mirror of
https://github.com/jamiepine/voicebox.git
synced 2026-09-27 14:15:16 -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
-4
@@ -93,12 +93,10 @@ function App() {
|
||||
}
|
||||
|
||||
serverStartingRef.current = true;
|
||||
const isRemote = useServerStore.getState().mode === 'remote';
|
||||
const customModelsDir = useServerStore.getState().customModelsDir;
|
||||
console.log(`Production mode: Starting bundled server... (remote: ${isRemote})`);
|
||||
console.log('Production mode: Starting bundled server...');
|
||||
|
||||
platform.lifecycle
|
||||
.startServer(isRemote, customModelsDir)
|
||||
.startServer(false)
|
||||
.then((serverUrl) => {
|
||||
console.log('Server is ready at:', serverUrl);
|
||||
// Update the server URL in the store with the dynamically assigned port
|
||||
|
||||
@@ -1,18 +1,17 @@
|
||||
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';
|
||||
import { apiClient } from '@/lib/api/client';
|
||||
import { formatAudioDuration } from '@/lib/utils/audio';
|
||||
import { debug } from '@/lib/utils/debug';
|
||||
import { usePlatform } from '@/platform/PlatformContext';
|
||||
import { usePlayerStore } from '@/stores/playerStore';
|
||||
import { usePlatform } from '@/platform/PlatformContext';
|
||||
|
||||
export function AudioPlayer() {
|
||||
const platform = usePlatform();
|
||||
const volumeLabelId = useId();
|
||||
const {
|
||||
audioUrl,
|
||||
audioId,
|
||||
@@ -360,7 +359,7 @@ export function AudioPlayer() {
|
||||
if (shouldAutoPlayNow) {
|
||||
// Clear the flag first
|
||||
usePlayerStore.getState().clearAutoPlayFlag();
|
||||
|
||||
|
||||
// Use a small delay to ensure audio element is fully ready
|
||||
setTimeout(() => {
|
||||
wavesurfer.play().catch((error) => {
|
||||
@@ -665,7 +664,7 @@ export function AudioPlayer() {
|
||||
// Handle shouldAutoPlay flag - for story mode auto-advance
|
||||
const shouldAutoPlay = usePlayerStore((state) => state.shouldAutoPlay);
|
||||
const clearAutoPlayFlag = usePlayerStore((state) => state.clearAutoPlayFlag);
|
||||
|
||||
|
||||
useEffect(() => {
|
||||
const wavesurfer = wavesurferRef.current;
|
||||
if (!wavesurfer || !shouldAutoPlay || duration === 0) {
|
||||
@@ -832,9 +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>
|
||||
@@ -849,8 +845,6 @@ export function AudioPlayer() {
|
||||
max={100}
|
||||
step={0.1}
|
||||
className="w-full"
|
||||
aria-label="Playback position"
|
||||
aria-valuetext={`${formatAudioDuration(currentTime)} of ${formatAudioDuration(duration)}`}
|
||||
/>
|
||||
)}
|
||||
{isLoading && (
|
||||
@@ -868,9 +862,7 @@ export function AudioPlayer() {
|
||||
|
||||
{/* Title */}
|
||||
{title && (
|
||||
<div className="text-sm font-medium truncate max-w-[200px] shrink-0 hidden lg:block">
|
||||
{title}
|
||||
</div>
|
||||
<div className="text-sm font-medium truncate max-w-[200px] shrink-0">{title}</div>
|
||||
)}
|
||||
|
||||
{/* Loop Button */}
|
||||
@@ -880,37 +872,26 @@ export function AudioPlayer() {
|
||||
onClick={toggleLoop}
|
||||
className={isLooping ? 'text-primary' : ''}
|
||||
title="Toggle loop"
|
||||
aria-label={isLooping ? 'Stop looping' : 'Loop'}
|
||||
>
|
||||
<Repeat className="h-4 w-4" />
|
||||
</Button>
|
||||
|
||||
{/* Volume Control */}
|
||||
<div
|
||||
className="flex items-center gap-2 shrink-0 w-[120px]"
|
||||
role="group"
|
||||
aria-label="Volume"
|
||||
>
|
||||
<div className="flex items-center gap-2 shrink-0 w-[120px]">
|
||||
<Button
|
||||
variant="ghost"
|
||||
size="icon"
|
||||
onClick={() => setVolume(volume > 0 ? 0 : 1)}
|
||||
className="h-8 w-8"
|
||||
aria-label={volume > 0 ? 'Mute' : 'Unmute'}
|
||||
>
|
||||
{volume > 0 ? <Volume2 className="h-4 w-4" /> : <VolumeX className="h-4 w-4" />}
|
||||
</Button>
|
||||
<span id={volumeLabelId} className="sr-only">
|
||||
Volume level, {Math.round(volume * 100)}%
|
||||
</span>
|
||||
<Slider
|
||||
value={[volume * 100]}
|
||||
onValueChange={handleVolumeChange}
|
||||
max={100}
|
||||
step={1}
|
||||
className="flex-1"
|
||||
aria-labelledby={volumeLabelId}
|
||||
aria-valuetext={`${Math.round(volume * 100)}%`}
|
||||
/>
|
||||
</div>
|
||||
|
||||
@@ -921,7 +902,6 @@ export function AudioPlayer() {
|
||||
onClick={handleClose}
|
||||
className="shrink-0"
|
||||
title="Close player"
|
||||
aria-label="Close player"
|
||||
>
|
||||
<X className="h-5 w-5" />
|
||||
</Button>
|
||||
|
||||
@@ -23,8 +23,8 @@ import {
|
||||
import { apiClient } from '@/lib/api/client';
|
||||
import { BOTTOM_SAFE_AREA_PADDING } from '@/lib/constants/ui';
|
||||
import { cn } from '@/lib/utils/cn';
|
||||
import { usePlatform } from '@/platform/PlatformContext';
|
||||
import { usePlayerStore } from '@/stores/playerStore';
|
||||
import { usePlatform } from '@/platform/PlatformContext';
|
||||
|
||||
interface AudioDevice {
|
||||
id: string;
|
||||
@@ -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
|
||||
@@ -168,7 +161,7 @@ export function AudioTab() {
|
||||
</Button>
|
||||
</div>
|
||||
) : (
|
||||
<div className="space-y-3">
|
||||
<div className="space-y-3 p-2">
|
||||
{allChannels.map((channel) => {
|
||||
const isSelected = selectedChannelId === channel.id;
|
||||
return (
|
||||
@@ -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>
|
||||
@@ -343,9 +341,7 @@ export function AudioTab() {
|
||||
<div className="flex flex-col items-center justify-center py-12 border-2 border-dashed border-muted rounded-md">
|
||||
<CheckCircle2 className="h-12 w-12 text-muted-foreground mb-4" />
|
||||
<p className="text-muted-foreground text-center">
|
||||
{platform.metadata.isTauri
|
||||
? 'No audio devices found'
|
||||
: 'Audio device selection requires Tauri'}
|
||||
{platform.metadata.isTauri ? 'No audio devices found' : 'Audio device selection requires Tauri'}
|
||||
</p>
|
||||
</div>
|
||||
)}
|
||||
|
||||
@@ -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';
|
||||
@@ -12,15 +12,14 @@ import {
|
||||
SelectValue,
|
||||
} from '@/components/ui/select';
|
||||
import { Textarea } from '@/components/ui/textarea';
|
||||
import { getLanguageOptionsForEngine, type LanguageCode } from '@/lib/constants/languages';
|
||||
import { useToast } from '@/components/ui/use-toast';
|
||||
import { LANGUAGE_OPTIONS } from '@/lib/constants/languages';
|
||||
import { useGenerationForm } from '@/lib/hooks/useGenerationForm';
|
||||
import { useProfile, useProfiles } from '@/lib/hooks/useProfiles';
|
||||
import { useStory } from '@/lib/hooks/useStories';
|
||||
import { useAddStoryItem, useStory } from '@/lib/hooks/useStories';
|
||||
import { cn } from '@/lib/utils/cn';
|
||||
import { useGenerationStore } from '@/stores/generationStore';
|
||||
import { useStoryStore } from '@/stores/storyStore';
|
||||
import { useUIStore } from '@/stores/uiStore';
|
||||
import { ParalinguisticInput } from './ParalinguisticInput';
|
||||
|
||||
interface FloatingGenerateBoxProps {
|
||||
isPlayerOpen?: boolean;
|
||||
@@ -44,7 +43,8 @@ export function FloatingGenerateBox({
|
||||
const selectedStoryId = useStoryStore((state) => state.selectedStoryId);
|
||||
const trackEditorHeight = useStoryStore((state) => state.trackEditorHeight);
|
||||
const { data: currentStory } = useStory(selectedStoryId);
|
||||
const addPendingStoryAdd = useGenerationStore((s) => s.addPendingStoryAdd);
|
||||
const addStoryItem = useAddStoryItem();
|
||||
const { toast } = useToast();
|
||||
|
||||
// Calculate if track editor is visible (on stories route with items)
|
||||
const hasTrackEditor = isStoriesRoute && currentStory && currentStory.items.length > 0;
|
||||
@@ -52,9 +52,25 @@ export function FloatingGenerateBox({
|
||||
const { form, handleSubmit, isPending } = useGenerationForm({
|
||||
onSuccess: async (generationId) => {
|
||||
setIsExpanded(false);
|
||||
// Defer the story add until TTS completes — useGenerationProgress handles it
|
||||
// If on stories route and a story is selected, add generation to story
|
||||
if (isStoriesRoute && selectedStoryId && generationId) {
|
||||
addPendingStoryAdd(generationId, selectedStoryId);
|
||||
try {
|
||||
await addStoryItem.mutateAsync({
|
||||
storyId: selectedStoryId,
|
||||
data: { generation_id: generationId },
|
||||
});
|
||||
toast({
|
||||
title: 'Added to story',
|
||||
description: `Generation added to "${currentStory?.name || 'story'}"`,
|
||||
});
|
||||
} catch (error) {
|
||||
toast({
|
||||
title: 'Failed to add to story',
|
||||
description:
|
||||
error instanceof Error ? error.message : 'Could not add generation to story',
|
||||
variant: 'destructive',
|
||||
});
|
||||
}
|
||||
}
|
||||
},
|
||||
});
|
||||
@@ -96,13 +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) {
|
||||
@@ -165,7 +174,7 @@ export function FloatingGenerateBox({
|
||||
isStoriesRoute
|
||||
? // Position aligned with story list: after sidebar + padding, width 360px
|
||||
'left-[calc(5rem+2rem)] w-[360px]'
|
||||
: 'left-[calc(5rem+2rem)] right-8 lg:right-auto lg:w-[calc((100%-5rem-4rem)/2-1rem)]',
|
||||
: 'left-[calc(5rem+2rem)] w-[calc((100%-5rem-4rem)/2-1rem)]',
|
||||
)}
|
||||
style={{
|
||||
// On stories route: offset by track editor height when visible
|
||||
@@ -178,7 +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}>
|
||||
@@ -203,57 +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" />
|
||||
@@ -288,7 +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',
|
||||
@@ -308,36 +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 }}
|
||||
@@ -345,28 +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>
|
||||
@@ -405,86 +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>
|
||||
);
|
||||
},
|
||||
);
|
||||
@@ -1,15 +1,13 @@
|
||||
import { useQueryClient } from '@tanstack/react-query';
|
||||
import {
|
||||
AudioWaveform,
|
||||
Download,
|
||||
FileArchive,
|
||||
Loader2,
|
||||
MoreHorizontal,
|
||||
Play,
|
||||
RotateCcw,
|
||||
Trash2,
|
||||
} from 'lucide-react';
|
||||
import { useEffect, useRef, useState } from 'react';
|
||||
import Loader from 'react-loaders';
|
||||
import { Button } from '@/components/ui/button';
|
||||
import {
|
||||
Dialog,
|
||||
@@ -38,8 +36,7 @@ import {
|
||||
useImportGeneration,
|
||||
} from '@/lib/hooks/useHistory';
|
||||
import { cn } from '@/lib/utils/cn';
|
||||
import { formatDate, formatDuration, formatEngineName } from '@/lib/utils/format';
|
||||
import { useGenerationStore } from '@/stores/generationStore';
|
||||
import { formatDate, formatDuration } from '@/lib/utils/format';
|
||||
import { usePlayerStore } from '@/stores/playerStore';
|
||||
|
||||
// OLD TABLE-BASED COMPONENT - REMOVED (can be found in git history)
|
||||
@@ -57,12 +54,9 @@ export function HistoryTable() {
|
||||
const [importDialogOpen, setImportDialogOpen] = useState(false);
|
||||
const [selectedFile, setSelectedFile] = useState<File | null>(null);
|
||||
const [deleteDialogOpen, setDeleteDialogOpen] = useState(false);
|
||||
const [generationToDelete, setGenerationToDelete] = useState<{ id: string; name: string } | null>(
|
||||
null,
|
||||
);
|
||||
const [generationToDelete, setGenerationToDelete] = useState<{ id: string; name: string } | null>(null);
|
||||
const limit = 20;
|
||||
const { toast } = useToast();
|
||||
const queryClient = useQueryClient();
|
||||
|
||||
const {
|
||||
data: historyData,
|
||||
@@ -77,7 +71,6 @@ export function HistoryTable() {
|
||||
const exportGeneration = useExportGeneration();
|
||||
const exportGenerationAudio = useExportGenerationAudio();
|
||||
const importGeneration = useImportGeneration();
|
||||
const addPendingGeneration = useGenerationStore((state) => state.addPendingGeneration);
|
||||
const setAudioWithAutoPlay = usePlayerStore((state) => state.setAudioWithAutoPlay);
|
||||
const restartCurrentAudio = usePlayerStore((state) => state.restartCurrentAudio);
|
||||
const currentAudioId = usePlayerStore((state) => state.audioId);
|
||||
@@ -201,20 +194,6 @@ export function HistoryTable() {
|
||||
}
|
||||
};
|
||||
|
||||
const handleRetry = async (generationId: string) => {
|
||||
try {
|
||||
const result = await apiClient.retryGeneration(generationId);
|
||||
addPendingGeneration(result.id);
|
||||
queryClient.invalidateQueries({ queryKey: ['history'] });
|
||||
} catch (error) {
|
||||
toast({
|
||||
title: 'Retry failed',
|
||||
description: error instanceof Error ? error.message : 'Could not retry generation',
|
||||
variant: 'destructive',
|
||||
});
|
||||
}
|
||||
};
|
||||
|
||||
const handleImportConfirm = () => {
|
||||
if (selectedFile) {
|
||||
importGeneration.mutate(selectedFile, {
|
||||
@@ -271,54 +250,25 @@ export function HistoryTable() {
|
||||
>
|
||||
{history.map((gen) => {
|
||||
const isCurrentlyPlaying = currentAudioId === gen.id && isPlaying;
|
||||
const isGenerating = gen.status === 'generating';
|
||||
const isFailed = gen.status === 'failed';
|
||||
const isPlayable = !isGenerating && !isFailed;
|
||||
return (
|
||||
<div
|
||||
key={gen.id}
|
||||
role={isPlayable ? 'button' : undefined}
|
||||
tabIndex={isPlayable ? 0 : undefined}
|
||||
className={cn(
|
||||
'flex items-stretch gap-4 h-26 border rounded-md p-3 bg-card transition-colors text-left w-full',
|
||||
isPlayable && 'hover:bg-muted/70 cursor-pointer',
|
||||
'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={
|
||||
isGenerating
|
||||
? `Generating speech for ${gen.profile_name}...`
|
||||
: isFailed
|
||||
? `Generation failed for ${gen.profile_name}`
|
||||
: isCurrentlyPlaying
|
||||
? `Sample from ${gen.profile_name}, ${formatDuration(gen.duration ?? 0)}, ${formatDate(gen.created_at)}. Playing. Press Enter to restart.`
|
||||
: `Sample from ${gen.profile_name}, ${formatDuration(gen.duration ?? 0)}, ${formatDate(gen.created_at)}. Press Enter to play.`
|
||||
}
|
||||
onMouseDown={(e) => {
|
||||
if (!isPlayable) return;
|
||||
// Don't trigger play if clicking on textarea or if text is selected
|
||||
const target = e.target as HTMLElement;
|
||||
if (target.closest('textarea') || window.getSelection()?.toString()) {
|
||||
return;
|
||||
}
|
||||
handlePlay(gen.id, gen.text, gen.profile_id);
|
||||
}}
|
||||
onKeyDown={(e) => {
|
||||
if (!isPlayable) return;
|
||||
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);
|
||||
}
|
||||
}}
|
||||
>
|
||||
{/* Status icon */}
|
||||
<div className="flex items-center shrink-0 w-10 justify-center overflow-hidden">
|
||||
<div className="scale-50">
|
||||
<Loader
|
||||
type={isGenerating ? 'line-scale' : 'line-scale-pulse-out-rapid'}
|
||||
active={isGenerating || isCurrentlyPlaying}
|
||||
/>
|
||||
</div>
|
||||
{/* Waveform icon */}
|
||||
<div className="flex items-center shrink-0">
|
||||
<AudioWaveform className="h-5 w-5 text-muted-foreground" />
|
||||
</div>
|
||||
|
||||
{/* Left side - Meta information */}
|
||||
@@ -329,22 +279,11 @@ export function HistoryTable() {
|
||||
<div className="flex items-center gap-2">
|
||||
<span className="text-xs text-muted-foreground">{gen.language}</span>
|
||||
<span className="text-xs text-muted-foreground">
|
||||
{formatEngineName(gen.engine, gen.model_size)}
|
||||
{formatDuration(gen.duration)}
|
||||
</span>
|
||||
{isFailed ? (
|
||||
<span className="text-xs text-destructive">Failed</span>
|
||||
) : !isGenerating ? (
|
||||
<span className="text-xs text-muted-foreground">
|
||||
{formatDuration(gen.duration ?? 0)}
|
||||
</span>
|
||||
) : null}
|
||||
</div>
|
||||
<div className="text-xs text-muted-foreground">
|
||||
{isGenerating ? (
|
||||
<span className="text-accent">Generating...</span>
|
||||
) : (
|
||||
formatDate(gen.created_at)
|
||||
)}
|
||||
{formatDate(gen.created_at)}
|
||||
</div>
|
||||
</div>
|
||||
|
||||
@@ -354,70 +293,57 @@ 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 ?? 0)}`}
|
||||
/>
|
||||
</div>
|
||||
|
||||
{/* Far right - Actions */}
|
||||
{/* Far right - Ellipsis actions */}
|
||||
<div
|
||||
className="w-10 shrink-0 flex justify-end items-center"
|
||||
className="w-10 shrink-0 flex justify-end"
|
||||
onMouseDown={(e) => e.stopPropagation()}
|
||||
onClick={(e) => e.stopPropagation()}
|
||||
>
|
||||
{isFailed ? (
|
||||
<Button
|
||||
variant="ghost"
|
||||
size="icon"
|
||||
className="h-8 w-8"
|
||||
aria-label="Retry generation"
|
||||
onClick={() => handleRetry(gen.id)}
|
||||
>
|
||||
<RotateCcw className="h-4 w-4" />
|
||||
</Button>
|
||||
) : isPlayable ? (
|
||||
<DropdownMenu>
|
||||
<DropdownMenuTrigger asChild>
|
||||
<Button
|
||||
variant="ghost"
|
||||
size="icon"
|
||||
className="h-8 w-8"
|
||||
aria-label="Actions"
|
||||
>
|
||||
<MoreHorizontal className="h-4 w-4" />
|
||||
</Button>
|
||||
</DropdownMenuTrigger>
|
||||
<DropdownMenuContent align="end">
|
||||
<DropdownMenuItem
|
||||
onClick={() => handlePlay(gen.id, gen.text, gen.profile_id)}
|
||||
>
|
||||
<Play className="mr-2 h-4 w-4" />
|
||||
Play
|
||||
</DropdownMenuItem>
|
||||
<DropdownMenuItem
|
||||
onClick={() => handleDownloadAudio(gen.id, gen.text)}
|
||||
disabled={exportGenerationAudio.isPending}
|
||||
>
|
||||
<Download className="mr-2 h-4 w-4" />
|
||||
Export Audio
|
||||
</DropdownMenuItem>
|
||||
<DropdownMenuItem
|
||||
onClick={() => handleExportPackage(gen.id, gen.text)}
|
||||
disabled={exportGeneration.isPending}
|
||||
>
|
||||
<FileArchive className="mr-2 h-4 w-4" />
|
||||
Export Package
|
||||
</DropdownMenuItem>
|
||||
<DropdownMenuItem
|
||||
onClick={() => handleDeleteClick(gen.id, gen.profile_name)}
|
||||
disabled={deleteGeneration.isPending}
|
||||
className="text-destructive focus:text-destructive"
|
||||
>
|
||||
<Trash2 className="mr-2 h-4 w-4" />
|
||||
Delete
|
||||
</DropdownMenuItem>
|
||||
</DropdownMenuContent>
|
||||
</DropdownMenu>
|
||||
) : null}
|
||||
<DropdownMenu>
|
||||
<DropdownMenuTrigger asChild>
|
||||
<Button
|
||||
variant="ghost"
|
||||
size="icon"
|
||||
className="h-8 w-8"
|
||||
aria-label="Actions"
|
||||
>
|
||||
<MoreHorizontal className="h-4 w-4" />
|
||||
</Button>
|
||||
</DropdownMenuTrigger>
|
||||
<DropdownMenuContent align="end">
|
||||
<DropdownMenuItem
|
||||
onClick={() => handlePlay(gen.id, gen.text, gen.profile_id)}
|
||||
>
|
||||
<Play className="mr-2 h-4 w-4" />
|
||||
Play
|
||||
</DropdownMenuItem>
|
||||
<DropdownMenuItem
|
||||
onClick={() => handleDownloadAudio(gen.id, gen.text)}
|
||||
disabled={exportGenerationAudio.isPending}
|
||||
>
|
||||
<Download className="mr-2 h-4 w-4" />
|
||||
Export Audio
|
||||
</DropdownMenuItem>
|
||||
<DropdownMenuItem
|
||||
onClick={() => handleExportPackage(gen.id, gen.text)}
|
||||
disabled={exportGeneration.isPending}
|
||||
>
|
||||
<FileArchive className="mr-2 h-4 w-4" />
|
||||
Export Package
|
||||
</DropdownMenuItem>
|
||||
<DropdownMenuItem
|
||||
onClick={() => handleDeleteClick(gen.id, gen.profile_name)}
|
||||
disabled={deleteGeneration.isPending}
|
||||
className="text-destructive focus:text-destructive"
|
||||
>
|
||||
<Trash2 className="mr-2 h-4 w-4" />
|
||||
Delete
|
||||
</DropdownMenuItem>
|
||||
</DropdownMenuContent>
|
||||
</DropdownMenu>
|
||||
</div>
|
||||
</div>
|
||||
);
|
||||
@@ -445,8 +371,7 @@ export function HistoryTable() {
|
||||
<DialogHeader>
|
||||
<DialogTitle>Delete Generation</DialogTitle>
|
||||
<DialogDescription>
|
||||
Are you sure you want to delete this generation from "{generationToDelete?.name}"?
|
||||
This action cannot be undone.
|
||||
Are you sure you want to delete this generation from "{generationToDelete?.name}"? This action cannot be undone.
|
||||
</DialogDescription>
|
||||
</DialogHeader>
|
||||
<DialogFooter>
|
||||
|
||||
@@ -13,7 +13,7 @@ import {
|
||||
} from '@/components/ui/dialog';
|
||||
import { useToast } from '@/components/ui/use-toast';
|
||||
import { ProfileList } from '@/components/VoiceProfiles/ProfileList';
|
||||
|
||||
import { BOTTOM_SAFE_AREA_PADDING } from '@/lib/constants/ui';
|
||||
import { useImportProfile } from '@/lib/hooks/useProfiles';
|
||||
import { cn } from '@/lib/utils/cn';
|
||||
import { usePlayerStore } from '@/stores/playerStore';
|
||||
@@ -77,9 +77,9 @@ export function MainEditor() {
|
||||
|
||||
return (
|
||||
// Main view: Profiles top left, Generator bottom left, History right
|
||||
<div className="grid grid-cols-1 lg:grid-cols-2 lg:gap-6 h-full min-h-0 overflow-hidden relative">
|
||||
<div className="grid grid-cols-1 lg:grid-cols-2 gap-6 h-full min-h-0 overflow-hidden relative">
|
||||
{/* Left Column */}
|
||||
<div className="flex flex-col min-h-0 overflow-hidden relative lg:overflow-hidden">
|
||||
<div className="flex flex-col min-h-0 overflow-hidden relative">
|
||||
{/* Scroll Mask - Always visible, behind content */}
|
||||
<div className="absolute top-0 left-0 right-0 h-16 bg-gradient-to-b from-background to-transparent z-0 pointer-events-none" />
|
||||
|
||||
@@ -110,7 +110,10 @@ export function MainEditor() {
|
||||
{/* Scrollable Content */}
|
||||
<div
|
||||
ref={scrollRef}
|
||||
className={cn('flex-1 min-h-0 overflow-y-auto pt-14 pb-4', isPlayerVisible && 'lg:pb-32')}
|
||||
className={cn(
|
||||
'flex-1 min-h-0 overflow-y-auto pt-14',
|
||||
isPlayerVisible ? BOTTOM_SAFE_AREA_PADDING : 'pb-4',
|
||||
)}
|
||||
>
|
||||
<div className="flex flex-col gap-6">
|
||||
<div className="shrink-0 flex flex-col">
|
||||
@@ -120,9 +123,6 @@ export function MainEditor() {
|
||||
</div>
|
||||
</div>
|
||||
|
||||
{/* Divider - single column only */}
|
||||
{/* <div className="border-t border-border -my-3 lg:hidden" /> */}
|
||||
|
||||
{/* Right Column - History */}
|
||||
<div className="flex flex-col min-h-0 overflow-hidden">
|
||||
<HistoryTable />
|
||||
|
||||
@@ -2,7 +2,7 @@ import { ModelManagement } from '@/components/ServerSettings/ModelManagement';
|
||||
|
||||
export function ModelsTab() {
|
||||
return (
|
||||
<div className="h-full flex flex-col">
|
||||
<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>
|
||||
@@ -89,42 +83,10 @@ 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
|
||||
id="keepServerRunning"
|
||||
className="mt-[6px]"
|
||||
checked={keepServerRunningOnClose}
|
||||
onCheckedChange={(checked: boolean) => {
|
||||
setKeepServerRunningOnClose(checked);
|
||||
@@ -153,39 +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"
|
||||
className="mt-[6px]"
|
||||
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>
|
||||
);
|
||||
|
||||
@@ -1,116 +0,0 @@
|
||||
import { Card, CardContent, CardDescription, CardHeader, CardTitle } from '@/components/ui/card';
|
||||
import { Checkbox } from '@/components/ui/checkbox';
|
||||
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);
|
||||
const normalizeAudio = useServerStore((state) => state.normalizeAudio);
|
||||
const setNormalizeAudio = useServerStore((state) => state.setNormalizeAudio);
|
||||
const autoplayOnGenerate = useServerStore((state) => state.autoplayOnGenerate);
|
||||
const setAutoplayOnGenerate = useServerStore((state) => state.setAutoplayOnGenerate);
|
||||
|
||||
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={5000}
|
||||
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 className="flex items-start gap-3">
|
||||
<Checkbox
|
||||
id="normalizeAudio"
|
||||
checked={normalizeAudio}
|
||||
onCheckedChange={setNormalizeAudio}
|
||||
className="mt-[6px]"
|
||||
/>
|
||||
<div className="space-y-1">
|
||||
<label
|
||||
htmlFor="normalizeAudio"
|
||||
className="text-sm font-medium leading-none cursor-pointer"
|
||||
>
|
||||
Normalize audio
|
||||
</label>
|
||||
<p className="text-sm text-muted-foreground">
|
||||
Adjusts output volume to a consistent level across generations.
|
||||
</p>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div className="flex items-start gap-3">
|
||||
<Checkbox
|
||||
id="autoplayOnGenerate"
|
||||
checked={autoplayOnGenerate}
|
||||
onCheckedChange={setAutoplayOnGenerate}
|
||||
className="mt-[6px]"
|
||||
/>
|
||||
<div className="space-y-1">
|
||||
<label
|
||||
htmlFor="autoplayOnGenerate"
|
||||
className="text-sm font-medium leading-none cursor-pointer"
|
||||
>
|
||||
Autoplay on generate
|
||||
</label>
|
||||
<p className="text-sm text-muted-foreground">
|
||||
Automatically play audio when a generation completes.
|
||||
</p>
|
||||
</div>
|
||||
</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>
|
||||
);
|
||||
}
|
||||
File diff suppressed because it is too large
Load Diff
@@ -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>
|
||||
|
||||
@@ -1,25 +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 { BOTTOM_SAFE_AREA_PADDING } from '@/lib/constants/ui';
|
||||
import { cn } from '@/lib/utils/cn';
|
||||
import { usePlatform } from '@/platform/PlatformContext';
|
||||
import { usePlayerStore } from '@/stores/playerStore';
|
||||
|
||||
export function ServerTab() {
|
||||
const platform = usePlatform();
|
||||
const isPlayerVisible = !!usePlayerStore((state) => state.audioUrl);
|
||||
return (
|
||||
<div
|
||||
className={cn('overflow-y-auto flex flex-col', isPlayerVisible && BOTTOM_SAFE_AREA_PADDING)}
|
||||
>
|
||||
<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
|
||||
|
||||
@@ -1,9 +1,9 @@
|
||||
import { Link, useMatchRoute } from '@tanstack/react-router';
|
||||
import { BookOpen, Box, Mic, Server, Speaker, Volume2 } from 'lucide-react';
|
||||
import { Box, BookOpen, Loader2, Mic, Server, Speaker, Volume2 } from 'lucide-react';
|
||||
import voiceboxLogo from '@/assets/voicebox-logo.png';
|
||||
import { cn } from '@/lib/utils/cn';
|
||||
import { useGenerationStore } from '@/stores/generationStore';
|
||||
import { usePlayerStore } from '@/stores/playerStore';
|
||||
import { version } from '../../package.json';
|
||||
|
||||
interface SidebarProps {
|
||||
isMacOS?: boolean;
|
||||
@@ -19,8 +19,10 @@ const tabs = [
|
||||
];
|
||||
|
||||
export function Sidebar({ isMacOS }: SidebarProps) {
|
||||
const isGenerating = useGenerationStore((state) => state.isGenerating);
|
||||
const audioUrl = usePlayerStore((state) => state.audioUrl);
|
||||
const isPlayerVisible = !!audioUrl;
|
||||
const matchRoute = useMatchRoute();
|
||||
const isPlayerOpen = !!usePlayerStore((s) => s.audioUrl);
|
||||
|
||||
return (
|
||||
<div
|
||||
@@ -40,7 +42,9 @@ export function Sidebar({ isMacOS }: SidebarProps) {
|
||||
const Icon = tab.icon;
|
||||
// For index route, use exact match; for others, use default matching
|
||||
const isActive =
|
||||
tab.path === '/' ? matchRoute({ to: '/', exact: true }) : matchRoute({ to: tab.path });
|
||||
tab.path === '/'
|
||||
? matchRoute({ to: '/', exact: true })
|
||||
: matchRoute({ to: tab.path });
|
||||
|
||||
return (
|
||||
<Link
|
||||
@@ -60,13 +64,20 @@ export function Sidebar({ isMacOS }: SidebarProps) {
|
||||
})}
|
||||
</div>
|
||||
|
||||
{/* Version */}
|
||||
<div
|
||||
className="mt-auto text-[10px] text-muted-foreground/50 transition-all duration-300"
|
||||
style={{ paddingBottom: isPlayerOpen ? '7rem' : undefined }}
|
||||
>
|
||||
v{version}
|
||||
</div>
|
||||
{/* Spacer to push loader to bottom */}
|
||||
<div className="flex-1" />
|
||||
|
||||
{/* Generation Loader */}
|
||||
{isGenerating && (
|
||||
<div
|
||||
className={cn(
|
||||
'w-full flex items-center justify-center transition-all duration-200',
|
||||
isPlayerVisible ? 'mb-[120px]' : 'mb-0',
|
||||
)}
|
||||
>
|
||||
<Loader2 className="h-6 w-6 text-accent animate-spin" />
|
||||
</div>
|
||||
)}
|
||||
</div>
|
||||
);
|
||||
}
|
||||
|
||||
@@ -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>
|
||||
);
|
||||
|
||||
@@ -13,11 +13,8 @@ import {
|
||||
sortableKeyboardCoordinates,
|
||||
verticalListSortingStrategy,
|
||||
} from '@dnd-kit/sortable';
|
||||
import { Link } from '@tanstack/react-router';
|
||||
import { AnimatePresence, motion } from 'framer-motion';
|
||||
import { Download, Plus } from 'lucide-react';
|
||||
import { useEffect, useMemo, useRef, useState } from 'react';
|
||||
import Loader from 'react-loaders';
|
||||
import { Button } from '@/components/ui/button';
|
||||
import { Input } from '@/components/ui/input';
|
||||
import { Popover, PopoverContent, PopoverTrigger } from '@/components/ui/popover';
|
||||
@@ -31,7 +28,6 @@ import {
|
||||
useStory,
|
||||
} from '@/lib/hooks/useStories';
|
||||
import { useStoryPlayback } from '@/lib/hooks/useStoryPlayback';
|
||||
import { useGenerationStore } from '@/stores/generationStore';
|
||||
import { useStoryStore } from '@/stores/storyStore';
|
||||
import { SortableStoryChatItem } from './StoryChatItem';
|
||||
|
||||
@@ -44,7 +40,6 @@ export function StoryContent() {
|
||||
const addStoryItem = useAddStoryItem();
|
||||
const { toast } = useToast();
|
||||
const scrollRef = useRef<HTMLDivElement>(null);
|
||||
const pendingCount = useGenerationStore((s) => s.pendingGenerationIds.size);
|
||||
|
||||
// Add generation popover state
|
||||
const [searchQuery, setSearchQuery] = useState('');
|
||||
@@ -58,9 +53,9 @@ export function StoryContent() {
|
||||
const query = searchQuery.toLowerCase();
|
||||
return historyData.items.filter(
|
||||
(gen) =>
|
||||
gen.status === 'completed' &&
|
||||
!storyGenerationIds.has(gen.id) &&
|
||||
(gen.text.toLowerCase().includes(query) || gen.profile_name.toLowerCase().includes(query)),
|
||||
(gen.text.toLowerCase().includes(query) ||
|
||||
gen.profile_name.toLowerCase().includes(query)),
|
||||
);
|
||||
}, [historyData, story, searchQuery]);
|
||||
|
||||
@@ -272,31 +267,7 @@ export function StoryContent() {
|
||||
<p className="text-sm text-muted-foreground mt-1">{story.description}</p>
|
||||
)}
|
||||
</div>
|
||||
<div className="flex gap-2 items-center">
|
||||
<AnimatePresence>
|
||||
{pendingCount > 0 && (
|
||||
<motion.div
|
||||
initial={{ opacity: 0, scale: 0.9, width: 0 }}
|
||||
animate={{ opacity: 1, scale: 1, width: 'auto' }}
|
||||
exit={{ opacity: 0, scale: 0.9, width: 0 }}
|
||||
transition={{ duration: 0.2 }}
|
||||
>
|
||||
<Link
|
||||
to="/"
|
||||
className="flex items-center gap-2 h-8 pl-1.5 pr-3 rounded-full bg-card border border-border hover:bg-muted/50 transition-all duration-200 cursor-pointer"
|
||||
>
|
||||
<div className="shrink-0 w-10 h-5 overflow-hidden flex items-center justify-center">
|
||||
<div className="scale-[0.45]">
|
||||
<Loader type="line-scale" active />
|
||||
</div>
|
||||
</div>
|
||||
<span className="text-xs text-muted-foreground whitespace-nowrap">
|
||||
Generating {pendingCount} {pendingCount === 1 ? 'audio' : 'audios'}
|
||||
</span>
|
||||
</Link>
|
||||
</motion.div>
|
||||
)}
|
||||
</AnimatePresence>
|
||||
<div className="flex gap-2">
|
||||
<Popover open={isAddOpen} onOpenChange={setIsAddOpen}>
|
||||
<PopoverTrigger asChild>
|
||||
<Button variant="outline" size="sm">
|
||||
@@ -316,7 +287,9 @@ export function StoryContent() {
|
||||
<div className="max-h-60 overflow-y-auto">
|
||||
{availableGenerations.length === 0 ? (
|
||||
<div className="p-4 text-center text-sm text-muted-foreground">
|
||||
{searchQuery ? 'No matching generations found' : 'No available generations'}
|
||||
{searchQuery
|
||||
? 'No matching generations found'
|
||||
: 'No available generations'}
|
||||
</div>
|
||||
) : (
|
||||
availableGenerations.map((gen) => (
|
||||
|
||||
@@ -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>
|
||||
|
||||
@@ -61,32 +61,14 @@ 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}, ${profile.language}. Select as voice for generation.`;
|
||||
|
||||
return (
|
||||
<>
|
||||
<Card
|
||||
className={cn(
|
||||
'cursor-pointer hover:shadow-md transition-all flex flex-col h-[162px]',
|
||||
'cursor-pointer hover:shadow-md transition-all flex flex-col',
|
||||
isSelected && 'ring-2 ring-primary shadow-md',
|
||||
)}
|
||||
onClick={handleSelect}
|
||||
tabIndex={0}
|
||||
role="button"
|
||||
aria-label={selectLabel}
|
||||
aria-pressed={isSelected}
|
||||
onKeyDown={handleKeyDown}
|
||||
>
|
||||
<CardHeader className="p-3 pb-2">
|
||||
<CardTitle className="flex items-center gap-1.5 text-base font-medium">
|
||||
|
||||
@@ -43,7 +43,7 @@ import {
|
||||
} from '@/lib/hooks/useProfiles';
|
||||
import { useSystemAudioCapture } from '@/lib/hooks/useSystemAudioCapture';
|
||||
import { useTranscription } from '@/lib/hooks/useTranscription';
|
||||
import { convertToWav, formatAudioDuration, getAudioDuration } from '@/lib/utils/audio';
|
||||
import { formatAudioDuration, getAudioDuration } from '@/lib/utils/audio';
|
||||
import { usePlatform } from '@/platform/PlatformContext';
|
||||
import { useServerStore } from '@/stores/serverStore';
|
||||
import { type ProfileFormDraft, useUIStore } from '@/stores/uiStore';
|
||||
@@ -505,23 +505,10 @@ export function ProfileForm() {
|
||||
language: data.language,
|
||||
});
|
||||
|
||||
// Convert non-WAV uploads to WAV so the backend can always use soundfile.
|
||||
// Recorded audio is already WAV (from useAudioRecording's convertToWav call).
|
||||
let fileToUpload: File = sampleFile;
|
||||
if (!sampleFile.type.includes('wav') && !sampleFile.name.toLowerCase().endsWith('.wav')) {
|
||||
try {
|
||||
const wavBlob = await convertToWav(sampleFile);
|
||||
const wavName = sampleFile.name.replace(/\.[^.]+$/, '.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,
|
||||
});
|
||||
|
||||
|
||||
@@ -41,11 +41,9 @@ export function ProfileList() {
|
||||
</CardContent>
|
||||
</Card>
|
||||
) : (
|
||||
<div className="flex gap-4 overflow-x-auto p-1 pb-1 lg:grid lg:grid-cols-3 lg:auto-rows-auto lg:overflow-x-visible lg:pb-[150px]">
|
||||
<div className="grid gap-4 grid-cols-3 auto-rows-auto p-1 pb-[150px]">
|
||||
{allProfiles.map((profile) => (
|
||||
<div key={profile.id} className="shrink-0 w-[200px] lg:w-auto lg:shrink">
|
||||
<ProfileCard profile={profile} />
|
||||
</div>
|
||||
<ProfileCard key={profile.id} profile={profile} />
|
||||
))}
|
||||
</div>
|
||||
)}
|
||||
|
||||
@@ -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>
|
||||
|
||||
@@ -1,5 +1,5 @@
|
||||
import { Check } from 'lucide-react';
|
||||
import * as React from 'react';
|
||||
import { Check } from 'lucide-react';
|
||||
import { cn } from '@/lib/utils/cn';
|
||||
|
||||
export interface CheckboxProps {
|
||||
|
||||
@@ -1,5 +1,4 @@
|
||||
@import "tailwindcss" source(".");
|
||||
@import "loaders.css/loaders.min.css";
|
||||
|
||||
@theme {
|
||||
--radius-sm: calc(var(--radius) - 4px);
|
||||
@@ -156,18 +155,3 @@
|
||||
animation: fadeIn 0.5s ease-out 0.15s forwards;
|
||||
opacity: 0;
|
||||
}
|
||||
|
||||
/* react-loaders */
|
||||
.line-scale-pulse-out-rapid > div,
|
||||
.line-scale > div {
|
||||
background-color: hsl(var(--accent)) !important;
|
||||
}
|
||||
|
||||
.loader-hidden {
|
||||
display: block;
|
||||
}
|
||||
|
||||
.loader-hidden > div > div {
|
||||
animation-play-state: paused !important;
|
||||
background-color: hsl(var(--muted-foreground)) !important;
|
||||
}
|
||||
|
||||
+39
-114
@@ -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 {
|
||||
@@ -200,12 +199,6 @@ class ApiClient {
|
||||
});
|
||||
}
|
||||
|
||||
async retryGeneration(generationId: string): Promise<GenerationResponse> {
|
||||
return this.request<GenerationResponse>(`/generate/${generationId}/retry`, {
|
||||
method: 'POST',
|
||||
});
|
||||
}
|
||||
|
||||
// History
|
||||
async listHistory(query?: HistoryQuery): Promise<HistoryListResponse> {
|
||||
const params = new URLSearchParams();
|
||||
@@ -258,13 +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 = `${this.getBaseUrl()}/history/import`;
|
||||
const formData = new FormData();
|
||||
formData.append('file', file);
|
||||
@@ -284,11 +271,6 @@ class ApiClient {
|
||||
return response.json();
|
||||
}
|
||||
|
||||
// Generation status SSE
|
||||
getGenerationStatusUrl(generationId: string): string {
|
||||
return `${this.getBaseUrl()}/generate/${generationId}/status`;
|
||||
}
|
||||
|
||||
// Audio
|
||||
getAudioUrl(audioId: string): string {
|
||||
return `${this.getBaseUrl()}/audio/${audioId}`;
|
||||
@@ -327,28 +309,8 @@ class ApiClient {
|
||||
return this.request<ModelStatusListResponse>('/models/status');
|
||||
}
|
||||
|
||||
async getModelsCacheDir(): Promise<{ path: string }> {
|
||||
return this.request<{ path: string }>('/models/cache-dir');
|
||||
}
|
||||
|
||||
async migrateModels(destination: string): Promise<{ source: string; destination: string }> {
|
||||
return this.request('/models/migrate', {
|
||||
method: 'POST',
|
||||
body: JSON.stringify({ destination }),
|
||||
});
|
||||
}
|
||||
|
||||
getMigrationProgressUrl(): string {
|
||||
return `${this.getBaseUrl()}/models/migrate/progress`;
|
||||
}
|
||||
|
||||
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),
|
||||
@@ -363,28 +325,11 @@ class ApiClient {
|
||||
});
|
||||
}
|
||||
|
||||
async unloadModel(modelName: string): Promise<{ message: string }> {
|
||||
return this.request<{ message: string }>(`/models/${modelName}/unload`, {
|
||||
method: 'POST',
|
||||
});
|
||||
}
|
||||
|
||||
async cancelDownload(modelName: string): Promise<{ message: string }> {
|
||||
return this.request<{ message: string }>('/models/download/cancel', {
|
||||
method: 'POST',
|
||||
body: JSON.stringify({ model_name: modelName } as ModelDownloadRequest),
|
||||
});
|
||||
}
|
||||
|
||||
// Task Management
|
||||
async getActiveTasks(): Promise<ActiveTasksResponse> {
|
||||
return this.request<ActiveTasksResponse>('/tasks/active');
|
||||
}
|
||||
|
||||
async clearAllTasks(): Promise<{ message: string }> {
|
||||
return this.request<{ message: string }>('/tasks/clear', { method: 'POST' });
|
||||
}
|
||||
|
||||
// Audio Channels
|
||||
async listChannels(): Promise<
|
||||
Array<{
|
||||
@@ -398,7 +343,10 @@ class ApiClient {
|
||||
return this.request('/channels');
|
||||
}
|
||||
|
||||
async createChannel(data: { name: string; device_ids: string[] }): Promise<{
|
||||
async createChannel(data: {
|
||||
name: string;
|
||||
device_ids: string[];
|
||||
}): Promise<{
|
||||
id: string;
|
||||
name: string;
|
||||
is_default: boolean;
|
||||
@@ -440,7 +388,10 @@ class ApiClient {
|
||||
return this.request(`/channels/${channelId}/voices`);
|
||||
}
|
||||
|
||||
async setChannelVoices(channelId: string, profileIds: string[]): Promise<{ message: string }> {
|
||||
async setChannelVoices(
|
||||
channelId: string,
|
||||
profileIds: string[],
|
||||
): Promise<{ message: string }> {
|
||||
return this.request(`/channels/${channelId}/voices`, {
|
||||
method: 'PUT',
|
||||
body: JSON.stringify({ profile_ids: profileIds }),
|
||||
@@ -451,30 +402,16 @@ class ApiClient {
|
||||
return this.request(`/profiles/${profileId}/channels`);
|
||||
}
|
||||
|
||||
async setProfileChannels(profileId: string, channelIds: string[]): Promise<{ message: string }> {
|
||||
async setProfileChannels(
|
||||
profileId: string,
|
||||
channelIds: string[],
|
||||
): Promise<{ message: string }> {
|
||||
return this.request(`/profiles/${profileId}/channels`, {
|
||||
method: 'PUT',
|
||||
body: JSON.stringify({ channel_ids: channelIds }),
|
||||
});
|
||||
}
|
||||
|
||||
// CUDA Backend Management
|
||||
async getCudaStatus(): Promise<CudaStatus> {
|
||||
return this.request<CudaStatus>('/backend/cuda-status');
|
||||
}
|
||||
|
||||
async downloadCudaBackend(): Promise<{ message: string; progress_key: string }> {
|
||||
return this.request<{ message: string; progress_key: string }>('/backend/download-cuda', {
|
||||
method: 'POST',
|
||||
});
|
||||
}
|
||||
|
||||
async deleteCudaBackend(): Promise<{ message: string }> {
|
||||
return this.request<{ message: string }>('/backend/cuda', {
|
||||
method: 'DELETE',
|
||||
});
|
||||
}
|
||||
|
||||
// Stories
|
||||
async listStories(): Promise<StoryResponse[]> {
|
||||
return this.request<StoryResponse[]>('/stories');
|
||||
@@ -531,33 +468,21 @@ class ApiClient {
|
||||
});
|
||||
}
|
||||
|
||||
async moveStoryItem(
|
||||
storyId: string,
|
||||
itemId: string,
|
||||
data: StoryItemMove,
|
||||
): Promise<StoryItemDetail> {
|
||||
async moveStoryItem(storyId: string, itemId: string, data: StoryItemMove): Promise<StoryItemDetail> {
|
||||
return this.request<StoryItemDetail>(`/stories/${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),
|
||||
|
||||
@@ -34,11 +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;
|
||||
normalize?: boolean;
|
||||
}
|
||||
|
||||
export interface GenerationResponse {
|
||||
@@ -46,14 +41,9 @@ export interface GenerationResponse {
|
||||
profile_id: string;
|
||||
text: string;
|
||||
language: string;
|
||||
audio_path?: string;
|
||||
duration?: number;
|
||||
audio_path: string;
|
||||
duration: number;
|
||||
seed?: number;
|
||||
instruct?: string;
|
||||
engine?: string;
|
||||
model_size?: string;
|
||||
status: 'generating' | 'completed' | 'failed';
|
||||
error?: string;
|
||||
created_at: string;
|
||||
}
|
||||
|
||||
@@ -88,29 +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 {
|
||||
@@ -127,29 +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[];
|
||||
}
|
||||
@@ -162,11 +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);
|
||||
}
|
||||
|
||||
|
||||
@@ -8,15 +8,14 @@ import { LANGUAGE_CODES, type LanguageCode } from '@/lib/constants/languages';
|
||||
import { useGeneration } from '@/lib/hooks/useGeneration';
|
||||
import { useModelDownloadToast } from '@/lib/hooks/useModelDownloadToast';
|
||||
import { useGenerationStore } from '@/stores/generationStore';
|
||||
import { useServerStore } from '@/stores/serverStore';
|
||||
import { usePlayerStore } from '@/stores/playerStore';
|
||||
|
||||
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>;
|
||||
@@ -29,10 +28,8 @@ interface UseGenerationFormOptions {
|
||||
export function useGenerationForm(options: UseGenerationFormOptions = {}) {
|
||||
const { toast } = useToast();
|
||||
const generation = useGeneration();
|
||||
const addPendingGeneration = useGenerationStore((state) => state.addPendingGeneration);
|
||||
const maxChunkChars = useServerStore((state) => state.maxChunkChars);
|
||||
const crossfadeMs = useServerStore((state) => state.crossfadeMs);
|
||||
const normalizeAudio = useServerStore((state) => state.normalizeAudio);
|
||||
const setAudioWithAutoPlay = usePlayerStore((state) => state.setAudioWithAutoPlay);
|
||||
const setIsGenerating = useGenerationStore((state) => state.setIsGenerating);
|
||||
const [downloadingModelName, setDownloadingModelName] = useState<string | null>(null);
|
||||
const [downloadingDisplayName, setDownloadingDisplayName] = useState<string | null>(null);
|
||||
|
||||
@@ -50,7 +47,6 @@ export function useGenerationForm(options: UseGenerationFormOptions = {}) {
|
||||
seed: undefined,
|
||||
modelSize: '1.7B',
|
||||
instruct: '',
|
||||
engine: 'qwen',
|
||||
...options.defaultValues,
|
||||
},
|
||||
});
|
||||
@@ -69,27 +65,11 @@ export function useGenerationForm(options: UseGenerationFormOptions = {}) {
|
||||
}
|
||||
|
||||
try {
|
||||
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';
|
||||
setIsGenerating(true);
|
||||
|
||||
const modelName = `qwen-tts-${data.modelSize}`;
|
||||
const displayName = data.modelSize === '1.7B' ? 'Qwen TTS 1.7B' : 'Qwen TTS 0.6B';
|
||||
|
||||
// Check if model needs downloading
|
||||
try {
|
||||
const modelStatus = await apiClient.getModelStatus();
|
||||
const model = modelStatus.models.find((m) => m.model_name === modelName);
|
||||
@@ -102,33 +82,24 @@ export function useGenerationForm(options: UseGenerationFormOptions = {}) {
|
||||
console.error('Failed to check model status:', error);
|
||||
}
|
||||
|
||||
const isQwen = engine === 'qwen';
|
||||
// This now returns immediately with status="generating"
|
||||
const result = await generation.mutateAsync({
|
||||
profile_id: selectedProfileId,
|
||||
text: data.text,
|
||||
language: data.language,
|
||||
seed: data.seed,
|
||||
model_size: isQwen ? data.modelSize : undefined,
|
||||
engine,
|
||||
instruct: isQwen ? data.instruct || undefined : undefined,
|
||||
max_chunk_chars: maxChunkChars,
|
||||
crossfade_ms: crossfadeMs,
|
||||
normalize: normalizeAudio,
|
||||
model_size: data.modelSize,
|
||||
instruct: data.instruct || undefined,
|
||||
});
|
||||
|
||||
// Track this generation for SSE status updates
|
||||
addPendingGeneration(result.id);
|
||||
|
||||
// Reset form immediately — user can start typing again
|
||||
form.reset({
|
||||
text: '',
|
||||
language: data.language,
|
||||
seed: undefined,
|
||||
modelSize: data.modelSize,
|
||||
instruct: '',
|
||||
engine: data.engine,
|
||||
toast({
|
||||
title: 'Generation complete!',
|
||||
description: `Audio generated (${result.duration.toFixed(2)}s)`,
|
||||
});
|
||||
|
||||
const audioUrl = apiClient.getAudioUrl(result.id);
|
||||
setAudioWithAutoPlay(audioUrl, result.id, selectedProfileId, data.text.substring(0, 50));
|
||||
|
||||
form.reset();
|
||||
options.onSuccess?.(result.id);
|
||||
} catch (error) {
|
||||
toast({
|
||||
@@ -137,6 +108,7 @@ export function useGenerationForm(options: UseGenerationFormOptions = {}) {
|
||||
variant: 'destructive',
|
||||
});
|
||||
} finally {
|
||||
setIsGenerating(false);
|
||||
setDownloadingModelName(null);
|
||||
setDownloadingDisplayName(null);
|
||||
}
|
||||
|
||||
@@ -1,154 +0,0 @@
|
||||
import { useQueryClient } from '@tanstack/react-query';
|
||||
import { useEffect, useRef } from 'react';
|
||||
import { useToast } from '@/components/ui/use-toast';
|
||||
import { apiClient } from '@/lib/api/client';
|
||||
import { useGenerationStore } from '@/stores/generationStore';
|
||||
import { usePlayerStore } from '@/stores/playerStore';
|
||||
import { useServerStore } from '@/stores/serverStore';
|
||||
|
||||
interface GenerationStatusEvent {
|
||||
id: string;
|
||||
status: 'generating' | 'completed' | 'failed' | 'not_found';
|
||||
duration?: number;
|
||||
error?: string;
|
||||
}
|
||||
|
||||
/**
|
||||
* Subscribes to SSE for all pending generations. When a generation completes,
|
||||
* invalidates the history query, removes it from pending, and auto-plays
|
||||
* if the player is idle.
|
||||
*/
|
||||
export function useGenerationProgress() {
|
||||
const queryClient = useQueryClient();
|
||||
const { toast } = useToast();
|
||||
const pendingIds = useGenerationStore((s) => s.pendingGenerationIds);
|
||||
const removePendingGeneration = useGenerationStore((s) => s.removePendingGeneration);
|
||||
const removePendingStoryAdd = useGenerationStore((s) => s.removePendingStoryAdd);
|
||||
const isPlaying = usePlayerStore((s) => s.isPlaying);
|
||||
const setAudioWithAutoPlay = usePlayerStore((s) => s.setAudioWithAutoPlay);
|
||||
const autoplayOnGenerate = useServerStore((s) => s.autoplayOnGenerate);
|
||||
|
||||
// Keep refs to avoid stale closures in EventSource handlers
|
||||
const isPlayingRef = useRef(isPlaying);
|
||||
const autoplayRef = useRef(autoplayOnGenerate);
|
||||
isPlayingRef.current = isPlaying;
|
||||
autoplayRef.current = autoplayOnGenerate;
|
||||
|
||||
// Track active EventSource instances
|
||||
const eventSourcesRef = useRef<Map<string, EventSource>>(new Map());
|
||||
|
||||
// Unmount-only cleanup — close all SSE connections when the hook is torn down
|
||||
useEffect(() => {
|
||||
const sources = eventSourcesRef.current;
|
||||
return () => {
|
||||
for (const source of sources.values()) {
|
||||
source.close();
|
||||
}
|
||||
sources.clear();
|
||||
};
|
||||
}, []);
|
||||
|
||||
useEffect(() => {
|
||||
const currentSources = eventSourcesRef.current;
|
||||
|
||||
// Close SSE connections for IDs no longer pending
|
||||
for (const [id, source] of currentSources.entries()) {
|
||||
if (!pendingIds.has(id)) {
|
||||
source.close();
|
||||
currentSources.delete(id);
|
||||
}
|
||||
}
|
||||
|
||||
// Open SSE connections for new pending IDs
|
||||
for (const id of pendingIds) {
|
||||
if (currentSources.has(id)) continue;
|
||||
|
||||
const url = apiClient.getGenerationStatusUrl(id);
|
||||
const source = new EventSource(url);
|
||||
|
||||
source.onmessage = (event) => {
|
||||
try {
|
||||
const data: GenerationStatusEvent = JSON.parse(event.data);
|
||||
|
||||
if (data.status === 'completed') {
|
||||
source.close();
|
||||
currentSources.delete(id);
|
||||
removePendingGeneration(id);
|
||||
|
||||
// Refresh history to pick up the completed generation
|
||||
queryClient.invalidateQueries({ queryKey: ['history'] });
|
||||
|
||||
// If this generation was queued for a story, add it now
|
||||
const storyId = removePendingStoryAdd(id);
|
||||
if (storyId) {
|
||||
apiClient
|
||||
.addStoryItem(storyId, { generation_id: id })
|
||||
.then(() => {
|
||||
queryClient.invalidateQueries({ queryKey: ['stories'] });
|
||||
queryClient.invalidateQueries({ queryKey: ['stories', storyId] });
|
||||
toast({
|
||||
title: 'Added to story',
|
||||
description: data.duration
|
||||
? `Audio generated (${data.duration.toFixed(2)}s) and added to story`
|
||||
: 'Audio generated and added to story',
|
||||
});
|
||||
})
|
||||
.catch(() => {
|
||||
toast({
|
||||
title: 'Generation complete',
|
||||
description: 'Audio generated but failed to add to story',
|
||||
variant: 'destructive',
|
||||
});
|
||||
});
|
||||
} else {
|
||||
// toast({
|
||||
// title: 'Generation complete!',
|
||||
// description: data.duration
|
||||
// ? `Audio generated (${data.duration.toFixed(2)}s)`
|
||||
// : 'Audio generated',
|
||||
// });
|
||||
}
|
||||
|
||||
// Auto-play if enabled and nothing is currently playing
|
||||
if (autoplayRef.current && !isPlayingRef.current) {
|
||||
const genAudioUrl = apiClient.getAudioUrl(id);
|
||||
setAudioWithAutoPlay(genAudioUrl, id, '', '');
|
||||
}
|
||||
} else if (data.status === 'failed' || data.status === 'not_found') {
|
||||
source.close();
|
||||
currentSources.delete(id);
|
||||
removePendingGeneration(id);
|
||||
removePendingStoryAdd(id);
|
||||
|
||||
queryClient.invalidateQueries({ queryKey: ['history'] });
|
||||
|
||||
toast({
|
||||
title: data.status === 'not_found' ? 'Generation not found' : 'Generation failed',
|
||||
description: data.error || 'An error occurred during generation',
|
||||
variant: 'destructive',
|
||||
});
|
||||
}
|
||||
} catch {
|
||||
// Ignore parse errors from heartbeats etc
|
||||
}
|
||||
};
|
||||
|
||||
source.onerror = () => {
|
||||
// EventSource auto-reconnects, but if we get repeated errors
|
||||
// just clean up
|
||||
source.close();
|
||||
currentSources.delete(id);
|
||||
removePendingGeneration(id);
|
||||
};
|
||||
|
||||
currentSources.set(id, source);
|
||||
}
|
||||
}, [
|
||||
pendingIds,
|
||||
removePendingGeneration,
|
||||
removePendingStoryAdd,
|
||||
queryClient,
|
||||
toast,
|
||||
setAudioWithAutoPlay,
|
||||
]);
|
||||
}
|
||||
@@ -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();
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
@@ -1,23 +1,23 @@
|
||||
import { useCallback, useEffect, useRef, useState } from 'react';
|
||||
import { apiClient } from '@/lib/api/client';
|
||||
import type { ActiveDownloadTask } from '@/lib/api/types';
|
||||
import { useGenerationStore } from '@/stores/generationStore';
|
||||
import type { ActiveDownloadTask } from '@/lib/api/types';
|
||||
|
||||
// Polling interval in milliseconds
|
||||
const POLL_INTERVAL = 30000;
|
||||
const POLL_INTERVAL = 2000;
|
||||
|
||||
/**
|
||||
* Hook to monitor active tasks (downloads and generations).
|
||||
* Polls the server periodically to catch downloads triggered from anywhere
|
||||
* (transcription, generation, explicit download, etc.).
|
||||
*
|
||||
*
|
||||
* Returns the active downloads so components can render download toasts.
|
||||
*/
|
||||
export function useRestoreActiveTasks() {
|
||||
const [activeDownloads, setActiveDownloads] = useState<ActiveDownloadTask[]>([]);
|
||||
const setIsGenerating = useGenerationStore((state) => state.setIsGenerating);
|
||||
const setActiveGenerationId = useGenerationStore((state) => state.setActiveGenerationId);
|
||||
const addPendingGeneration = useGenerationStore((state) => state.addPendingGeneration);
|
||||
|
||||
|
||||
// Track which downloads we've seen to detect new ones
|
||||
const seenDownloadsRef = useRef<Set<string>>(new Set());
|
||||
|
||||
@@ -25,15 +25,15 @@ export function useRestoreActiveTasks() {
|
||||
try {
|
||||
const tasks = await apiClient.getActiveTasks();
|
||||
|
||||
// Restore pending generations (e.g., after page refresh)
|
||||
// Update generation state
|
||||
if (tasks.generations.length > 0) {
|
||||
setIsGenerating(true);
|
||||
setActiveGenerationId(tasks.generations[0].task_id);
|
||||
for (const gen of tasks.generations) {
|
||||
addPendingGeneration(gen.task_id);
|
||||
}
|
||||
} else {
|
||||
// Only clear if we were tracking a generation
|
||||
const currentId = useGenerationStore.getState().activeGenerationId;
|
||||
if (currentId) {
|
||||
setIsGenerating(false);
|
||||
setActiveGenerationId(null);
|
||||
}
|
||||
}
|
||||
@@ -41,14 +41,14 @@ export function useRestoreActiveTasks() {
|
||||
// Update active downloads
|
||||
// Keep track of all active downloads (including new ones)
|
||||
const currentDownloadNames = new Set(tasks.downloads.map((d) => d.model_name));
|
||||
|
||||
|
||||
// Remove completed downloads from our seen set
|
||||
for (const name of seenDownloadsRef.current) {
|
||||
if (!currentDownloadNames.has(name)) {
|
||||
seenDownloadsRef.current.delete(name);
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
// Add new downloads to seen set
|
||||
for (const download of tasks.downloads) {
|
||||
seenDownloadsRef.current.add(download.model_name);
|
||||
@@ -59,7 +59,7 @@ export function useRestoreActiveTasks() {
|
||||
// Silently fail - server might be temporarily unavailable
|
||||
console.debug('Failed to fetch active tasks:', error);
|
||||
}
|
||||
}, [setActiveGenerationId, addPendingGeneration]);
|
||||
}, [setIsGenerating, setActiveGenerationId]);
|
||||
|
||||
useEffect(() => {
|
||||
// Fetch immediately on mount
|
||||
|
||||
+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;
|
||||
});
|
||||
}
|
||||
|
||||
/**
|
||||
|
||||
@@ -21,25 +21,10 @@ export function formatDate(date: string | Date): string {
|
||||
} else {
|
||||
dateObj = date;
|
||||
}
|
||||
|
||||
|
||||
return formatDistance(dateObj, new Date(), { addSuffix: true }).replace(/^about /i, '');
|
||||
}
|
||||
|
||||
const ENGINE_DISPLAY_NAMES: Record<string, string> = {
|
||||
qwen: 'Qwen',
|
||||
luxtts: 'LuxTTS',
|
||||
chatterbox: 'Chatterbox',
|
||||
chatterbox_turbo: 'Chatterbox Turbo',
|
||||
};
|
||||
|
||||
export function formatEngineName(engine?: string, modelSize?: string): string {
|
||||
const name = ENGINE_DISPLAY_NAMES[engine ?? 'qwen'] ?? engine ?? 'Qwen';
|
||||
if (engine === 'qwen' && modelSize) {
|
||||
return `${name} ${modelSize}`;
|
||||
}
|
||||
return name;
|
||||
}
|
||||
|
||||
export function formatFileSize(bytes: number): string {
|
||||
if (bytes === 0) return '0 Bytes';
|
||||
const k = 1024;
|
||||
|
||||
@@ -49,9 +49,8 @@ export interface PlatformAudio {
|
||||
}
|
||||
|
||||
export interface PlatformLifecycle {
|
||||
startServer(remote?: boolean, modelsDir?: string | null): Promise<string>;
|
||||
startServer(remote?: boolean): Promise<string>;
|
||||
stopServer(): Promise<void>;
|
||||
restartServer(modelsDir?: string | null): Promise<string>;
|
||||
setKeepServerRunning(keep: boolean): Promise<void>;
|
||||
setupWindowCloseHandler(): Promise<void>;
|
||||
onServerReady?: () => void;
|
||||
|
||||
@@ -8,10 +8,8 @@ import { Sidebar } from '@/components/Sidebar';
|
||||
import { StoriesTab } from '@/components/StoriesTab/StoriesTab';
|
||||
import { Toaster } from '@/components/ui/toaster';
|
||||
import { VoicesTab } from '@/components/VoicesTab/VoicesTab';
|
||||
import { useGenerationProgress } from '@/lib/hooks/useGenerationProgress';
|
||||
import { useModelDownloadToast } from '@/lib/hooks/useModelDownloadToast';
|
||||
import { MODEL_DISPLAY_NAMES, useRestoreActiveTasks } from '@/lib/hooks/useRestoreActiveTasks';
|
||||
|
||||
// Simple platform check that works in both web and Tauri
|
||||
const isMacOS = () => navigator.platform.toLowerCase().includes('mac');
|
||||
|
||||
@@ -20,9 +18,6 @@ function RootLayout() {
|
||||
// Monitor active downloads/generations and show toasts for them
|
||||
const activeDownloads = useRestoreActiveTasks();
|
||||
|
||||
// Subscribe to SSE for pending generations — handles completion, auto-play, and history refresh
|
||||
useGenerationProgress();
|
||||
|
||||
return (
|
||||
<AppFrame>
|
||||
<div className="flex flex-1 min-h-0 overflow-hidden">
|
||||
|
||||
@@ -1,58 +1,15 @@
|
||||
import { create } from 'zustand';
|
||||
|
||||
interface GenerationState {
|
||||
/** IDs of generations currently in progress */
|
||||
pendingGenerationIds: Set<string>;
|
||||
/** Whether any generation is in progress (derived from pendingGenerationIds) */
|
||||
isGenerating: boolean;
|
||||
/** Map of generationId → storyId for deferred story additions */
|
||||
pendingStoryAdds: Map<string, string>;
|
||||
addPendingGeneration: (id: string) => void;
|
||||
removePendingGeneration: (id: string) => void;
|
||||
addPendingStoryAdd: (generationId: string, storyId: string) => void;
|
||||
removePendingStoryAdd: (generationId: string) => string | undefined;
|
||||
setActiveGenerationId: (id: string | null) => void;
|
||||
activeGenerationId: string | null;
|
||||
setIsGenerating: (generating: boolean) => void;
|
||||
setActiveGenerationId: (id: string | null) => void;
|
||||
}
|
||||
|
||||
export const useGenerationStore = create<GenerationState>((set, get) => ({
|
||||
pendingGenerationIds: new Set(),
|
||||
export const useGenerationStore = create<GenerationState>((set) => ({
|
||||
isGenerating: false,
|
||||
activeGenerationId: null,
|
||||
pendingStoryAdds: new Map(),
|
||||
|
||||
addPendingGeneration: (id) =>
|
||||
set((state) => {
|
||||
const next = new Set(state.pendingGenerationIds);
|
||||
next.add(id);
|
||||
return { pendingGenerationIds: next, isGenerating: true };
|
||||
}),
|
||||
|
||||
removePendingGeneration: (id) =>
|
||||
set((state) => {
|
||||
const next = new Set(state.pendingGenerationIds);
|
||||
next.delete(id);
|
||||
return { pendingGenerationIds: next, isGenerating: next.size > 0 };
|
||||
}),
|
||||
|
||||
addPendingStoryAdd: (generationId, storyId) =>
|
||||
set((state) => {
|
||||
const next = new Map(state.pendingStoryAdds);
|
||||
next.set(generationId, storyId);
|
||||
return { pendingStoryAdds: next };
|
||||
}),
|
||||
|
||||
removePendingStoryAdd: (generationId) => {
|
||||
const storyId = get().pendingStoryAdds.get(generationId);
|
||||
if (storyId) {
|
||||
set((state) => {
|
||||
const next = new Map(state.pendingStoryAdds);
|
||||
next.delete(generationId);
|
||||
return { pendingStoryAdds: next };
|
||||
});
|
||||
}
|
||||
return storyId;
|
||||
},
|
||||
|
||||
setIsGenerating: (generating) => set({ isGenerating: generating }),
|
||||
setActiveGenerationId: (id) => set({ activeGenerationId: id }),
|
||||
}));
|
||||
|
||||
@@ -13,21 +13,6 @@ interface ServerStore {
|
||||
|
||||
keepServerRunningOnClose: boolean;
|
||||
setKeepServerRunningOnClose: (keepRunning: boolean) => void;
|
||||
|
||||
maxChunkChars: number;
|
||||
setMaxChunkChars: (value: number) => void;
|
||||
|
||||
crossfadeMs: number;
|
||||
setCrossfadeMs: (value: number) => void;
|
||||
|
||||
normalizeAudio: boolean;
|
||||
setNormalizeAudio: (value: boolean) => void;
|
||||
|
||||
autoplayOnGenerate: boolean;
|
||||
setAutoplayOnGenerate: (value: boolean) => void;
|
||||
|
||||
customModelsDir: string | null;
|
||||
setCustomModelsDir: (dir: string | null) => void;
|
||||
}
|
||||
|
||||
export const useServerStore = create<ServerStore>()(
|
||||
@@ -44,21 +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 }),
|
||||
|
||||
normalizeAudio: true,
|
||||
setNormalizeAudio: (value) => set({ normalizeAudio: value }),
|
||||
|
||||
autoplayOnGenerate: true,
|
||||
setAutoplayOnGenerate: (value) => set({ autoplayOnGenerate: value }),
|
||||
|
||||
customModelsDir: null,
|
||||
setCustomModelsDir: (dir) => set({ customModelsDir: dir }),
|
||||
}),
|
||||
{
|
||||
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
|
||||
+2
-36
@@ -45,14 +45,10 @@ class Generation(Base):
|
||||
profile_id = Column(String, ForeignKey("profiles.id"), nullable=False)
|
||||
text = Column(Text, nullable=False)
|
||||
language = Column(String, default="en")
|
||||
audio_path = Column(String, nullable=True)
|
||||
duration = Column(Float, nullable=True)
|
||||
audio_path = Column(String, nullable=False)
|
||||
duration = Column(Float, nullable=False)
|
||||
seed = Column(Integer)
|
||||
instruct = Column(Text)
|
||||
engine = Column(String, default="qwen")
|
||||
model_size = Column(String, nullable=True)
|
||||
status = Column(String, default="completed") # generating, completed, failed
|
||||
error = Column(Text, nullable=True)
|
||||
created_at = Column(DateTime, default=datetime.utcnow)
|
||||
|
||||
|
||||
@@ -292,36 +288,6 @@ def _run_migrations(engine):
|
||||
conn.commit()
|
||||
print("Added avatar_path column to profiles")
|
||||
|
||||
# Migration: Add status and error columns to generations table
|
||||
if 'generations' in inspector.get_table_names():
|
||||
columns = {col['name'] for col in inspector.get_columns('generations')}
|
||||
if 'status' not in columns:
|
||||
print("Migrating generations: adding status column")
|
||||
with engine.connect() as conn:
|
||||
conn.execute(text("ALTER TABLE generations ADD COLUMN status VARCHAR DEFAULT 'completed'"))
|
||||
conn.commit()
|
||||
print("Added status column to generations")
|
||||
if 'error' not in columns:
|
||||
print("Migrating generations: adding error column")
|
||||
with engine.connect() as conn:
|
||||
conn.execute(text("ALTER TABLE generations ADD COLUMN error TEXT"))
|
||||
conn.commit()
|
||||
print("Added error column to generations")
|
||||
if 'engine' not in columns:
|
||||
print("Migrating generations: adding engine column")
|
||||
with engine.connect() as conn:
|
||||
conn.execute(text("ALTER TABLE generations ADD COLUMN engine VARCHAR DEFAULT 'qwen'"))
|
||||
conn.commit()
|
||||
print("Added engine column to generations")
|
||||
# Re-read columns after engine migration (variable name shadows outer `engine`)
|
||||
columns = {col['name'] for col in inspector.get_columns('generations')}
|
||||
if 'model_size' not in columns:
|
||||
print("Migrating generations: adding model_size column")
|
||||
with engine.connect() as conn:
|
||||
conn.execute(text("ALTER TABLE generations ADD COLUMN model_size VARCHAR"))
|
||||
conn.commit()
|
||||
print("Added model_size column to generations")
|
||||
|
||||
|
||||
def get_db():
|
||||
"""Get database session (generator for dependency injection)."""
|
||||
|
||||
+1
-42
@@ -29,10 +29,6 @@ async def create_generation(
|
||||
seed: Optional[int],
|
||||
db: Session,
|
||||
instruct: Optional[str] = None,
|
||||
generation_id: Optional[str] = None,
|
||||
status: str = "completed",
|
||||
engine: Optional[str] = "qwen",
|
||||
model_size: Optional[str] = None,
|
||||
) -> GenerationResponse:
|
||||
"""
|
||||
Create a new generation history entry.
|
||||
@@ -46,16 +42,12 @@ async def create_generation(
|
||||
seed: Random seed used (if any)
|
||||
db: Database session
|
||||
instruct: Natural language instruction used (if any)
|
||||
generation_id: Pre-assigned ID (for async generation flow)
|
||||
status: Generation status (generating, completed, failed)
|
||||
engine: TTS engine used (qwen, luxtts, chatterbox, chatterbox_turbo)
|
||||
model_size: Model size variant (1.7B, 0.6B) — only relevant for qwen
|
||||
|
||||
Returns:
|
||||
Created generation entry
|
||||
"""
|
||||
db_generation = DBGeneration(
|
||||
id=generation_id or str(uuid.uuid4()),
|
||||
id=str(uuid.uuid4()),
|
||||
profile_id=profile_id,
|
||||
text=text,
|
||||
language=language,
|
||||
@@ -63,9 +55,6 @@ async def create_generation(
|
||||
duration=duration,
|
||||
seed=seed,
|
||||
instruct=instruct,
|
||||
engine=engine,
|
||||
model_size=model_size,
|
||||
status=status,
|
||||
created_at=datetime.utcnow(),
|
||||
)
|
||||
|
||||
@@ -76,32 +65,6 @@ async def create_generation(
|
||||
return GenerationResponse.model_validate(db_generation)
|
||||
|
||||
|
||||
async def update_generation_status(
|
||||
generation_id: str,
|
||||
status: str,
|
||||
db: Session,
|
||||
audio_path: Optional[str] = None,
|
||||
duration: Optional[float] = None,
|
||||
error: Optional[str] = None,
|
||||
) -> Optional[GenerationResponse]:
|
||||
"""Update the status of a generation (used by async generation flow)."""
|
||||
generation = db.query(DBGeneration).filter_by(id=generation_id).first()
|
||||
if not generation:
|
||||
return None
|
||||
|
||||
generation.status = status
|
||||
if audio_path is not None:
|
||||
generation.audio_path = audio_path
|
||||
if duration is not None:
|
||||
generation.duration = duration
|
||||
if error is not None:
|
||||
generation.error = error
|
||||
|
||||
db.commit()
|
||||
db.refresh(generation)
|
||||
return GenerationResponse.model_validate(generation)
|
||||
|
||||
|
||||
async def get_generation(
|
||||
generation_id: str,
|
||||
db: Session,
|
||||
@@ -180,10 +143,6 @@ async def list_generations(
|
||||
duration=generation.duration,
|
||||
seed=generation.seed,
|
||||
instruct=generation.instruct,
|
||||
engine=generation.engine or "qwen",
|
||||
model_size=generation.model_size,
|
||||
status=generation.status or "completed",
|
||||
error=generation.error,
|
||||
created_at=generation.created_at,
|
||||
))
|
||||
|
||||
|
||||
+113
-1022
File diff suppressed because it is too large
Load Diff
+11
-51
@@ -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)$")
|
||||
|
||||
|
||||
class VoiceProfileResponse(BaseModel):
|
||||
@@ -52,15 +52,11 @@ class ProfileSampleResponse(BaseModel):
|
||||
class GenerationRequest(BaseModel):
|
||||
"""Request model for voice generation."""
|
||||
profile_id: str
|
||||
text: str = Field(..., min_length=1, max_length=50000)
|
||||
language: str = Field(default="en", pattern="^(zh|en|ja|ko|de|fr|ru|pt|es|it|he)$")
|
||||
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)")
|
||||
normalize: bool = Field(default=True, description="Normalize output audio volume")
|
||||
|
||||
|
||||
class GenerationResponse(BaseModel):
|
||||
@@ -69,14 +65,10 @@ class GenerationResponse(BaseModel):
|
||||
profile_id: str
|
||||
text: str
|
||||
language: str
|
||||
audio_path: Optional[str] = None
|
||||
duration: Optional[float] = None
|
||||
seed: Optional[int] = None
|
||||
instruct: Optional[str] = None
|
||||
engine: Optional[str] = "qwen"
|
||||
model_size: Optional[str] = None
|
||||
status: str = "completed"
|
||||
error: Optional[str] = None
|
||||
audio_path: str
|
||||
duration: float
|
||||
seed: Optional[int]
|
||||
instruct: Optional[str]
|
||||
created_at: datetime
|
||||
|
||||
class Config:
|
||||
@@ -98,14 +90,10 @@ class HistoryResponse(BaseModel):
|
||||
profile_name: str
|
||||
text: str
|
||||
language: str
|
||||
audio_path: Optional[str] = None
|
||||
duration: Optional[float] = None
|
||||
seed: Optional[int] = None
|
||||
instruct: Optional[str] = None
|
||||
engine: Optional[str] = "qwen"
|
||||
model_size: Optional[str] = None
|
||||
status: str = "completed"
|
||||
error: Optional[str] = None
|
||||
audio_path: str
|
||||
duration: float
|
||||
seed: Optional[int]
|
||||
instruct: Optional[str]
|
||||
created_at: datetime
|
||||
|
||||
class Config:
|
||||
@@ -139,30 +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
|
||||
@@ -179,21 +149,11 @@ class ModelDownloadRequest(BaseModel):
|
||||
model_name: str
|
||||
|
||||
|
||||
class ModelMigrateRequest(BaseModel):
|
||||
"""Request model for migrating models to a new directory."""
|
||||
destination: str
|
||||
|
||||
|
||||
class ActiveDownloadTask(BaseModel):
|
||||
"""Response model for active download task."""
|
||||
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
|
||||
|
||||
+6
-6
@@ -270,14 +270,11 @@ async def add_item_to_story(
|
||||
generation_created_at=generation.created_at,
|
||||
)
|
||||
|
||||
# Get track from data or default to 0
|
||||
track = data.track if data.track is not None else 0
|
||||
|
||||
# Calculate start_time_ms if not provided
|
||||
if data.start_time_ms is not None:
|
||||
start_time_ms = data.start_time_ms
|
||||
else:
|
||||
# Find the maximum end time on the target track only
|
||||
# Find the maximum end time (start_time_ms + duration_ms) of existing items
|
||||
existing_items = db.query(
|
||||
DBStoryItem,
|
||||
DBGeneration
|
||||
@@ -285,11 +282,11 @@ async def add_item_to_story(
|
||||
DBGeneration,
|
||||
DBStoryItem.generation_id == DBGeneration.id
|
||||
).filter(
|
||||
DBStoryItem.story_id == story_id,
|
||||
DBStoryItem.track == track,
|
||||
DBStoryItem.story_id == story_id
|
||||
).all()
|
||||
|
||||
if not existing_items:
|
||||
# First item starts at 0
|
||||
start_time_ms = 0
|
||||
else:
|
||||
max_end_time_ms = 0
|
||||
@@ -300,6 +297,9 @@ async def add_item_to_story(
|
||||
# Add 200ms gap after the last item
|
||||
start_time_ms = max_end_time_ms + 200
|
||||
|
||||
# Get track from data or default to 0
|
||||
track = data.track if data.track is not None else 0
|
||||
|
||||
# Create item
|
||||
item = DBStoryItem(
|
||||
id=str(uuid.uuid4()),
|
||||
|
||||
@@ -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=[],
|
||||
|
||||
@@ -4,10 +4,6 @@
|
||||
"workspaces": {
|
||||
"": {
|
||||
"name": "voicebox",
|
||||
"dependencies": {
|
||||
"loaders.css": "^0.1.2",
|
||||
"react-loaders": "^3.0.1",
|
||||
},
|
||||
"devDependencies": {
|
||||
"@biomejs/biome": "2.3.12",
|
||||
"@types/node": "^20.0.0",
|
||||
@@ -17,7 +13,7 @@
|
||||
},
|
||||
"app": {
|
||||
"name": "@voicebox/app",
|
||||
"version": "0.1.13",
|
||||
"version": "0.1.11",
|
||||
"dependencies": {
|
||||
"@dnd-kit/core": "^6.3.1",
|
||||
"@dnd-kit/sortable": "^10.0.0",
|
||||
@@ -72,7 +68,7 @@
|
||||
},
|
||||
"landing": {
|
||||
"name": "@voicebox/landing",
|
||||
"version": "0.1.13",
|
||||
"version": "0.1.11",
|
||||
"dependencies": {
|
||||
"@radix-ui/react-separator": "^1.1.8",
|
||||
"@radix-ui/react-slot": "^1.2.4",
|
||||
@@ -97,7 +93,7 @@
|
||||
},
|
||||
"tauri": {
|
||||
"name": "@voicebox/tauri",
|
||||
"version": "0.1.13",
|
||||
"version": "0.1.11",
|
||||
"dependencies": {
|
||||
"@tauri-apps/api": "^2.0.0",
|
||||
"@tauri-apps/plugin-dialog": "^2.0.0",
|
||||
@@ -120,7 +116,7 @@
|
||||
},
|
||||
"web": {
|
||||
"name": "@voicebox/web",
|
||||
"version": "0.1.13",
|
||||
"version": "0.1.11",
|
||||
"dependencies": {
|
||||
"@tanstack/react-query": "^5.0.0",
|
||||
"react": "^18.3.0",
|
||||
@@ -129,7 +125,6 @@
|
||||
"zustand": "^4.5.0",
|
||||
},
|
||||
"devDependencies": {
|
||||
"@tailwindcss/vite": "^4.0.0",
|
||||
"@types/react": "^18.3.0",
|
||||
"@types/react-dom": "^18.3.0",
|
||||
"@typescript-eslint/eslint-plugin": "^7.0.0",
|
||||
@@ -682,8 +677,6 @@
|
||||
|
||||
"class-variance-authority": ["[email protected]", "", { "dependencies": { "clsx": "^2.1.1" } }, "sha512-Ka+9Trutv7G8M6WT6SeiRWz792K5qEqIGEGzXKhAE6xOWAY6pPH8U+9IY3oCMv6kqTmLsv7Xh/2w2RigkePMsg=="],
|
||||
|
||||
"classnames": ["[email protected]", "", {}, "sha512-saHYOzhIQs6wy2sVxTM6bUDsQO4F50V9RQ22qBpEdCW+I+/Wmke2HOl6lS6dTpdxVhb88/I6+Hs+438c3lfUow=="],
|
||||
|
||||
"client-only": ["[email protected]", "", {}, "sha512-IV3Ou0jSMzZrd3pZ48nLkT9DA7Ag1pnPzaiQhpW7c3RbcqqzvzzVu+L8gfqMp/8IM2MQtSiqaCxrrcfu8I8rMA=="],
|
||||
|
||||
"clsx": ["[email protected]", "", {}, "sha512-eYm0QWBtUrBWZWG0d386OGAw16Z995PiOVo2B7bjWSbHedGl5e0ZWaq65kOGgUSNesEIDkB9ISbTg/JK9dhCZA=="],
|
||||
@@ -880,8 +873,6 @@
|
||||
|
||||
"lines-and-columns": ["[email protected]", "", {}, "sha512-7ylylesZQ/PV29jhEDl3Ufjo6ZX7gCqJr5F7PKrqc93v7fzSymt1BpwEU8nAUXs8qzzvqhbjhK5QZg6Mt/HkBg=="],
|
||||
|
||||
"loaders.css": ["[email protected]", "", {}, "sha512-Rhowlq24ey1VOeor+3wYOt9+MjaxBOJm1u4KlQgNC3+0xJ0LS4wq4iG57D/BPzvuD/7HHDGQOWJ+81oR2EI9bQ=="],
|
||||
|
||||
"locate-path": ["[email protected]", "", { "dependencies": { "p-locate": "^5.0.0" } }, "sha512-iPZK6eYjbxRu3uB4/WZ3EsEIMJFMqAoopl3R+zuq0UjcAm/MO6KCweDgPfP3elTztoKP3KtnVHxTn2NHBSDVUw=="],
|
||||
|
||||
"lodash.merge": ["[email protected]", "", {}, "sha512-0KpjqXRVvrYyCsX1swR/XTK0va6VQkQM6MNo7PqW77ByjAhoARA8EfrP1N4+KlKj8YS0ZUCtRT/YUuhyYDujIQ=="],
|
||||
@@ -968,8 +959,6 @@
|
||||
|
||||
"prelude-ls": ["[email protected]", "", {}, "sha512-vkcDPrRZo1QZLbn5RLGPpg/WmIQ65qoWWhcGKf/b5eplkkarX0m9z8ppCat4mlOqUsWpyNuYgO3VRyrYHSzX5g=="],
|
||||
|
||||
"prop-types": ["[email protected]", "", { "dependencies": { "loose-envify": "^1.4.0", "object-assign": "^4.1.1", "react-is": "^16.13.1" } }, "sha512-oj87CgZICdulUohogVAR7AjlC0327U4el4L6eAvOqCeudMDVU0NThNaV+b9Df4dXgSP1gXMTnPdhfe/2qDH5cg=="],
|
||||
|
||||
"punycode": ["[email protected]", "", {}, "sha512-vYt7UD1U9Wg6138shLtLOvdAu+8DsC/ilFtEVHcH+wydcSpNE20AfSOduf6MkRFahL5FY7X1oU7nKVZFtfq8Fg=="],
|
||||
|
||||
"queue-microtask": ["[email protected]", "", {}, "sha512-NuaNSa6flKT5JaSYQzJok04JzTL1CA6aGhv5rfLW3PgqA+M2ChpZQnAC8h8i4ZFkBS8X5RqkDBHA7r4hej3K9A=="],
|
||||
@@ -980,10 +969,6 @@
|
||||
|
||||
"react-hook-form": ["[email protected]", "", { "peerDependencies": { "react": "^16.8.0 || ^17 || ^18 || ^19" } }, "sha512-9SUJKCGKo8HUSsCO+y0CtqkqI5nNuaDqTxyqPsZPqIwudpj4rCrAz/jZV+jn57bx5gtZKOh3neQu94DXMc+w5w=="],
|
||||
|
||||
"react-is": ["[email protected]", "", {}, "sha512-24e6ynE2H+OKt4kqsOvNd8kBpV65zoxbA4BVsEOB3ARVWQki/DHzaUoC5KuON/BiccDaCCTZBuOcfZs70kR8bQ=="],
|
||||
|
||||
"react-loaders": ["[email protected]", "", { "dependencies": { "classnames": "^2.2.3" }, "peerDependencies": { "prop-types": ">=15.6.0", "react": ">=15" } }, "sha512-4igMNqs9Fb3d4Z+0UHIGQNJsw/37gX0nUO8QxupnEKRn1dtyYC1LGwk5GuaoDciMQCQc/MmPwb4Fn6ZfdoX1FQ=="],
|
||||
|
||||
"react-refresh": ["[email protected]", "", {}, "sha512-z6F7K9bV85EfseRCp2bzrpyQ0Gkw1uLoCel9XBVWPg/TjRj94SkJzUTGfOa4bs7iJvBWtQG0Wq7wnI0syw3EBQ=="],
|
||||
|
||||
"react-remove-scroll": ["[email protected]", "", { "dependencies": { "react-remove-scroll-bar": "^2.3.7", "react-style-singleton": "^2.2.3", "tslib": "^2.1.0", "use-callback-ref": "^1.3.3", "use-sidecar": "^1.1.3" }, "peerDependencies": { "@types/react": "*", "react": "^16.8.0 || ^17.0.0 || ^18.0.0 || ^19.0.0 || ^19.0.0-rc" }, "optionalPeers": ["@types/react"] }, "sha512-Iqb9NjCCTt6Hf+vOdNIZGdTiH1QSqr27H/Ek9sv/a97gfueI/5h1s3yRi1nngzMUaOOToin5dI1dXKdXiF+u0Q=="],
|
||||
|
||||
@@ -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.
|
||||
@@ -1,67 +0,0 @@
|
||||
# Voicebox Issue Pain Points (Snapshot)
|
||||
|
||||
## Scope
|
||||
|
||||
- Dataset: **128 total issues** (**107 open**, **21 closed**)
|
||||
- Source: GitHub issues in `jamiepine/voicebox`
|
||||
- Classification: keyword/theme clustering
|
||||
- Note: counts below are **non-exclusive** (one issue can belong to multiple pain points)
|
||||
|
||||
## Most Common Pain Points (Open Issues)
|
||||
|
||||
| Rank | Pain Point | Open Issues | What users are reporting |
|
||||
|---|---|---:|---|
|
||||
| 1 | Model download & offline reliability | **32** | Downloads failing/stalling, cache/offline behavior inconsistent, wrong model size selected, Errno issues |
|
||||
| 2 | GPU/backend compatibility | **22** | GPU not detected, backend fallback surprises, platform-specific runtime failures (Windows/Mac) |
|
||||
| 3 | Export/save/file persistence | **15** | Export fails, "failed to fetch/download audio", samples/profiles not saving |
|
||||
| 4 | Language/accent quality & coverage | **14** | Missing language support, accent mismatch, robotic outputs |
|
||||
| 5 | Update/restart safety + long-op controls | **4** | Auto-restart without warning, update confusion, lack of cancel/pause controls |
|
||||
|
||||
## Representative Issues by Pain Point
|
||||
|
||||
### 1) Model download & offline reliability (32)
|
||||
|
||||
- [#159](https://github.com/jamiepine/voicebox/issues/159) - Qwen download fails with Errno 22
|
||||
- [#151](https://github.com/jamiepine/voicebox/issues/151) - Model loading hangs / server crashes
|
||||
- [#150](https://github.com/jamiepine/voicebox/issues/150) - Internet required despite downloaded models
|
||||
- [#149](https://github.com/jamiepine/voicebox/issues/149) - Cancel/pause controls for large downloads
|
||||
- [#96](https://github.com/jamiepine/voicebox/issues/96) - 0.6B selection still uses/downloads 1.7B
|
||||
|
||||
### 2) GPU/backend compatibility (22)
|
||||
|
||||
- [#164](https://github.com/jamiepine/voicebox/issues/164) - Windows: no GPU usage + multiple breakages
|
||||
- [#141](https://github.com/jamiepine/voicebox/issues/141) - Using CPU only, GPU not used
|
||||
- [#131](https://github.com/jamiepine/voicebox/issues/131) - Numpy ABI mismatch in bundled app
|
||||
- [#130](https://github.com/jamiepine/voicebox/issues/130) - Intel Mac tensor/padding generation error
|
||||
- [#127](https://github.com/jamiepine/voicebox/issues/127) - GPU not found
|
||||
|
||||
### 3) Export/save/file persistence (15)
|
||||
|
||||
- [#148](https://github.com/jamiepine/voicebox/issues/148) - Japanese export fails on 0.1.12
|
||||
- [#143](https://github.com/jamiepine/voicebox/issues/143) - Samples not saving
|
||||
- [#134](https://github.com/jamiepine/voicebox/issues/134) - Can't save profile
|
||||
- [#105](https://github.com/jamiepine/voicebox/issues/105) - Export audio fails (failed to fetch)
|
||||
- [#49](https://github.com/jamiepine/voicebox/issues/49) - Export filename/location ignored on Windows
|
||||
|
||||
### 4) Language/accent quality & coverage (14)
|
||||
|
||||
- [#162](https://github.com/jamiepine/voicebox/issues/162) - Persian audio request/problem
|
||||
- [#117](https://github.com/jamiepine/voicebox/issues/117) - Arabic language support
|
||||
- [#113](https://github.com/jamiepine/voicebox/issues/113) - Polish language support
|
||||
- [#109](https://github.com/jamiepine/voicebox/issues/109) - Ukrainian support
|
||||
- [#100](https://github.com/jamiepine/voicebox/issues/100) - Non-US accent quality issues
|
||||
|
||||
### 5) Update/restart safety + controls (4)
|
||||
|
||||
- [#164](https://github.com/jamiepine/voicebox/issues/164) - Update behavior + usability failures
|
||||
- [#136](https://github.com/jamiepine/voicebox/issues/136) - Auto-restart without warning
|
||||
- [#86](https://github.com/jamiepine/voicebox/issues/86) - Unexpected restart with no confirmation
|
||||
- [#149](https://github.com/jamiepine/voicebox/issues/149) - Need pause/cancel and pre-download confirmation
|
||||
|
||||
## Additional Signal
|
||||
|
||||
- There is also a large **feature-request/misc** bucket (**36 open**) that is competing with stability triage (audiobook, Linux build, additional ASR/TTS models, integrations).
|
||||
|
||||
## Takeaway
|
||||
|
||||
Most user pain is concentrated in four stability areas: **download/offline path**, **GPU/backend detection**, **save/export reliability**, and **language/accent correctness**. Addressing those first should reduce the majority of current support friction.
|
||||
Some files were not shown because too many files have changed in this diff Show More
Reference in New Issue
Block a user