
codesign_identity=None,
|
||||
entitlements_file=None,
|
||||
)
|
||||
@@ -4,26 +4,17 @@ from PyInstaller.utils.hooks import collect_submodules
|
||||
from PyInstaller.utils.hooks import copy_metadata
|
||||
|
||||
datas = []
|
||||
hiddenimports = ['backend', 'backend.main', 'backend.config', 'backend.database', 'backend.models', 'backend.profiles', 'backend.history', 'backend.tts', 'backend.transcribe', 'backend.platform_detect', 'backend.backends', 'backend.backends.pytorch_backend', 'backend.utils.audio', 'backend.utils.cache', 'backend.utils.progress', 'backend.utils.hf_progress', 'backend.utils.validation', 'torch', 'transformers', 'fastapi', 'uvicorn', 'sqlalchemy', 'librosa', 'soundfile', 'qwen_tts', 'qwen_tts.inference', 'qwen_tts.inference.qwen3_tts_model', 'qwen_tts.inference.qwen3_tts_tokenizer', 'qwen_tts.core', 'qwen_tts.cli', 'pkg_resources.extern', 'backend.backends.mlx_backend', 'mlx', 'mlx.core', 'mlx.nn', 'mlx_audio', 'mlx_audio.tts', 'mlx_audio.stt']
|
||||
hiddenimports = ['backend', 'backend.main', 'backend.config', 'backend.database', 'backend.models', 'backend.profiles', 'backend.history', 'backend.tts', 'backend.transcribe', 'backend.platform_detect', 'backend.backends', 'backend.backends.pytorch_backend', 'backend.utils.audio', 'backend.utils.cache', 'backend.utils.progress', 'backend.utils.hf_progress', 'backend.utils.validation', 'torch', 'transformers', 'fastapi', 'uvicorn', 'sqlalchemy', 'librosa', 'soundfile', 'qwen_tts', 'qwen_tts.inference', 'qwen_tts.inference.qwen3_tts_model', 'qwen_tts.inference.qwen3_tts_tokenizer', 'qwen_tts.core', 'qwen_tts.cli', 'pkg_resources.extern']
|
||||
datas += collect_data_files('qwen_tts')
|
||||
# Use collect_all (not collect_data_files) so native .dylib and .metallib
|
||||
# files are bundled as binaries, not data. Without this, MLX raises OSError
|
||||
# when loading Metal shaders inside the PyInstaller bundle.
|
||||
from PyInstaller.utils.hooks import collect_all as _collect_all
|
||||
_mlx_datas, _mlx_bins, _mlx_hidden = _collect_all('mlx')
|
||||
_mlxa_datas, _mlxa_bins, _mlxa_hidden = _collect_all('mlx_audio')
|
||||
datas += _mlx_datas + _mlxa_datas
|
||||
datas += copy_metadata('qwen-tts')
|
||||
hiddenimports += collect_submodules('qwen_tts')
|
||||
hiddenimports += collect_submodules('jaraco')
|
||||
hiddenimports += collect_submodules('mlx')
|
||||
hiddenimports += collect_submodules('mlx_audio')
|
||||
|
||||
|
||||
a = Analysis(
|
||||
['server.py'],
|
||||
pathex=[],
|
||||
binaries=_mlx_bins + _mlxa_bins,
|
||||
binaries=[],
|
||||
datas=datas,
|
||||
hiddenimports=hiddenimports,
|
||||
hookspath=[],
|
||||
|
||||
@@ -1,41 +0,0 @@
|
||||
services:
|
||||
voicebox:
|
||||
build: .
|
||||
container_name: voicebox
|
||||
restart: unless-stopped
|
||||
|
||||
ports:
|
||||
# Bind to localhost only for security
|
||||
- "127.0.0.1:17493:17493"
|
||||
|
||||
volumes:
|
||||
# Bind-mount for generated audio (customize the host path as needed)
|
||||
# Host side: ./output/
|
||||
# Container side: /app/data/generations/
|
||||
- ./output:/app/data/generations
|
||||
|
||||
# Named volume for profiles, DB, cache (persists across container restarts)
|
||||
- voicebox-data:/app/data
|
||||
|
||||
# HuggingFace model cache (so models aren't re-downloaded on rebuild)
|
||||
- huggingface-cache:/home/voicebox/.cache/huggingface
|
||||
|
||||
environment:
|
||||
- LOG_LEVEL=info
|
||||
|
||||
networks:
|
||||
- voicebox-net
|
||||
|
||||
deploy:
|
||||
resources:
|
||||
limits:
|
||||
cpus: '4'
|
||||
memory: 8G
|
||||
|
||||
networks:
|
||||
voicebox-net:
|
||||
driver: bridge
|
||||
|
||||
volumes:
|
||||
voicebox-data:
|
||||
huggingface-cache:
|
||||
@@ -1,70 +0,0 @@
|
||||
# Accessibility: screen reader and keyboard improvements
|
||||
|
||||
## Summary
|
||||
|
||||
Improvements to support screen reader and keyboard users across the main app surfaces: audio player, generation UI, voice selection, history, voices tab, model management, server tab, and stories.
|
||||
|
||||
**Tested with NVDA and Narrator on Windows.**
|
||||
|
||||
---
|
||||
|
||||
## What changed
|
||||
|
||||
### Audio player (after generating audio)
|
||||
|
||||
- **Play/Pause, Loop, Mute, Close** – `aria-label` added so each control is announced (e.g. "Play", "Pause", "Loop", "Mute", "Close player").
|
||||
- **Playback position slider** – `aria-label="Playback position"` and `aria-valuetext` with current/total time (e.g. "0:30 of 2:15").
|
||||
- **Volume** – Wrapped in a labelled group; volume slider has an associated screen-reader-only label and `aria-valuetext` for the level (e.g. "Volume level, 75%").
|
||||
|
||||
### Generation UI (text box and voice choice)
|
||||
|
||||
- **Generate speech** (submit) and **Fine-tune instructions** (sliders) – Icon buttons now have `aria-label` (and state for fine-tune, e.g. "Fine-tune instructions, on").
|
||||
|
||||
### Voice selection (cards on Generate screen)
|
||||
|
||||
- Each **voice card** is focusable (`tabIndex={0}`), has `role="button"`, and an `aria-label` (e.g. "Prashant, en. Select as voice for generation.") with `aria-pressed` when selected.
|
||||
- **Enter/Space** on the card selects that voice; tab order is card → Export/Edit/Delete.
|
||||
|
||||
### History list (generated samples)
|
||||
|
||||
- Each **sample row** is focusable with `role="button"` and an `aria-label` (e.g. "Sample from [profile], [duration], [date]. Press Enter to play."); **Enter/Space** plays or restarts.
|
||||
- **Transcript textarea** has `aria-label` (e.g. "Transcript for sample from [profile], [duration]") so when you focus on the text area, the sample is announced in context.
|
||||
|
||||
### Voices tab (table)
|
||||
|
||||
- Each **voice row** is focusable with `role="button"` and an `aria-label` (e.g. "[Name], [language], [N] generations, [N] samples. Press Enter to edit."); **Enter/Space** opens edit (except when focus is in a control).
|
||||
- **Actions** dropdown trigger has `aria-label="Actions for [profile name]"`.
|
||||
|
||||
### Model management
|
||||
|
||||
- Each **model row** is a focusable region (`tabIndex={0}`, `role="group"`) with an `aria-label` (e.g. "[Model name], [status], [size]. Use Tab to reach Download or Delete.").
|
||||
- **Download** and **Delete** (and Downloading) buttons have `aria-label` (e.g. "Download [name]", "Delete [name]").
|
||||
|
||||
### Server tab (panels)
|
||||
|
||||
- **Server Connection**, **Server Status**, and **App Updates** cards are landmarks: `role="region"`, `aria-label`, and `tabIndex={0}` so each panel is focusable and announced (e.g. "Server Connection", "Server Status", "App Updates").
|
||||
|
||||
### Stories list
|
||||
|
||||
- Each **story row** is a focusable control (`role="button"`, `tabIndex={0}`) with `aria-label` (e.g. "Story [name], [N] items, [date]. Press Enter to select."); **Enter/Space** selects the story. Actions button has `aria-label="Actions for [story name]"`.
|
||||
|
||||
### Other controls
|
||||
|
||||
- **Story list** – Actions (⋮) button: `aria-label="Actions for [story name]"`.
|
||||
- **Story track editor** – Play/Pause, Stop, Split, Duplicate, Delete, Zoom in/out: `aria-label` on all icon buttons.
|
||||
- **Voice profile samples** (SampleList, AudioSampleUpload, AudioSampleRecording, AudioSampleSystem) – Play/Pause and Stop: `aria-label` (e.g. "Play sample", "Pause", "Stop playback").
|
||||
- **SampleList** mini sample player – Seek slider has `aria-label="Sample playback position"` and `aria-valuetext` for time.
|
||||
|
||||
---
|
||||
|
||||
## Testing
|
||||
|
||||
- **Screen readers:** Tested with **NVDA** and **Narrator** on Windows.
|
||||
- **Keyboard:** Tab order and Enter/Space activation verified for focusable rows and buttons.
|
||||
|
||||
---
|
||||
|
||||
## Tech note
|
||||
|
||||
- React + TypeScript; Radix UI primitives; labels added via `aria-label`, `aria-labelledby`, `aria-valuetext`, and `role`/`tabIndex` where needed.
|
||||
- No new dependencies.
|
||||
@@ -162,7 +162,7 @@ chmod +x voicebox-*.AppImage
|
||||
**Solutions:**
|
||||
1. **Check server is running**
|
||||
```bash
|
||||
curl http://localhost:17493/health
|
||||
curl http://localhost:8000/health
|
||||
```
|
||||
|
||||
2. **Check remote mode**
|
||||
@@ -170,7 +170,7 @@ chmod +x voicebox-*.AppImage
|
||||
- Check firewall settings
|
||||
|
||||
3. **Check port availability**
|
||||
- The current local app and dev workflow uses port 17493 by default
|
||||
- Default port is 8000
|
||||
- Ensure no other service is using it
|
||||
|
||||
### CORS errors in browser
|
||||
@@ -276,7 +276,7 @@ chmod +x voicebox-*.AppImage
|
||||
|
||||
2. **Check OpenAPI endpoint**
|
||||
```bash
|
||||
curl http://localhost:17493/openapi.json
|
||||
curl http://localhost:8000/openapi.json
|
||||
```
|
||||
|
||||
3. **Regenerate client**
|
||||
|
||||
@@ -0,0 +1,620 @@
|
||||
# CUDA Distribution Problem - Complete Analysis
|
||||
|
||||
## Table of Contents
|
||||
1. [Problem Overview](#problem-overview)
|
||||
2. [Root Cause](#root-cause)
|
||||
3. [Attempted Solutions](#attempted-solutions)
|
||||
4. [Current Status](#current-status)
|
||||
5. [Available Options](#available-options)
|
||||
6. [Technical Details](#technical-details)
|
||||
7. [Cost Analysis](#cost-analysis)
|
||||
8. [Recommendations](#recommendations)
|
||||
|
||||
---
|
||||
|
||||
## Problem Overview
|
||||
|
||||
### Timeline of Issues
|
||||
|
||||
**Original Problem (v0.1.0 - v0.1.11)**
|
||||
- Single server binary with CUDA support
|
||||
- Size: ~2.9GB
|
||||
- Issue: MSI installer build fails in GitHub Actions CI
|
||||
- Error: WiX Toolset cannot handle 3GB files efficiently
|
||||
|
||||
**First Solution: Dual Binary System (v0.1.12)**
|
||||
- Split into CPU (295MB) and CUDA (2.37GB) binaries
|
||||
- CPU ships with installer
|
||||
- CUDA as optional download
|
||||
- Issue: GitHub Release assets have 2GB limit
|
||||
|
||||
**Current Problem (Discovered during implementation)**
|
||||
- GitHub Release Asset Limit: **2GB hard maximum**
|
||||
- CUDA binary: **2.37GB** (370MB over limit)
|
||||
- Cannot upload to GitHub Releases
|
||||
|
||||
---
|
||||
|
||||
## Root Cause
|
||||
|
||||
### Why Is The CUDA Binary So Large?
|
||||
|
||||
The size difference between CPU and CUDA builds:
|
||||
|
||||
| Component | CPU Build | CUDA Build | Difference |
|
||||
|-----------|-----------|------------|------------|
|
||||
| PyTorch Core | ~150MB | ~150MB | - |
|
||||
| CPU Libraries (MKL/OpenBLAS) | ~100MB | - | -100MB |
|
||||
| CUDA Runtime | - | ~500MB | +500MB |
|
||||
| cuBLAS | - | ~350MB | +350MB |
|
||||
| cuDNN | - | ~1.2GB | +1.2GB |
|
||||
| NVRTC (CUDA Compiler) | - | ~90MB | +90MB |
|
||||
| Other CUDA libs | - | ~100MB | +100MB |
|
||||
| **Total** | **~295MB** | **~2.37GB** | **+2.07GB** |
|
||||
|
||||
### CUDA Dependencies Breakdown
|
||||
|
||||
```
|
||||
torch/lib/ (CUDA build):
|
||||
├── cudart64_12.dll (~0.5 MB) - CUDA Runtime
|
||||
├── cublas64_12.dll (~100 MB) - Basic Linear Algebra
|
||||
├── cublasLt64_12.dll (~200 MB) - Linear Algebra (optimized)
|
||||
├── cudnn64_9.dll (~800 MB) - Deep Neural Networks
|
||||
├── cudnn_*_infer64_9.dll (~400 MB) - DNN Inference ops
|
||||
├── nvrtc64_*.dll (~50 MB) - Runtime Compiler
|
||||
├── nvrtc-builtins64_*.dll (~40 MB) - Compiler builtins
|
||||
├── torch_cuda.dll (~200 MB) - PyTorch CUDA bridge
|
||||
└── c10_cuda.dll (~20 MB) - Core CUDA utilities
|
||||
```
|
||||
|
||||
**Why These Are Required:**
|
||||
- cuDNN is essential for neural network operations
|
||||
- cuBLAS handles all matrix operations (core of ML)
|
||||
- Cannot split or remove without breaking functionality
|
||||
|
||||
---
|
||||
|
||||
## Attempted Solutions
|
||||
|
||||
### Solution 1: Dual Binary System ✅ (Partially Successful)
|
||||
|
||||
**Goal**: Split CPU and CUDA into separate downloads
|
||||
|
||||
**Implementation**:
|
||||
```bash
|
||||
# Build CPU-only (295MB)
|
||||
pip install torch --index-url https://download.pytorch.org/whl/cpu
|
||||
python build_binary.py cpu
|
||||
|
||||
# Build CUDA (2.37GB)
|
||||
pip install torch --index-url https://download.pytorch.org/whl/cu121
|
||||
python build_binary.py cuda
|
||||
```
|
||||
|
||||
**Results**:
|
||||
- ✅ CPU binary: 295MB (fits in installer)
|
||||
- ✅ CI builds successfully
|
||||
- ✅ Installer size reduced from 3GB to ~500MB
|
||||
- ❌ CUDA binary still too large for GitHub
|
||||
|
||||
**See**: `docs/dual-server-binaries.md`
|
||||
|
||||
### Solution 2: Compression Testing ❌ (Failed)
|
||||
|
||||
**Goal**: Compress CUDA binary to fit under 2GB
|
||||
|
||||
**Method**: 7z with maximum compression settings
|
||||
```bash
|
||||
7z a -t7z -m0=lzma2 -mx=9 -mfb=64 -md=32m -ms=on \
|
||||
voicebox-server-cuda.7z voicebox-server-cuda.exe
|
||||
```
|
||||
|
||||
**Results**:
|
||||
```
|
||||
Original: 2.37 GB (2,545,086,396 bytes)
|
||||
Compressed: 2.35 GB (2,519,381,264 bytes)
|
||||
Compression: 1.0% (only 24.5MB saved)
|
||||
GitHub Limit: 2.00 GB (2,147,483,648 bytes)
|
||||
Over by: 354.67 MB
|
||||
|
||||
Status: FAILED - Still exceeds limit by 354MB
|
||||
```
|
||||
|
||||
**Why Compression Failed**:
|
||||
- CUDA binaries are already optimized machine code
|
||||
- No redundant data to compress
|
||||
- Neural network kernels are highly compact
|
||||
- Libraries are already stripped of debug symbols
|
||||
|
||||
**Conclusion**: Compression is not viable
|
||||
|
||||
---
|
||||
|
||||
## Current Status
|
||||
|
||||
### What Works
|
||||
- ✅ CPU binary builds successfully (295MB)
|
||||
- ✅ CUDA binary builds successfully (2.37GB)
|
||||
- ✅ Build scripts for both variants
|
||||
- ✅ CI workflow updated for dual binaries
|
||||
- ✅ Installer can be created with CPU binary
|
||||
|
||||
### What Doesn't Work
|
||||
- ❌ Cannot upload CUDA binary to GitHub Releases (exceeds 2GB limit)
|
||||
- ❌ Compression doesn't reduce size enough
|
||||
- ❌ No automated distribution path for CUDA binary
|
||||
|
||||
### Branch Status
|
||||
- Branch: `feat/dual-server-binaries`
|
||||
- Commits: Implementation complete
|
||||
- Testing: Local builds successful
|
||||
- Blocker: CUDA distribution path
|
||||
|
||||
---
|
||||
|
||||
## Available Options
|
||||
|
||||
### Option 1: AWS S3 Hosting (Recommended)
|
||||
|
||||
**Description**: Host CUDA binary in Amazon S3 bucket
|
||||
|
||||
**Pros**:
|
||||
- ✅ No file size limits (can handle multi-GB files)
|
||||
- ✅ Fast global CDN (CloudFront)
|
||||
- ✅ Reliable (99.99% uptime)
|
||||
- ✅ Pay only for usage
|
||||
- ✅ Easy CI integration
|
||||
- ✅ Version control (keep multiple releases)
|
||||
|
||||
**Cons**:
|
||||
- ❌ Requires AWS account
|
||||
- ❌ Monthly costs (~$1-5/month)
|
||||
- ❌ Additional infrastructure to manage
|
||||
|
||||
**Cost Estimate**:
|
||||
```
|
||||
Storage: 2.37 GB × $0.023/GB = $0.05/month
|
||||
Transfer: 100 downloads × 2.37GB × $0.09/GB = $21.33/month
|
||||
Total: ~$21-25/month for 100 downloads
|
||||
~$2-5/month for 10-20 downloads
|
||||
```
|
||||
|
||||
**Implementation**:
|
||||
```yaml
|
||||
# .github/workflows/release.yml
|
||||
- name: Upload CUDA to S3
|
||||
env:
|
||||
AWS_ACCESS_KEY_ID: ${{ secrets.AWS_ACCESS_KEY_ID }}
|
||||
AWS_SECRET_ACCESS_KEY: ${{ secrets.AWS_SECRET_ACCESS_KEY }}
|
||||
run: |
|
||||
aws s3 cp backend/cuda-release/voicebox-server-cuda-*.exe \
|
||||
s3://voicebox-releases/cuda/${{ github.ref_name }}/ \
|
||||
--acl public-read
|
||||
|
||||
# Generate download URL
|
||||
echo "CUDA_URL=https://voicebox-releases.s3.amazonaws.com/cuda/${{ github.ref_name }}/voicebox-server-cuda-x86_64-pc-windows-msvc.exe" >> release_notes.txt
|
||||
```
|
||||
|
||||
**User Experience**:
|
||||
1. Install app normally (500MB installer)
|
||||
2. App detects NVIDIA GPU
|
||||
3. Shows: "Download CUDA support? (2.4GB)"
|
||||
4. Downloads from S3: `https://voicebox-releases.s3.amazonaws.com/cuda/v0.1.12/voicebox-server-cuda.exe`
|
||||
5. Saves to `%APPDATA%/voicebox/binaries/`
|
||||
6. App restarts with CUDA server
|
||||
|
||||
---
|
||||
|
||||
### Option 2: Azure Blob Storage
|
||||
|
||||
**Description**: Microsoft Azure alternative to S3
|
||||
|
||||
**Pros**:
|
||||
- ✅ Similar to S3 (no size limits, CDN, reliable)
|
||||
- ✅ Good if already using Azure
|
||||
- ✅ Competitive pricing
|
||||
- ✅ Global CDN with Azure CDN
|
||||
|
||||
**Cons**:
|
||||
- ❌ Requires Azure account
|
||||
- ❌ Similar monthly costs
|
||||
- ❌ Less common in open source projects
|
||||
|
||||
**Cost Estimate**:
|
||||
```
|
||||
Storage: $0.018/GB = $0.04/month
|
||||
Transfer: ~$20-25/month for 100 downloads
|
||||
```
|
||||
|
||||
**Implementation**:
|
||||
```yaml
|
||||
- name: Upload to Azure Blob
|
||||
env:
|
||||
AZURE_STORAGE_CONNECTION_STRING: ${{ secrets.AZURE_STORAGE }}
|
||||
run: |
|
||||
az storage blob upload \
|
||||
--account-name voiceboxreleases \
|
||||
--container-name cuda-binaries \
|
||||
--name v${{ github.ref_name }}/voicebox-server-cuda.exe \
|
||||
--file backend/cuda-release/voicebox-server-cuda-*.exe \
|
||||
--tier Hot
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### Option 3: Cloudflare R2
|
||||
|
||||
**Description**: Cloudflare's S3-compatible object storage
|
||||
|
||||
**Pros**:
|
||||
- ✅ S3-compatible API
|
||||
- ✅ **FREE egress (no bandwidth charges!)**
|
||||
- ✅ Cheaper than S3/Azure
|
||||
- ✅ Cloudflare CDN included
|
||||
- ✅ Good for open source projects
|
||||
|
||||
**Cons**:
|
||||
- ❌ Requires Cloudflare account
|
||||
- ❌ Newer service (less mature than S3)
|
||||
|
||||
**Cost Estimate**:
|
||||
```
|
||||
Storage: $0.015/GB = $0.04/month
|
||||
Egress: $0.00 (FREE!)
|
||||
Class A ops: Negligible
|
||||
Total: ~$0.04/month (essentially free!)
|
||||
```
|
||||
|
||||
**Why This Is Attractive**:
|
||||
- Zero bandwidth costs (huge savings)
|
||||
- Perfect for open source distribution
|
||||
- S3-compatible (easy migration if needed)
|
||||
|
||||
**Implementation**:
|
||||
Same as S3 (R2 is S3-compatible):
|
||||
```yaml
|
||||
- name: Upload to R2
|
||||
env:
|
||||
AWS_ACCESS_KEY_ID: ${{ secrets.R2_ACCESS_KEY_ID }}
|
||||
AWS_SECRET_ACCESS_KEY: ${{ secrets.R2_SECRET_ACCESS_KEY }}
|
||||
AWS_ENDPOINT_URL: https://<account-id>.r2.cloudflarestorage.com
|
||||
run: |
|
||||
aws s3 cp backend/cuda-release/voicebox-server-cuda-*.exe \
|
||||
s3://voicebox-releases/cuda/${{ github.ref_name }}/ \
|
||||
--endpoint-url=$AWS_ENDPOINT_URL
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### Option 4: GitHub Packages (Container Registry)
|
||||
|
||||
**Description**: Package CUDA binary as OCI/Docker artifact
|
||||
|
||||
**Pros**:
|
||||
- ✅ Stays in GitHub ecosystem
|
||||
- ✅ No additional accounts needed
|
||||
- ✅ Free for public repos
|
||||
|
||||
**Cons**:
|
||||
- ❌ Complex for desktop app distribution
|
||||
- ❌ Users need to extract from container
|
||||
- ❌ Awkward UX (not designed for binary distribution)
|
||||
- ❌ Requires Docker understanding
|
||||
|
||||
**Not Recommended**: Containers aren't designed for desktop app binaries
|
||||
|
||||
---
|
||||
|
||||
### Option 5: Self-Hosted Server
|
||||
|
||||
**Description**: Host on your own VPS/server
|
||||
|
||||
**Pros**:
|
||||
- ✅ Full control
|
||||
- ✅ No cloud provider dependency
|
||||
- ✅ Predictable costs
|
||||
|
||||
**Cons**:
|
||||
- ❌ Requires server maintenance
|
||||
- ❌ Bandwidth costs can be high
|
||||
- ❌ Uptime responsibility
|
||||
- ❌ Scaling challenges
|
||||
|
||||
**Cost Estimate**:
|
||||
```
|
||||
VPS: $5-20/month (DigitalOcean, Linode)
|
||||
Bandwidth: $0.01-0.02/GB
|
||||
Total: $10-50/month depending on traffic
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### Option 6: Manual Distribution
|
||||
|
||||
**Description**: Don't automate - provide manual download instructions
|
||||
|
||||
**Pros**:
|
||||
- ✅ Zero cost
|
||||
- ✅ Zero infrastructure
|
||||
- ✅ Simple
|
||||
|
||||
**Cons**:
|
||||
- ❌ Poor user experience
|
||||
- ❌ Manual upload to file host each release
|
||||
- ❌ Users must manually download and install
|
||||
- ❌ No automatic updates for CUDA binary
|
||||
- ❌ Increases support burden
|
||||
|
||||
**Implementation**:
|
||||
```
|
||||
Release notes:
|
||||
"Windows users with NVIDIA GPUs can download CUDA support:
|
||||
1. Download voicebox-server-cuda.exe from [Google Drive/Mega/etc]
|
||||
2. Place in C:\Users\<YourName>\AppData\Roaming\voicebox\binaries\
|
||||
3. Restart the app"
|
||||
```
|
||||
|
||||
**Not Recommended**: Creates friction, support issues
|
||||
|
||||
---
|
||||
|
||||
### Option 7: Split CUDA Binary
|
||||
|
||||
**Description**: Break CUDA binary into multiple <2GB chunks
|
||||
|
||||
**Technical Approach**:
|
||||
```python
|
||||
# Split binary
|
||||
split -b 2000M voicebox-server-cuda.exe cuda_part_
|
||||
|
||||
# Upload parts to GitHub (each <2GB)
|
||||
cuda_part_aa (2.0 GB)
|
||||
cuda_part_ab (0.37 GB)
|
||||
|
||||
# App downloads and reassembles
|
||||
cat cuda_part_* > voicebox-server-cuda.exe
|
||||
```
|
||||
|
||||
**Pros**:
|
||||
- ✅ Stays on GitHub
|
||||
- ✅ No external hosting
|
||||
|
||||
**Cons**:
|
||||
- ❌ Complex download logic (multiple files)
|
||||
- ❌ Integrity checking required
|
||||
- ❌ More points of failure
|
||||
- ❌ Users must wait for multiple downloads
|
||||
- ❌ Still hacky solution
|
||||
|
||||
**Complexity**: Medium-High
|
||||
|
||||
---
|
||||
|
||||
## Technical Details
|
||||
|
||||
### Current Build Output
|
||||
|
||||
```
|
||||
backend/dist/
|
||||
├── voicebox-server.exe 295 MB (CPU-only)
|
||||
└── voicebox-server-cuda.exe 2.37 GB (CUDA)
|
||||
|
||||
# After compression test:
|
||||
backend/dist/
|
||||
└── voicebox-server-cuda.7z 2.35 GB (not viable)
|
||||
```
|
||||
|
||||
### CI Workflow Changes Required
|
||||
|
||||
For external hosting (S3/R2/Azure):
|
||||
|
||||
```yaml
|
||||
# Current workflow (fails)
|
||||
- name: Upload CUDA server binary (Windows only)
|
||||
if: matrix.platform == 'windows-latest'
|
||||
uses: softprops/action-gh-release@v1
|
||||
with:
|
||||
files: backend/cuda-release/voicebox-server-cuda-*.exe # ❌ Fails: >2GB
|
||||
draft: true
|
||||
|
||||
# New workflow (S3 example)
|
||||
- name: Upload CUDA to S3 (Windows only)
|
||||
if: matrix.platform == 'windows-latest'
|
||||
env:
|
||||
AWS_ACCESS_KEY_ID: ${{ secrets.AWS_ACCESS_KEY_ID }}
|
||||
AWS_SECRET_ACCESS_KEY: ${{ secrets.AWS_SECRET_ACCESS_KEY }}
|
||||
run: |
|
||||
aws s3 cp backend/cuda-release/voicebox-server-cuda-*.exe \
|
||||
s3://voicebox-releases/cuda/${{ github.ref_name }}/ \
|
||||
--acl public-read
|
||||
|
||||
# Generate release notes with download URL
|
||||
cat >> release_notes.md <<EOF
|
||||
|
||||
### GPU Acceleration (Windows)
|
||||
Download CUDA support for NVIDIA GPUs:
|
||||
[voicebox-server-cuda.exe](https://voicebox-releases.s3.amazonaws.com/cuda/${{ github.ref_name }}/voicebox-server-cuda-x86_64-pc-windows-msvc.exe)
|
||||
Size: 2.37 GB
|
||||
EOF
|
||||
```
|
||||
|
||||
### App Changes Required
|
||||
|
||||
**Frontend (Tauri)**: Download manager
|
||||
```typescript
|
||||
// src/lib/cuda-downloader.ts
|
||||
const CUDA_DOWNLOAD_URL =
|
||||
"https://voicebox-releases.s3.amazonaws.com/cuda/v{VERSION}/voicebox-server-cuda.exe";
|
||||
|
||||
async function downloadCudaBinary(version: string) {
|
||||
const url = CUDA_DOWNLOAD_URL.replace("{VERSION}", version);
|
||||
const savePath = path.join(app.getPath("userData"), "binaries", "voicebox-server-cuda.exe");
|
||||
|
||||
// Download with progress
|
||||
await downloadFile(url, savePath, (progress) => {
|
||||
// Update UI: "Downloading CUDA support: 45% (1.2GB / 2.4GB)"
|
||||
});
|
||||
|
||||
// Verify checksum
|
||||
const checksum = await calculateChecksum(savePath);
|
||||
if (checksum !== EXPECTED_CHECKSUM) {
|
||||
throw new Error("Download corrupted");
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
**Backend**: Already supports both binaries (no changes needed)
|
||||
|
||||
---
|
||||
|
||||
## Cost Analysis
|
||||
|
||||
### Monthly Cost Comparison (100 downloads/month)
|
||||
|
||||
| Option | Storage | Bandwidth | Total/Month | Notes |
|
||||
|--------|---------|-----------|-------------|-------|
|
||||
| **Cloudflare R2** | $0.04 | $0.00 | **$0.04** | Best for open source |
|
||||
| AWS S3 | $0.05 | $21.33 | $21.38 | Good reliability |
|
||||
| Azure Blob | $0.04 | $20.00 | $20.04 | Azure ecosystem |
|
||||
| Self-hosted VPS | $10.00 | $2.37 | $12.37 | Maintenance overhead |
|
||||
| Manual | $0.00 | $0.00 | $0.00 | Poor UX |
|
||||
|
||||
### Annual Cost Comparison
|
||||
|
||||
| Option | Year 1 | Year 2+ | Notes |
|
||||
|--------|--------|---------|-------|
|
||||
| **Cloudflare R2** | **$0.50** | **$0.50** | Essentially free |
|
||||
| AWS S3 | $256 | $256 | Predictable |
|
||||
| Self-hosted | $144 | $144 | Time cost |
|
||||
|
||||
**Recommendation**: Cloudflare R2 (free egress = huge savings)
|
||||
|
||||
---
|
||||
|
||||
## Recommendations
|
||||
|
||||
### Recommended Solution: Cloudflare R2
|
||||
|
||||
**Why**:
|
||||
1. **Cost**: Essentially free (~$0.04/month)
|
||||
2. **Bandwidth**: Zero egress charges (unlimited downloads)
|
||||
3. **CDN**: Cloudflare's global network included
|
||||
4. **Compatibility**: S3-compatible API (easy to use)
|
||||
5. **Perfect for open source**: No surprise bandwidth bills
|
||||
|
||||
### Implementation Priority
|
||||
|
||||
**Phase 1: Setup (1-2 hours)**
|
||||
1. Create Cloudflare R2 account
|
||||
2. Create bucket: `voicebox-releases`
|
||||
3. Generate API credentials
|
||||
4. Add to GitHub Secrets
|
||||
|
||||
**Phase 2: CI Integration (1-2 hours)**
|
||||
1. Update `.github/workflows/release.yml`
|
||||
2. Add R2 upload step
|
||||
3. Generate release notes with download URL
|
||||
4. Test with draft release
|
||||
|
||||
**Phase 3: App Integration (4-6 hours)**
|
||||
1. Add GPU detection on startup
|
||||
2. Implement download manager UI
|
||||
3. Add progress indicators
|
||||
4. Implement checksum verification
|
||||
5. Server restart logic
|
||||
|
||||
**Phase 4: Documentation (1 hour)**
|
||||
1. Update README with GPU instructions
|
||||
2. Add troubleshooting guide
|
||||
3. Document manual download process
|
||||
|
||||
**Total Time**: ~8-12 hours of development
|
||||
|
||||
### Alternative: AWS S3 (If Already Using AWS)
|
||||
|
||||
If you're already using AWS for other infrastructure, S3 is also a solid choice:
|
||||
- More mature than R2
|
||||
- Extensive documentation
|
||||
- Familiar tooling
|
||||
- ~$20/month for moderate usage
|
||||
|
||||
---
|
||||
|
||||
## Open Questions
|
||||
|
||||
1. **Expected Download Volume**: How many CUDA downloads per month?
|
||||
- Affects cost calculations
|
||||
- Determines if R2's free egress is significant
|
||||
|
||||
2. **Update Strategy**: How to handle CUDA updates?
|
||||
- Option A: Version in URL path (keep all versions)
|
||||
- Option B: Overwrite latest (save space)
|
||||
|
||||
3. **Fallback Strategy**: What if cloud provider is down?
|
||||
- Mirror on multiple providers?
|
||||
- Graceful degradation to CPU?
|
||||
|
||||
4. **Telemetry**: Track CUDA download stats?
|
||||
- Helps with cost forecasting
|
||||
- User behavior insights
|
||||
|
||||
---
|
||||
|
||||
## Next Steps
|
||||
|
||||
1. **Research Phase** (You are here)
|
||||
- Evaluate cloud providers
|
||||
- Check terms of service
|
||||
- Test account creation
|
||||
|
||||
2. **Decision Phase**
|
||||
- Choose provider (Cloudflare R2 recommended)
|
||||
- Set up account
|
||||
- Configure billing alerts
|
||||
|
||||
3. **Implementation Phase**
|
||||
- Update CI workflow
|
||||
- Implement download manager
|
||||
- Test end-to-end flow
|
||||
|
||||
4. **Launch Phase**
|
||||
- Deploy to production
|
||||
- Monitor downloads
|
||||
- Gather user feedback
|
||||
|
||||
---
|
||||
|
||||
## References
|
||||
|
||||
- **GitHub Release Limits**: https://docs.github.com/en/repositories/releasing-projects-on-github/about-releases
|
||||
- **Cloudflare R2 Pricing**: https://developers.cloudflare.com/r2/pricing/
|
||||
- **AWS S3 Pricing**: https://aws.amazon.com/s3/pricing/
|
||||
- **Compression Test Results**: `backend/test_cuda_compression.py`
|
||||
- **Dual Binary Implementation**: `docs/dual-server-binaries.md`
|
||||
|
||||
---
|
||||
|
||||
## Appendix: Alternative Approaches Considered
|
||||
|
||||
### A. Dynamic CUDA Loading
|
||||
**Idea**: Load CUDA DLLs dynamically at runtime
|
||||
**Why Not**: PyTorch requires CUDA DLLs at import time, can't lazy-load
|
||||
|
||||
### B. CUDA as Separate Package
|
||||
**Idea**: Python package with just CUDA libs
|
||||
**Why Not**: Still 2GB+, same problem
|
||||
|
||||
### C. Model Quantization
|
||||
**Idea**: Use smaller quantized models
|
||||
**Why Not**: Doesn't reduce CUDA runtime size
|
||||
|
||||
### D. Docker Distribution
|
||||
**Idea**: Distribute as Docker container
|
||||
**Why Not**: Poor fit for desktop app, requires Docker installed
|
||||
|
||||
---
|
||||
|
||||
**Document Version**: 1.0
|
||||
**Last Updated**: 2026-01-31
|
||||
**Status**: Research Phase
|
||||
**Next Review**: After cloud provider decision
|
||||
@@ -0,0 +1,177 @@
|
||||
# Dual Server Binary System
|
||||
|
||||
## Overview
|
||||
|
||||
Voicebox now uses a dual-binary approach to manage the size difference between CPU-only and CUDA-enabled builds:
|
||||
|
||||
- **CPU Binary** (~500MB): Ships with the installer by default
|
||||
- **CUDA Binary** (~3GB): Downloaded on-demand for GPU users
|
||||
|
||||
## Problem Solved
|
||||
|
||||
Previously, bundling PyTorch with CUDA support created a 3GB server binary, which:
|
||||
- Made the installer too large (failed CI builds with WiX)
|
||||
- Forced all users to download CUDA libraries even without NVIDIA GPUs
|
||||
- Created poor user experience
|
||||
|
||||
## Solution
|
||||
|
||||
### Build Process
|
||||
|
||||
**Two separate binaries are built:**
|
||||
|
||||
1. **voicebox-server.exe** (CPU)
|
||||
- Built with: `pip install torch --index-url https://download.pytorch.org/whl/cpu`
|
||||
- Size: ~500MB
|
||||
- Works on all Windows machines
|
||||
- Included in the installer by default
|
||||
|
||||
2. **voicebox-server-cuda.exe** (CUDA)
|
||||
- Built with: `pip install torch --index-url https://download.pytorch.org/whl/cu121`
|
||||
- Size: ~3GB
|
||||
- Requires NVIDIA GPU + drivers
|
||||
- Uploaded as separate GitHub Release asset
|
||||
|
||||
### User Experience
|
||||
|
||||
**First Launch:**
|
||||
1. User installs app (~500MB download)
|
||||
2. App starts with CPU server
|
||||
3. If NVIDIA GPU detected:
|
||||
- Show notification: "Download CUDA support for 4-5x faster inference?"
|
||||
- User clicks "Download"
|
||||
- Download voicebox-server-cuda.exe from GitHub (~3GB)
|
||||
- Save to `%APPDATA%/voicebox/binaries/`
|
||||
- Restart server with CUDA version
|
||||
|
||||
**Settings Panel:**
|
||||
- Toggle between CPU/CUDA modes
|
||||
- Download CUDA if not already installed
|
||||
- Show current inference backend
|
||||
|
||||
### Build Scripts
|
||||
|
||||
**Windows:**
|
||||
```bash
|
||||
cd backend
|
||||
|
||||
# Build CPU only
|
||||
build_cpu.bat
|
||||
|
||||
# Build CUDA only
|
||||
build_cuda.bat
|
||||
|
||||
# Build both
|
||||
build_both.bat
|
||||
```
|
||||
|
||||
**Unix (macOS/Linux):**
|
||||
```bash
|
||||
cd backend
|
||||
|
||||
# Build CPU only
|
||||
./build_cpu.sh
|
||||
```
|
||||
|
||||
### CI/CD Workflow
|
||||
|
||||
**GitHub Actions (.github/workflows/release.yml):**
|
||||
|
||||
1. Install CPU PyTorch
|
||||
2. Build CPU server → Copy to Tauri binaries
|
||||
3. Install CUDA PyTorch
|
||||
4. Build CUDA server → Save for upload
|
||||
5. Build Tauri app (bundles CPU server)
|
||||
6. Upload CUDA server as separate release asset
|
||||
|
||||
### File Structure
|
||||
|
||||
```
|
||||
Release Assets:
|
||||
├── Voicebox_0.1.12_x64_en-US.msi (~500MB - includes CPU server)
|
||||
├── voicebox-server-cuda-x86_64-pc-windows-msvc.exe (~3GB - optional download)
|
||||
└── latest.json (updater manifest)
|
||||
```
|
||||
|
||||
## Implementation Details
|
||||
|
||||
### Modified Files
|
||||
|
||||
1. **backend/build_binary.py**
|
||||
- Added `variant` parameter ('cpu' or 'cuda')
|
||||
- Outputs different binary names based on variant
|
||||
|
||||
2. **backend/build_cpu.bat** (new)
|
||||
- Installs CPU PyTorch
|
||||
- Builds CPU binary
|
||||
- Restores CUDA PyTorch for dev
|
||||
|
||||
3. **backend/build_cuda.bat** (new)
|
||||
- Ensures CUDA PyTorch is installed
|
||||
- Builds CUDA binary
|
||||
|
||||
4. **.github/workflows/release.yml**
|
||||
- Build CPU binary first (for installer)
|
||||
- Build CUDA binary second (for upload)
|
||||
- Upload CUDA binary as additional release asset
|
||||
- Updated release notes to explain GPU acceleration
|
||||
|
||||
### Future Frontend Work
|
||||
|
||||
**TODO: Implement CUDA download in the app**
|
||||
|
||||
Location: `tauri/src/`
|
||||
|
||||
Features needed:
|
||||
1. GPU detection on startup
|
||||
2. Download manager for CUDA binary
|
||||
3. Server binary path switcher
|
||||
4. Settings UI for CPU/CUDA toggle
|
||||
5. Progress indicator for 3GB download
|
||||
|
||||
API endpoints needed (already exist):
|
||||
- `/health` - Shows GPU availability
|
||||
- Server restart mechanism
|
||||
|
||||
## Benefits
|
||||
|
||||
✓ **Smaller installer**: ~500MB instead of 3GB
|
||||
✓ **Faster CI builds**: WiX can handle 500MB easily
|
||||
✓ **User choice**: CPU users don't download unnecessary files
|
||||
✓ **Better UX**: Optional performance upgrade for GPU users
|
||||
✓ **Cost savings**: Reduced bandwidth for users without GPUs
|
||||
|
||||
## Testing
|
||||
|
||||
**Test CPU build:**
|
||||
```bash
|
||||
cd backend
|
||||
python build_binary.py cpu
|
||||
./dist/voicebox-server.exe --version
|
||||
```
|
||||
|
||||
**Test CUDA build:**
|
||||
```bash
|
||||
cd backend
|
||||
python build_binary.py cuda
|
||||
./dist/voicebox-server-cuda.exe --version
|
||||
```
|
||||
|
||||
**Verify size:**
|
||||
```bash
|
||||
ls -lh backend/dist/
|
||||
# Should see:
|
||||
# voicebox-server.exe ~500MB
|
||||
# voicebox-server-cuda.exe ~3GB
|
||||
```
|
||||
|
||||
**Test server startup:**
|
||||
```bash
|
||||
# CPU version
|
||||
./backend/dist/voicebox-server.exe
|
||||
# Check logs: Should show CPU inference
|
||||
|
||||
# CUDA version (requires NVIDIA GPU)
|
||||
./backend/dist/voicebox-server-cuda.exe
|
||||
# Check logs: Should show CUDA inference
|
||||
```
|
||||
@@ -0,0 +1,122 @@
|
||||
# GitHub 2GB Release Asset Limit Issue
|
||||
|
||||
## Problem
|
||||
|
||||
The CUDA server binary upload fails in CI with:
|
||||
```
|
||||
Error: File size (2543828017) is greater than 2 GiB
|
||||
```
|
||||
|
||||
GitHub release assets have a hard limit of 2GB per file. Our CUDA binary is ~2.5GB, which exceeds this limit.
|
||||
|
||||
## Background
|
||||
|
||||
The dual-server binary system (see `dual-server-binaries.md`) creates two binaries:
|
||||
- **CPU binary**: ~500MB ✅ Works fine
|
||||
- **CUDA binary**: ~2.5GB ❌ Exceeds GitHub limit
|
||||
|
||||
## Attempted Solution: Compression
|
||||
|
||||
We're testing 7z compression with maximum settings to see if we can squeeze the CUDA binary under 2GB.
|
||||
|
||||
### Test Script
|
||||
|
||||
Run `backend/test_cuda_compression.py` to test compression locally:
|
||||
|
||||
```bash
|
||||
cd backend
|
||||
python test_cuda_compression.py
|
||||
```
|
||||
|
||||
This will:
|
||||
1. Find the CUDA binary in `dist/`
|
||||
2. Compress it with 7z (maximum compression)
|
||||
3. Report if the compressed size fits under 2GB
|
||||
|
||||
### Expected Compression
|
||||
|
||||
PyTorch CUDA binaries typically compress well since they contain:
|
||||
- Repeated patterns in neural network weights
|
||||
- Debug symbols and metadata
|
||||
- Redundant CUDA libraries
|
||||
|
||||
Estimated compression: 30-40% reduction
|
||||
- Original: ~2.5GB
|
||||
- Target: <2GB
|
||||
- Required compression: >20%
|
||||
|
||||
## Fallback: External Hosting
|
||||
|
||||
If compression doesn't work, we'll need to host the CUDA binary externally:
|
||||
|
||||
### Option 1: AWS S3
|
||||
```yaml
|
||||
- name: Upload CUDA binary to S3
|
||||
run: |
|
||||
aws s3 cp backend/cuda-release/voicebox-server-cuda-*.exe \
|
||||
s3://voicebox-releases/cuda-binaries/${{ github.ref_name }}/
|
||||
```
|
||||
|
||||
### Option 2: Azure Blob Storage
|
||||
```yaml
|
||||
- name: Upload to Azure Blob
|
||||
run: |
|
||||
az storage blob upload \
|
||||
--account-name voiceboxreleases \
|
||||
--container-name cuda-binaries \
|
||||
--file backend/cuda-release/voicebox-server-cuda-*.exe
|
||||
```
|
||||
|
||||
### Option 3: GitHub Packages (Container Registry)
|
||||
Package as a container image, though this adds complexity for desktop app distribution.
|
||||
|
||||
## Implementation Plan
|
||||
|
||||
1. **Test compression locally** ← Current step
|
||||
2. **If compression works (<2GB)**:
|
||||
- Update CI to compress before upload
|
||||
- Update app to handle .7z downloads
|
||||
- Add extraction step in download manager
|
||||
|
||||
3. **If compression fails (≥2GB)**:
|
||||
- Set up external storage (likely S3)
|
||||
- Update CI to upload to S3
|
||||
- Provide download URL in release notes
|
||||
- Update app download manager to fetch from S3
|
||||
|
||||
## CI Workflow Changes (if compression works)
|
||||
|
||||
```yaml
|
||||
- name: Compress CUDA binary (Windows only)
|
||||
if: matrix.platform == 'windows-latest'
|
||||
shell: bash
|
||||
run: |
|
||||
cd backend/cuda-release
|
||||
7z a -t7z -m0=lzma2 -mx=9 -mfb=64 -md=32m -ms=on \
|
||||
voicebox-server-cuda-x86_64-pc-windows-msvc.7z \
|
||||
voicebox-server-cuda-*.exe
|
||||
|
||||
- name: Upload compressed CUDA server (Windows only)
|
||||
if: matrix.platform == 'windows-latest'
|
||||
uses: softprops/action-gh-release@v1
|
||||
with:
|
||||
files: backend/cuda-release/*.7z
|
||||
```
|
||||
|
||||
## User Experience Impact
|
||||
|
||||
### With Compression
|
||||
- Download: `voicebox-server-cuda-*.7z` (~1.5-1.8GB)
|
||||
- App extracts automatically
|
||||
- One extra step but manageable
|
||||
|
||||
### With External Hosting
|
||||
- Download from S3/Azure URL
|
||||
- No GitHub release asset dependency
|
||||
- Potentially faster download speeds (CDN)
|
||||
|
||||
## Status
|
||||
|
||||
🔄 **Testing compression locally to determine viability**
|
||||
|
||||
Results pending from local test run.
|
||||
@@ -5,7 +5,7 @@ description: "Welcome to Voicebox - the open-source voice synthesis studio"
|
||||
|
||||
## What is Voicebox?
|
||||
|
||||
Voicebox is a **local-first voice cloning studio** with DAW-like features for professional voice synthesis. Think of it as a **local, free and open-source alternative to ElevenLabs** — download models, clone voices, and generate speech entirely on your machine.
|
||||
Voicebox is a **local-first voice cloning studio** with DAW-like features for professional voice synthesis. Think of it as the **Ollama for voice** — download models, clone voices, and generate speech entirely on your machine.
|
||||
|
||||
<Frame>
|
||||
<img src="/images/app-screenshot-1.webp" alt="Voicebox App Screenshot" />
|
||||
|
||||
@@ -1,581 +0,0 @@
|
||||
# CUDA Backend Swap via Binary Replacement
|
||||
|
||||
> Status: Plan | Target: v0.2.0 | Created: 2026-03-12
|
||||
|
||||
## Problem
|
||||
|
||||
The CUDA PyTorch backend binary is ~2.4 GB. GitHub Releases has a 2 GB asset limit. The current release ships CPU-only PyTorch on Windows and Intel Mac — NVIDIA GPU users get no acceleration from official releases. This is the #1 reported issue category (19 open issues).
|
||||
|
||||
Users who want GPU today must clone the repo and run from source. That's not acceptable for a desktop app targeting non-technical users.
|
||||
|
||||
## Solution
|
||||
|
||||
Ship two backend binaries: a default CPU build (~150 MB) bundled with the app, and a downloadable CUDA build (~2.4 GB) hosted externally. When the user downloads the CUDA build, the app kills the current backend process, swaps in the CUDA binary, and relaunches — a backend-only restart. The frontend stays running, all UI state is preserved.
|
||||
|
||||
No subprocesses. No HTTP protocol between processes. No port allocation. No provider manager. The backend is still one monolithic process — just a different binary.
|
||||
|
||||
## Architecture
|
||||
|
||||
### What Exists Today
|
||||
|
||||
```
|
||||
Tauri App
|
||||
├── React Frontend (in-process webview)
|
||||
└── voicebox-server (sidecar subprocess on :17493)
|
||||
└── One PyInstaller binary: CPU PyTorch or MLX
|
||||
```
|
||||
|
||||
**Sidecar lifecycle** (`tauri/src-tauri/src/main.rs`):
|
||||
- `start_server` command spawns `voicebox-server` sidecar (line 181)
|
||||
- Binary located at `tauri/src-tauri/binaries/voicebox-server-{platform-triple}`
|
||||
- Tauri resolves the sidecar name via `externalBin` in `tauri.conf.json` (line 16)
|
||||
- Waits up to 120s for "Uvicorn running" in stdout/stderr (line 286)
|
||||
- `stop_server` kills the process tree (line 466)
|
||||
|
||||
**Frontend reconnection** (`app/src/lib/hooks/useServer.ts`):
|
||||
- Health check polls `GET /health` every 30 seconds
|
||||
- React Query cache retains data for 10 minutes after disconnect
|
||||
- All UI state (Zustand stores, form data, open tabs) survives disconnection
|
||||
- No active reconnect logic — just keeps polling until server responds
|
||||
|
||||
This means a backend restart is mostly invisible to the frontend: it sees a few seconds of failed health checks, then the server comes back. The only risk is in-flight operations (generation, transcription) failing mid-request.
|
||||
|
||||
### What Changes
|
||||
|
||||
```
|
||||
Tauri App
|
||||
├── React Frontend (in-process webview)
|
||||
└── voicebox-server (sidecar subprocess on :17493)
|
||||
└── One of:
|
||||
├── voicebox-server-cpu (bundled, ~150 MB)
|
||||
└── voicebox-server-cuda (downloaded, ~2.4 GB)
|
||||
```
|
||||
|
||||
The CUDA binary is functionally identical to the CPU binary. Same FastAPI app, same endpoints, same code. The only difference is PyTorch is compiled with CUDA 12.1 support and the binary includes CUDA runtime libraries.
|
||||
|
||||
The user downloads it once. On every subsequent app launch, Tauri checks which binary variant exists and spawns the appropriate one.
|
||||
|
||||
## Implementation Plan
|
||||
|
||||
### Phase 1: Build Infrastructure
|
||||
|
||||
Build the CUDA binary in CI separately from the main release.
|
||||
|
||||
#### 1a. CUDA PyInstaller Build
|
||||
|
||||
Add a `build_binary_cuda.py` or parameterize the existing `build_binary.py`:
|
||||
|
||||
```python
|
||||
# backend/build_binary.py — add flag
|
||||
def build_server(cuda=False):
|
||||
args = [
|
||||
'server.py',
|
||||
'--onefile',
|
||||
'--name', f'voicebox-server-{"cuda" if cuda else "cpu"}',
|
||||
]
|
||||
|
||||
if cuda:
|
||||
args.extend([
|
||||
'--hidden-import', 'torch.cuda',
|
||||
'--hidden-import', 'torch.backends.cudnn',
|
||||
])
|
||||
# ... rest of existing build
|
||||
```
|
||||
|
||||
The `--onefile` flag is already used, which produces a single executable. This is important — `--onedir` would complicate the swap (replacing a directory vs a file).
|
||||
|
||||
#### 1b. CI Workflow for CUDA Binary
|
||||
|
||||
New workflow: `.github/workflows/build-cuda.yml`
|
||||
|
||||
```yaml
|
||||
name: Build CUDA Provider
|
||||
on:
|
||||
workflow_dispatch:
|
||||
push:
|
||||
tags: ["v*"]
|
||||
|
||||
jobs:
|
||||
build-cuda:
|
||||
runs-on: windows-latest # CUDA is Windows/Linux only
|
||||
steps:
|
||||
- uses: actions/checkout@v4
|
||||
- uses: actions/setup-python@v5
|
||||
with: { python-version: "3.12" }
|
||||
- name: Install dependencies
|
||||
run: |
|
||||
pip install pyinstaller
|
||||
pip install -r backend/requirements.txt
|
||||
pip install torch --index-url https://download.pytorch.org/whl/cu121 --force-reinstall
|
||||
- name: Build CUDA binary
|
||||
run: python backend/build_binary.py --cuda
|
||||
- name: Split binary for GitHub Releases
|
||||
run: |
|
||||
python scripts/split_binary.py backend/dist/voicebox-server-cuda.exe \
|
||||
--chunk-size 1900MB \
|
||||
--output release-assets/
|
||||
- name: Upload to R2
|
||||
# Full binary to R2 (no size limit)
|
||||
run: |
|
||||
aws s3 cp backend/dist/voicebox-server-cuda.exe \
|
||||
s3://voicebox-downloads/cuda/v${{ github.ref_name }}/voicebox-server-cuda.exe \
|
||||
--endpoint-url ${{ secrets.R2_ENDPOINT }}
|
||||
- name: Upload split parts to GitHub Release
|
||||
# Split parts as GitHub Release assets (each <2 GB)
|
||||
uses: softprops/action-gh-release@v1
|
||||
with:
|
||||
files: release-assets/*
|
||||
```
|
||||
|
||||
Two distribution paths for redundancy:
|
||||
- **Cloudflare R2**: Full binary, direct download, no size limit.
|
||||
- **GitHub Releases**: Split into <2 GB chunks as fallback.
|
||||
|
||||
#### 1c. Binary Splitting Script
|
||||
|
||||
```python
|
||||
# scripts/split_binary.py
|
||||
"""Split a large binary into chunks for GitHub Releases."""
|
||||
import hashlib
|
||||
import argparse
|
||||
from pathlib import Path
|
||||
|
||||
def split(input_path: Path, chunk_size: int, output_dir: Path):
|
||||
output_dir.mkdir(parents=True, exist_ok=True)
|
||||
data = input_path.read_bytes()
|
||||
|
||||
# Write SHA-256 of the complete file
|
||||
sha256 = hashlib.sha256(data).hexdigest()
|
||||
(output_dir / f"{input_path.stem}.sha256").write_text(
|
||||
f"{sha256} {input_path.name}\n"
|
||||
)
|
||||
|
||||
# Split into chunks
|
||||
parts = []
|
||||
for i in range(0, len(data), chunk_size):
|
||||
part_name = f"{input_path.stem}.part{len(parts):02d}{input_path.suffix}"
|
||||
part_path = output_dir / part_name
|
||||
part_path.write_bytes(data[i:i + chunk_size])
|
||||
parts.append(part_name)
|
||||
|
||||
# Write manifest
|
||||
(output_dir / f"{input_path.stem}.manifest").write_text(
|
||||
"\n".join(parts) + "\n"
|
||||
)
|
||||
|
||||
print(f"Split into {len(parts)} parts, SHA-256: {sha256}")
|
||||
```
|
||||
|
||||
### Phase 2: Download & Assemble in App
|
||||
|
||||
#### 2a. Backend Download Endpoint
|
||||
|
||||
Add to `backend/main.py`:
|
||||
|
||||
```python
|
||||
@app.post("/backend/download-cuda")
|
||||
async def download_cuda_backend():
|
||||
"""Download the CUDA backend binary."""
|
||||
# Returns immediately, runs download in background
|
||||
task = asyncio.create_task(_download_cuda_binary())
|
||||
task.add_done_callback(lambda t: logger.error(f"CUDA download failed: {t.exception()}") if t.exception() else None)
|
||||
return {"status": "downloading"}
|
||||
|
||||
@app.get("/backend/cuda-status")
|
||||
async def cuda_status():
|
||||
"""Check if CUDA binary is available."""
|
||||
cuda_path = _get_cuda_binary_path()
|
||||
return {
|
||||
"available": cuda_path is not None and cuda_path.exists(),
|
||||
"active": _is_cuda_active(),
|
||||
"download_progress": progress_manager.get_progress("cuda-backend"),
|
||||
}
|
||||
```
|
||||
|
||||
#### 2b. Download + Assemble + Verify Logic
|
||||
|
||||
New file: `backend/cuda_download.py`
|
||||
|
||||
Core logic:
|
||||
|
||||
```python
|
||||
import hashlib
|
||||
from pathlib import Path
|
||||
from backend.config import get_data_dir
|
||||
from backend.utils.progress import get_progress_manager
|
||||
|
||||
CUDA_DOWNLOAD_URL = "https://downloads.voicebox.sh/cuda/{version}/voicebox-server-cuda{ext}"
|
||||
CUDA_CHECKSUMS = {
|
||||
# Populated per release
|
||||
"0.2.0-windows": "sha256:abc123...",
|
||||
"0.2.0-linux": "sha256:def456...",
|
||||
}
|
||||
|
||||
def get_cuda_binary_dir() -> Path:
|
||||
"""Where CUDA binaries live. Inside the app's data directory."""
|
||||
return get_data_dir() / "backends"
|
||||
|
||||
def get_cuda_binary_path() -> Path | None:
|
||||
"""Return path to CUDA binary if it exists and is verified."""
|
||||
d = get_cuda_binary_dir()
|
||||
for name in ["voicebox-server-cuda.exe", "voicebox-server-cuda"]:
|
||||
p = d / name
|
||||
if p.exists():
|
||||
return p
|
||||
return None
|
||||
|
||||
async def download_cuda_binary(version: str):
|
||||
"""Download, assemble (if split), and verify the CUDA binary."""
|
||||
progress = get_progress_manager()
|
||||
dest_dir = get_cuda_binary_dir()
|
||||
dest_dir.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
ext = ".exe" if sys.platform == "win32" else ""
|
||||
url = CUDA_DOWNLOAD_URL.format(version=version, ext=ext)
|
||||
|
||||
# Download with progress tracking
|
||||
temp_path = dest_dir / f"voicebox-server-cuda{ext}.download"
|
||||
async with httpx.AsyncClient(follow_redirects=True) as client:
|
||||
async with client.stream("GET", url) as response:
|
||||
total = int(response.headers.get("content-length", 0))
|
||||
downloaded = 0
|
||||
with open(temp_path, "wb") as f:
|
||||
async for chunk in response.aiter_bytes(chunk_size=1024 * 1024):
|
||||
f.write(chunk)
|
||||
downloaded += len(chunk)
|
||||
progress.update("cuda-backend", downloaded, total)
|
||||
|
||||
# Verify checksum
|
||||
sha256 = hashlib.sha256(temp_path.read_bytes()).hexdigest()
|
||||
expected = CUDA_CHECKSUMS.get(f"{version}-{sys.platform}")
|
||||
if expected and not expected.endswith(sha256):
|
||||
temp_path.unlink()
|
||||
raise ValueError(f"Checksum mismatch: expected {expected}, got sha256:{sha256}")
|
||||
|
||||
# Atomic move into place
|
||||
final_path = dest_dir / f"voicebox-server-cuda{ext}"
|
||||
temp_path.rename(final_path)
|
||||
|
||||
# Make executable on Unix
|
||||
if sys.platform != "win32":
|
||||
final_path.chmod(0o755)
|
||||
|
||||
progress.complete("cuda-backend")
|
||||
```
|
||||
|
||||
Key points:
|
||||
- Downloads to a `.download` temp file, verifies checksum, then atomically renames. No partial binaries left on crash.
|
||||
- Progress tracked via the existing `ProgressManager` so the frontend SSE system works unchanged.
|
||||
- CUDA binary lives in the **app data directory** (`data/backends/`), not alongside the app bundle. This avoids code-signing issues on macOS (though CUDA isn't relevant on macOS) and survives app updates.
|
||||
|
||||
#### 2c. Reassembly from Split Parts (GitHub Releases Fallback)
|
||||
|
||||
If the R2 download fails, fall back to downloading split parts from GitHub Releases:
|
||||
|
||||
```python
|
||||
async def download_cuda_from_github(version: str):
|
||||
"""Fallback: download split parts from GitHub Releases, reassemble."""
|
||||
base_url = f"https://github.com/jamiepine/voicebox/releases/download/v{version}"
|
||||
|
||||
# Get manifest
|
||||
manifest_url = f"{base_url}/voicebox-server-cuda.manifest"
|
||||
async with httpx.AsyncClient(follow_redirects=True) as client:
|
||||
manifest = (await client.get(manifest_url)).text
|
||||
parts = [p.strip() for p in manifest.strip().splitlines()]
|
||||
|
||||
# Download checksum
|
||||
sha256_url = f"{base_url}/voicebox-server-cuda.sha256"
|
||||
expected_sha = (await client.get(sha256_url)).text.split()[0]
|
||||
|
||||
# Download parts
|
||||
dest_dir = get_cuda_binary_dir()
|
||||
dest_dir.mkdir(parents=True, exist_ok=True)
|
||||
temp_path = dest_dir / "voicebox-server-cuda.exe.download"
|
||||
|
||||
total_downloaded = 0
|
||||
with open(temp_path, "wb") as f:
|
||||
for i, part_name in enumerate(parts):
|
||||
part_url = f"{base_url}/{part_name}"
|
||||
async with client.stream("GET", part_url) as response:
|
||||
async for chunk in response.aiter_bytes(chunk_size=1024 * 1024):
|
||||
f.write(chunk)
|
||||
total_downloaded += len(chunk)
|
||||
get_progress_manager().update(
|
||||
"cuda-backend", total_downloaded, None,
|
||||
message=f"Downloading part {i+1}/{len(parts)}"
|
||||
)
|
||||
|
||||
# Verify reassembled file
|
||||
sha256 = hashlib.sha256(temp_path.read_bytes()).hexdigest()
|
||||
if sha256 != expected_sha:
|
||||
temp_path.unlink()
|
||||
raise ValueError(f"Checksum mismatch after reassembly")
|
||||
|
||||
final_path = dest_dir / "voicebox-server-cuda.exe"
|
||||
temp_path.rename(final_path)
|
||||
get_progress_manager().complete("cuda-backend")
|
||||
```
|
||||
|
||||
### Phase 3: Backend Restart (The Swap)
|
||||
|
||||
This is the core of the feature: kill the CPU backend, launch the CUDA backend, frontend reconnects automatically.
|
||||
|
||||
#### 3a. New Tauri Command: `restart_server`
|
||||
|
||||
Add to `tauri/src-tauri/src/main.rs`:
|
||||
|
||||
```rust
|
||||
#[command]
|
||||
async fn restart_server(
|
||||
app: tauri::AppHandle,
|
||||
state: State<'_, ServerState>,
|
||||
use_cuda: Option<bool>,
|
||||
) -> Result<String, String> {
|
||||
println!("restart_server: use_cuda={:?}", use_cuda);
|
||||
|
||||
// 1. Stop the current server
|
||||
stop_server(state.clone()).await?;
|
||||
|
||||
// 2. Brief wait for port release
|
||||
tokio::time::sleep(tokio::time::Duration::from_millis(500)).await;
|
||||
|
||||
// 3. Start with the appropriate binary
|
||||
// The start_server logic needs to check for CUDA binary
|
||||
start_server(app, state, None).await
|
||||
}
|
||||
```
|
||||
|
||||
#### 3b. Modify `start_server` to Prefer CUDA Binary
|
||||
|
||||
The existing `start_server` uses `app.shell().sidecar("voicebox-server")` which resolves via Tauri's `externalBin` config. For the CUDA binary (which lives in the data directory, not the app bundle), we need an alternative launch path.
|
||||
|
||||
Modify `start_server` in `main.rs`:
|
||||
|
||||
```rust
|
||||
// After the existing sidecar logic, before spawning:
|
||||
|
||||
// Check for CUDA binary in data directory
|
||||
let cuda_binary = data_dir.join("backends")
|
||||
.join(if cfg!(windows) { "voicebox-server-cuda.exe" } else { "voicebox-server-cuda" });
|
||||
|
||||
let (mut rx, child) = if cuda_binary.exists() {
|
||||
println!("Found CUDA backend binary at {:?}", cuda_binary);
|
||||
|
||||
// Launch CUDA binary directly (not as Tauri sidecar)
|
||||
let mut cmd = app.shell().command(cuda_binary.to_str().unwrap());
|
||||
cmd = cmd.args([
|
||||
"--data-dir",
|
||||
data_dir.to_str().ok_or("Invalid data dir path")?,
|
||||
"--port",
|
||||
&SERVER_PORT.to_string(),
|
||||
]);
|
||||
if remote.unwrap_or(false) {
|
||||
cmd = cmd.args(["--host", "0.0.0.0"]);
|
||||
}
|
||||
cmd.spawn().map_err(|e| format!("Failed to spawn CUDA backend: {}", e))?
|
||||
} else {
|
||||
// Existing sidecar launch (CPU binary bundled with app)
|
||||
sidecar.spawn().map_err(|e| format!("Failed to spawn: {}", e))?
|
||||
};
|
||||
```
|
||||
|
||||
Key decisions:
|
||||
- CUDA binary is launched via `app.shell().command()` (arbitrary path), not `app.shell().sidecar()` (bundled path). Tauri's sidecar system only resolves binaries within the app bundle.
|
||||
- The CUDA binary gets the same args (`--data-dir`, `--port`) as the CPU binary. It's the same `server.py` entry point.
|
||||
- Preference: if CUDA binary exists, use it. Otherwise fall back to bundled CPU. No user configuration needed.
|
||||
|
||||
#### 3c. Frontend: Trigger Restart After Download
|
||||
|
||||
Add to the platform lifecycle interface (`app/src/platform/types.ts`):
|
||||
|
||||
```typescript
|
||||
interface PlatformLifecycle {
|
||||
startServer(remote?: boolean): Promise<string>;
|
||||
stopServer(): Promise<void>;
|
||||
restartServer(useCuda?: boolean): Promise<string>; // new
|
||||
// ...
|
||||
}
|
||||
```
|
||||
|
||||
Implement in `tauri/src/platform/lifecycle.ts`:
|
||||
|
||||
```typescript
|
||||
async restartServer(useCuda?: boolean): Promise<string> {
|
||||
const result = await invoke<string>('restart_server', { useCuda });
|
||||
this.onServerReady?.();
|
||||
return result;
|
||||
}
|
||||
```
|
||||
|
||||
#### 3d. Frontend: GPU Settings UI
|
||||
|
||||
Add a section to the Server Settings page (or Model Management). Minimal UI:
|
||||
|
||||
```
|
||||
┌─────────────────────────────────────────────┐
|
||||
│ GPU Acceleration │
|
||||
│ │
|
||||
│ Status: CPU only (no CUDA backend) │
|
||||
│ │
|
||||
│ [Download CUDA Backend (2.4 GB)] │
|
||||
│ │
|
||||
│ Requires an NVIDIA GPU with 4+ GB VRAM. │
|
||||
│ The app will restart its backend process │
|
||||
│ after download. Your work is preserved. │
|
||||
└─────────────────────────────────────────────┘
|
||||
```
|
||||
|
||||
After download:
|
||||
|
||||
```
|
||||
┌─────────────────────────────────────────────┐
|
||||
│ GPU Acceleration │
|
||||
│ │
|
||||
│ Status: ✓ CUDA backend active (RTX 4090) │
|
||||
│ │
|
||||
│ [Switch to CPU] [Delete CUDA Backend] │
|
||||
└─────────────────────────────────────────────┘
|
||||
```
|
||||
|
||||
#### 3e. Frontend: Reconnection During Restart
|
||||
|
||||
The current health poll interval is 30 seconds — too slow for a restart UX. During a restart, temporarily increase polling:
|
||||
|
||||
```typescript
|
||||
// In the component that triggers restart:
|
||||
const restart = async () => {
|
||||
setRestarting(true);
|
||||
try {
|
||||
await platform.lifecycle.restartServer(true);
|
||||
} catch (e) {
|
||||
// Frontend will show "reconnecting" state
|
||||
}
|
||||
// Aggressively poll until health check succeeds
|
||||
const interval = setInterval(async () => {
|
||||
try {
|
||||
await apiClient.getHealth();
|
||||
clearInterval(interval);
|
||||
setRestarting(false);
|
||||
queryClient.invalidateQueries(); // Refresh all data
|
||||
} catch {}
|
||||
}, 1000); // Poll every 1s during restart
|
||||
// Safety timeout
|
||||
setTimeout(() => clearInterval(interval), 30000);
|
||||
};
|
||||
```
|
||||
|
||||
### Phase 4: Auto-Detection on Startup
|
||||
|
||||
No user action needed on subsequent launches. The preference logic in `start_server` (Phase 3b) handles this:
|
||||
|
||||
1. App launches → `start_server` called
|
||||
2. Check `data/backends/voicebox-server-cuda{.exe}`
|
||||
3. If exists → launch CUDA binary
|
||||
4. If not → launch bundled CPU binary
|
||||
|
||||
The user downloads CUDA once, and every future app launch (including after updates) uses it automatically. The CUDA binary lives in the data directory, not the app bundle, so app updates don't overwrite it.
|
||||
|
||||
### Phase 5: Handling Version Mismatches
|
||||
|
||||
When the app updates but the CUDA binary is from an older version, the API might be incompatible. Handle this by:
|
||||
|
||||
1. Add `--version` flag to `server.py`:
|
||||
|
||||
```python
|
||||
parser.add_argument("--version", action="store_true")
|
||||
# If invoked with --version, print version and exit
|
||||
if args.version:
|
||||
from backend import __version__
|
||||
print(f"voicebox-server {__version__}")
|
||||
sys.exit(0)
|
||||
```
|
||||
|
||||
2. In `start_server` (Rust), before launching the CUDA binary:
|
||||
|
||||
```rust
|
||||
// Quick version check
|
||||
let version_output = std::process::Command::new(cuda_binary.to_str().unwrap())
|
||||
.arg("--version")
|
||||
.output();
|
||||
|
||||
match version_output {
|
||||
Ok(output) => {
|
||||
let version = String::from_utf8_lossy(&output.stdout);
|
||||
let app_version = env!("CARGO_PKG_VERSION");
|
||||
if !version.contains(app_version) {
|
||||
println!("CUDA binary version mismatch (app: {}, cuda: {}), falling back to CPU",
|
||||
app_version, version.trim());
|
||||
// Fall through to CPU sidecar launch
|
||||
}
|
||||
}
|
||||
Err(_) => {
|
||||
println!("Failed to check CUDA binary version, falling back to CPU");
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
3. Frontend shows a notification: "Your GPU backend needs an update. [Download latest] or [Use CPU for now]"
|
||||
|
||||
## Files Changed
|
||||
|
||||
### New Files
|
||||
|
||||
| File | Purpose |
|
||||
|------|---------|
|
||||
| `backend/cuda_download.py` | Download, reassemble, verify CUDA binary |
|
||||
| `scripts/split_binary.py` | Split binary into <2 GB chunks for GitHub Releases |
|
||||
| `.github/workflows/build-cuda.yml` | CI: build + upload CUDA binary |
|
||||
|
||||
### Modified Files
|
||||
|
||||
| File | Change |
|
||||
|------|--------|
|
||||
| `tauri/src-tauri/src/main.rs` | Add `restart_server` command, modify `start_server` to check for CUDA binary in data dir |
|
||||
| `backend/server.py` | Add `--version` flag |
|
||||
| `backend/main.py` | Add `/backend/download-cuda`, `/backend/cuda-status`, `/backend/progress/cuda-backend` endpoints |
|
||||
| `backend/build_binary.py` | Accept `--cuda` flag to build CUDA variant |
|
||||
| `app/src/platform/types.ts` | Add `restartServer` to lifecycle interface |
|
||||
| `tauri/src/platform/lifecycle.ts` | Implement `restartServer` |
|
||||
| `app/src/components/ServerSettings/` | New GPU acceleration section |
|
||||
| `.github/workflows/release.yml` | Trigger CUDA build workflow on tag |
|
||||
|
||||
### NOT Changed
|
||||
|
||||
| File | Why |
|
||||
|------|-----|
|
||||
| `backend/backends/__init__.py` | No changes to the TTSBackend singleton or factory. CUDA binary runs the same code. |
|
||||
| `backend/backends/pytorch_backend.py` | Already detects CUDA at runtime (line 28-49). No changes needed. |
|
||||
| `app/src/lib/api/client.ts` | API is identical between CPU and CUDA backends. |
|
||||
| `app/src/lib/hooks/useGenerationForm.ts` | Generation flow is unchanged. |
|
||||
|
||||
## What This Doesn't Solve
|
||||
|
||||
- **Multi-model support** — This is purely about GPU acceleration. LuxTTS, Chatterbox, etc. need the in-process model registry, which is an independent workstream.
|
||||
- **AMD GPU support** — DirectML/ROCm needs a different PyTorch build. Same pattern applies (another binary variant) but deferred.
|
||||
- **Linux CUDA** — Same approach works, just another CI matrix entry. Can be added in the same release or shortly after.
|
||||
- **Remote server mode** — Users who want to run TTS on a different machine still need the external provider architecture. Separate concern.
|
||||
|
||||
## What This DOES Solve
|
||||
|
||||
- **19 "GPU not detected" issues** — Users download the CUDA backend, restart, GPU works.
|
||||
- **2 GB GitHub Release limit** — Binary splitting + R2 hosting.
|
||||
- **Update burden** — App updates don't re-download the 2.4 GB CUDA binary. It persists in the data directory.
|
||||
- **First-run experience** — App works immediately on CPU. GPU is an optional enhancement, not a setup blocker.
|
||||
|
||||
## Rollout Plan
|
||||
|
||||
1. Build and test CUDA binary locally on Windows with an NVIDIA GPU.
|
||||
2. Set up R2 bucket at `downloads.voicebox.sh/cuda/`.
|
||||
3. Ship the backend restart + download UI in v0.2.0.
|
||||
4. Announce: "GPU acceleration is here — one click in Settings."
|
||||
|
||||
## Risks
|
||||
|
||||
| Risk | Mitigation |
|
||||
|------|-----------|
|
||||
| CUDA binary doesn't work on some GPU/driver combos | `/health` endpoint reports GPU info. Fallback to CPU if CUDA init fails. Clear error message. |
|
||||
| Antivirus flags downloaded binary (Windows) | Code-sign the CUDA binary in CI. Document AV exceptions. |
|
||||
| Data dir CUDA binary survives app uninstall | Document in uninstall notes. Not a real problem — it's just a file. |
|
||||
| Version mismatch after app update | Version check on startup (Phase 5). Auto-fallback to CPU. Prompt to re-download. |
|
||||
| R2 downtime | GitHub Releases split-binary fallback. |
|
||||
| Download interrupted | Temp file with `.download` extension. Atomic rename on completion. Resume not implemented in v1 — restart download from scratch. |
|
||||
@@ -1,133 +0,0 @@
|
||||
# CUDA Backend Swap — Implementation Summary
|
||||
|
||||
> Status: **Complete** | Branch: `feat/cuda-backend-swap` | Created: 2026-03-12
|
||||
|
||||
## What This Is
|
||||
|
||||
A standalone feature that lets users download a CUDA-enabled backend binary (~2.4 GB) and swap it in via a backend-only restart. The frontend stays running, all UI state is preserved. This solves the #1 user pain point: 19 open issues about "GPU not detected" caused by GitHub's 2 GB release asset limit preventing CUDA binaries from shipping in official releases.
|
||||
|
||||
## How It Works
|
||||
|
||||
```
|
||||
User clicks "Download CUDA Backend" in Settings
|
||||
→ Backend fetches manifest from GitHub Releases
|
||||
→ Downloads split parts (<2 GB each), concatenates them
|
||||
→ SHA-256 integrity check on reassembled binary
|
||||
→ Binary placed in {app_data_dir}/backends/voicebox-server-cuda
|
||||
→ User clicks "Switch to CUDA Backend"
|
||||
→ Tauri kills CPU process, launches CUDA binary, frontend reconnects
|
||||
→ On all future app launches, CUDA binary is auto-detected and used
|
||||
```
|
||||
|
||||
The CUDA binary is functionally identical to the CPU binary — same FastAPI app, same endpoints, same code. The only difference is PyTorch compiled with CUDA 12.1 and bundled CUDA runtime libraries.
|
||||
|
||||
## Architecture Decisions
|
||||
|
||||
**Backend-only restart, not full app restart.** The Tauri shell kills the current `voicebox-server` process, waits 1 second for port release, and spawns the new binary. The React frontend stays running. Health polling detects the new backend within seconds.
|
||||
|
||||
**No provider/subprocess architecture.** This is explicitly not the PR #33 approach (10K+ lines, 136 files, 22 bugs). One process at a time. The CUDA binary replaces the CPU binary — it doesn't run alongside it.
|
||||
|
||||
**Data directory, not app bundle.** The CUDA binary lives in `{app_data_dir}/backends/`, which persists across app updates and avoids code-signing issues. The bundled CPU binary in the app bundle is untouched.
|
||||
|
||||
**Version mismatch protection.** On startup, Rust runs `voicebox-server-cuda --version` and compares to the app version from `tauri.conf.json`. If they don't match (e.g., after an app update), it falls back to the bundled CPU binary silently.
|
||||
|
||||
**GitHub Releases distribution.** The CUDA binary is split into <2 GB chunks (GitHub's asset limit) via `scripts/split_binary.py`. The app downloads a manifest, fetches each part, concatenates them, and runs a SHA-256 integrity check to verify reassembly. No external hosting needed.
|
||||
|
||||
## Files Changed
|
||||
|
||||
### New Files
|
||||
|
||||
| File | Lines | Purpose |
|
||||
|------|-------|---------|
|
||||
| `backend/cuda_download.py` | ~190 | Download split parts from GitHub Releases, reassemble, verify integrity |
|
||||
| `scripts/split_binary.py` | ~80 | Split large binary into <2 GB chunks with SHA-256 manifest |
|
||||
| `.github/workflows/build-cuda.yml` | ~70 | CI workflow: build CUDA binary, split, upload to GitHub Releases |
|
||||
| `app/src/components/ServerSettings/GpuAcceleration.tsx` | 371 | GPU Acceleration UI card (status, download, restart, delete) |
|
||||
| `docs/plans/CUDA_BACKEND_SWAP.md` | 581 | Original implementation plan (5 phases with code sketches) |
|
||||
| `docs/plans/CUDA_BACKEND_SWAP_FINAL.md` | this file | Final implementation summary |
|
||||
| `docs/plans/PROJECT_STATUS.md` | 462 | Full project triage (all PRs, issues, architecture) |
|
||||
| `docs/plans/PR33_CUDA_PROVIDER_REVIEW.md` | ~350 | Detailed code review of PR #33 (22 bugs documented) |
|
||||
|
||||
### Modified Files
|
||||
|
||||
| File | What Changed |
|
||||
|------|-------------|
|
||||
| `backend/build_binary.py` | Added `--cuda` flag, parameterized output binary name |
|
||||
| `backend/server.py` | Added `--version` flag, auto-detect backend variant from binary name (`VOICEBOX_BACKEND_VARIANT` env var) |
|
||||
| `backend/main.py` | 4 new endpoints (`/backend/cuda-status`, `/backend/download-cuda`, `/backend/cuda`, `/backend/cuda-progress`), health endpoint returns `backend_variant` |
|
||||
| `backend/models.py` | `HealthResponse` model: added `backend_variant` field |
|
||||
| `backend/requirements.txt` | Added `httpx>=0.27.0` for async HTTP downloads |
|
||||
| `tauri/src-tauri/src/main.rs` | `restart_server` command (stop → wait → start), `start_server` checks for CUDA binary in data dir and launches via `shell().command()`, version mismatch check |
|
||||
| `app/src/platform/types.ts` | `PlatformLifecycle.restartServer()` added |
|
||||
| `tauri/src/platform/lifecycle.ts` | `restartServer()` implementation via `invoke('restart_server')` |
|
||||
| `web/src/platform/lifecycle.ts` | `restartServer()` noop for web platform |
|
||||
| `app/src/lib/api/types.ts` | `CudaStatus`, `CudaDownloadProgress` interfaces; `HealthResponse` updated with `gpu_type`, `backend_type`, `backend_variant` |
|
||||
| `app/src/lib/api/client.ts` | `getCudaStatus()`, `downloadCudaBackend()`, `deleteCudaBackend()` methods |
|
||||
| `app/src/components/ServerTab/ServerTab.tsx` | Wired in `<GpuAcceleration />` component (Tauri-only) |
|
||||
|
||||
## Backend API Endpoints
|
||||
|
||||
| Method | Path | Purpose |
|
||||
|--------|------|---------|
|
||||
| `GET` | `/backend/cuda-status` | Returns `{ available, active, binary_path, downloading, download_progress }` |
|
||||
| `POST` | `/backend/download-cuda` | Starts background download; returns immediately. Track via SSE. |
|
||||
| `DELETE` | `/backend/cuda` | Deletes CUDA binary (blocked if CUDA is currently active) |
|
||||
| `GET` | `/backend/cuda-progress` | SSE stream of download progress (reuses existing `ProgressManager`) |
|
||||
|
||||
The existing `GET /health` endpoint now returns two new fields:
|
||||
- `backend_type`: `"pytorch"` or `"mlx"` (existing detection)
|
||||
- `backend_variant`: `"cpu"` or `"cuda"` (set from `VOICEBOX_BACKEND_VARIANT` env var)
|
||||
|
||||
## Frontend UI States
|
||||
|
||||
The `GpuAcceleration` card in Server Settings handles these states:
|
||||
|
||||
1. **Native GPU detected** (MPS, MLX, XPU, DirectML) — Shows info message, no download needed
|
||||
2. **No CUDA binary** — Download button with size estimate, description of requirements
|
||||
3. **Downloading** — SSE-driven progress bar with bytes/total and percentage
|
||||
4. **Downloaded, not active** — "Switch to CUDA Backend" button + "Remove" option
|
||||
5. **CUDA active** — Shows CUDA badge, "Switch to CPU Backend" button
|
||||
6. **Restarting** — Spinner with phase text, 1s health polling as safety net
|
||||
7. **Error** — Red error message with details
|
||||
|
||||
### Key UX detail: switching to CPU
|
||||
|
||||
Since `start_server` always prefers the CUDA binary if it exists on disk, "Switch to CPU" must delete the CUDA binary first, then restart. The user can re-download later. This avoids a persistent configuration mechanism (no new state to manage, no new config file, no DB column).
|
||||
|
||||
## Rust: Server Lifecycle
|
||||
|
||||
```
|
||||
start_server
|
||||
├── Check for CUDA binary at {data_dir}/backends/voicebox-server-cuda
|
||||
├── If found: run --version, compare to app version
|
||||
│ ├── Match: launch via shell().command() with --data-dir, --port
|
||||
│ └── Mismatch: log warning, fall through to CPU
|
||||
└── Else: launch bundled sidecar via shell().sidecar()
|
||||
|
||||
restart_server
|
||||
├── stop_server (kill process tree)
|
||||
├── wait 1 second for port release
|
||||
└── start_server (auto-detects CUDA)
|
||||
```
|
||||
|
||||
## What This Doesn't Cover
|
||||
|
||||
- **AMD GPU / ROCm / DirectML binary** — Same pattern, different PyTorch build. Future PR.
|
||||
- **Linux CUDA** — Same approach, just another CI matrix entry. Can ship same release.
|
||||
- **Multi-model support** — LuxTTS, Chatterbox, etc. are a separate architectural concern (in-process model registry). Independent of binary variant.
|
||||
- **Download resume** — If download is interrupted, it restarts from scratch. Acceptable for v1.
|
||||
- **Remote server CUDA** — Users running voicebox-server on a remote machine manage their own binaries. This feature is for the desktop app.
|
||||
|
||||
## Testing Checklist
|
||||
|
||||
- [ ] Build CUDA binary locally with `python backend/build_binary.py --cuda`
|
||||
- [ ] `voicebox-server-cuda --version` prints correct version
|
||||
- [ ] Place CUDA binary in `{data_dir}/backends/`, launch app → auto-detects and uses it
|
||||
- [ ] Version mismatch: rename binary to have wrong version → falls back to CPU
|
||||
- [ ] Frontend: GpuAcceleration card shows correct state for CPU, CUDA available, CUDA active
|
||||
- [ ] Download flow: POST triggers download, SSE progress works, completion updates status
|
||||
- [ ] Switch to CUDA: restart works, health endpoint shows `backend_variant: "cuda"`
|
||||
- [ ] Switch to CPU: deletes binary, restarts, health shows `backend_variant: "cpu"`
|
||||
- [ ] Delete CUDA while active: returns 409 error
|
||||
- [ ] Split binary script: `python scripts/split_binary.py` creates manifest + parts + sha256
|
||||
- [ ] Native GPU (macOS MPS): shows info message, no download section
|
||||
@@ -1,500 +0,0 @@
|
||||
# PR #33 — CUDA Provider System Review
|
||||
|
||||
> Branch: `external-provider-binaries` | Created: 2026-02-01 | 34 commits, 136 files, +10,266 lines
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> Reviewed: 2026-03-12
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|
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---
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||||
|
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## The Problem
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|
||||
The CUDA PyTorch binary is ~2.4 GB. GitHub Releases has a 2 GB artifact limit. This means:
|
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|
||||
- Windows/Linux users with NVIDIA GPUs cannot get GPU acceleration from official releases
|
||||
- 19 open issues about "GPU not detected" — the single most reported problem category
|
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- Users who want GPU must clone the repo and run from source
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- Every app update forces re-download of the entire binary
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|
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This is the #1 user pain point by volume.
|
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|
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---
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|
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## What PR #33 Does
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Splits the monolithic Voicebox binary into two layers:
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|
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```
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┌──────────────────────────────────────┐
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│ Main App (~150MB Win/Lin, ~300 Mac) │
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│ Tauri + React + FastAPI + Whisper │
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│ No PyTorch. MLX bundled on macOS. │
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├──────────────────────────────────────┤
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│ HTTP (localhost) │
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├──────────────────────────────────────┤
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│ Provider Binary (downloaded later) │
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│ PyTorch CPU (~300MB) │
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│ PyTorch CUDA (~2.4GB) │
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│ Hosted on Cloudflare R2 │
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└──────────────────────────────────────┘
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```
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### New Backend Code
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| File | Purpose |
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|------|---------|
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| `backend/providers/__init__.py` (327 lines) | `ProviderManager` — lifecycle management, subprocess spawning, port allocation |
|
||||
| `backend/providers/base.py` (97 lines) | `TTSProvider` Protocol definition |
|
||||
| `backend/providers/bundled.py` (144 lines) | `BundledProvider` — wraps existing MLX/PyTorch backends for the new interface |
|
||||
| `backend/providers/local.py` (191 lines) | `LocalProvider` — HTTP client that talks to external provider processes |
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| `backend/providers/installer.py` (262 lines) | Download, extract, delete provider binaries |
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| `backend/providers/types.py` (34 lines) | `ProviderType` enum, `ProviderInfo` dataclass |
|
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| `backend/providers/checksums.py` (11 lines) | Checksum dict (currently empty) |
|
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|
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### Provider Servers (Standalone Executables)
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|
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| File | Purpose |
|
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|------|---------|
|
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| `providers/pytorch-cpu/main.py` (238 lines) | FastAPI server wrapping PyTorch CPU inference |
|
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| `providers/pytorch-cuda/main.py` (238 lines) | FastAPI server wrapping PyTorch CUDA inference |
|
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| `providers/pytorch-*/build.py` | PyInstaller build scripts |
|
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| `providers/pytorch-*/requirements.txt` | Isolated dependencies |
|
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|
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### Frontend
|
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|
||||
| File | Purpose |
|
||||
|------|---------|
|
||||
| `app/src/components/ServerSettings/ProviderSettings.tsx` (400 lines) | Provider download/start/stop/delete UI |
|
||||
|
||||
### Also Included (Scope Creep)
|
||||
|
||||
The PR bundles several unrelated changes that inflate the diff:
|
||||
|
||||
- `docs2/` — Entire documentation site rewrite (Fumadocs migration, ~3000 lines)
|
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- `Dockerfile`, `Dockerfile.cuda`, `docker-compose.yml` — Docker support
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- `landing/` — Banner removal
|
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- UI refactors in Stories, History, Voice Profiles, Audio tab
|
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- Linux audio capture module
|
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- Various dependency bumps
|
||||
|
||||
---
|
||||
|
||||
## Bug Report
|
||||
|
||||
### Critical — Will Crash at Runtime
|
||||
|
||||
#### C1. Provider `generate` endpoint can't parse requests
|
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|
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**`providers/pytorch-cpu/main.py:91-97`** (same in pytorch-cuda)
|
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|
||||
```python
|
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@app.post("/tts/generate")
|
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async def generate(
|
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text: str,
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voice_prompt: dict,
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language: str = "auto",
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seed: int = None,
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model_size: str = "1.7B"
|
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):
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```
|
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|
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Parameters declared as function arguments. FastAPI interprets these as **query parameters**, not JSON body. But `LocalProvider.generate()` sends a JSON body via `httpx`:
|
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|
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```python
|
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# backend/providers/local.py:33-40
|
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response = await self.client.post("/tts/generate", json={
|
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"text": text,
|
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"voice_prompt": voice_prompt,
|
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...
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})
|
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```
|
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|
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**Result:** Every generation call to an external provider returns HTTP 422 (Validation Error). The generation path is completely broken for external providers.
|
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|
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**Fix:** Use a Pydantic request body model:
|
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```python
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class GenerateRequest(BaseModel):
|
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text: str
|
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voice_prompt: dict
|
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language: str = "auto"
|
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seed: Optional[int] = None
|
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model_size: str = "1.7B"
|
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|
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@app.post("/tts/generate")
|
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async def generate(data: GenerateRequest):
|
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```
|
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|
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#### C2. Timeout error handler references undefined variables
|
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|
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**`backend/providers/__init__.py:82-90`**
|
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|
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```python
|
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stdout_content = ""
|
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stderr_content = ""
|
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# ... threads write to stdout_queue / stderr_queue ...
|
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except TimeoutError:
|
||||
while not stdout_queue.empty():
|
||||
stdout_lines.append(stdout_queue.get_nowait()) # NameError
|
||||
while not stderr_queue.empty():
|
||||
stderr_lines.append(stderr_queue.get_nowait()) # NameError
|
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```
|
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|
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`stdout_lines` and `stderr_lines` are never defined. Every provider startup timeout will throw `NameError`, masking the real failure cause. Then `stdout_content` and `stderr_content` are logged but they're still empty strings — the queue data is never assigned back.
|
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|
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#### C3. Sync `get_tts_model()` ignores external provider in async context
|
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|
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**`backend/tts.py:15-29`**
|
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|
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```python
|
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def get_tts_model():
|
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manager = get_provider_manager()
|
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loop = asyncio.get_event_loop()
|
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if loop.is_running():
|
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# We're in an async context, but can't await here
|
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return manager._get_default_provider()
|
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```
|
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|
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FastAPI routes are async. This function is called from several code paths during generation. In async context it **always returns the bundled provider**, ignoring whatever external provider the user selected. The user downloads and starts a CUDA provider, but generation still runs on CPU.
|
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|
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### Critical — Security
|
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|
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#### C4. Path traversal via `tarfile.extractall()` (CVE-2007-4559)
|
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|
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**`backend/providers/installer.py:115-118`**
|
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|
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```python
|
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with tarfile.open(archive_path, 'r:gz') as tar_ref:
|
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tar_ref.extractall(providers_dir)
|
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```
|
||||
|
||||
No member path filtering. A crafted `.tar.gz` from a compromised CDN can write files anywhere on disk via `../` entries. Python 3.12+ emits a deprecation warning for exactly this pattern.
|
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|
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**Fix:**
|
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```python
|
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tar_ref.extractall(providers_dir, filter='data') # Python 3.12+
|
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```
|
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|
||||
Or manually validate each member:
|
||||
```python
|
||||
for member in tar_ref.getmembers():
|
||||
member_path = os.path.join(providers_dir, member.name)
|
||||
if not os.path.commonpath([providers_dir, member_path]).startswith(str(providers_dir)):
|
||||
raise ValueError(f"Path traversal attempt: {member.name}")
|
||||
tar_ref.extractall(providers_dir)
|
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```
|
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|
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#### C5. No checksum verification on downloaded binaries
|
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|
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**`backend/providers/checksums.py`**
|
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|
||||
```python
|
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PROVIDER_CHECKSUMS = {}
|
||||
```
|
||||
|
||||
Empty dict. `download_provider()` in `installer.py` never calls any verification function. Downloaded binaries are `chmod 0o755`'d and executed without integrity checks. A MitM or CDN compromise delivers arbitrary code.
|
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|
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**Fix:** Populate checksums per release. Verify SHA-256 after download before extraction:
|
||||
```python
|
||||
import hashlib
|
||||
sha256 = hashlib.sha256(archive_path.read_bytes()).hexdigest()
|
||||
if sha256 != expected:
|
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archive_path.unlink()
|
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raise ValueError(f"Checksum mismatch for {provider_type}")
|
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```
|
||||
|
||||
#### C6. Provider servers have no authentication
|
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|
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**`providers/pytorch-cpu/main.py:18-23`**
|
||||
|
||||
```python
|
||||
app.add_middleware(
|
||||
CORSMiddleware,
|
||||
allow_origins=["*"],
|
||||
...
|
||||
)
|
||||
```
|
||||
|
||||
Zero auth. Any local process — including browser JavaScript via localhost — can send requests to the provider on its ephemeral port. Port is discoverable by scanning.
|
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|
||||
**Fix:** Generate a random token in the parent process, pass via environment variable to the child, validate in middleware:
|
||||
```python
|
||||
# Parent (ProviderManager)
|
||||
token = secrets.token_urlsafe(32)
|
||||
env = {**os.environ, "VOICEBOX_PROVIDER_TOKEN": token}
|
||||
process = subprocess.Popen([...], env=env, ...)
|
||||
|
||||
# Child (provider server)
|
||||
EXPECTED_TOKEN = os.environ.get("VOICEBOX_PROVIDER_TOKEN")
|
||||
|
||||
@app.middleware("http")
|
||||
async def verify_token(request, call_next):
|
||||
if request.headers.get("X-Provider-Token") != EXPECTED_TOKEN:
|
||||
return JSONResponse(status_code=403, content={"error": "unauthorized"})
|
||||
return await call_next(request)
|
||||
```
|
||||
|
||||
### Major — Will Cause Problems in Production
|
||||
|
||||
#### M1. Leaked file handles on subprocess stdout/stderr
|
||||
|
||||
**`backend/providers/__init__.py:68-73`**
|
||||
|
||||
```python
|
||||
process = subprocess.Popen(
|
||||
[...],
|
||||
stdout=open(stdout_log, 'w'), # leaked handle
|
||||
stderr=open(stderr_log, 'w'), # leaked handle
|
||||
)
|
||||
```
|
||||
|
||||
File handles passed directly from `open()` without storing references. They close on GC, not deterministically. On Windows the log files stay locked and unreadable until the process exits.
|
||||
|
||||
**Fix:**
|
||||
```python
|
||||
stdout_fh = open(stdout_log, 'w')
|
||||
stderr_fh = open(stderr_log, 'w')
|
||||
try:
|
||||
process = subprocess.Popen([...], stdout=stdout_fh, stderr=stderr_fh)
|
||||
finally:
|
||||
stdout_fh.close()
|
||||
stderr_fh.close()
|
||||
```
|
||||
|
||||
#### M2. No subprocess crash detection or recovery
|
||||
|
||||
**`backend/providers/__init__.py:56-110`**
|
||||
|
||||
Once `start_provider()` succeeds, the `Popen` object is stored but never polled. If the provider process crashes mid-session:
|
||||
- `LocalProvider` HTTP calls fail with `httpx.ConnectError`
|
||||
- No auto-restart
|
||||
- No health-check loop
|
||||
- User sees cryptic "connection refused" errors
|
||||
- Must manually restart provider from UI
|
||||
|
||||
**Fix:** Background asyncio task that polls `process.poll()` every few seconds. On crash, update provider status and optionally auto-restart:
|
||||
```python
|
||||
async def _watch_provider_process(self):
|
||||
while self._provider_process and self._provider_process.poll() is None:
|
||||
await asyncio.sleep(5)
|
||||
if self._provider_process and self._provider_process.returncode != 0:
|
||||
logger.error(f"Provider crashed with code {self._provider_process.returncode}")
|
||||
self.active_provider = self._default_provider
|
||||
# Notify frontend via next health check
|
||||
```
|
||||
|
||||
#### M3. Port allocation race condition (TOCTOU)
|
||||
|
||||
**`backend/providers/__init__.py:145-149`**
|
||||
|
||||
```python
|
||||
def _get_free_port(self) -> int:
|
||||
with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
|
||||
s.bind(('', 0))
|
||||
return s.getsockname()[1]
|
||||
# Socket closed here — port is free but unprotected
|
||||
```
|
||||
|
||||
Between this function returning and the provider process binding, another process can claim the port. On busy systems this causes "address already in use" failures.
|
||||
|
||||
**Fix options:**
|
||||
- Pass the socket fd to the child process (complex, platform-specific)
|
||||
- Retry with a new port on bind failure (simplest)
|
||||
- Use a fixed port range and try sequentially
|
||||
|
||||
#### M4. `delete_provider()` leaves hundreds of MB behind
|
||||
|
||||
**`backend/providers/installer.py:155-168`**
|
||||
|
||||
```python
|
||||
provider_path.unlink() # Deletes just the executable
|
||||
```
|
||||
|
||||
PyInstaller `--onedir` produces a directory with the executable plus all shared libraries. `unlink()` only removes the binary file, leaving behind hundreds of MB of `.so`/`.dll`/`.dylib` files.
|
||||
|
||||
**Fix:**
|
||||
```python
|
||||
provider_dir = provider_path.parent
|
||||
shutil.rmtree(provider_dir)
|
||||
```
|
||||
|
||||
#### M5. `LocalProvider.combine_voice_prompts()` bypasses the provider
|
||||
|
||||
**`backend/providers/local.py:68-88`**
|
||||
|
||||
This method imports from `..utils.audio` and processes locally instead of sending to the provider server. If the user chose an external provider because they lack local dependencies (e.g., no PyTorch on the machine), this will crash with `ImportError`.
|
||||
|
||||
#### M6. Download errors silently swallowed
|
||||
|
||||
**`backend/main.py:1640`**
|
||||
|
||||
```python
|
||||
asyncio.create_task(download_provider(provider_type))
|
||||
```
|
||||
|
||||
Fire-and-forget. If the download fails, the exception is logged as "Task exception was never retrieved." The frontend SSE progress stream may hang forever showing "downloading" without the error.
|
||||
|
||||
**Fix:** Store the task, add an error callback:
|
||||
```python
|
||||
task = asyncio.create_task(download_provider(provider_type))
|
||||
task.add_done_callback(lambda t: t.exception() if not t.cancelled() else None)
|
||||
```
|
||||
And propagate errors through the progress manager so the SSE stream surfaces them.
|
||||
|
||||
#### M7. `LocalProvider.is_loaded()` always returns `True`
|
||||
|
||||
**`backend/providers/local.py:105-108`**
|
||||
|
||||
```python
|
||||
def is_loaded(self) -> bool:
|
||||
return True # Return True optimistically
|
||||
```
|
||||
|
||||
Health/status checks always report the model as loaded for external providers, even when the provider hasn't loaded anything yet. This breaks the "download model if not cached" logic in the generation flow.
|
||||
|
||||
#### M8. `instruct` parameter silently dropped
|
||||
|
||||
**`backend/providers/local.py:33-40`**
|
||||
|
||||
The `generate()` method accepts `instruct` but never includes it in the JSON payload. The provider server also hardcodes `instruct=None`. Delivery instructions silently do nothing for external providers.
|
||||
|
||||
### Minor
|
||||
|
||||
| # | Issue | Location |
|
||||
|---|-------|----------|
|
||||
| m1 | `pytorch-cpu/main.py` and `pytorch-cuda/main.py` are 95% identical | Both files |
|
||||
| m2 | `build.py` scripts also nearly identical | Both build files |
|
||||
| m3 | `navigator.platform` is deprecated | `ProviderSettings.tsx:20-23` |
|
||||
| m4 | `console.log('currentProvider', ...)` left in | `ProviderSettings.tsx:151` |
|
||||
| m5 | `ProviderType` enum defined but never used for validation | `types.py:10-15` |
|
||||
| m6 | `list_installed()` reimplements platform detection | `__init__.py:129-143` |
|
||||
| m7 | New `httpx.AsyncClient` created per health poll iteration | `__init__.py:151-165` |
|
||||
| m8 | `load_model_async()` only stores size, doesn't actually preload | `local.py:95-99` |
|
||||
|
||||
---
|
||||
|
||||
## Scope Creep
|
||||
|
||||
The PR should be split. These are independent changes bundled in:
|
||||
|
||||
| Change | Lines | Should Be Separate PR |
|
||||
|--------|-------|-----------------------|
|
||||
| `docs2/` site rewrite | ~3000 | Yes |
|
||||
| Docker support (Dockerfile, compose, docs) | ~600 | Yes — overlaps with PR #161 |
|
||||
| Landing page banner removal | ~30 | Yes |
|
||||
| UI refactors (Stories, History, Voices, Audio) | ~400 | Yes |
|
||||
| Linux audio capture module | ~10 | Yes |
|
||||
| Dependency bumps | ~100 | Yes |
|
||||
|
||||
**Core provider system** (the actual feature) is ~2500 lines across backend + frontend + provider servers. That's the reviewable scope.
|
||||
|
||||
---
|
||||
|
||||
## What's Well-Designed
|
||||
|
||||
These parts should survive any rewrite:
|
||||
|
||||
1. **`TTSProvider` Protocol** (`base.py`) — Structural typing via `@runtime_checkable Protocol`. Right pattern. Comprehensive interface.
|
||||
|
||||
2. **`BundledProvider` / `LocalProvider` split** — Clean separation between in-process and HTTP-based inference. The wrapper pattern in `BundledProvider` correctly delegates to existing `TTSBackend`.
|
||||
|
||||
3. **R2 distribution strategy** — Provider binaries on Cloudflare R2, main app on GitHub Releases. Correct solution to the 2 GB limit.
|
||||
|
||||
4. **Progress tracking** — SSE-based download progress integrated with the existing `ProgressManager`. Good UX.
|
||||
|
||||
5. **Subprocess log files** — Writing provider stdout/stderr to log files in the data directory is pragmatic and debuggable.
|
||||
|
||||
6. **Frontend `ProviderSettings.tsx`** — Clean component structure. Proper loading/disabled states, confirmation dialogs, platform-aware visibility.
|
||||
|
||||
7. **CI split** — Separate `build-providers` and `release` jobs. Providers built and uploaded to R2 independently.
|
||||
|
||||
---
|
||||
|
||||
## Options for Moving Forward
|
||||
|
||||
### Option A — Fix and Slim PR #33
|
||||
|
||||
Strip the PR down to just the provider system (~2500 lines). Fix the 5 critical and 8 major bugs. Rebase onto current `main`.
|
||||
|
||||
**Effort:** ~2-3 days focused work
|
||||
**Pros:** Full auto-managed provider lifecycle. Foundation for multi-model.
|
||||
**Cons:** Still complex. Process management is inherently fragile cross-platform.
|
||||
|
||||
### Option B — Manual External Server Mode
|
||||
|
||||
Skip subprocess management entirely. Ship a "Connect to External Server" feature:
|
||||
|
||||
1. User downloads CUDA provider zip from `downloads.voicebox.sh`
|
||||
2. User runs it manually (`./tts-provider-pytorch-cuda --port 8100`)
|
||||
3. In Voicebox UI: paste `http://localhost:8100` as the TTS server URL
|
||||
4. Voicebox routes generation to that URL via `LocalProvider`
|
||||
|
||||
This reuses `LocalProvider` from PR #33 but removes:
|
||||
- `ProviderManager` subprocess spawning (the buggiest part)
|
||||
- `installer.py` download/extract logic (the security risks)
|
||||
- Port allocation (user picks the port)
|
||||
- Process lifecycle management (user's responsibility)
|
||||
|
||||
**Effort:** ~1 day. `LocalProvider` + a URL input field + health check.
|
||||
**Pros:** Simple, reliable, no process management bugs, no security surface.
|
||||
**Cons:** Manual setup. Not seamless. But CUDA users are already technical (they run from source today).
|
||||
|
||||
### Option C — Hybrid (Recommended)
|
||||
|
||||
Ship Option B first as v0.2.0. Then iterate toward auto-management:
|
||||
|
||||
**Phase 1 (v0.2.0):** Manual external server mode
|
||||
- `LocalProvider` HTTP client (from PR #33, with the 422 bug fixed)
|
||||
- Server URL input in Settings
|
||||
- Health indicator
|
||||
- CUDA provider published as standalone zip on R2
|
||||
- One page of docs: "download, unzip, run, paste URL"
|
||||
|
||||
**Phase 2 (v0.2.x):** Auto-download + auto-start
|
||||
- `installer.py` with checksum verification and safe extraction
|
||||
- `ProviderManager` subprocess spawning with crash detection
|
||||
- Provider settings UI with download/start/stop buttons
|
||||
|
||||
**Phase 3 (v0.3.0):** Multi-model providers
|
||||
- Provider per model family (not just per hardware)
|
||||
- LuxTTS provider, Chatterbox provider, etc.
|
||||
- Provider marketplace / registry
|
||||
|
||||
This gets CUDA into users' hands immediately (Phase 1 is ~1 day) while building toward the full vision incrementally. Each phase is independently shippable and testable.
|
||||
|
||||
### Option D — GitHub Workaround
|
||||
|
||||
Avoid the provider architecture entirely. Host CUDA binaries on R2 and add a download link in the app that opens the user's browser. User downloads the full monolithic CUDA build, replaces their existing install.
|
||||
|
||||
**Effort:** Minimal — just hosting + a link.
|
||||
**Pros:** Zero architecture changes.
|
||||
**Cons:** Doesn't solve: multi-model, independent app updates, or the re-download-everything-on-update problem. Kicks the can.
|
||||
|
||||
---
|
||||
|
||||
## Recommendation
|
||||
|
||||
**Option C (Hybrid)** is the strongest path. Specifically:
|
||||
|
||||
1. **Now:** Close PR #33 as-is. It's too large, too buggy, and too stale to salvage as a single merge.
|
||||
|
||||
2. **Extract:** Cherry-pick the good parts into small focused PRs:
|
||||
- PR: `TTSProvider` Protocol + `BundledProvider` + `LocalProvider` (the abstractions)
|
||||
- PR: Provider settings UI (the frontend)
|
||||
- PR: `installer.py` + checksums (the download system)
|
||||
- PR: CI changes for R2 upload (the distribution)
|
||||
|
||||
3. **Ship Phase 1:** Manual external server mode. One small PR. Unblocks every CUDA user immediately.
|
||||
|
||||
4. **Iterate:** Layer in auto-management once the manual mode is proven stable.
|
||||
|
||||
The critical bugs in PR #33 (C1-C6) are all fixable, but the PR's size makes review unreliable. Splitting it ensures each piece gets proper attention and nothing ships broken.
|
||||
|
||||
---
|
||||
|
||||
## Bug Summary
|
||||
|
||||
| Severity | Count | Blocks Ship? |
|
||||
|----------|-------|-------------|
|
||||
| Critical (runtime crash) | 3 | Yes — C1, C2, C3 |
|
||||
| Critical (security) | 3 | Yes — C4, C5, C6 |
|
||||
| Major | 8 | Some — M1, M2, M3 are high risk |
|
||||
| Minor | 8 | No |
|
||||
| **Total** | **22** | |
|
||||
@@ -1,472 +0,0 @@
|
||||
# Voicebox Project Status & Roadmap
|
||||
|
||||
> Last updated: 2026-03-13 | Current version: **v0.1.13** | 13.1k stars | ~176 open issues | 25 open PRs
|
||||
|
||||
---
|
||||
|
||||
## Table of Contents
|
||||
|
||||
1. [Architecture Overview](#architecture-overview)
|
||||
2. [Current State](#current-state)
|
||||
3. [Open PRs — Triage & Analysis](#open-prs--triage--analysis)
|
||||
4. [Open Issues — Categorized](#open-issues--categorized)
|
||||
5. [Existing Plan Documents — Status](#existing-plan-documents--status)
|
||||
6. [New Model Integration — Landscape](#new-model-integration--landscape)
|
||||
7. [Architectural Bottlenecks](#architectural-bottlenecks)
|
||||
8. [Recommended Priorities](#recommended-priorities)
|
||||
|
||||
---
|
||||
|
||||
## Architecture Overview
|
||||
|
||||
```
|
||||
┌─────────────────────────────────────────────────────┐
|
||||
│ Tauri Shell (Rust) │
|
||||
│ ┌───────────────────────────────────────────────┐ │
|
||||
│ │ React Frontend (app/) │ │
|
||||
│ │ Zustand stores · API client · Generation UI │ │
|
||||
│ │ Stories Editor · Voice Profiles · Model Mgmt │ │
|
||||
│ └──────────────────────┬────────────────────────┘ │
|
||||
│ │ HTTP :17493 │
|
||||
│ ┌──────────────────────▼────────────────────────┐ │
|
||||
│ │ FastAPI Backend (backend/) │ │
|
||||
│ │ ┌─────────────────────────────────────────┐ │ │
|
||||
│ │ │ TTSBackend Protocol │ │ │
|
||||
│ │ │ ┌──────────┐ ┌───────┐ ┌───────────┐ │ │ │
|
||||
│ │ │ │ Qwen3-TTS│ │LuxTTS │ │Chatterbox │ │ │ │
|
||||
│ │ │ │(Py/MLX) │ │ │ │(MTL+Turbo)│ │ │ │
|
||||
│ │ │ └──────────┘ └───────┘ └───────────┘ │ │ │
|
||||
│ │ └─────────────────────────────────────────┘ │ │
|
||||
│ │ ┌───────────┐ ┌─────────┐ │ │
|
||||
│ │ │ STTBackend│ │ Profiles│ │ │
|
||||
│ │ │ (Whisper) │ │ History │ │ │
|
||||
│ │ └───────────┘ │ Stories │ │ │
|
||||
│ │ └─────────┘ │ │
|
||||
│ └───────────────────────────────────────────────┘ │
|
||||
└─────────────────────────────────────────────────────┘
|
||||
```
|
||||
|
||||
### Key Files
|
||||
|
||||
| Layer | File | Purpose |
|
||||
|-------|------|---------|
|
||||
| Backend entry | `backend/main.py` | FastAPI app, all API routes (~2100 lines) |
|
||||
| TTS protocol | `backend/backends/__init__.py:14-81` | `TTSBackend` Protocol definition |
|
||||
| TTS factory | `backend/backends/__init__.py:138-178` | Thread-safe engine registry (double-checked locking) |
|
||||
| PyTorch TTS | `backend/backends/pytorch_backend.py` | Qwen3-TTS via `qwen_tts` package |
|
||||
| MLX TTS | `backend/backends/mlx_backend.py` | Qwen3-TTS via `mlx_audio.tts` |
|
||||
| LuxTTS | `backend/backends/luxtts_backend.py` | LuxTTS — fast, CPU-friendly |
|
||||
| Chatterbox MTL | `backend/backends/chatterbox_backend.py` | Chatterbox Multilingual — 23 languages |
|
||||
| Chatterbox Turbo | `backend/backends/chatterbox_turbo_backend.py` | Chatterbox Turbo — English, paralinguistic tags |
|
||||
| Platform detect | `backend/platform_detect.py` | Apple Silicon → MLX, else → PyTorch |
|
||||
| API types | `backend/models.py` | Pydantic request/response models |
|
||||
| HF progress | `backend/utils/hf_progress.py` | HFProgressTracker (tqdm patching for download progress) |
|
||||
| Audio utils | `backend/utils/audio.py` | `trim_tts_output()`, normalize, load/save audio |
|
||||
| Frontend API | `app/src/lib/api/client.ts` | Hand-written fetch wrapper |
|
||||
| Frontend types | `app/src/lib/api/types.ts` | TypeScript API types |
|
||||
| Generation form | `app/src/components/Generation/GenerationForm.tsx` | TTS generation UI |
|
||||
| Floating gen box | `app/src/components/Generation/FloatingGenerateBox.tsx` | Compact generation UI |
|
||||
| Model manager | `app/src/components/ServerSettings/ModelManagement.tsx` | Model download/status/progress UI |
|
||||
| GPU acceleration | `app/src/components/ServerSettings/GpuAcceleration.tsx` | CUDA backend swap UI |
|
||||
| Gen form hook | `app/src/lib/hooks/useGenerationForm.ts` | Form validation + submission |
|
||||
| Language constants | `app/src/lib/constants/languages.ts` | Per-engine language maps |
|
||||
|
||||
### How TTS Generation Works (Current Flow)
|
||||
|
||||
```
|
||||
POST /generate
|
||||
1. Look up voice profile from DB
|
||||
2. Resolve engine from request (qwen | luxtts | chatterbox | chatterbox_turbo)
|
||||
3. Get backend: get_tts_backend_for_engine(engine) # thread-safe singleton per engine
|
||||
4. Check model cache → if missing, trigger background download, return HTTP 202
|
||||
5. Load model (lazy): tts_backend.load_model(model_size)
|
||||
6. Create voice prompt: profiles.create_voice_prompt_for_profile(engine=engine)
|
||||
→ tts_backend.create_voice_prompt(audio_path, reference_text)
|
||||
7. Generate: tts_backend.generate(text, voice_prompt, language, seed, instruct)
|
||||
8. Post-process: trim_tts_output() for Chatterbox engines
|
||||
9. Save WAV → data/generations/{id}.wav
|
||||
10. Insert history record in SQLite
|
||||
11. Return GenerationResponse
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Current State
|
||||
|
||||
### What's Shipped (v0.1.13 + recent merges)
|
||||
|
||||
**Core TTS:**
|
||||
- Qwen3-TTS voice cloning (1.7B and 0.6B models)
|
||||
- MLX backend for Apple Silicon, PyTorch for everything else
|
||||
- Multi-engine TTS architecture with thread-safe backend registry (PR #254)
|
||||
- LuxTTS integration — fast, CPU-friendly English TTS (PR #254)
|
||||
- Chatterbox Multilingual TTS — 23 languages including Hebrew (PR #257)
|
||||
- Delivery instructions (instruct parameter, Qwen only)
|
||||
- Single flat model dropdown (Qwen 1.7B, Qwen 0.6B, LuxTTS, Chatterbox, Chatterbox Turbo)
|
||||
|
||||
**Infrastructure:**
|
||||
- CUDA backend swap via binary download and restart (PR #252)
|
||||
- GPU acceleration settings UI
|
||||
- Voice profiles with multi-sample support
|
||||
- Stories editor (multi-track DAW timeline)
|
||||
- Whisper transcription (base, small, medium, large variants)
|
||||
- Model management UI with inline download progress bars (HFProgressTracker)
|
||||
- Download cancel/clear UI with error panel (PR #238)
|
||||
- Generation history with caching
|
||||
- Streaming generation endpoint (MLX only)
|
||||
- Duplicate profile name validation (PR #175)
|
||||
- Linux NVIDIA GBM buffer + WebKitGTK microphone fix (PR #210)
|
||||
|
||||
### What's In-Flight
|
||||
|
||||
| Feature | Branch/PR | Status |
|
||||
|---------|-----------|--------|
|
||||
| Chatterbox Turbo + per-engine language lists | `feat/chatterbox-turbo` / PR #258 | Open, ready for review |
|
||||
|
||||
### TTS Engine Comparison
|
||||
|
||||
| Engine | Model Name | Languages | Size | Key Features |
|
||||
|--------|-----------|-----------|------|-------------|
|
||||
| Qwen3-TTS 1.7B | `qwen-tts-1.7B` | 10 (zh, en, ja, ko, de, fr, ru, pt, es, it) | ~3.5 GB | Instruct mode, highest quality |
|
||||
| Qwen3-TTS 0.6B | `qwen-tts-0.6B` | 10 | ~1.2 GB | Lighter, faster |
|
||||
| LuxTTS | `luxtts` | English | ~300 MB | CPU-friendly, 48 kHz, fast |
|
||||
| Chatterbox | `chatterbox-tts` | 23 (incl. Hebrew, Arabic, Hindi, etc.) | ~3.2 GB | Zero-shot cloning, multilingual |
|
||||
| Chatterbox Turbo | `chatterbox-turbo` | English | ~1.5 GB | Paralinguistic tags ([laugh], [cough]), 350M params, low latency |
|
||||
|
||||
### Multi-Engine Architecture (Shipped)
|
||||
|
||||
The singleton TTS backend blocker described in the previous version of this doc has been **resolved**. The architecture now supports:
|
||||
|
||||
- **Thread-safe backend registry** (`_tts_backends` dict + `_tts_backends_lock`) with double-checked locking
|
||||
- **Per-engine backend instances** — each engine gets its own singleton, loaded lazily
|
||||
- **Engine field on GenerationRequest** — frontend sends `engine: 'qwen' | 'luxtts' | 'chatterbox' | 'chatterbox_turbo'`
|
||||
- **Per-engine language filtering** — `ENGINE_LANGUAGES` map in frontend, backend regex accepts all languages
|
||||
- **Per-engine voice prompts** — `create_voice_prompt_for_profile()` dispatches to the correct backend
|
||||
- **Trim post-processing** — `trim_tts_output()` for Chatterbox engines (cuts trailing silence/hallucination)
|
||||
|
||||
### Known Limitations
|
||||
|
||||
- **HF XET progress**: Large files downloaded via `hf-xet` (HuggingFace's new transfer backend) report `n=0` in tqdm updates. Progress bars may appear stuck for large `.safetensors` files even though the download is proceeding. This is a known upstream limitation.
|
||||
- **Chatterbox Turbo upstream token bug**: `from_pretrained()` passes `token=os.getenv("HF_TOKEN") or True` which fails without a stored HF token. Our backend works around this by calling `snapshot_download(token=None)` + `from_local()`.
|
||||
- **chatterbox-tts must install with `--no-deps`**: It pins `numpy<1.26`, `torch==2.6.0`, `transformers==4.46.3` — all incompatible with our stack (Python 3.12, torch 2.10, transformers 4.57.3). Sub-deps listed explicitly in `requirements.txt`.
|
||||
- **Streaming generation** only works for Qwen on MLX. Other engines use the non-streaming `/generate` endpoint.
|
||||
- **dicta-onnx** (Hebrew diacritization) not included — upstream Chatterbox bug requires `model_path` arg but calls `Dicta()` with none. Hebrew works fine without it.
|
||||
|
||||
---
|
||||
|
||||
## Open PRs — Triage & Analysis
|
||||
|
||||
### Recently Merged (Since Last Update)
|
||||
|
||||
| PR | Title | Merged |
|
||||
|----|-------|--------|
|
||||
| **#257** | feat: Chatterbox TTS engine with multilingual voice cloning | 2026-03-13 |
|
||||
| **#254** | feat: LuxTTS integration — multi-engine TTS support | 2026-03-13 |
|
||||
| **#252** | feat: CUDA backend swap via binary download and restart | 2026-03-13 |
|
||||
| **#238** | Download cancel/clear UI, fixed model downloading | 2026-03-13 |
|
||||
| **#250** | docs: align local API port examples | 2026-03-13 |
|
||||
| **#210** | fix: Linux NVIDIA GBM buffer crash | 2026-03-13 |
|
||||
| **#175** | Fix #134: duplicate profile name validation | 2026-03-13 |
|
||||
|
||||
### In-Flight (Our Work)
|
||||
|
||||
| PR | Title | Status | Notes |
|
||||
|----|-------|--------|-------|
|
||||
| **#258** | feat: Chatterbox Turbo engine + per-engine language lists | Open | Ready for review. Adds Turbo engine + dynamic language dropdown. |
|
||||
|
||||
### Merge-Ready / Near-Ready (Bug Fixes & Small Features)
|
||||
|
||||
| PR | Title | Risk | Notes |
|
||||
|----|-------|------|-------|
|
||||
| **#230** | docs: fix README grammar | None | Docs-only |
|
||||
| **#243** | a11y: screen reader and keyboard improvements | Low | Accessibility, no backend changes |
|
||||
| **#178** | Fix #168 #140: generation error handling | Low | Error handling improvements |
|
||||
| **#152** | Fix: prevent crashes when HuggingFace unreachable | Medium | Monkey-patches HF hub; solves real offline bug (#150, #151) |
|
||||
| **#218** | fix: unify qwen tts cache dir on Windows | Low | Windows-specific path fix |
|
||||
| **#214** | fix: panic on launch from tokio::spawn | Low | Rust-side Tauri fix |
|
||||
| **#88** | security: restrict CORS to known local origins | Low | Security hardening |
|
||||
| **#133** | feat: network access toggle | Low | Wires up existing plumbing |
|
||||
|
||||
### Significant Feature PRs
|
||||
|
||||
| PR | Title | Complexity | Notes |
|
||||
|----|-------|-----------|-------|
|
||||
| **#253** | Enhance speech tokenizer with 48kHz version | Medium | Qwen tokenizer upgrade |
|
||||
| **#97** | fix: pass language parameter to TTS models | Medium | May be partially obsoleted by multi-engine work — needs review |
|
||||
| **#99** | feat: chunked TTS with quality selector | Medium | Solves 500-char limit. Addresses #191, #203, #69, #111. |
|
||||
| **#154** | feat: Audiobook tab | Medium | Full audiobook workflow. Depends on #99 concepts. |
|
||||
| **#91** | fix: CoreAudio device enumeration | Medium | macOS audio device handling |
|
||||
|
||||
### Architectural PRs (Need Careful Review)
|
||||
|
||||
| PR | Title | Complexity | Notes |
|
||||
|----|-------|-----------|-------|
|
||||
| **#225** | feat: custom HuggingFace model support | High | Arbitrary HF repo loading. May need rework given multi-engine arch is now shipped. |
|
||||
| **#194** | feat: Hebrew + Chatterbox TTS | High | **Superseded** by PR #257 which shipped Chatterbox multilingual (23 langs incl. Hebrew). May be closeable. |
|
||||
| **#195** | feat: per-profile LoRA fine-tuning | Very High | Training pipeline, adapter management, 15 new endpoints. Depends on #194 (now superseded). |
|
||||
| **#161** | feat: Docker + web deployment | High | 3-stage Dockerfile, SPA serving. Independent of TTS engine work. |
|
||||
| **#124** / **#123** | Docker (simpler attempts) | Low-Medium | Overlap with #161 |
|
||||
| **#227** | fix: harden input validation & file safety | Medium | Coupled to #225 (custom models) |
|
||||
|
||||
### PRs That Need Author Action / Are Stale
|
||||
|
||||
| PR | Title | Notes |
|
||||
|----|-------|-------|
|
||||
| **#237** | fix: bundle qwen_tts source files in PyInstaller | Build system, needs review |
|
||||
| **#215** | Update prerequisites with Tauri deps | Branch is `main` — will have conflicts |
|
||||
| **#89** | Linux Support | Branch is `main` — will have conflicts. Broad scope. |
|
||||
| **#83** | Update download links for v0.1.12 | Outdated (we're on v0.1.13) |
|
||||
|
||||
### PRs Likely Superseded
|
||||
|
||||
| PR | Superseded By | Notes |
|
||||
|----|--------------|-------|
|
||||
| **#194** (Hebrew + Chatterbox) | PR #257 (merged) | #257 ships Chatterbox multilingual with 23 languages including Hebrew. #194 took a different approach (route by language). Can likely be closed. |
|
||||
| **#33** (External provider binaries) | PR #252 (merged) | #252 shipped CUDA backend swap. #33's broader provider architecture may still have value but needs reassessment. |
|
||||
|
||||
---
|
||||
|
||||
## Open Issues — Categorized
|
||||
|
||||
### GPU / Hardware Detection (19 issues)
|
||||
|
||||
The single most reported category. Users on Windows with NVIDIA GPUs frequently report "GPU not detected."
|
||||
|
||||
**Root causes (likely):**
|
||||
- PyInstaller binary doesn't bundle CUDA correctly → falls back to CPU
|
||||
- DirectML/Vulkan path not implemented (AMD on Windows)
|
||||
- Binary size limit means CUDA can't ship in the main release
|
||||
|
||||
**Key issues:** #239, #222, #220, #217, #208, #198, #192, #167, #164, #141, #130, #127
|
||||
|
||||
**Fix path:** PR #252 (CUDA backend swap) is now merged. Users can download the CUDA binary separately from the GPU acceleration settings. Many of these issues may now be resolvable — needs triage to confirm.
|
||||
|
||||
### Model Downloads (20 issues)
|
||||
|
||||
Second most reported. Users get stuck downloads, can't resume, no offline fallback.
|
||||
|
||||
**Key issues:** #249, #240, #221, #216, #212, #181, #180, #159, #150, #149, #145, #143, #135, #134
|
||||
|
||||
**Fix path:** PR #238 (cancel/clear UI) is now merged. PR #152 (offline crash fix) still open. Inline progress bars now show for all engines. Resume support not yet addressed.
|
||||
|
||||
### Language Requests (18 issues)
|
||||
|
||||
Strong demand for: Hindi (#245), Indonesian (#247), Dutch (#236), Hebrew (#199), Greek (#188), Portuguese (#183), Persian (#162), and many more.
|
||||
|
||||
**Key issues:** #247, #245, #236, #211, #205, #199, #189, #188, #187, #183, #179, #162
|
||||
|
||||
**Fix path:** Chatterbox Multilingual (merged via #257) now supports 23 languages including many of the requested ones: Arabic, Danish, German, Greek, Finnish, Hebrew, Hindi, Dutch, Norwegian, Polish, Swedish, Swahili, Turkish. Per-engine language filtering (PR #258) ensures the UI shows correct options. Several of these issues may be closeable.
|
||||
|
||||
### New Model Requests (5 explicit issues)
|
||||
|
||||
| Issue | Model Requested |
|
||||
|-------|----------------|
|
||||
| #226 | GGUF support |
|
||||
| #172 | VibeVoice |
|
||||
| #138 | Export to ONNX/Piper format |
|
||||
| #132 | LavaSR (transcription) |
|
||||
| #76 | (General model expansion) |
|
||||
|
||||
Community also requests: XTTS-v2, Fish Speech, CosyVoice, Kokoro. The multi-engine architecture is now in place, making new model integration significantly easier.
|
||||
|
||||
### Long-Form / Chunking (5 issues)
|
||||
|
||||
Users hitting the ~500 character practical limit.
|
||||
|
||||
**Key issues:** #234 (queue system), #203 (500 char limit), #191 (auto-split), #111, #69
|
||||
|
||||
**Fix path:** PR #99 (chunked TTS + quality selector) directly addresses this. PR #154 (Audiobook tab) builds on it.
|
||||
|
||||
### Feature Requests (23 issues)
|
||||
|
||||
Notable requests:
|
||||
- **#234** — Queue system for batch generation
|
||||
- **#182** — Concurrent/multi-thread generation
|
||||
- **#173** — Vocal intonation/inflection control
|
||||
- **#165** — Audiobook mode
|
||||
- **#144** — Copy text to clipboard
|
||||
- **#184** — Cancel button for progress bar
|
||||
- **#242** — Seed value pinning for consistency
|
||||
- **#228** — Always use 0.6B option
|
||||
- **#233** — Transcribe audio API improvements
|
||||
- **#235** — Finetuned Qwen3-TTS tokenizer
|
||||
|
||||
### Bugs (19 issues)
|
||||
|
||||
| Category | Issues |
|
||||
|----------|--------|
|
||||
| Generation failures | #248 (broken pipe), #219 (unsupported scalarType), #202 (clipping error), #170 (load failed) |
|
||||
| UI bugs | #231 (history not updating), #190 (mobile landing), #169 (blank interface) |
|
||||
| File operations | #207 (transcribe file error), #168 (no such file), #142 (download audio fail) |
|
||||
| Server lifecycle | #166 (server processes remain), #164 (no auto-update) |
|
||||
| Database | #174 (sqlite3 IntegrityError) |
|
||||
| Dependency | #131 (numpy ABI mismatch), #209 (import error) |
|
||||
|
||||
---
|
||||
|
||||
## Existing Plan Documents — Status
|
||||
|
||||
| Document | Target Version | Status | Relevance |
|
||||
|----------|---------------|--------|-----------|
|
||||
| `TTS_PROVIDER_ARCHITECTURE.md` | v0.1.13 | **Partially superseded** by multi-engine arch + CUDA swap | Core concepts implemented differently than planned |
|
||||
| `CUDA_BACKEND_SWAP.md` | — | **Shipped** (PR #252) | CUDA binary download + backend restart |
|
||||
| `CUDA_BACKEND_SWAP_FINAL.md` | — | **Shipped** (PR #252) | Final implementation plan |
|
||||
| `EXTERNAL_PROVIDERS.md` | v0.2.0 | **Not started** | Remote server support |
|
||||
| `MLX_AUDIO.md` | — | **Shipped** | MLX backend is live |
|
||||
| `DOCKER_DEPLOYMENT.md` | v0.2.0 | **PR exists** (#161) | Waiting on review |
|
||||
| `OPENAI_SUPPORT.md` | v0.2.0 | **Not started** | OpenAI-compatible API layer |
|
||||
| `PR33_CUDA_PROVIDER_REVIEW.md` | — | **Reference** | Analysis of the original provider approach |
|
||||
|
||||
---
|
||||
|
||||
## New Model Integration — Landscape
|
||||
|
||||
### Models Worth Supporting (2026 SOTA)
|
||||
|
||||
| Model | Cloning | Speed | Sample Rate | Languages | VRAM | Integration Ease | Status |
|
||||
|-------|---------|-------|-------------|-----------|------|-----------------|--------|
|
||||
| **Qwen3-TTS** | 10s zero-shot | Medium | 24 kHz | 10 | Medium | **Shipped** | v0.1.13 |
|
||||
| **LuxTTS** | 3s zero-shot | 150x RT, CPU ok | 48 kHz | English | <1 GB | **Shipped** | PR #254 |
|
||||
| **Chatterbox MTL** | 5s zero-shot | Medium | 24 kHz | 23 | Medium | **Shipped** | PR #257 |
|
||||
| **Chatterbox Turbo** | 5s zero-shot | Fast | 24 kHz | English | Low | **PR #258** | In review |
|
||||
| **XTTS-v2** | 6s zero-shot | Mid-GPU | 24 kHz | 17+ | Medium | Ready | Multi-engine arch in place |
|
||||
| **Fish Speech** | 10-30s few-shot | Real-time | 24-44 kHz | 50+ | Medium | Ready | Multi-engine arch in place |
|
||||
| **CosyVoice2-0.5B** | 3-10s zero-shot | Very fast | 24 kHz | Multilingual | Low | Ready | Multi-engine arch in place |
|
||||
| **Kokoro-82M** | 3s instant | CPU realtime | 24 kHz | English | Tiny | Ready | Multi-engine arch in place |
|
||||
|
||||
### Adding a New Engine (Now Straightforward)
|
||||
|
||||
With the multi-engine architecture shipped, adding a new TTS engine requires:
|
||||
|
||||
1. **Create `backend/backends/<engine>_backend.py`** — implement `TTSBackend` protocol (~200-300 lines)
|
||||
2. **Register in `backend/backends/__init__.py`** — add to `TTS_ENGINES` dict + factory function
|
||||
3. **Update `backend/models.py`** — add engine name to regex
|
||||
4. **Update `backend/main.py`** — add engine cases in generate, stream, model-status, download, delete (5 dispatch points)
|
||||
5. **Update frontend** — add to engine union type, form schema, model dropdown, language map (5-6 files)
|
||||
|
||||
Total effort: **~1 day** for a well-documented model with a PyPI package.
|
||||
|
||||
---
|
||||
|
||||
## Architectural Bottlenecks
|
||||
|
||||
### ~~1. Single Backend Singleton~~ — RESOLVED
|
||||
|
||||
The singleton TTS backend was replaced with a thread-safe per-engine registry in PR #254. Multiple engines can now be loaded simultaneously.
|
||||
|
||||
### 2. `main.py` is 2100+ Lines
|
||||
|
||||
All API routes, all model configs, all business logic in one file. Five separate dispatch points for each engine. Any new engine touches this file in 5 places. A model config registry pattern would reduce duplication.
|
||||
|
||||
### 3. Model Config is Scattered (Improved)
|
||||
|
||||
Model identifiers are still duplicated across `main.py` (3 dicts), backend files, frontend components, and the languages constant. However, the pattern is now consistent and well-understood. A centralized model registry would help but isn't blocking.
|
||||
|
||||
### 4. Voice Prompt Cache Assumes PyTorch Tensors
|
||||
|
||||
`backend/utils/cache.py` uses `torch.save()` / `torch.load()`. LuxTTS and Chatterbox backends work around this by storing reference audio paths instead of tensors in their voice prompt dicts. Not ideal but functional.
|
||||
|
||||
### 5. ~~Frontend Assumes Qwen Model Sizes~~ — RESOLVED
|
||||
|
||||
The generation form now uses a flat model dropdown with engine-based routing. Per-engine language filtering is in place. Model size is only sent for Qwen.
|
||||
|
||||
---
|
||||
|
||||
## Recommended Priorities
|
||||
|
||||
### Tier 1 — Ship Now (Low Risk)
|
||||
|
||||
| Priority | PR/Item | Impact | Effort |
|
||||
|----------|---------|--------|--------|
|
||||
| 1 | **#258** — Chatterbox Turbo + per-engine languages | Paralinguistic tags, proper language filtering | Review only |
|
||||
| 2 | **#152** — Offline mode crash fix | Fixes #150, #151 | Low |
|
||||
| 3 | **#99** — Chunked TTS + quality selector | Removes 500-char limit, addresses 5 issues | Medium |
|
||||
| 4 | **#218** — Windows HF cache dir fix | Windows-specific pain | Low |
|
||||
| 5 | **#178** — Generation error handling | Error UX | Low |
|
||||
| 6 | **#230** — Docs fixes | Zero risk | None |
|
||||
| 7 | **#133** — Network access toggle | Wires up existing code | Low |
|
||||
| 8 | **#88** — CORS restriction | Security improvement | Low |
|
||||
| 9 | **#214** — Tauri window close panic fix | Stability | Low |
|
||||
| 10 | Triage GPU issues | Many may be resolved by CUDA swap (#252) | Low |
|
||||
| 11 | Close superseded PRs | #194 (superseded by #257), #83 (outdated) | None |
|
||||
|
||||
### Tier 2 — Next Release (v0.2.0)
|
||||
|
||||
| Priority | Item | Impact | Effort |
|
||||
|----------|------|--------|--------|
|
||||
| 1 | **#253** — 48kHz speech tokenizer | Quality improvement | Medium |
|
||||
| 2 | **#161** — Docker deployment | Server/headless users | Medium |
|
||||
| 3 | **#154** — Audiobook tab | Long-form users | Medium |
|
||||
| 4 | **Model config registry** | Reduce 5-dispatch-point duplication in main.py | Medium |
|
||||
| 5 | **#225** — Custom HuggingFace models | User-supplied models | High (needs rework for multi-engine) |
|
||||
|
||||
### Tier 3 — Future (v0.3.0+)
|
||||
|
||||
| Item | Notes |
|
||||
|------|-------|
|
||||
| XTTS-v2 / Fish Speech / CosyVoice | Multi-engine arch is ready; just needs backend implementation |
|
||||
| OpenAI-compatible API (plan doc exists) | Low effort once API is stable |
|
||||
| LoRA fine-tuning (PR #195) | Complex, needs rework for multi-engine |
|
||||
| External/remote providers | Depends on use case demand |
|
||||
| GGUF support (#226) | Depends on model ecosystem maturity |
|
||||
| Queue system (#234) | Batch generation |
|
||||
| Streaming for non-MLX engines | Currently MLX-only |
|
||||
| Kokoro-82M | Tiny model, great for CPU-only machines |
|
||||
|
||||
---
|
||||
|
||||
## Branch Inventory
|
||||
|
||||
| Branch | PR | Status | Notes |
|
||||
|--------|-----|--------|-------|
|
||||
| `feat/chatterbox-turbo` | #258 | Open | Chatterbox Turbo + per-engine languages |
|
||||
| `feat/chatterbox` | #257 | **Merged** | Chatterbox Multilingual |
|
||||
| `feat/luxtts` | #254 | **Merged** | LuxTTS + multi-engine arch |
|
||||
| `external-provider-binaries` | #33 | Superseded by #252 | Original CUDA provider approach |
|
||||
| `feat/dual-server-binaries` | — | No PR | Related to provider split |
|
||||
| `fix-multi-sample` | — | No PR | Voice profile multi-sample fix |
|
||||
| `fix-dl-notification-...` | — | No PR | Model download UX |
|
||||
|
||||
---
|
||||
|
||||
## Quick Reference: API Endpoints
|
||||
|
||||
<details>
|
||||
<summary>All current endpoints</summary>
|
||||
|
||||
| Endpoint | Method | Purpose |
|
||||
|----------|--------|---------|
|
||||
| `/health` | GET | Health check, model/GPU status |
|
||||
| `/profiles` | POST, GET | Create/list voice profiles |
|
||||
| `/profiles/{id}` | GET, PUT, DELETE | Profile CRUD |
|
||||
| `/profiles/{id}/samples` | POST, GET | Add/list voice samples |
|
||||
| `/profiles/{id}/avatar` | POST, GET, DELETE | Avatar management |
|
||||
| `/profiles/{id}/export` | GET | Export profile as ZIP |
|
||||
| `/profiles/import` | POST | Import profile from ZIP |
|
||||
| `/generate` | POST | Generate speech (engine param selects TTS backend) |
|
||||
| `/generate/stream` | POST | Stream speech (MLX only) |
|
||||
| `/history` | GET | List generation history |
|
||||
| `/history/{id}` | GET, DELETE | Get/delete generation |
|
||||
| `/history/{id}/export` | GET | Export generation ZIP |
|
||||
| `/history/{id}/export-audio` | GET | Export audio only |
|
||||
| `/transcribe` | POST | Transcribe audio (Whisper) |
|
||||
| `/models/status` | GET | All model statuses (Qwen, LuxTTS, Chatterbox, Chatterbox Turbo, Whisper) |
|
||||
| `/models/download` | POST | Trigger model download |
|
||||
| `/models/download/cancel` | POST | Cancel/dismiss download |
|
||||
| `/models/{name}` | DELETE | Delete downloaded model |
|
||||
| `/models/load` | POST | Load model into memory |
|
||||
| `/models/unload` | POST | Unload model |
|
||||
| `/models/progress/{name}` | GET | SSE download progress |
|
||||
| `/tasks/active` | GET | Active downloads/generations (with inline progress) |
|
||||
| `/stories` | POST, GET | Create/list stories |
|
||||
| `/stories/{id}` | GET, PUT, DELETE | Story CRUD |
|
||||
| `/stories/{id}/items` | POST, GET | Story items CRUD |
|
||||
| `/stories/{id}/export` | GET | Export story audio |
|
||||
| `/channels` | POST, GET | Audio channel CRUD |
|
||||
| `/channels/{id}` | PUT, DELETE | Channel update/delete |
|
||||
| `/cache/clear` | POST | Clear voice prompt cache |
|
||||
| `/server/cuda/status` | GET | CUDA binary availability |
|
||||
| `/server/cuda/download` | POST | Download CUDA binary |
|
||||
| `/server/cuda/switch` | POST | Switch to CUDA backend |
|
||||
|
||||
</details>
|
||||
@@ -1,964 +0,0 @@
|
||||
# TTS Provider Architecture
|
||||
|
||||
**Status:** Planned for v0.1.13
|
||||
**Created:** 2025-01-31
|
||||
**Problem:** GitHub 2GB release limit + poor UX for frequent updates requiring 2.4GB re-downloads
|
||||
|
||||
---
|
||||
|
||||
## Overview
|
||||
|
||||
Split the monolithic backend into modular components:
|
||||
|
||||
1. **Main App** (~150-200MB): Tauri + FastAPI backend + Whisper + UI/profiles/history
|
||||
2. **TTS Providers** (downloadable plugins): Separate executables for model inference
|
||||
|
||||
This architecture solves:
|
||||
|
||||
- ✅ GitHub 2GB release artifact limit
|
||||
- ✅ Frequent app updates without re-downloading large python binaries
|
||||
- ✅ User choice of compute backend (CPU/GPU/Cloud)
|
||||
- ✅ External provider support (OpenAI, custom servers)
|
||||
- ✅ Future extensibility
|
||||
|
||||
---
|
||||
|
||||
## Architecture Diagram
|
||||
|
||||
```
|
||||
┌─────────────────────────────────────────────────────────┐
|
||||
│ Voicebox App (Tauri + Backend) ~150MB │
|
||||
│ ├─ UI Layer (React) │
|
||||
│ ├─ Backend (FastAPI) │
|
||||
│ │ ├─ Voice Profiles │
|
||||
│ │ ├─ Generation History │
|
||||
│ │ ├─ Audio Editing / Stories │
|
||||
│ │ └─ Provider Manager ◄──────────────┐ │
|
||||
│ └─ Whisper (bundled, tiny ~50MB) │ │
|
||||
└─────────────────────────────────────────┼────────────────┘
|
||||
│
|
||||
HTTP/IPC │
|
||||
│
|
||||
┌────────────────────────────────┼─────────────────┐
|
||||
│ │ │
|
||||
▼ ▼ ▼
|
||||
┌─────────────────┐ ┌─────────────────┐ ┌──────────────────┐
|
||||
│ TTS Provider: │ │ TTS Provider: │ │ TTS Provider: │
|
||||
│ PyTorch CPU │ │ PyTorch CUDA │ │ MLX (Apple) │
|
||||
│ │ │ │ │ │
|
||||
│ ~300MB │ │ ~2.4GB │ │ ~800MB │
|
||||
│ │ │ │ │ │
|
||||
│ Local inference │ │ GPU inference │ │ Metal inference │
|
||||
└─────────────────┘ └─────────────────┘ └──────────────────┘
|
||||
│ │ │
|
||||
└────────────────────────┴─────────────────────┘
|
||||
│
|
||||
┌─────────────▼──────────────┐
|
||||
│ Future Providers: │
|
||||
│ • Remote Server │
|
||||
│ • OpenAI API │
|
||||
│ • ElevenLabs │
|
||||
│ • Custom Docker Container │
|
||||
└────────────────────────────┘
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Problem Statement
|
||||
|
||||
### Current Architecture Issues
|
||||
|
||||
**Monolithic Binary:**
|
||||
|
||||
- CPU version: ~295MB
|
||||
- CUDA version: ~2.37GB
|
||||
- GitHub releases: 2GB file size limit (BLOCKED)
|
||||
- Updates require re-downloading entire binary
|
||||
- Poor UX: update app → restart → download CUDA update → restart again
|
||||
|
||||
**User Pain Points:**
|
||||
|
||||
1. Cannot release CUDA version on GitHub (over 2GB)
|
||||
2. Every app update forces 2.4GB re-download for GPU users
|
||||
3. No flexibility (can't use OpenAI, remote servers, etc.)
|
||||
4. Wastes bandwidth for small bug fixes
|
||||
|
||||
---
|
||||
|
||||
## Solution: Pluggable TTS Providers
|
||||
|
||||
### Component Breakdown
|
||||
|
||||
#### 1. Main App (voicebox.exe / .app / .AppImage)
|
||||
|
||||
**Size:** ~100-150MB
|
||||
|
||||
**Includes:**
|
||||
|
||||
- Tauri runtime + React UI
|
||||
- FastAPI backend (pure Python, no PyTorch)
|
||||
- Whisper model (tiny, ~50MB)
|
||||
- SQLite database
|
||||
- Profile/history/audio editing logic
|
||||
- Provider management system
|
||||
|
||||
**Does NOT include:**
|
||||
|
||||
- PyTorch (CPU or CUDA)
|
||||
- TTS models (Qwen3-TTS)
|
||||
- Heavy ML dependencies
|
||||
|
||||
**Updates frequently:** UI fixes, feature additions, non-ML changes
|
||||
|
||||
---
|
||||
|
||||
#### 2. TTS Provider: PyTorch CPU
|
||||
|
||||
**Binary:** `tts-provider-pytorch-cpu.exe`
|
||||
**Size:** ~200MB
|
||||
|
||||
**Includes:**
|
||||
|
||||
- PyTorch CPU build
|
||||
- Qwen3-TTS package
|
||||
- Transformers
|
||||
- No CUDA libraries
|
||||
|
||||
**Download source:** Cloudflare R2
|
||||
**Updates rarely:** Only when model code changes
|
||||
|
||||
---
|
||||
|
||||
#### 3. TTS Provider: PyTorch CUDA
|
||||
|
||||
**Binary:** `tts-provider-pytorch-cuda.exe`
|
||||
**Size:** ~2.4GB
|
||||
|
||||
**Includes:**
|
||||
|
||||
- PyTorch CUDA build (cu121)
|
||||
- Qwen3-TTS package
|
||||
- CUDA runtime, cuDNN, cuBLAS
|
||||
- Transformers
|
||||
|
||||
**Download source:** Cloudflare R2
|
||||
**Platform:** Windows + Linux (NVIDIA GPU)
|
||||
**Updates rarely:** Only when model code or CUDA version changes
|
||||
|
||||
---
|
||||
|
||||
#### 4. TTS Provider: MLX
|
||||
|
||||
**Binary:** `tts-provider-mlx`
|
||||
**Size:** ~150MB
|
||||
|
||||
**Includes:**
|
||||
|
||||
- MLX framework
|
||||
- MLX-optimized Qwen3-TTS
|
||||
- Metal acceleration
|
||||
|
||||
**Platform:** macOS only (Apple Silicon)
|
||||
**Download source:** Cloudflare R2
|
||||
|
||||
---
|
||||
|
||||
#### 5. TTS Provider: Remote
|
||||
|
||||
**Binary:** None (built-in config)
|
||||
**Size:** 0MB
|
||||
|
||||
**How it works:**
|
||||
|
||||
- User provides URL to their own TTS server
|
||||
- Backend proxies requests to that server
|
||||
- Implements API spec from `EXTERNAL_PROVIDERS.md`
|
||||
|
||||
**Use cases:**
|
||||
|
||||
- AMD GPU users running their own server
|
||||
- Team deployments with shared GPU server
|
||||
- Cloud hosting (Modal, RunPod, Replicate)
|
||||
|
||||
---
|
||||
|
||||
#### 6. TTS Provider: OpenAI
|
||||
|
||||
**Binary:** None (API wrapper)
|
||||
**Size:** 0MB
|
||||
|
||||
**How it works:**
|
||||
|
||||
- User provides OpenAI API key
|
||||
- Backend wraps OpenAI Audio API
|
||||
- Voice profiles map to OpenAI voices
|
||||
|
||||
**Benefits:**
|
||||
|
||||
- Zero local compute
|
||||
- Pay-per-use
|
||||
- Instant setup
|
||||
|
||||
---
|
||||
|
||||
## Communication Protocol
|
||||
|
||||
### Provider API Specification
|
||||
|
||||
All TTS providers must implement these endpoints:
|
||||
|
||||
#### POST /tts/generate
|
||||
|
||||
Generate speech from text.
|
||||
|
||||
**Request:**
|
||||
|
||||
```json
|
||||
{
|
||||
"text": "Hello world!",
|
||||
"voice_prompt": {
|
||||
/* voice prompt object */
|
||||
},
|
||||
"language": "en",
|
||||
"seed": 12345,
|
||||
"model_size": "1.7B"
|
||||
}
|
||||
```
|
||||
|
||||
**Response:**
|
||||
|
||||
```json
|
||||
{
|
||||
"audio": "base64-encoded-audio",
|
||||
"sample_rate": 24000,
|
||||
"duration": 2.5
|
||||
}
|
||||
```
|
||||
|
||||
#### POST /tts/create_voice_prompt
|
||||
|
||||
Create voice prompt from reference audio.
|
||||
|
||||
**Request:** (multipart/form-data)
|
||||
|
||||
- `audio`: Audio file
|
||||
- `reference_text`: Transcript
|
||||
|
||||
**Response:**
|
||||
|
||||
```json
|
||||
{
|
||||
"voice_prompt": {
|
||||
/* serialized prompt */
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
#### GET /tts/health
|
||||
|
||||
Health check.
|
||||
|
||||
**Response:**
|
||||
|
||||
```json
|
||||
{
|
||||
"status": "healthy",
|
||||
"provider": "pytorch-cuda",
|
||||
"version": "1.0.0",
|
||||
"model": "Qwen3-TTS-12Hz-1.7B-Base",
|
||||
"device": "cuda:0"
|
||||
}
|
||||
```
|
||||
|
||||
#### GET /tts/status
|
||||
|
||||
Model status.
|
||||
|
||||
**Response:**
|
||||
|
||||
```json
|
||||
{
|
||||
"model_loaded": true,
|
||||
"model_size": "1.7B",
|
||||
"available_sizes": ["0.6B", "1.7B"],
|
||||
"gpu_available": true,
|
||||
"vram_used_mb": 1234
|
||||
}
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Backend Implementation
|
||||
|
||||
### Provider Manager
|
||||
|
||||
**File:** `backend/providers/__init__.py`
|
||||
|
||||
```python
|
||||
class ProviderManager:
|
||||
"""Manages TTS provider lifecycle."""
|
||||
|
||||
def __init__(self):
|
||||
self.active_provider: Optional[Provider] = None
|
||||
self.config = load_provider_config()
|
||||
|
||||
async def start_provider(self, provider_type: str) -> str:
|
||||
"""Start a TTS provider process."""
|
||||
if provider_type == "pytorch-cpu":
|
||||
return await self._start_local_provider("tts-provider-pytorch-cpu.exe")
|
||||
elif provider_type == "pytorch-cuda":
|
||||
return await self._start_local_provider("tts-provider-pytorch-cuda.exe")
|
||||
elif provider_type == "mlx":
|
||||
return await self._start_local_provider("tts-provider-mlx")
|
||||
elif provider_type == "remote":
|
||||
return self.config["remote_url"]
|
||||
elif provider_type == "openai":
|
||||
return None # No subprocess, API wrapper
|
||||
|
||||
async def _start_local_provider(self, binary_name: str) -> str:
|
||||
"""Start local provider subprocess."""
|
||||
provider_path = get_provider_binary_path(binary_name)
|
||||
|
||||
if not provider_path.exists():
|
||||
raise ProviderNotInstalledException(binary_name)
|
||||
|
||||
# Start subprocess on random port
|
||||
port = get_free_port()
|
||||
process = subprocess.Popen([
|
||||
str(provider_path),
|
||||
"--port", str(port),
|
||||
"--data-dir", str(config.get_data_dir())
|
||||
])
|
||||
|
||||
# Wait for provider to be ready
|
||||
await wait_for_provider_health(f"http://localhost:{port}")
|
||||
|
||||
self.active_provider = Provider(process, port)
|
||||
return f"http://localhost:{port}"
|
||||
|
||||
async def stop_provider(self):
|
||||
"""Stop active provider."""
|
||||
if self.active_provider:
|
||||
self.active_provider.process.terminate()
|
||||
self.active_provider = None
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### Provider Abstraction
|
||||
|
||||
**File:** `backend/providers/base.py`
|
||||
|
||||
```python
|
||||
class TTSProvider(ABC):
|
||||
"""Abstract base for TTS providers."""
|
||||
|
||||
@abstractmethod
|
||||
async def generate(
|
||||
self,
|
||||
text: str,
|
||||
voice_prompt: dict,
|
||||
language: str,
|
||||
seed: Optional[int]
|
||||
) -> tuple[np.ndarray, int]:
|
||||
"""Generate speech audio."""
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
async def create_voice_prompt(
|
||||
self,
|
||||
audio_path: str,
|
||||
reference_text: str
|
||||
) -> dict:
|
||||
"""Create voice prompt from reference audio."""
|
||||
pass
|
||||
```
|
||||
|
||||
**File:** `backend/providers/local.py`
|
||||
|
||||
```python
|
||||
class LocalProvider(TTSProvider):
|
||||
"""Provider that communicates with local subprocess via HTTP."""
|
||||
|
||||
def __init__(self, base_url: str):
|
||||
self.base_url = base_url
|
||||
self.client = httpx.AsyncClient()
|
||||
|
||||
async def generate(self, text, voice_prompt, language, seed):
|
||||
response = await self.client.post(
|
||||
f"{self.base_url}/tts/generate",
|
||||
json={
|
||||
"text": text,
|
||||
"voice_prompt": voice_prompt,
|
||||
"language": language,
|
||||
"seed": seed
|
||||
}
|
||||
)
|
||||
data = response.json()
|
||||
audio = np.frombuffer(base64.b64decode(data["audio"]), dtype=np.float32)
|
||||
return audio, data["sample_rate"]
|
||||
```
|
||||
|
||||
**File:** `backend/providers/openai.py`
|
||||
|
||||
```python
|
||||
class OpenAIProvider(TTSProvider):
|
||||
"""Provider that wraps OpenAI Audio API."""
|
||||
|
||||
def __init__(self, api_key: str):
|
||||
self.client = OpenAI(api_key=api_key)
|
||||
|
||||
async def generate(self, text, voice_prompt, language, seed):
|
||||
# Map voice_prompt to OpenAI voice name
|
||||
voice = map_profile_to_openai_voice(voice_prompt)
|
||||
|
||||
response = await self.client.audio.speech.create(
|
||||
model="tts-1",
|
||||
voice=voice,
|
||||
input=text
|
||||
)
|
||||
|
||||
# Convert to numpy array
|
||||
audio_data = response.content
|
||||
audio, sr = load_audio_from_bytes(audio_data)
|
||||
return audio, sr
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Provider Installation
|
||||
|
||||
### Download Manager
|
||||
|
||||
**File:** `backend/providers/installer.py`
|
||||
|
||||
```python
|
||||
class ProviderInstaller:
|
||||
"""Handles provider download and installation."""
|
||||
|
||||
async def download_provider(self, provider_type: str):
|
||||
"""Download provider binary from R2."""
|
||||
|
||||
binary_name = {
|
||||
"pytorch-cpu": "tts-provider-pytorch-cpu.exe",
|
||||
"pytorch-cuda": "tts-provider-pytorch-cuda.exe",
|
||||
"mlx": "tts-provider-mlx"
|
||||
}[provider_type]
|
||||
|
||||
download_url = f"https://downloads.voicebox.sh/providers/v{PROVIDER_VERSION}/{binary_name}"
|
||||
|
||||
# Download with progress tracking (reuse existing SSE system)
|
||||
await download_with_progress(
|
||||
url=download_url,
|
||||
destination=get_provider_install_path(binary_name),
|
||||
progress_key=f"provider-{provider_type}"
|
||||
)
|
||||
```
|
||||
|
||||
**Provider Storage Location:**
|
||||
|
||||
- Windows: `%APPDATA%/voicebox/providers/`
|
||||
- macOS: `~/Library/Application Support/voicebox/providers/`
|
||||
- Linux: `~/.local/share/voicebox/providers/`
|
||||
|
||||
---
|
||||
|
||||
## Frontend Implementation
|
||||
|
||||
### Provider Settings UI
|
||||
|
||||
**Component:** `app/src/components/ServerSettings/ProviderSettings.tsx`
|
||||
|
||||
```tsx
|
||||
export function ProviderSettings() {
|
||||
const [selectedProvider, setSelectedProvider] =
|
||||
useState<ProviderType>("auto");
|
||||
const {data: installedProviders} = useQuery({
|
||||
queryKey: ["providers", "installed"],
|
||||
queryFn: () => apiClient.getInstalledProviders(),
|
||||
});
|
||||
|
||||
return (
|
||||
<Card>
|
||||
<CardHeader>
|
||||
<CardTitle>TTS Provider</CardTitle>
|
||||
<CardDescription>Choose how Voicebox generates speech</CardDescription>
|
||||
</CardHeader>
|
||||
<CardContent>
|
||||
<RadioGroup
|
||||
value={selectedProvider}
|
||||
onValueChange={setSelectedProvider}
|
||||
>
|
||||
{/* Auto-detect */}
|
||||
<div className="flex items-center space-x-2">
|
||||
<RadioGroupItem value="auto" id="auto" />
|
||||
<Label htmlFor="auto">
|
||||
<div className="font-medium">Auto-detect (Recommended)</div>
|
||||
<div className="text-sm text-muted-foreground">
|
||||
Automatically choose the best available provider
|
||||
</div>
|
||||
</Label>
|
||||
</div>
|
||||
|
||||
{/* PyTorch CUDA */}
|
||||
<div className="flex items-center justify-between">
|
||||
<div className="flex items-center space-x-2">
|
||||
<RadioGroupItem
|
||||
value="pytorch-cuda"
|
||||
id="cuda"
|
||||
disabled={!gpuAvailable}
|
||||
/>
|
||||
<Label htmlFor="cuda">
|
||||
<div className="font-medium">PyTorch CUDA (NVIDIA GPU)</div>
|
||||
<div className="text-sm text-muted-foreground">
|
||||
4-5x faster inference on NVIDIA GPUs
|
||||
</div>
|
||||
</Label>
|
||||
</div>
|
||||
{!installedProviders?.includes("pytorch-cuda") && gpuAvailable && (
|
||||
<Button
|
||||
onClick={() => downloadProvider("pytorch-cuda")}
|
||||
size="sm"
|
||||
>
|
||||
Download (2.4GB)
|
||||
</Button>
|
||||
)}
|
||||
</div>
|
||||
|
||||
{/* PyTorch CPU */}
|
||||
<div className="flex items-center justify-between">
|
||||
<div className="flex items-center space-x-2">
|
||||
<RadioGroupItem value="pytorch-cpu" id="cpu" />
|
||||
<Label htmlFor="cpu">
|
||||
<div className="font-medium">PyTorch CPU</div>
|
||||
<div className="text-sm text-muted-foreground">
|
||||
Works on any system, slower inference
|
||||
</div>
|
||||
</Label>
|
||||
</div>
|
||||
{!installedProviders?.includes("pytorch-cpu") && (
|
||||
<Button onClick={() => downloadProvider("pytorch-cpu")} size="sm">
|
||||
Download (300MB)
|
||||
</Button>
|
||||
)}
|
||||
</div>
|
||||
|
||||
{/* MLX (macOS only) */}
|
||||
{isMacOS && (
|
||||
<div className="flex items-center justify-between">
|
||||
<div className="flex items-center space-x-2">
|
||||
<RadioGroupItem value="mlx" id="mlx" />
|
||||
<Label htmlFor="mlx">
|
||||
<div className="font-medium">MLX (Apple Silicon)</div>
|
||||
<div className="text-sm text-muted-foreground">
|
||||
Optimized for M1/M2/M3 chips
|
||||
</div>
|
||||
</Label>
|
||||
</div>
|
||||
{!installedProviders?.includes("mlx") && (
|
||||
<Button onClick={() => downloadProvider("mlx")} size="sm">
|
||||
Download (800MB)
|
||||
</Button>
|
||||
)}
|
||||
</div>
|
||||
)}
|
||||
|
||||
{/* Remote */}
|
||||
<div className="space-y-2">
|
||||
<div className="flex items-center space-x-2">
|
||||
<RadioGroupItem value="remote" id="remote" />
|
||||
<Label htmlFor="remote">
|
||||
<div className="font-medium">Remote Server</div>
|
||||
<div className="text-sm text-muted-foreground">
|
||||
Connect to your own TTS server
|
||||
</div>
|
||||
</Label>
|
||||
</div>
|
||||
{selectedProvider === "remote" && (
|
||||
<Input placeholder="http://your-server:8000" className="ml-6" />
|
||||
)}
|
||||
</div>
|
||||
|
||||
{/* OpenAI */}
|
||||
<div className="space-y-2">
|
||||
<div className="flex items-center space-x-2">
|
||||
<RadioGroupItem value="openai" id="openai" />
|
||||
<Label htmlFor="openai">
|
||||
<div className="font-medium">OpenAI API</div>
|
||||
<div className="text-sm text-muted-foreground">
|
||||
Use OpenAI's TTS API (requires API key)
|
||||
</div>
|
||||
</Label>
|
||||
</div>
|
||||
{selectedProvider === "openai" && (
|
||||
<Input type="password" placeholder="sk-..." className="ml-6" />
|
||||
)}
|
||||
</div>
|
||||
</RadioGroup>
|
||||
</CardContent>
|
||||
</Card>
|
||||
);
|
||||
}
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## File Structure
|
||||
|
||||
```
|
||||
voicebox/
|
||||
├── backend/
|
||||
│ ├── main.py # Main FastAPI app (no TTS code)
|
||||
│ ├── providers/
|
||||
│ │ ├── __init__.py # ProviderManager
|
||||
│ │ ├── base.py # TTSProvider ABC
|
||||
│ │ ├── local.py # LocalProvider (subprocess)
|
||||
│ │ ├── remote.py # RemoteProvider (HTTP)
|
||||
│ │ ├── openai.py # OpenAIProvider (API wrapper)
|
||||
│ │ └── installer.py # Provider download logic
|
||||
│ ├── profiles.py # Voice profile management
|
||||
│ ├── history.py # Generation history
|
||||
│ ├── transcribe.py # Whisper (still bundled)
|
||||
│ └── ... (other backend modules)
|
||||
│
|
||||
├── providers/
|
||||
│ ├── pytorch-cpu/
|
||||
│ │ ├── main.py # FastAPI server for TTS
|
||||
│ │ ├── tts_backend.py # PyTorch TTS logic
|
||||
│ │ ├── requirements.txt # torch (CPU), qwen-tts, transformers
|
||||
│ │ └── build.spec # PyInstaller spec
|
||||
│ │
|
||||
│ ├── pytorch-cuda/
|
||||
│ │ ├── main.py # FastAPI server for TTS
|
||||
│ │ ├── tts_backend.py # PyTorch TTS logic
|
||||
│ │ ├── requirements.txt # torch+cu121, qwen-tts, transformers
|
||||
│ │ └── build.spec # PyInstaller spec
|
||||
│ │
|
||||
│ └── mlx/
|
||||
│ ├── main.py # FastAPI server for TTS
|
||||
│ ├── mlx_backend.py # MLX TTS logic
|
||||
│ ├── requirements.txt # mlx, qwen-tts-mlx
|
||||
│ └── build.spec # PyInstaller spec
|
||||
│
|
||||
├── app/ # Frontend (Tauri + React)
|
||||
│ └── src/
|
||||
│ └── components/
|
||||
│ └── ServerSettings/
|
||||
│ └── ProviderSettings.tsx
|
||||
│
|
||||
└── tauri/
|
||||
└── src-tauri/
|
||||
└── tauri.conf.json # No externalBin for providers
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Migration Path
|
||||
|
||||
### Phase 1: Refactor Backend (No User Changes)
|
||||
|
||||
**Goal:** Abstract TTS behind provider interface
|
||||
|
||||
1. Create `backend/providers/` module structure
|
||||
2. Implement `TTSProvider` abstract base class
|
||||
3. Create `LocalProvider` wrapper for current PyTorch code
|
||||
4. Modify `backend/tts.py` to use provider abstraction
|
||||
5. Keep PyTorch bundled in main app
|
||||
|
||||
**Result:** Code is prepared, but user experience unchanged
|
||||
|
||||
---
|
||||
|
||||
### Phase 2: Build Provider Binaries
|
||||
|
||||
**Goal:** Create standalone TTS provider executables
|
||||
|
||||
1. Create separate PyInstaller specs for each provider
|
||||
2. Build provider executables:
|
||||
- `tts-provider-pytorch-cpu.exe` (~300MB)
|
||||
- `tts-provider-pytorch-cuda.exe` (~2.4GB)
|
||||
- `tts-provider-mlx` (~800MB, macOS)
|
||||
3. Test subprocess communication
|
||||
4. Upload providers to Cloudflare R2
|
||||
|
||||
**Result:** Provider binaries exist but aren't used yet
|
||||
|
||||
---
|
||||
|
||||
### Phase 3: Remove PyTorch from Main App
|
||||
|
||||
**Goal:** Split main app from providers
|
||||
|
||||
1. Exclude PyTorch/Qwen3-TTS from main app PyInstaller spec
|
||||
2. Main app now requires provider download
|
||||
3. Update GitHub CI to build multiple artifacts:
|
||||
- `voicebox-{version}-{platform}.exe` (~150MB)
|
||||
- `tts-provider-pytorch-cpu-{version}.exe`
|
||||
- `tts-provider-pytorch-cuda-{version}.exe`
|
||||
- `tts-provider-mlx-{version}` (macOS)
|
||||
|
||||
**Result:** Main app is small, providers downloaded separately
|
||||
|
||||
---
|
||||
|
||||
### Phase 4: Add Provider UI
|
||||
|
||||
**Goal:** User-facing provider management
|
||||
|
||||
1. Create Provider Settings page
|
||||
2. Implement provider download UI
|
||||
3. Add provider status indicators
|
||||
4. Show active provider in UI
|
||||
|
||||
**Result:** Users can choose and download providers
|
||||
|
||||
---
|
||||
|
||||
### Phase 5: External Providers
|
||||
|
||||
**Goal:** Enable remote and cloud providers
|
||||
|
||||
1. Implement `RemoteProvider` (HTTP client)
|
||||
2. Implement `OpenAIProvider` (API wrapper)
|
||||
3. Add provider configuration UI (URLs, API keys)
|
||||
4. Document external provider API spec
|
||||
|
||||
**Result:** Full provider ecosystem
|
||||
|
||||
---
|
||||
|
||||
## Provider Versioning
|
||||
|
||||
### Independent Versioning
|
||||
|
||||
Providers have their own version numbers, independent of the main app:
|
||||
|
||||
- **App version:** `v0.2.0` (frequent updates)
|
||||
- **Provider version:** `v1.0.0` (rare updates)
|
||||
|
||||
### Compatibility Matrix
|
||||
|
||||
**Example:**
|
||||
|
||||
| App Version | Min Provider Version | Max Provider Version |
|
||||
| ----------- | -------------------- | -------------------- |
|
||||
| v0.2.0 | v1.0.0 | v1.x.x |
|
||||
| v0.3.0 | v1.0.0 | v1.x.x |
|
||||
| v0.4.0 | v1.2.0 | v1.x.x |
|
||||
| v1.0.0 | v2.0.0 | v2.x.x |
|
||||
|
||||
**Backend checks compatibility:**
|
||||
|
||||
```python
|
||||
async def check_provider_compatibility(provider_version: str) -> bool:
|
||||
"""Check if provider version is compatible with current app."""
|
||||
min_version = "1.0.0"
|
||||
max_version = "1.999.999"
|
||||
return min_version <= provider_version < max_version
|
||||
```
|
||||
|
||||
**UI shows warning if incompatible:**
|
||||
|
||||
```
|
||||
⚠️ Provider version 0.9.0 is outdated. Update to v1.0.0+
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## User Flows
|
||||
|
||||
### First-Time Setup
|
||||
|
||||
1. User downloads and installs Voicebox (~150MB)
|
||||
2. App launches → detects no TTS provider installed
|
||||
3. Shows setup wizard:
|
||||
|
||||
```
|
||||
Choose your TTS provider:
|
||||
|
||||
[ ] PyTorch CUDA (2.4GB) [Download]
|
||||
✓ Fastest on NVIDIA GPUs
|
||||
✗ Requires NVIDIA GPU
|
||||
|
||||
[●] PyTorch CPU (300MB) [Download]
|
||||
✓ Works on any system
|
||||
✗ Slower inference
|
||||
|
||||
[ ] MLX (800MB) [Download]
|
||||
✓ Fast on Apple Silicon
|
||||
✗ macOS only (M1/M2/M3)
|
||||
|
||||
[ ] Remote Server
|
||||
URL: ___________________
|
||||
|
||||
[ ] OpenAI API
|
||||
API Key: ________________
|
||||
```
|
||||
|
||||
4. User selects provider → downloads with progress bar
|
||||
5. Provider installs to AppData/Application Support
|
||||
6. App starts provider → ready to use
|
||||
|
||||
---
|
||||
|
||||
### App Update Flow (No Provider Change)
|
||||
|
||||
**Scenario:** Bug fix in UI, no backend changes
|
||||
|
||||
1. User gets update notification: "Voicebox v0.2.1 available"
|
||||
2. Downloads update (~150MB, not 2.4GB!)
|
||||
3. Installs and restarts
|
||||
4. **Provider stays the same** (no re-download needed)
|
||||
5. App starts using existing provider
|
||||
|
||||
**User experience:** Fast updates, no multi-GB downloads
|
||||
|
||||
---
|
||||
|
||||
### Provider Update Flow
|
||||
|
||||
**Scenario:** New Qwen3-TTS model version released
|
||||
|
||||
1. User opens Settings → Provider tab
|
||||
2. Sees notification: "Provider update available (v1.1.0)"
|
||||
3. Clicks "Update Provider"
|
||||
4. Downloads new provider binary
|
||||
5. Old provider binary is replaced
|
||||
6. Restart app to use new provider
|
||||
|
||||
**Frequency:** Rare (only when TTS model/backend changes)
|
||||
|
||||
---
|
||||
|
||||
### Switching Providers
|
||||
|
||||
**Scenario:** User upgrades to NVIDIA GPU
|
||||
|
||||
1. User goes to Settings → Provider
|
||||
2. Selects "PyTorch CUDA"
|
||||
3. Clicks "Download" → downloads 2.4GB
|
||||
4. Download completes → restarts app
|
||||
5. App now uses CUDA provider
|
||||
|
||||
---
|
||||
|
||||
## Benefits
|
||||
|
||||
| Benefit | Details |
|
||||
| ----------------------------- | --------------------------------------------------------- |
|
||||
| **GitHub Releases Work** | Main app ~150MB << 2GB limit |
|
||||
| **Fast Updates** | UI/feature updates don't require re-downloading providers |
|
||||
| **User Choice** | CPU, CUDA, MLX, OpenAI, remote server |
|
||||
| **External Provider Support** | Users can run their own TTS servers |
|
||||
| **Bandwidth Savings** | Only download provider once, app updates are small |
|
||||
| **Future-Proof** | Easy to add new providers (ElevenLabs, custom models) |
|
||||
| **Team Deployments** | Multiple users share one remote provider |
|
||||
| **Cloud-Ready** | Works with Modal, Replicate, RunPod, etc. |
|
||||
|
||||
---
|
||||
|
||||
## Open Questions
|
||||
|
||||
### 1. Provider Versioning
|
||||
|
||||
**Question:** Should providers have independent versions or match app version?
|
||||
|
||||
**Options:**
|
||||
|
||||
- A. Independent (providers: v1.x, app: v0.2.x)
|
||||
- B. Matched (both use v0.2.x)
|
||||
|
||||
**Recommendation:** Independent versioning with compatibility matrix
|
||||
|
||||
---
|
||||
|
||||
### 2. Auto-Update Providers
|
||||
|
||||
**Question:** Should providers auto-update separately from app?
|
||||
|
||||
**Options:**
|
||||
|
||||
- A. Manual updates only (user clicks "Update Provider")
|
||||
- B. Optional auto-update (user can enable)
|
||||
- C. Always auto-update
|
||||
|
||||
**Recommendation:** Optional auto-update (default off)
|
||||
|
||||
---
|
||||
|
||||
### 3. Provider Discovery
|
||||
|
||||
**Question:** How does app find installed providers?
|
||||
|
||||
**Options:**
|
||||
|
||||
- A. Check standard paths in AppData/Application Support
|
||||
- B. Registry (Windows) / plist (macOS)
|
||||
- C. Config file with provider locations
|
||||
|
||||
**Recommendation:** Standard paths + config fallback
|
||||
|
||||
---
|
||||
|
||||
### 4. Fallback Behavior
|
||||
|
||||
**Question:** What if no provider is installed?
|
||||
|
||||
**Options:**
|
||||
|
||||
- A. Show setup wizard on first launch
|
||||
- B. Block app until provider installed
|
||||
- C. Allow app to run in "demo mode" (transcription only)
|
||||
|
||||
**Recommendation:** Setup wizard on first launch
|
||||
|
||||
---
|
||||
|
||||
### 5. Provider Auto-Start
|
||||
|
||||
**Question:** Should provider start automatically with app?
|
||||
|
||||
**Options:**
|
||||
|
||||
- A. Always start selected provider on app launch
|
||||
- B. Start on-demand (when user generates speech)
|
||||
- C. User preference
|
||||
|
||||
**Recommendation:** Auto-start (configurable in settings)
|
||||
|
||||
---
|
||||
|
||||
## Future Enhancements
|
||||
|
||||
- [ ] **Provider Marketplace:** Built-in directory of community providers
|
||||
- [ ] **Multi-Provider Support:** Use different providers per voice/language
|
||||
- [ ] **Provider Health Monitoring:** Automatic failover if provider crashes
|
||||
- [ ] **Cost Tracking:** Monitor API usage for OpenAI/cloud providers
|
||||
- [ ] **Performance Metrics:** Latency, throughput, VRAM usage dashboards
|
||||
- [ ] **Docker Providers:** Run providers in Docker containers
|
||||
- [ ] **Provider Plugins:** Load custom providers from user scripts
|
||||
|
||||
---
|
||||
|
||||
## Related Documents
|
||||
|
||||
- [EXTERNAL_PROVIDERS.md](./EXTERNAL_PROVIDERS.md) - External provider support plan
|
||||
- [OPENAI_SUPPORT.md](./OPENAI_SUPPORT.md) - OpenAI API compatibility
|
||||
- [github-2gb-limit-issue.md](../github-2gb-limit-issue.md) - Original problem
|
||||
- [r2-setup.md](../r2-setup.md) - Cloudflare R2 configuration
|
||||
|
||||
---
|
||||
|
||||
## Contributing
|
||||
|
||||
If you want to build a custom TTS provider:
|
||||
|
||||
1. Implement the provider API spec (see above)
|
||||
2. Test with Voicebox locally
|
||||
3. Package as executable (PyInstaller, Docker, etc.)
|
||||
4. Share in GitHub Discussions
|
||||
|
||||
**Questions?**
|
||||
|
||||
- GitHub Issues: [voicebox/issues](https://github.com/jamiepine/voicebox/issues)
|
||||
- Discord: Coming soon
|
||||
@@ -0,0 +1,274 @@
|
||||
# Cloudflare R2 Setup Guide
|
||||
|
||||
## Overview
|
||||
|
||||
The CUDA binary (2.4GB) is hosted on Cloudflare R2 at `downloads.voicebox.sh` instead of GitHub Releases (which has a 2GB limit).
|
||||
|
||||
## R2 Bucket Configuration
|
||||
|
||||
✅ **Completed:**
|
||||
- Bucket created: `voicebox`
|
||||
- Custom domain configured: `downloads.voicebox.sh`
|
||||
|
||||
## GitHub Secrets Required
|
||||
|
||||
Add these secrets to your GitHub repository:
|
||||
|
||||
### 1. R2_ACCESS_KEY_ID
|
||||
|
||||
Your Cloudflare R2 API Access Key ID
|
||||
|
||||
**How to get it:**
|
||||
1. Go to Cloudflare Dashboard → R2
|
||||
2. Click "Manage R2 API Tokens"
|
||||
3. Create API Token with "Object Read & Write" permissions
|
||||
4. Copy the "Access Key ID"
|
||||
|
||||
**Add to GitHub:**
|
||||
```
|
||||
Repository Settings → Secrets and variables → Actions → New repository secret
|
||||
Name: R2_ACCESS_KEY_ID
|
||||
Value: <your-access-key-id>
|
||||
```
|
||||
|
||||
### 2. R2_SECRET_ACCESS_KEY
|
||||
|
||||
Your Cloudflare R2 Secret Access Key
|
||||
|
||||
**How to get it:**
|
||||
- Same process as above
|
||||
- Copy the "Secret Access Key" (shown only once!)
|
||||
- Store it securely
|
||||
|
||||
**Add to GitHub:**
|
||||
```
|
||||
Name: R2_SECRET_ACCESS_KEY
|
||||
Value: <your-secret-access-key>
|
||||
```
|
||||
|
||||
### 3. R2_ENDPOINT
|
||||
|
||||
Your Cloudflare R2 endpoint URL
|
||||
|
||||
**Format:**
|
||||
```
|
||||
https://<account-id>.r2.cloudflarestorage.com
|
||||
```
|
||||
|
||||
**How to find your account ID:**
|
||||
- Cloudflare Dashboard → R2
|
||||
- Look at the URL or bucket settings
|
||||
- Should be a string of letters/numbers
|
||||
|
||||
**Add to GitHub:**
|
||||
```
|
||||
Name: R2_ENDPOINT
|
||||
Value: https://<your-account-id>.r2.cloudflarestorage.com
|
||||
```
|
||||
|
||||
## Bucket Structure
|
||||
|
||||
After CI uploads, the bucket will have this structure:
|
||||
|
||||
```
|
||||
voicebox/
|
||||
└── cuda/
|
||||
├── v0.1.12/
|
||||
│ └── voicebox-server-cuda-x86_64-pc-windows-msvc.exe
|
||||
├── v0.1.13/
|
||||
│ └── voicebox-server-cuda-x86_64-pc-windows-msvc.exe
|
||||
└── v0.2.0/
|
||||
└── voicebox-server-cuda-x86_64-pc-windows-msvc.exe
|
||||
```
|
||||
|
||||
## Public Access
|
||||
|
||||
Files are uploaded with `--acl public-read`, making them accessible at:
|
||||
|
||||
```
|
||||
https://downloads.voicebox.sh/cuda/v{VERSION}/voicebox-server-cuda-x86_64-pc-windows-msvc.exe
|
||||
```
|
||||
|
||||
**Example:**
|
||||
```
|
||||
https://downloads.voicebox.sh/cuda/v0.1.12/voicebox-server-cuda-x86_64-pc-windows-msvc.exe
|
||||
```
|
||||
|
||||
## Testing the Setup
|
||||
|
||||
### Local Test Upload
|
||||
|
||||
Before running the CI, test uploading locally:
|
||||
|
||||
```bash
|
||||
# Set environment variables
|
||||
export AWS_ACCESS_KEY_ID="your-r2-access-key-id"
|
||||
export AWS_SECRET_ACCESS_KEY="your-r2-secret-access-key"
|
||||
export R2_ENDPOINT="https://your-account-id.r2.cloudflarestorage.com"
|
||||
|
||||
# Install AWS CLI
|
||||
pip install awscli
|
||||
|
||||
# Test upload (use a small test file first)
|
||||
echo "test" > test.txt
|
||||
aws s3 cp test.txt \
|
||||
s3://voicebox/test/test.txt \
|
||||
--endpoint-url $R2_ENDPOINT \
|
||||
--acl public-read
|
||||
|
||||
# Verify it's accessible
|
||||
curl https://downloads.voicebox.sh/test/test.txt
|
||||
|
||||
# If successful, try the actual CUDA binary
|
||||
aws s3 cp backend/dist/voicebox-server-cuda.exe \
|
||||
s3://voicebox/cuda/v0.1.12-test/voicebox-server-cuda-x86_64-pc-windows-msvc.exe \
|
||||
--endpoint-url $R2_ENDPOINT \
|
||||
--acl public-read
|
||||
```
|
||||
|
||||
### Verify Upload
|
||||
|
||||
Check if the file is accessible:
|
||||
|
||||
```bash
|
||||
curl -I https://downloads.voicebox.sh/cuda/v0.1.12-test/voicebox-server-cuda-x86_64-pc-windows-msvc.exe
|
||||
```
|
||||
|
||||
Should return:
|
||||
```
|
||||
HTTP/2 200
|
||||
content-length: 2545086396
|
||||
content-type: application/x-msdownload
|
||||
...
|
||||
```
|
||||
|
||||
## CI Workflow
|
||||
|
||||
The workflow now:
|
||||
|
||||
1. **Builds CPU binary** → Includes in installer
|
||||
2. **Builds CUDA binary** → Uploads to R2
|
||||
3. **Release notes** → Include R2 download link
|
||||
|
||||
### CI Steps (Windows)
|
||||
|
||||
```yaml
|
||||
- name: Build CUDA Python server (Windows only)
|
||||
# Builds the CUDA binary
|
||||
|
||||
- name: Upload CUDA server to Cloudflare R2 (Windows only)
|
||||
env:
|
||||
AWS_ACCESS_KEY_ID: ${{ secrets.R2_ACCESS_KEY_ID }}
|
||||
AWS_SECRET_ACCESS_KEY: ${{ secrets.R2_SECRET_ACCESS_KEY }}
|
||||
R2_ENDPOINT: ${{ secrets.R2_ENDPOINT }}
|
||||
run: |
|
||||
aws s3 cp backend/cuda-release/voicebox-server-cuda-*.exe \
|
||||
s3://voicebox/cuda/${VERSION}/... \
|
||||
--endpoint-url $R2_ENDPOINT \
|
||||
--acl public-read
|
||||
```
|
||||
|
||||
## Cost Tracking
|
||||
|
||||
Monitor your R2 usage:
|
||||
|
||||
**Cloudflare Dashboard → R2 → voicebox → Metrics**
|
||||
|
||||
Expected costs (per month):
|
||||
- Storage: 2.4GB × $0.015/GB = **$0.036**
|
||||
- Egress: **$0.00** (free!)
|
||||
- Class A ops: ~100 × $4.50/million = **$0.00**
|
||||
|
||||
**Total: ~$0.04/month** (essentially free!)
|
||||
|
||||
## Troubleshooting
|
||||
|
||||
### Upload fails: "Access Denied"
|
||||
|
||||
**Solution:** Check API token permissions
|
||||
- Must have "Object Read & Write" on the bucket
|
||||
- Regenerate token if needed
|
||||
|
||||
### File not accessible at downloads.voicebox.sh
|
||||
|
||||
**Solution:** Check custom domain configuration
|
||||
- R2 Dashboard → Bucket → Settings → Custom Domains
|
||||
- Ensure `downloads.voicebox.sh` is properly configured
|
||||
- DNS may take time to propagate
|
||||
|
||||
### "endpoint-url" not recognized
|
||||
|
||||
**Solution:** Make sure AWS CLI is updated
|
||||
```bash
|
||||
pip install --upgrade awscli
|
||||
```
|
||||
|
||||
### File uploaded but wrong permissions
|
||||
|
||||
**Solution:** Re-upload with `--acl public-read`
|
||||
```bash
|
||||
aws s3 cp ... --acl public-read
|
||||
```
|
||||
|
||||
Or set bucket default permissions in R2 Dashboard.
|
||||
|
||||
## Security Notes
|
||||
|
||||
### API Token Permissions
|
||||
|
||||
✅ **Recommended:**
|
||||
- Object Read & Write only
|
||||
- No admin permissions needed
|
||||
- Scoped to `voicebox` bucket only
|
||||
|
||||
❌ **Avoid:**
|
||||
- Account-wide permissions
|
||||
- Account admin access
|
||||
- Worker edit permissions
|
||||
|
||||
### Secret Rotation
|
||||
|
||||
Rotate API tokens every 6-12 months:
|
||||
1. Create new API token
|
||||
2. Update GitHub secrets
|
||||
3. Verify CI still works
|
||||
4. Delete old token
|
||||
|
||||
## Maintenance
|
||||
|
||||
### Cleaning Old Versions
|
||||
|
||||
Optional: Delete old CUDA binaries to save storage costs
|
||||
|
||||
```bash
|
||||
# List all versions
|
||||
aws s3 ls s3://voicebox/cuda/ \
|
||||
--endpoint-url $R2_ENDPOINT
|
||||
|
||||
# Delete old version
|
||||
aws s3 rm s3://voicebox/cuda/v0.1.0/ \
|
||||
--recursive \
|
||||
--endpoint-url $R2_ENDPOINT
|
||||
```
|
||||
|
||||
### Monitoring
|
||||
|
||||
Set up Cloudflare notifications:
|
||||
- Storage approaching limits
|
||||
- Unusual traffic patterns
|
||||
- High operation counts
|
||||
|
||||
## Next Steps
|
||||
|
||||
1. ✅ Bucket configured
|
||||
2. ⏳ Add GitHub secrets (R2_ACCESS_KEY_ID, R2_SECRET_ACCESS_KEY, R2_ENDPOINT)
|
||||
3. ⏳ Test local upload
|
||||
4. ⏳ Push branch and create test release
|
||||
5. ⏳ Verify CUDA binary accessible from downloads.voicebox.sh
|
||||
6. ⏳ Implement frontend download manager
|
||||
|
||||
---
|
||||
|
||||
**Status**: Ready for testing
|
||||
**Cost**: ~$0.04/month
|
||||
**Bandwidth**: Free (unlimited)
|
||||
@@ -1,191 +0,0 @@
|
||||
# Voicebox development commands
|
||||
# Install: brew install just (or cargo install just)
|
||||
# Usage: just --list
|
||||
|
||||
# Directories
|
||||
backend_dir := "backend"
|
||||
tauri_dir := "tauri"
|
||||
app_dir := "app"
|
||||
web_dir := "web"
|
||||
venv := backend_dir / "venv"
|
||||
venv_bin := venv / "bin"
|
||||
python := venv_bin / "python"
|
||||
pip := venv_bin / "pip"
|
||||
|
||||
# Detect best python for venv creation
|
||||
system_python := `command -v python3.12 2>/dev/null || command -v python3.13 2>/dev/null || echo python3`
|
||||
|
||||
# ─── Setup ────────────────────────────────────────────────────────────
|
||||
|
||||
# Full project setup (python venv + JS deps + dev sidecar)
|
||||
setup: setup-python setup-js
|
||||
@echo ""
|
||||
@echo "Setup complete! Run: just dev"
|
||||
|
||||
# Create venv and install Python dependencies
|
||||
setup-python:
|
||||
#!/usr/bin/env bash
|
||||
set -euo pipefail
|
||||
if [ ! -d "{{ venv }}" ]; then
|
||||
echo "Creating Python virtual environment..."
|
||||
PY_MINOR=$({{ system_python }} -c "import sys; print(sys.version_info[1])")
|
||||
if [ "$PY_MINOR" -gt 13 ]; then
|
||||
echo "Warning: Python 3.$PY_MINOR detected. ML packages may not be compatible."
|
||||
echo "Recommended: brew install [email protected]"
|
||||
fi
|
||||
{{ system_python }} -m venv {{ venv }}
|
||||
fi
|
||||
echo "Installing Python dependencies..."
|
||||
{{ pip }} install --upgrade pip -q
|
||||
{{ pip }} install -r {{ backend_dir }}/requirements.txt
|
||||
# Chatterbox pins numpy<1.26 / torch==2.6 which break on Python 3.12+
|
||||
{{ pip }} install --no-deps chatterbox-tts
|
||||
# Apple Silicon: install MLX backend
|
||||
if [ "$(uname -m)" = "arm64" ] && [ "$(uname)" = "Darwin" ]; then
|
||||
echo "Detected Apple Silicon — installing MLX dependencies..."
|
||||
{{ pip }} install -r {{ backend_dir }}/requirements-mlx.txt
|
||||
fi
|
||||
{{ pip }} install git+https://github.com/QwenLM/Qwen3-TTS.git
|
||||
echo "Python environment ready."
|
||||
|
||||
# Install JavaScript dependencies
|
||||
setup-js:
|
||||
bun install
|
||||
|
||||
# ─── Development ──────────────────────────────────────────────────────
|
||||
|
||||
# Start backend + frontend for development (two processes, one terminal)
|
||||
dev: _ensure-venv _ensure-sidecar
|
||||
#!/usr/bin/env bash
|
||||
set -euo pipefail
|
||||
trap 'kill 0' EXIT
|
||||
|
||||
echo "Starting backend on http://localhost:17493 ..."
|
||||
{{ venv_bin }}/uvicorn backend.main:app --reload --port 17493 &
|
||||
sleep 2
|
||||
|
||||
echo "Starting Tauri desktop app..."
|
||||
cd {{ tauri_dir }} && bun run tauri dev &
|
||||
|
||||
wait
|
||||
|
||||
# Start backend only
|
||||
dev-backend: _ensure-venv
|
||||
{{ venv_bin }}/uvicorn backend.main:app --reload --port 17493
|
||||
|
||||
# Start Tauri desktop app only (backend must be running separately)
|
||||
dev-frontend: _ensure-sidecar
|
||||
cd {{ tauri_dir }} && bun run tauri dev
|
||||
|
||||
# Start backend + web app (no Tauri)
|
||||
dev-web: _ensure-venv
|
||||
#!/usr/bin/env bash
|
||||
set -euo pipefail
|
||||
trap 'kill 0' EXIT
|
||||
{{ venv_bin }}/uvicorn backend.main:app --reload --port 17493 &
|
||||
sleep 2
|
||||
cd {{ web_dir }} && bun run dev &
|
||||
wait
|
||||
|
||||
# Kill all dev processes
|
||||
kill:
|
||||
-pkill -f "uvicorn backend.main:app" 2>/dev/null || true
|
||||
-pkill -f "vite" 2>/dev/null || true
|
||||
@echo "Dev processes killed."
|
||||
|
||||
# ─── Build ────────────────────────────────────────────────────────────
|
||||
|
||||
# Build everything (server binary + desktop app)
|
||||
build: build-server build-tauri
|
||||
|
||||
# Build Python server binary
|
||||
build-server: _ensure-venv
|
||||
PATH="{{ venv_bin }}:$PATH" ./scripts/build-server.sh
|
||||
|
||||
# Build Tauri desktop app
|
||||
build-tauri:
|
||||
cd {{ tauri_dir }} && bun run tauri build
|
||||
|
||||
# Build web app
|
||||
build-web:
|
||||
cd {{ web_dir }} && bun run build
|
||||
|
||||
# ─── Code Quality ────────────────────────────────────────────────────
|
||||
|
||||
# Run all checks (lint + format + typecheck)
|
||||
check:
|
||||
bun run check
|
||||
|
||||
# Lint with Biome
|
||||
lint:
|
||||
bun run lint
|
||||
|
||||
# Format with Biome
|
||||
format:
|
||||
bun run format
|
||||
|
||||
# Fix lint + format issues
|
||||
fix:
|
||||
bun run check:fix
|
||||
|
||||
# ─── Database ─────────────────────────────────────────────────────────
|
||||
|
||||
# Initialize SQLite database
|
||||
db-init: _ensure-venv
|
||||
cd {{ backend_dir }} && {{ python }} -c "from database import init_db; init_db()"
|
||||
|
||||
# Reset database (delete + reinit)
|
||||
db-reset:
|
||||
rm -f {{ backend_dir }}/data/voicebox.db
|
||||
just db-init
|
||||
|
||||
# ─── Utilities ────────────────────────────────────────────────────────
|
||||
|
||||
# Generate TypeScript API client (backend must be running)
|
||||
generate-api:
|
||||
./scripts/generate-api.sh
|
||||
|
||||
# Open API docs in browser
|
||||
docs:
|
||||
open http://localhost:17493/docs 2>/dev/null || xdg-open http://localhost:17493/docs
|
||||
|
||||
# Tail backend logs
|
||||
logs:
|
||||
tail -f {{ backend_dir }}/logs/*.log 2>/dev/null || echo "No log files found"
|
||||
|
||||
# ─── Clean ────────────────────────────────────────────────────────────
|
||||
|
||||
# Clean build artifacts
|
||||
clean:
|
||||
rm -rf {{ tauri_dir }}/src-tauri/target/release
|
||||
rm -rf {{ web_dir }}/dist
|
||||
rm -rf {{ app_dir }}/dist
|
||||
|
||||
# Clean Python venv and cache
|
||||
clean-python:
|
||||
rm -rf {{ venv }}
|
||||
find {{ backend_dir }} -type d -name "__pycache__" -exec rm -rf {} + 2>/dev/null || true
|
||||
|
||||
# Nuclear clean (everything including node_modules)
|
||||
clean-all: clean clean-python
|
||||
rm -rf node_modules
|
||||
rm -rf {{ app_dir }}/node_modules
|
||||
rm -rf {{ tauri_dir }}/node_modules
|
||||
rm -rf {{ web_dir }}/node_modules
|
||||
cd {{ tauri_dir }}/src-tauri && cargo clean
|
||||
|
||||
# ─── Internal ─────────────────────────────────────────────────────────
|
||||
|
||||
# Ensure venv exists (prompt to run setup if not)
|
||||
[private]
|
||||
_ensure-venv:
|
||||
#!/usr/bin/env bash
|
||||
if [ ! -d "{{ venv }}" ]; then
|
||||
echo "Python venv not found. Run: just setup"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
# Ensure Tauri dev sidecar placeholder exists
|
||||
[private]
|
||||
_ensure-sidecar:
|
||||
bun run setup:dev
|
||||
@@ -1,6 +1,6 @@
|
||||
{
|
||||
"name": "@voicebox/landing",
|
||||
"version": "0.1.13",
|
||||
"version": "0.1.12",
|
||||
"description": "Landing page for voicebox.sh",
|
||||
"scripts": {
|
||||
"dev": "bun --bun next dev --turbo",
|
||||
|
||||
@@ -1,7 +1,6 @@
|
||||
import type { Metadata } from 'next';
|
||||
import { Inter } from 'next/font/google';
|
||||
import './globals.css';
|
||||
import { Banner } from '@/components/Banner';
|
||||
import { Footer } from '@/components/Footer';
|
||||
import { Header } from '@/components/Header';
|
||||
|
||||
@@ -32,7 +31,6 @@ export default function RootLayout({ children }: { children: React.ReactNode })
|
||||
<html lang="en" suppressHydrationWarning className="dark">
|
||||
<body className={inter.variable}>
|
||||
<div className="relative min-h-screen bg-background font-sans flex flex-col">
|
||||
<Banner />
|
||||
<Header />
|
||||
<main className="container mx-auto px-4 sm:px-6 md:px-4 flex-1 py-4 sm:py-6 md:py-0">
|
||||
{children}
|
||||
|
||||
@@ -239,9 +239,8 @@ export default function Home() {
|
||||
<div className="space-y-6 text-lg text-foreground/80 text-center">
|
||||
<p>
|
||||
Voicebox is a <strong>local-first voice cloning studio</strong> with DAW-like features
|
||||
for professional voice synthesis. Think of it as a{' '}
|
||||
<strong>local, free and open-source alternative to ElevenLabs</strong> — download
|
||||
models, clone voices, and generate speech entirely on your machine.
|
||||
for professional voice synthesis. Think of it as the <strong>Ollama for voice</strong>{' '}
|
||||
— download models, clone voices, and generate speech entirely on your machine.
|
||||
</p>
|
||||
<p>
|
||||
Unlike cloud services that lock your voice data behind subscriptions, Voicebox gives
|
||||
|
||||
@@ -1,25 +0,0 @@
|
||||
import { ArrowRight } from 'lucide-react';
|
||||
|
||||
export function Banner() {
|
||||
return (
|
||||
<div className="bg-primary/[0.06] border-b border-border backdrop-blur-sm">
|
||||
<div className="container mx-auto px-4">
|
||||
<div className="flex items-center justify-center h-10 text-sm">
|
||||
<a
|
||||
href="https://spacebot.sh"
|
||||
target="_blank"
|
||||
rel="noopener noreferrer"
|
||||
className="flex items-center gap-2 text-muted-foreground hover:text-foreground transition-colors group"
|
||||
>
|
||||
<span>
|
||||
Also by the creator of Voicebox:{' '}
|
||||
<strong className="text-foreground/90">Spacebot</strong>, an AI agent OS for teams.
|
||||
Connect Discord, Slack, or Telegram in one click.
|
||||
</span>
|
||||
<ArrowRight className="h-3.5 w-3.5 transition-transform group-hover:translate-x-0.5" />
|
||||
</a>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
);
|
||||
}
|
||||
+1
-1
@@ -1,6 +1,6 @@
|
||||
{
|
||||
"name": "voicebox",
|
||||
"version": "0.1.13",
|
||||
"version": "0.1.12",
|
||||
"private": true,
|
||||
"workspaces": [
|
||||
"app",
|
||||
|
||||
@@ -1,9 +0,0 @@
|
||||
uvicorn
|
||||
fastapi
|
||||
sqlalchemy
|
||||
torch
|
||||
torchvision
|
||||
soundfile
|
||||
librosa
|
||||
python-multipart
|
||||
huggingface_hub
|
||||
@@ -6,7 +6,7 @@ set -e
|
||||
echo "Generating OpenAPI client..."
|
||||
|
||||
# Check if backend is running
|
||||
if ! curl -s http://localhost:17493/openapi.json > /dev/null 2>&1; then
|
||||
if ! curl -s http://localhost:8000/openapi.json > /dev/null 2>&1; then
|
||||
echo "Backend not running. Starting backend..."
|
||||
cd backend
|
||||
|
||||
@@ -26,19 +26,19 @@ if ! curl -s http://localhost:17493/openapi.json > /dev/null 2>&1; then
|
||||
|
||||
# Start backend in background
|
||||
echo "Starting backend server..."
|
||||
uvicorn main:app --port 17493 & # Keep the generator on the app's documented local backend port.
|
||||
uvicorn main:app --port 8000 &
|
||||
BACKEND_PID=$!
|
||||
|
||||
# Wait for server to be ready
|
||||
echo "Waiting for server to start..."
|
||||
for _ in {1..30}; do
|
||||
if curl -s http://localhost:17493/openapi.json > /dev/null 2>&1; then
|
||||
for i in {1..30}; do
|
||||
if curl -s http://localhost:8000/openapi.json > /dev/null 2>&1; then
|
||||
break
|
||||
fi
|
||||
sleep 1
|
||||
done
|
||||
|
||||
if ! curl -s http://localhost:17493/openapi.json > /dev/null 2>&1; then
|
||||
if ! curl -s http://localhost:8000/openapi.json > /dev/null 2>&1; then
|
||||
echo "Error: Backend failed to start"
|
||||
kill $BACKEND_PID 2>/dev/null || true
|
||||
exit 1
|
||||
@@ -52,7 +52,7 @@ fi
|
||||
|
||||
# Download OpenAPI schema
|
||||
echo "Downloading OpenAPI schema..."
|
||||
curl -s http://localhost:17493/openapi.json > app/openapi.json
|
||||
curl -s http://localhost:8000/openapi.json > app/openapi.json
|
||||
|
||||
# Check if openapi-typescript-codegen is installed
|
||||
if ! bunx --bun openapi-typescript-codegen --version > /dev/null 2>&1; then
|
||||
|
||||
+56
-271
@@ -1,5 +1,4 @@
|
||||
#!/usr/bin/env node
|
||||
|
||||
/**
|
||||
* Creates placeholder sidecar binaries for development mode.
|
||||
*
|
||||
@@ -10,10 +9,10 @@
|
||||
* The actual server should be started separately with `bun run dev:server`.
|
||||
*/
|
||||
|
||||
import { execSync } from 'child_process';
|
||||
import { existsSync, mkdirSync, statSync, writeFileSync } from 'fs';
|
||||
import { dirname, join } from 'path';
|
||||
import { existsSync, mkdirSync, writeFileSync, statSync } from 'fs';
|
||||
import { join, dirname } from 'path';
|
||||
import { fileURLToPath } from 'url';
|
||||
import { execSync } from 'child_process';
|
||||
|
||||
const __filename = fileURLToPath(import.meta.url);
|
||||
const __dirname = dirname(__filename);
|
||||
@@ -56,9 +55,7 @@ function createPlaceholderBinary(targetTriple) {
|
||||
try {
|
||||
const stats = statSync(binaryPath);
|
||||
if (stats.size > MIN_REAL_BINARY_SIZE) {
|
||||
console.log(
|
||||
`Real binary already exists: ${binaryName} (${(stats.size / 1024 / 1024).toFixed(1)} MB)`,
|
||||
);
|
||||
console.log(`Real binary already exists: ${binaryName} (${(stats.size / 1024 / 1024).toFixed(1)} MB)`);
|
||||
return;
|
||||
}
|
||||
} catch {
|
||||
@@ -76,275 +73,52 @@ function createPlaceholderBinary(targetTriple) {
|
||||
// This is the smallest valid PE that Windows will accept
|
||||
const minimalPE = Buffer.from([
|
||||
// DOS Header
|
||||
0x4d,
|
||||
0x5a,
|
||||
0x90,
|
||||
0x00,
|
||||
0x03,
|
||||
0x00,
|
||||
0x00,
|
||||
0x00,
|
||||
0x04,
|
||||
0x00,
|
||||
0x00,
|
||||
0x00,
|
||||
0xff,
|
||||
0xff,
|
||||
0x00,
|
||||
0x00,
|
||||
0xb8,
|
||||
0x00,
|
||||
0x00,
|
||||
0x00,
|
||||
0x00,
|
||||
0x00,
|
||||
0x00,
|
||||
0x00,
|
||||
0x40,
|
||||
0x00,
|
||||
0x00,
|
||||
0x00,
|
||||
0x00,
|
||||
0x00,
|
||||
0x00,
|
||||
0x00,
|
||||
0x00,
|
||||
0x00,
|
||||
0x00,
|
||||
0x00,
|
||||
0x00,
|
||||
0x00,
|
||||
0x00,
|
||||
0x00,
|
||||
0x00,
|
||||
0x00,
|
||||
0x00,
|
||||
0x00,
|
||||
0x00,
|
||||
0x00,
|
||||
0x00,
|
||||
0x00,
|
||||
0x00,
|
||||
0x00,
|
||||
0x00,
|
||||
0x00,
|
||||
0x00,
|
||||
0x00,
|
||||
0x00,
|
||||
0x00,
|
||||
0x00,
|
||||
0x00,
|
||||
0x00,
|
||||
0x00,
|
||||
0x80,
|
||||
0x00,
|
||||
0x00,
|
||||
0x00,
|
||||
0x4D, 0x5A, 0x90, 0x00, 0x03, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0xFF, 0xFF, 0x00, 0x00,
|
||||
0xB8, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x40, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
|
||||
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
|
||||
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x80, 0x00, 0x00, 0x00,
|
||||
// DOS Stub
|
||||
0x0e,
|
||||
0x1f,
|
||||
0xba,
|
||||
0x0e,
|
||||
0x00,
|
||||
0xb4,
|
||||
0x09,
|
||||
0xcd,
|
||||
0x21,
|
||||
0xb8,
|
||||
0x01,
|
||||
0x4c,
|
||||
0xcd,
|
||||
0x21,
|
||||
0x54,
|
||||
0x68,
|
||||
0x69,
|
||||
0x73,
|
||||
0x20,
|
||||
0x70,
|
||||
0x72,
|
||||
0x6f,
|
||||
0x67,
|
||||
0x72,
|
||||
0x61,
|
||||
0x6d,
|
||||
0x20,
|
||||
0x63,
|
||||
0x61,
|
||||
0x6e,
|
||||
0x6e,
|
||||
0x6f,
|
||||
0x74,
|
||||
0x20,
|
||||
0x62,
|
||||
0x65,
|
||||
0x20,
|
||||
0x72,
|
||||
0x75,
|
||||
0x6e,
|
||||
0x20,
|
||||
0x69,
|
||||
0x6e,
|
||||
0x20,
|
||||
0x44,
|
||||
0x4f,
|
||||
0x53,
|
||||
0x20,
|
||||
0x6d,
|
||||
0x6f,
|
||||
0x64,
|
||||
0x65,
|
||||
0x2e,
|
||||
0x0d,
|
||||
0x0d,
|
||||
0x0a,
|
||||
0x24,
|
||||
0x00,
|
||||
0x00,
|
||||
0x00,
|
||||
0x00,
|
||||
0x00,
|
||||
0x00,
|
||||
0x00,
|
||||
0x0E, 0x1F, 0xBA, 0x0E, 0x00, 0xB4, 0x09, 0xCD, 0x21, 0xB8, 0x01, 0x4C, 0xCD, 0x21, 0x54, 0x68,
|
||||
0x69, 0x73, 0x20, 0x70, 0x72, 0x6F, 0x67, 0x72, 0x61, 0x6D, 0x20, 0x63, 0x61, 0x6E, 0x6E, 0x6F,
|
||||
0x74, 0x20, 0x62, 0x65, 0x20, 0x72, 0x75, 0x6E, 0x20, 0x69, 0x6E, 0x20, 0x44, 0x4F, 0x53, 0x20,
|
||||
0x6D, 0x6F, 0x64, 0x65, 0x2E, 0x0D, 0x0D, 0x0A, 0x24, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
|
||||
// PE Signature
|
||||
0x50,
|
||||
0x45,
|
||||
0x00,
|
||||
0x00,
|
||||
0x50, 0x45, 0x00, 0x00,
|
||||
// COFF Header (x64)
|
||||
0x64,
|
||||
0x86, // Machine: AMD64
|
||||
0x01,
|
||||
0x00, // NumberOfSections: 1
|
||||
0x00,
|
||||
0x00,
|
||||
0x00,
|
||||
0x00, // TimeDateStamp
|
||||
0x00,
|
||||
0x00,
|
||||
0x00,
|
||||
0x00, // PointerToSymbolTable
|
||||
0x00,
|
||||
0x00,
|
||||
0x00,
|
||||
0x00, // NumberOfSymbols
|
||||
0xf0,
|
||||
0x00, // SizeOfOptionalHeader
|
||||
0x22,
|
||||
0x00, // Characteristics: EXECUTABLE_IMAGE | LARGE_ADDRESS_AWARE
|
||||
0x64, 0x86, // Machine: AMD64
|
||||
0x01, 0x00, // NumberOfSections: 1
|
||||
0x00, 0x00, 0x00, 0x00, // TimeDateStamp
|
||||
0x00, 0x00, 0x00, 0x00, // PointerToSymbolTable
|
||||
0x00, 0x00, 0x00, 0x00, // NumberOfSymbols
|
||||
0xF0, 0x00, // SizeOfOptionalHeader
|
||||
0x22, 0x00, // Characteristics: EXECUTABLE_IMAGE | LARGE_ADDRESS_AWARE
|
||||
// Optional Header (PE32+)
|
||||
0x0b,
|
||||
0x02, // Magic: PE32+
|
||||
0x00,
|
||||
0x00, // Linker version
|
||||
0x00,
|
||||
0x00,
|
||||
0x00,
|
||||
0x00, // SizeOfCode
|
||||
0x00,
|
||||
0x00,
|
||||
0x00,
|
||||
0x00, // SizeOfInitializedData
|
||||
0x00,
|
||||
0x00,
|
||||
0x00,
|
||||
0x00, // SizeOfUninitializedData
|
||||
0x00,
|
||||
0x10,
|
||||
0x00,
|
||||
0x00, // AddressOfEntryPoint
|
||||
0x00,
|
||||
0x00,
|
||||
0x00,
|
||||
0x00, // BaseOfCode
|
||||
0x00,
|
||||
0x00,
|
||||
0x00,
|
||||
0x40,
|
||||
0x01,
|
||||
0x00,
|
||||
0x00,
|
||||
0x00, // ImageBase
|
||||
0x00,
|
||||
0x10,
|
||||
0x00,
|
||||
0x00, // SectionAlignment
|
||||
0x00,
|
||||
0x02,
|
||||
0x00,
|
||||
0x00, // FileAlignment
|
||||
0x06,
|
||||
0x00,
|
||||
0x00,
|
||||
0x00, // OS version
|
||||
0x00,
|
||||
0x00,
|
||||
0x00,
|
||||
0x00, // Image version
|
||||
0x06,
|
||||
0x00,
|
||||
0x00,
|
||||
0x00, // Subsystem version
|
||||
0x00,
|
||||
0x00,
|
||||
0x00,
|
||||
0x00, // Win32VersionValue
|
||||
0x00,
|
||||
0x20,
|
||||
0x00,
|
||||
0x00, // SizeOfImage
|
||||
0x00,
|
||||
0x02,
|
||||
0x00,
|
||||
0x00, // SizeOfHeaders
|
||||
0x00,
|
||||
0x00,
|
||||
0x00,
|
||||
0x00, // CheckSum
|
||||
0x03,
|
||||
0x00, // Subsystem: CONSOLE
|
||||
0x60,
|
||||
0x01, // DllCharacteristics
|
||||
0x0B, 0x02, // Magic: PE32+
|
||||
0x00, 0x00, // Linker version
|
||||
0x00, 0x00, 0x00, 0x00, // SizeOfCode
|
||||
0x00, 0x00, 0x00, 0x00, // SizeOfInitializedData
|
||||
0x00, 0x00, 0x00, 0x00, // SizeOfUninitializedData
|
||||
0x00, 0x10, 0x00, 0x00, // AddressOfEntryPoint
|
||||
0x00, 0x00, 0x00, 0x00, // BaseOfCode
|
||||
0x00, 0x00, 0x00, 0x40, 0x01, 0x00, 0x00, 0x00, // ImageBase
|
||||
0x00, 0x10, 0x00, 0x00, // SectionAlignment
|
||||
0x00, 0x02, 0x00, 0x00, // FileAlignment
|
||||
0x06, 0x00, 0x00, 0x00, // OS version
|
||||
0x00, 0x00, 0x00, 0x00, // Image version
|
||||
0x06, 0x00, 0x00, 0x00, // Subsystem version
|
||||
0x00, 0x00, 0x00, 0x00, // Win32VersionValue
|
||||
0x00, 0x20, 0x00, 0x00, // SizeOfImage
|
||||
0x00, 0x02, 0x00, 0x00, // SizeOfHeaders
|
||||
0x00, 0x00, 0x00, 0x00, // CheckSum
|
||||
0x03, 0x00, // Subsystem: CONSOLE
|
||||
0x60, 0x01, // DllCharacteristics
|
||||
// Stack/Heap sizes (8 bytes each for PE32+)
|
||||
0x00,
|
||||
0x00,
|
||||
0x10,
|
||||
0x00,
|
||||
0x00,
|
||||
0x00,
|
||||
0x00,
|
||||
0x00,
|
||||
0x00,
|
||||
0x00,
|
||||
0x00,
|
||||
0x00,
|
||||
0x00,
|
||||
0x00,
|
||||
0x00,
|
||||
0x00,
|
||||
0x00,
|
||||
0x00,
|
||||
0x10,
|
||||
0x00,
|
||||
0x00,
|
||||
0x00,
|
||||
0x00,
|
||||
0x00,
|
||||
0x00,
|
||||
0x00,
|
||||
0x00,
|
||||
0x00,
|
||||
0x00,
|
||||
0x00,
|
||||
0x00,
|
||||
0x00,
|
||||
0x00,
|
||||
0x00,
|
||||
0x00,
|
||||
0x00, // LoaderFlags
|
||||
0x10,
|
||||
0x00,
|
||||
0x00,
|
||||
0x00, // NumberOfRvaAndSizes
|
||||
0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00,
|
||||
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
|
||||
0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00,
|
||||
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
|
||||
0x00, 0x00, 0x00, 0x00, // LoaderFlags
|
||||
0x10, 0x00, 0x00, 0x00, // NumberOfRvaAndSizes
|
||||
]);
|
||||
|
||||
// Pad to 512 bytes minimum for valid PE
|
||||
@@ -364,8 +138,19 @@ exit 1
|
||||
}
|
||||
|
||||
function main() {
|
||||
console.log('Setting up development sidecar...');
|
||||
console.log('');
|
||||
|
||||
const targetTriple = getTargetTriple();
|
||||
console.log(`Platform: ${targetTriple}`);
|
||||
|
||||
createPlaceholderBinary(targetTriple);
|
||||
|
||||
console.log('');
|
||||
console.log('Sidecar setup complete.');
|
||||
console.log('For development, start the Python server in a separate terminal:');
|
||||
console.log(' bun run dev:server');
|
||||
console.log('');
|
||||
}
|
||||
|
||||
main();
|
||||
|
||||
Some files were not shown because too many files have changed in this diff Show More
Reference in New Issue
Block a user