diff --git a/docs/.gitignore b/docs/.gitignore index 70eeb4ba..9e429e49 100644 --- a/docs/.gitignore +++ b/docs/.gitignore @@ -1,3 +1,26 @@ -node_modules -.mintlify +# deps +/node_modules + +# generated content +.source + +# test & build +/coverage +/.next/ +/out/ +/build +*.tsbuildinfo + +# misc .DS_Store +*.pem +/.pnp +.pnp.js +npm-debug.log* +yarn-debug.log* +yarn-error.log* + +# others +.env*.local +.vercel +next-env.d.ts \ No newline at end of file diff --git a/docs/AUTOUPDATER.md b/docs/AUTOUPDATER.md deleted file mode 100644 index 06de6002..00000000 --- a/docs/AUTOUPDATER.md +++ /dev/null @@ -1,192 +0,0 @@ -# Auto-Updater Documentation - -Voicebox includes automatic updates powered by Tauri's updater plugin. This document explains how it works for both users and developers. - -## 1. Generate Signing Keys - -Run this command to generate your signing keypair: - -```bash -cd tauri && bun tauri signer generate -w ~/.tauri/voicebox.key -``` - -This creates: -- **Private key**: `~/.tauri/voicebox.key` (keep this secret!) -- **Public key**: `~/.tauri/voicebox.key.pub` - -## 2. Update Configuration - -Copy the content from `~/.tauri/voicebox.key.pub` and replace the placeholder in `tauri/src-tauri/tauri.conf.json`: - -```json -{ - "plugins": { - "updater": { - "pubkey": "PASTE_PUBLIC_KEY_CONTENT_HERE", - "endpoints": [ - "https://github.com/YOUR_USERNAME/voicebox/releases/latest/download/latest.json" - ] - } - } -} -``` - -Update the endpoint URL with your actual GitHub username/organization. - -## 3. Building with Signatures - -When building releases, set these environment variables: - -**macOS/Linux:** -```bash -export TAURI_SIGNING_PRIVATE_KEY="$(cat ~/.tauri/voicebox.key)" -export TAURI_SIGNING_PRIVATE_KEY_PASSWORD="" -bun run build -``` - -**Windows PowerShell:** -```powershell -$env:TAURI_SIGNING_PRIVATE_KEY = Get-Content ~/.tauri/voicebox.key -Raw -$env:TAURI_SIGNING_PRIVATE_KEY_PASSWORD = "" -bun run build -``` - -## 4. GitHub Release Setup - -When you create a GitHub release, the build process will generate: -- Installers for each platform -- `.sig` signature files -- `latest.json` update manifest - -### Manual Release Process - -1. Build the app with signing keys set -2. Create a new GitHub release -3. Upload all files from `tauri/src-tauri/target/release/bundle/` -4. Create `latest.json` in your release assets: - -```json -{ - "version": "0.2.0", - "notes": "Bug fixes and improvements", - "pub_date": "2026-01-25T12:00:00Z", - "platforms": { - "darwin-aarch64": { - "signature": "CONTENT_FROM_.app.tar.gz.sig", - "url": "https://github.com/YOUR_USERNAME/voicebox/releases/download/v0.2.0/voicebox_0.2.0_aarch64.dmg" - }, - "darwin-x86_64": { - "signature": "CONTENT_FROM_.app.tar.gz.sig", - "url": "https://github.com/YOUR_USERNAME/voicebox/releases/download/v0.2.0/voicebox_0.2.0_x64.dmg" - }, - "linux-x86_64": { - "signature": "CONTENT_FROM_.AppImage.sig", - "url": "https://github.com/YOUR_USERNAME/voicebox/releases/download/v0.2.0/voicebox_0.2.0_amd64.AppImage" - }, - "windows-x86_64": { - "signature": "CONTENT_FROM_.msi.sig", - "url": "https://github.com/YOUR_USERNAME/voicebox/releases/download/v0.2.0/voicebox_0.2.0_x64_en-US.msi" - } - } -} -``` - -### Automated GitHub Actions (Recommended) - -Create `.github/workflows/release.yml`: - -```yaml -name: Release - -on: - push: - tags: - - 'v*' - -jobs: - release: - strategy: - matrix: - platform: [macos-latest, ubuntu-22.04, windows-latest] - - runs-on: ${{ matrix.platform }} - - steps: - - uses: actions/checkout@v4 - - - name: Setup Bun - uses: oven-sh/setup-bun@v1 - - - name: Install Rust - uses: dtolnay/rust-toolchain@stable - - - name: Install dependencies (Ubuntu) - if: matrix.platform == 'ubuntu-22.04' - run: | - sudo apt-get update - sudo apt-get install -y libwebkit2gtk-4.1-dev libappindicator3-dev librsvg2-dev patchelf - - - name: Install dependencies - run: bun install - - - name: Build - env: - TAURI_SIGNING_PRIVATE_KEY: ${{ secrets.TAURI_SIGNING_PRIVATE_KEY }} - TAURI_SIGNING_PRIVATE_KEY_PASSWORD: ${{ secrets.TAURI_SIGNING_PRIVATE_KEY_PASSWORD }} - run: bun run build - - - name: Upload Release - uses: softprops/action-gh-release@v1 - with: - files: tauri/src-tauri/target/release/bundle/**/* - env: - GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} -``` - -Add your private key to GitHub secrets: -- Go to Settings → Secrets and variables → Actions -- Add `TAURI_SIGNING_PRIVATE_KEY` with the content of `~/.tauri/voicebox.key` -- Add `TAURI_SIGNING_PRIVATE_KEY_PASSWORD` (empty string if no password) - -## Frontend Integration - -The frontend integration is complete with automatic update notifications and manual update checks: - -- **Update Notification Banner** - Appears automatically when updates are available -- **Settings Panel** - Manual "Check for Updates" button in Settings tab -- **Update Hook** - React hook handles all update operations - -See `docs/AUTOUPDATER_QUICKSTART.md` for a quick setup guide. - -## Security Notes - -- Never commit your private key to version control -- Store private keys securely (use GitHub secrets for CI/CD) -- The public key in `tauri.conf.json` is safe to commit -- Updates are cryptographically verified before installation -- HTTP endpoints are blocked by default (HTTPS only) - -## Testing Updates - -1. Build version 0.1.0 and install it -2. Update version in `tauri.conf.json` to 0.2.0 -3. Build version 0.2.0 with signatures -4. Create a local server or GitHub release with `latest.json` -5. Run version 0.1.0 and trigger update check -6. Verify update downloads and installs correctly - -## Troubleshooting - -**"Invalid signature" error:** -- Verify public key matches the private key used to sign -- Ensure signature files (.sig) are uploaded correctly - -**"No update available" when one exists:** -- Check endpoint URL is correct -- Verify `latest.json` format matches specification -- Ensure version in latest.json is higher than current version - -**Build fails with signing:** -- Confirm environment variables are set correctly -- Check private key file exists and is readable -- Verify private key format (should start with `dW50cnVzdGVkIGNvbW1lbnQ6`) diff --git a/docs/AUTOUPDATER_QUICKSTART.md b/docs/AUTOUPDATER_QUICKSTART.md deleted file mode 100644 index 1baa1b95..00000000 --- a/docs/AUTOUPDATER_QUICKSTART.md +++ /dev/null @@ -1,116 +0,0 @@ -# Autoupdater Quick Start - -The Tauri v2 autoupdater has been fully configured and integrated. Follow these steps to activate it. - -## What's Already Done - -✅ Rust plugin installed and initialized -✅ Tauri configuration set up with updater settings -✅ Permissions granted for update operations -✅ GitHub Actions workflow updated with signing support -✅ Frontend components created and integrated -✅ Update notifications on app startup -✅ Manual update check in Settings tab - -## Required Steps (5 minutes) - -### 1. Generate Signing Keys - -```bash -bun run generate:keys -``` - -This creates: -- Private key: `~/.tauri/voicebox.key` (keep secret!) -- Public key: `~/.tauri/voicebox.key.pub` (safe to share) - -### 2. Update Tauri Config - -Open `tauri/src-tauri/tauri.conf.json` and: - -1. Replace `"REPLACE_WITH_YOUR_PUBLIC_KEY"` with the content from `~/.tauri/voicebox.key.pub` -2. Update the endpoint URL with your GitHub username: - ```json - "endpoints": [ - "https://github.com/YOUR_USERNAME/voicebox/releases/latest/download/latest.json" - ] - ``` - -### 3. Add GitHub Secrets - -Go to your repo Settings → Secrets and variables → Actions: - -1. Add `TAURI_SIGNING_PRIVATE_KEY`: - ```bash - cat ~/.tauri/voicebox.key - ``` - Copy the entire output and paste as the secret value - -2. Add `TAURI_SIGNING_PRIVATE_KEY_PASSWORD`: - Leave empty (or add your password if you set one) - -### 4. Test the Setup - -To test locally before creating a release: - -```bash -bun run build:release -``` - -This will verify your keys are set up correctly. - -## How It Works - -### For Users -1. App checks for updates on startup (only in Tauri builds) -2. If an update is available, a banner appears at the top -3. Users can click "Install Now" to download and install -4. App restarts automatically after installation - -### For Developers -1. Create a new git tag: `git tag v0.2.0 && git push --tags` -2. GitHub Actions builds signed releases for all platforms -3. Uploads installers and generates `latest.json` manifest -4. Users running older versions will be notified automatically - -## UI Components - -### Update Notification Banner -- Shows at top of app when update is available -- Appears automatically on startup -- Displays download/install progress - -### Settings Panel -- Located in Settings tab -- Shows current version -- Manual "Check for Updates" button -- Update status and progress - -## Troubleshooting - -**"Public key not configured"** -- Make sure you copied the entire content from `voicebox.key.pub` -- The key should start with `dW50cnVzdGVkIGNvbW1lbnQ6` - -**"Failed to check for updates"** -- Endpoint URL might be incorrect -- No releases published yet (expected for first setup) - -**Build fails with signing error** -- Check that GitHub secrets are set correctly -- Verify private key file exists at `~/.tauri/voicebox.key` - -## Next Release Workflow - -1. Update version in `tauri/src-tauri/tauri.conf.json` -2. Commit changes -3. Create and push tag: `git tag v0.2.0 && git push --tags` -4. GitHub Actions will automatically build and create a draft release -5. Review the release and publish it -6. Users will be notified of the update - -## See Also - -- Full documentation: `docs/AUTOUPDATER.md` -- Build script: `scripts/prepare-release.sh` -- GitHub workflow: `.github/workflows/release.yml` diff --git a/docs2/MIGRATION.md b/docs/MIGRATION.md similarity index 100% rename from docs2/MIGRATION.md rename to docs/MIGRATION.md diff --git a/docs/README.md b/docs/README.md index ee3b91c6..68109407 100644 --- a/docs/README.md +++ b/docs/README.md @@ -1,64 +1,45 @@ -# Voicebox Documentation +# fumadocs-ui-template -This directory contains the documentation for Voicebox, built with [Mintlify](https://mintlify.com). +This is a Next.js application generated with +[Create Fumadocs](https://github.com/fuma-nama/fumadocs). -## Development - -### Prerequisites - -Install Mintlify globally using bun: +Run development server: ```bash -bun add -g mintlify +npm run dev +# or +pnpm dev +# or +yarn dev ``` -Or use the helper script: +Open http://localhost:3000 with your browser to see the result. -```bash -bun run install:mintlify -``` +## Explore -### Running Locally +In the project, you can see: -```bash -bun run dev -``` +- `lib/source.ts`: Code for content source adapter, [`loader()`](https://fumadocs.dev/docs/headless/source-api) provides the interface to access your content. +- `lib/layout.shared.tsx`: Shared options for layouts, optional but preferred to keep. -This will start the Mintlify dev server. +| Route | Description | +| ------------------------- | ------------------------------------------------------ | +| `app/(home)` | The route group for your landing page and other pages. | +| `app/docs` | The documentation layout and pages. | +| `app/api/search/route.ts` | The Route Handler for search. | -The docs will be available at `http://localhost:3000` +### Fumadocs MDX -### Structure +A `source.config.ts` config file has been included, you can customise different options like frontmatter schema. -``` -docs/ -├── mint.json # Mintlify configuration -├── custom.css # Custom styles -├── overview/ # Getting started & feature docs -├── guides/ # User guides -├── api/ # API reference -├── development/ # Developer documentation -├── logo/ # Logo assets -└── public/ # Static assets -``` +Read the [Introduction](https://fumadocs.dev/docs/mdx) for further details. -### Writing Docs +## Learn More -- Use `.mdx` files for all documentation pages -- Follow the existing structure in `mint.json` for navigation -- Use Mintlify components for enhanced formatting (Card, CardGroup, Accordion, etc.) -- Reference the [Mintlify documentation](https://mintlify.com/docs) for available components +To learn more about Next.js and Fumadocs, take a look at the following +resources: -## Deployment - -Docs are automatically deployed when changes are pushed to the main branch. - -To manually deploy: - -```bash -mintlify deploy -``` - -## Contributing - -See [CONTRIBUTING.md](../CONTRIBUTING.md) for contribution guidelines. +- [Next.js Documentation](https://nextjs.org/docs) - learn about Next.js + features and API. +- [Learn Next.js](https://nextjs.org/learn) - an interactive Next.js tutorial. +- [Fumadocs](https://fumadocs.dev) - learn about Fumadocs diff --git a/docs/TROUBLESHOOTING.md b/docs/TROUBLESHOOTING.md deleted file mode 100644 index 6f0317a0..00000000 --- a/docs/TROUBLESHOOTING.md +++ /dev/null @@ -1,308 +0,0 @@ -# Troubleshooting Guide - -Common issues and solutions for Voicebox. - -## Installation Issues - -### macOS: "Voicebox cannot be opened because it is from an unidentified developer" - -**Solution:** -1. Right-click the `.dmg` file -2. Select "Open" -3. Click "Open" in the security dialog -4. Alternatively, go to System Settings → Privacy & Security → Allow Voicebox - -### Windows: "Windows protected your PC" - -**Solution:** -1. Click "More info" -2. Click "Run anyway" -3. Windows Defender may flag new software; this is normal for unsigned apps - -### Linux: AppImage won't run - -**Solution:** -```bash -chmod +x voicebox-*.AppImage -./voicebox-*.AppImage -``` - -## Runtime Issues - -### Server won't start - -**Symptoms:** App opens but shows "Server not connected" - -**Solutions:** -1. **Check Python installation** - ```bash - python --version # Should be 3.11+ - ``` - -2. **Check server binary exists** - - Look in `tauri/src-tauri/binaries/` for your platform - - Binary should match your system architecture - -3. **Check permissions** - ```bash - # macOS/Linux - chmod +x tauri/src-tauri/binaries/voicebox-server-* - ``` - -4. **Check logs** - - macOS: Open Console.app and search for "voicebox" - - Linux: Check `~/.local/share/voicebox/` for logs - - Windows: Check Event Viewer - -### "Model download failed" - -**Symptoms:** First generation fails with download error - -**Solutions:** -1. **Check internet connection** - - Models download from HuggingFace Hub (~2-4GB) - - First download may take several minutes - -2. **Check disk space** - - Models are cached in `~/.cache/huggingface/` - - Ensure at least 5GB free space - -3. **Manual download** (if automatic fails) - ```bash - pip install huggingface_hub - huggingface-cli download Qwen/Qwen3-TTS-12Hz-1.7B-Base - ``` - -### "Out of memory" errors - -**Symptoms:** Generation fails with CUDA/VRAM errors - -**Solutions:** -1. **Use smaller model** - - Switch to 0.6B model instead of 1.7B - - Settings → Model Management → Load 0.6B - -2. **Close other applications** - - Free up GPU memory - - Close browser tabs, other ML apps - -3. **Use CPU mode** - - Slower but works without GPU - - Backend automatically falls back to CPU - -### MLX "Failed to load the default metallib" error (Apple Silicon) - -**Symptoms:** Generation fails with "library not found" or "metallib" errors - -**Solutions:** -1. **Rebuild server binary** - ```bash - bun run build:server - ``` - The build script should automatically include MLX Metal shader libraries. - -2. **Check MLX installation** - ```bash - pip install -r backend/requirements-mlx.txt - ``` - -3. **Verify backend detection** - - Check server logs for "Backend: MLX" - - If showing "Backend: PYTORCH", MLX may not be installed correctly - -### Audio playback issues - -**Symptoms:** Generated audio won't play - -**Solutions:** -1. **Check audio format** - - Audio is saved as WAV files - - Ensure your system supports WAV playback - -2. **Try downloading audio** - - Right-click → Download - - Play in external player - -3. **Check browser permissions** (web version) - - Allow audio autoplay in browser settings - -### Slow generation - -**Symptoms:** Generation takes >30 seconds - -**Solutions:** -1. **Check backend type** (Apple Silicon) - - Check Settings → Server Status - - Should show "Backend: MLX" on Apple Silicon - - If showing "Backend: PYTORCH", install MLX: `pip install -r backend/requirements-mlx.txt` - - MLX provides 4-5x faster inference on Apple Silicon - -2. **Use GPU** (if available) - - Check Settings → Server Status - - Should show "GPU available: true" - - Apple Silicon: Should show "Metal (Apple Silicon via MLX)" - - Windows/Linux: Should show "CUDA" if GPU available - -3. **Enable caching** - - Voice prompts are cached automatically - - Second generation with same voice should be faster - -4. **Use smaller model** - - 0.6B model is faster than 1.7B - - Quality difference is minimal for most voices - -5. **Check system resources** - - Close other CPU/GPU intensive apps - - Ensure adequate RAM (8GB+ recommended) - -## API Issues - -### "Connection refused" when using API - -**Solutions:** -1. **Check server is running** - ```bash - curl http://localhost:8000/health - ``` - -2. **Check remote mode** - - If connecting remotely, ensure server is started with `--host 0.0.0.0` - - Check firewall settings - -3. **Check port availability** - - Default port is 8000 - - Ensure no other service is using it - -### CORS errors in browser - -**Solutions:** -1. **Use desktop app** (recommended) - - Desktop app doesn't have CORS restrictions - -2. **Configure CORS** (for web deployment) - - Update `backend/main.py` CORS settings - - Add your domain to allowed origins - -## Update Issues - -### "Update check failed" - -**Solutions:** -1. **Check internet connection** - - Updates are fetched from GitHub releases - -2. **Check GitHub access** - - Ensure `github.com` is accessible - - Check firewall/proxy settings - -3. **Manual update** - - Download latest release from GitHub - - Install manually - -### "Invalid signature" error - -**Solutions:** -1. **Re-download installer** - - Signature may be corrupted - - Download fresh copy from GitHub - -2. **Check release integrity** - - Verify `.sig` file matches installer - - Report issue if signature is invalid - -## Data Issues - -### Profiles disappeared - -**Solutions:** -1. **Check data directory** - - macOS: `~/Library/Application Support/voicebox/` - - Windows: `%APPDATA%/voicebox/` - - Linux: `~/.local/share/voicebox/` - -2. **Check database** - - Database: `data/voicebox.db` - - Ensure file exists and is readable - -3. **Restore from backup** - - Profiles can be exported/imported - - Check for backup files - -### "Database locked" error - -**Solutions:** -1. **Close other instances** - - Ensure only one Voicebox instance is running - -2. **Restart app** - - Close and reopen Voicebox - -3. **Check file permissions** - - Ensure database file is writable - - Check directory permissions - -## Development Issues - -### Build fails - -**Solutions:** -1. **Check Rust installation** - ```bash - rustc --version - rustup update - ``` - -2. **Check Tauri dependencies** - ```bash - cd tauri - bun install - ``` - -3. **Clean build** - ```bash - cd tauri/src-tauri - cargo clean - cd ../.. - bun run build - ``` - -### API client generation fails - -**Solutions:** -1. **Start backend server** - ```bash - bun run dev:server - ``` - -2. **Check OpenAPI endpoint** - ```bash - curl http://localhost:8000/openapi.json - ``` - -3. **Regenerate client** - ```bash - bun run generate:api - ``` - -## Still Having Issues? - -1. **Check existing issues** - - Search GitHub issues for similar problems - - Check closed issues for solutions - -2. **Create new issue** - - Include: - - OS and version - - Voicebox version - - Steps to reproduce - - Error messages/logs - - Screenshots (if applicable) - -3. **Get help** - - Check documentation in `docs/` - - Review `backend/README.md` for API details - - See `CONTRIBUTING.md` for development help - ---- - -For more help, open an issue on [GitHub](https://github.com/jamiepine/voicebox/issues). diff --git a/docs/api/authentication.mdx b/docs/api/authentication.mdx deleted file mode 100644 index e4e72808..00000000 --- a/docs/api/authentication.mdx +++ /dev/null @@ -1,55 +0,0 @@ ---- -title: "Authentication" -description: "API authentication and security" ---- - -## Current Status - - - Authentication is not currently implemented in Voicebox. The API is intended for local use only. - - -## Local Usage - -For local development and usage: -- API runs on `localhost:17493` -- No authentication required -- Access restricted to local machine - -## Future Implementation - -Authentication will be added in a future release for: -- Remote deployments -- Multi-user access -- Production environments - -Planned authentication methods: -- API keys -- OAuth 2.0 -- JWT tokens - -## Security Best Practices - -Until authentication is implemented: - - - - Use WireGuard or Tailscale for remote access - - - Run behind nginx with basic auth - - - Restrict access to trusted IPs only - - - Don't expose to public internet - - - -## Coming Soon - -- API key management -- User accounts -- Rate limiting -- Access control diff --git a/docs/api/generation.mdx b/docs/api/generation.mdx deleted file mode 100644 index c9fff59c..00000000 --- a/docs/api/generation.mdx +++ /dev/null @@ -1,119 +0,0 @@ ---- -title: "Generation API" -description: "Generate speech from text" ---- - -## Generate Speech - -```http -POST /generate -``` - -**Request:** -```json -{ - "text": "Hello world", - "profile_id": "abc123", - "language": "en" -} -``` - -**Response:** -```json -{ - "id": "gen123", - "text": "Hello world", - "profile_id": "abc123", - "language": "en", - "audio_url": "/audio/gen123.wav", - "duration": 2.3, - "created_at": "2024-01-29T12:00:00Z" -} -``` - -## List History - -```http -GET /history -``` - -**Query Parameters:** -- `profile_id` (optional) - Filter by voice profile -- `limit` (optional) - Number of results (default: 50) -- `offset` (optional) - Pagination offset - -**Response:** -```json -{ - "generations": [ - { - "id": "gen123", - "text": "Hello world", - "profile_id": "abc123", - "duration": 2.3, - "created_at": "2024-01-29T12:00:00Z" - } - ], - "total": 100 -} -``` - -## Get Generation - -```http -GET /history/{id} -``` - -**Response:** -```json -{ - "id": "gen123", - "text": "Hello world", - "profile_id": "abc123", - "language": "en", - "audio_url": "/audio/gen123.wav", - "duration": 2.3, - "created_at": "2024-01-29T12:00:00Z" -} -``` - -## Delete Generation - -```http -DELETE /history/{id} -``` - -**Response:** -```json -{ - "success": true -} -``` - -## TypeScript Example - -```typescript -import { VoiceboxClient } from '@/lib/api' - -const client = new VoiceboxClient({ - baseUrl: 'http://localhost:17493' -}) - -// Generate speech -const generation = await client.generate({ - text: 'Hello world', - profile_id: 'abc123', - language: 'en' -}) - -// Get audio URL -const audioUrl = generation.audio_url - -// List history -const history = await client.listHistory({ - profile_id: 'abc123', - limit: 20 -}) -``` - -For full API documentation, visit `http://localhost:17493/docs` when the server is running. diff --git a/docs/api/overview.mdx b/docs/api/overview.mdx deleted file mode 100644 index c862a332..00000000 --- a/docs/api/overview.mdx +++ /dev/null @@ -1,219 +0,0 @@ ---- -title: "API Overview" -description: "Integrate voice synthesis into your applications with the Voicebox REST API" ---- - -## Introduction - -Voicebox exposes a full REST API that allows you to integrate voice synthesis into your own applications. The API runs on `http://localhost:17493` by default. - - - When Voicebox is running, visit the auto-generated API documentation at `http://localhost:17493/docs` - - -## Base URL - -``` -http://localhost:17493 -``` - -For remote deployments, replace `localhost` with your server's IP or hostname. - -## Authentication - - - Currently, the API does not require authentication for local development. Authentication will be added in a future release for production deployments. - - -## Quick Example - -Here's a simple example of generating speech: - -```bash -# Generate speech -curl -X POST http://localhost:17493/generate \ - -H "Content-Type: application/json" \ - -d '{ - "text": "Hello world", - "profile_id": "abc123", - "language": "en" - }' -``` - -## API Endpoints - -The Voicebox API is organized into several categories: - - - - Create, list, update, and delete voice profiles - - - Generate speech from text using voice profiles - - - Record and transcribe audio - - - Create and manage multi-voice stories (coming soon) - - - -## Core Endpoints - -### Voice Profiles - -```http -GET /profiles # List all profiles -POST /profiles # Create a new profile -GET /profiles/{id} # Get profile details -PUT /profiles/{id} # Update a profile -DELETE /profiles/{id} # Delete a profile -POST /profiles/{id}/samples # Add voice sample -``` - -### Generation - -```http -POST /generate # Generate speech -GET /history # List generation history -GET /history/{id} # Get generation details -DELETE /history/{id} # Delete from history -``` - -### Recordings - -```http -POST /recordings # Start recording -POST /recordings/stop # Stop recording -POST /transcribe # Transcribe audio -``` - -## Response Format - -All API responses follow a consistent JSON format: - -```json -{ - "success": true, - "data": { - // Response data - }, - "error": null -} -``` - -Error responses: - -```json -{ - "success": false, - "data": null, - "error": { - "message": "Error description", - "code": "ERROR_CODE" - } -} -``` - -## Data Models - -### Voice Profile - -```json -{ - "id": "abc123", - "name": "John Smith", - "language": "en", - "description": "Professional narrator voice", - "created_at": "2024-01-29T12:00:00Z", - "samples": [ - { - "id": "sample123", - "audio_path": "/path/to/sample.wav", - "duration": 15.5 - } - ] -} -``` - -### Generation - -```json -{ - "id": "gen123", - "text": "Hello world", - "profile_id": "abc123", - "language": "en", - "audio_path": "/path/to/output.wav", - "duration": 2.3, - "created_at": "2024-01-29T12:00:00Z" -} -``` - -## TypeScript Client - -Voicebox provides an auto-generated TypeScript client with full type safety: - -```typescript -import { VoiceboxClient } from '@/lib/api' - -const client = new VoiceboxClient({ - baseUrl: 'http://localhost:17493' -}) - -// Create a profile -const profile = await client.createProfile({ - name: 'John Smith', - language: 'en' -}) - -// Generate speech -const generation = await client.generate({ - text: 'Hello world', - profile_id: profile.id, - language: 'en' -}) -``` - -The client is automatically generated from the OpenAPI schema. See [Development Setup](/development/setup#generate-openapi-client) for details. - -## Rate Limiting - - - Currently, there are no rate limits for local usage. Rate limiting will be added in a future release for production deployments. - - -## WebSocket Support - - - Real-time streaming generation via WebSockets is planned for a future release. - - -## Use Cases - - - - Generate dynamic dialogue for NPCs and characters - - - Automate voiceovers for videos and podcasts - - - Build text-to-speech tools for visually impaired users - - - Create custom voice interfaces - - - -## Next Steps - - - - Learn how to manage voice profiles - - - Generate speech from text - - diff --git a/docs/api/recordings.mdx b/docs/api/recordings.mdx deleted file mode 100644 index dd0552d2..00000000 --- a/docs/api/recordings.mdx +++ /dev/null @@ -1,95 +0,0 @@ ---- -title: "Recordings API" -description: "Record and transcribe audio" ---- - -## Start Recording - -```http -POST /recordings/start -``` - -**Request:** -```json -{ - "source": "microphone" -} -``` - -**Response:** -```json -{ - "recording_id": "rec123", - "status": "recording" -} -``` - -## Stop Recording - -```http -POST /recordings/stop -``` - -**Request:** -```json -{ - "recording_id": "rec123" -} -``` - -**Response:** -```json -{ - "recording_id": "rec123", - "audio_url": "/audio/rec123.wav", - "duration": 15.5 -} -``` - -## Transcribe Audio - -```http -POST /transcribe -``` - -**Request:** (multipart/form-data) -``` -audio: -language: "en" (optional) -``` - -**Response:** -```json -{ - "text": "Transcribed speech text here", - "language": "en", - "duration": 15.5, - "confidence": 0.95 -} -``` - -## TypeScript Example - -```typescript -import { VoiceboxClient } from '@/lib/api' - -const client = new VoiceboxClient({ - baseUrl: 'http://localhost:17493' -}) - -// Start recording -const recording = await client.startRecording({ - source: 'microphone' -}) - -// ... record audio ... - -// Stop recording -const result = await client.stopRecording(recording.id) - -// Transcribe -const transcription = await client.transcribe(audioFile, 'en') -console.log(transcription.text) -``` - -For full API documentation, visit `http://localhost:17493/docs` when the server is running. diff --git a/docs/api/voice-profiles.mdx b/docs/api/voice-profiles.mdx deleted file mode 100644 index 3715e44c..00000000 --- a/docs/api/voice-profiles.mdx +++ /dev/null @@ -1,149 +0,0 @@ ---- -title: "Voice Profiles API" -description: "Manage voice profiles programmatically" ---- - -## Endpoints - -### List Profiles - -```http -GET /profiles -``` - -**Response:** -```json -{ - "profiles": [ - { - "id": "abc123", - "name": "John Smith", - "language": "en", - "description": "Professional narrator", - "created_at": "2024-01-29T12:00:00Z", - "sample_count": 2 - } - ] -} -``` - -### Get Profile - -```http -GET /profiles/{id} -``` - -**Response:** -```json -{ - "id": "abc123", - "name": "John Smith", - "language": "en", - "description": "Professional narrator", - "created_at": "2024-01-29T12:00:00Z", - "samples": [ - { - "id": "sample123", - "duration": 15.5, - "created_at": "2024-01-29T12:00:00Z" - } - ] -} -``` - -### Create Profile - -```http -POST /profiles -``` - -**Request:** -```json -{ - "name": "John Smith", - "language": "en", - "description": "Professional narrator" -} -``` - -**Response:** -```json -{ - "id": "abc123", - "name": "John Smith", - "language": "en", - "description": "Professional narrator", - "created_at": "2024-01-29T12:00:00Z" -} -``` - -### Update Profile - -```http -PUT /profiles/{id} -``` - -**Request:** -```json -{ - "name": "Updated Name", - "description": "Updated description" -} -``` - -### Delete Profile - -```http -DELETE /profiles/{id} -``` - -**Response:** -```json -{ - "success": true -} -``` - -### Add Voice Sample - -```http -POST /profiles/{id}/samples -``` - -**Request:** (multipart/form-data) -``` -audio: -``` - -**Response:** -```json -{ - "sample_id": "sample123", - "duration": 15.5 -} -``` - -## TypeScript Example - -```typescript -import { VoiceboxClient } from '@/lib/api' - -const client = new VoiceboxClient({ - baseUrl: 'http://localhost:17493' -}) - -// Create profile -const profile = await client.createProfile({ - name: 'John Smith', - language: 'en', - description: 'Professional narrator' -}) - -// Add sample -await client.addSample(profile.id, audioFile) - -// List all profiles -const profiles = await client.listProfiles() -``` - -For full API documentation, visit `http://localhost:17493/docs` when the server is running. diff --git a/docs2/app/(home)/layout.tsx b/docs/app/(home)/layout.tsx similarity index 100% rename from docs2/app/(home)/layout.tsx rename to docs/app/(home)/layout.tsx diff --git a/docs2/app/(home)/page.tsx b/docs/app/(home)/page.tsx similarity index 100% rename from docs2/app/(home)/page.tsx rename to docs/app/(home)/page.tsx diff --git a/docs2/app/api/search/route.ts b/docs/app/api/search/route.ts similarity index 100% rename from docs2/app/api/search/route.ts rename to docs/app/api/search/route.ts diff --git a/docs2/app/docs/[[...slug]]/page.tsx 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-.nav-anchor:hover { - @apply text-[#BF9E40]; -} - -/* Icon wrapper on hover */ -.nav-anchor:hover div { - background: #BF9E40 !important; - filter: brightness(1) !important; -} - -/* Icon SVG on hover */ -.nav-anchor:hover svg { - @apply bg-white !important; -} diff --git a/docs/developer/contributing.mdx b/docs/developer/contributing.mdx deleted file mode 100644 index d67a2f4b..00000000 --- a/docs/developer/contributing.mdx +++ /dev/null @@ -1,326 +0,0 @@ ---- -title: "Contributing" -description: "How to contribute to Voicebox" ---- - -Thank you for your interest in contributing to Voicebox! This guide will help you get started. - -## Code of Conduct - -- Be respectful and inclusive -- Welcome newcomers and help them learn -- Focus on constructive feedback -- Respect different viewpoints and experiences - -## Getting Started - -Before you start contributing, make sure you have: - -1. **Read the documentation** to understand how Voicebox works -2. **Set up your development environment** - see [Development Setup](/development/setup) -3. **Explored the codebase** to understand the project structure -4. **Checked existing issues** to see if someone else is working on something similar - -## Ways to Contribute - - - - Found a bug? Open an issue with reproduction steps - - - Have an idea? Start a discussion or open an issue - - - Fix typos, add examples, or clarify instructions - - - Fix bugs, add features, or optimize performance - - - -## Development Workflow - -### 1. Fork & Clone - -```bash -# Fork the repository on GitHub -# Then clone your fork -git clone https://github.com/YOUR_USERNAME/voicebox.git -cd voicebox -``` - -### 2. Create a Branch - -Use descriptive branch names: - -```bash -# For features -git checkout -b feature/voice-effects - -# For bug fixes -git checkout -b fix/audio-playback-issue - -# For documentation -git checkout -b docs/api-examples -``` - -### 3. Make Your Changes - -Follow these guidelines: - - - - **TypeScript/React:** - - Use TypeScript strict mode - - Prefer functional components with hooks - - Use named exports - - Format with Biome (runs automatically) - - **Python:** - - Follow PEP 8 - - Use type hints - - Use async/await for I/O - - Document functions with docstrings - - **Rust:** - - Follow Rust conventions - - Use meaningful names - - Handle errors explicitly - - Run `rustfmt` - - - - Write clear, descriptive commit messages: - - ```bash - # Good - git commit -m "Add voice profile export feature" - git commit -m "Fix audio playback stopping after 30 seconds" - - # Avoid - git commit -m "Update code" - git commit -m "Fix bug" - ``` - - Format: - - Use imperative mood ("Add feature" not "Added feature") - - Keep first line under 50 characters - - Add detailed description if needed - - - - - Test your changes manually in the app - - Ensure backend API endpoints work - - Check for TypeScript/Python errors - - Verify UI components render correctly - - Add automated tests when possible - - - -### 4. Push & Create PR - -```bash -# Push your branch -git push origin feature/your-feature-name - -# Then create a pull request on GitHub -``` - -## Pull Request Guidelines - -When creating a pull request: - - - - Examples: - - "Add voice profile export functionality" - - "Fix audio playback stopping after 30 seconds" - - "Improve generation speed with caching" - - - - Include: - - What changes you made - - Why you made them - - How to test them - - Screenshots (for UI changes) - - Reference related issues - - - - - Update relevant docs if behavior changes - - Add API documentation for new endpoints - - Update README if needed - - - - - [ ] Code follows style guidelines - - [ ] Documentation updated - - [ ] Changes tested - - [ ] No breaking changes (or documented) - - [ ] CHANGELOG.md updated - - - -## Project Structure - -Understanding the codebase: - -``` -voicebox/ -├── app/ # Shared React frontend -│ ├── src/ -│ │ ├── components/ # UI components -│ │ ├── lib/ # Utilities and API client -│ │ ├── hooks/ # React hooks -│ │ └── stores/ # Zustand state stores -├── backend/ # Python FastAPI server -│ ├── main.py # API routes -│ ├── tts.py # Voice synthesis logic -│ ├── database.py # SQLite operations -│ └── models.py # Pydantic models -├── tauri/ # Desktop app wrapper -│ └── src-tauri/ # Rust backend -├── web/ # Web deployment -├── landing/ # Marketing website -└── scripts/ # Build & release scripts -``` - -## Areas for Contribution - -### Bug Fixes - -- Check [existing issues](https://github.com/jamiepine/voicebox/issues) for bugs -- Test your fix thoroughly -- Add regression tests if possible - -### New Features - -- Check the [roadmap](https://github.com/jamiepine/voicebox#roadmap) for planned features -- Discuss major features in an issue first -- Keep features focused and well-scoped - -### Documentation - -- Improve clarity and fix typos -- Add code examples -- Create tutorials or guides -- Document API endpoints - -### UI/UX Improvements - -- Improve accessibility -- Enhance visual design -- Optimize performance -- Add animations/transitions - -### Infrastructure - -- Improve build process -- Add CI/CD improvements -- Optimize bundle size -- Add testing infrastructure - -## API Development - -When adding new API endpoints: - - - - In `backend/main.py`: - - ```python - @app.post("/api/new-endpoint") - async def new_endpoint(data: RequestModel) -> ResponseModel: - """Endpoint description.""" - # Implementation - return response - ``` - - - - In `backend/models.py`: - - ```python - class RequestModel(BaseModel): - field: str - - class ResponseModel(BaseModel): - result: str - ``` - - - - ```bash - bun run generate:api - ``` - - This updates the TypeScript client with type-safe bindings. - - - - Add documentation in `/docs/api/` - - - -## Testing - -Currently testing is primarily manual. When adding tests: - -**Backend:** -```bash -cd backend -pytest -``` - -**Frontend:** -```bash -bun run test -``` - -**E2E (future):** -```bash -bun run test:e2e -``` - -## Release Process - -Releases are managed by maintainers using `bumpversion`: - -```bash -# Bump version (patch, minor, or major) -bumpversion patch - -# Push with tags -git push && git push --tags -``` - -GitHub Actions automatically builds and publishes releases when tags are pushed. - -## Community - -- **GitHub Issues:** Bug reports and feature requests -- **GitHub Discussions:** General questions and ideas -- **Discord:** Real-time chat (coming soon) - -## Recognition - -Contributors are recognized in: -- [CHANGELOG.md](https://github.com/jamiepine/voicebox/blob/main/CHANGELOG.md) -- GitHub contributor list -- Release notes - -## License - -By contributing, you agree that your contributions will be licensed under the MIT License. - -## Questions? - -If you have questions: - -1. Check the [documentation](/overview/introduction) -2. Search [existing issues](https://github.com/jamiepine/voicebox/issues) -3. Open a new issue or discussion -4. See [CONTRIBUTING.md](https://github.com/jamiepine/voicebox/blob/main/CONTRIBUTING.md) in the repo - -Thank you for contributing to Voicebox! 🎉 diff --git a/docs/developer/setup.mdx b/docs/developer/setup.mdx deleted file mode 100644 index c2c44f24..00000000 --- a/docs/developer/setup.mdx +++ /dev/null @@ -1,239 +0,0 @@ ---- -title: "Development Setup" -description: "Set up your local development environment for Voicebox" ---- - -## Prerequisites - -Before you begin, ensure you have the following installed: - - - - [Download Bun](https://bun.sh) - ```bash - curl -fsSL https://bun.sh/install | bash - ``` - - - [Download Python](https://python.org) - ```bash - python --version - ``` - - - [Install Rust](https://rustup.rs) - ```bash - rustc --version - ``` - - - -## Clone the Repository - -```bash -git clone https://github.com/jamiepine/voicebox.git -cd voicebox -``` - -## Quick Setup (Recommended) - -The easiest way to get started is using the Makefile: - -```bash -# Setup everything -make setup - -# Start development -make dev -``` - - - The Makefile is available on macOS and Linux. Windows users should follow the manual setup below. - - -## Manual Setup - -### 1. Install JavaScript Dependencies - -```bash -bun install -``` - -This installs dependencies for: -- `app/` - Shared React frontend -- `tauri/` - Tauri desktop wrapper -- `web/` - Web deployment wrapper - -### 2. Set Up Python Backend - -```bash -cd backend - -# Create virtual environment -python -m venv venv - -# Activate virtual environment -source venv/bin/activate # macOS/Linux -# or -venv\Scripts\activate # Windows - -# Install Python dependencies -pip install -r requirements.txt - -# Install MLX dependencies (Apple Silicon only - for faster inference) -# On Apple Silicon, this enables native Metal acceleration -if [[ $(uname -m) == "arm64" ]]; then - pip install -r requirements-mlx.txt -fi - -# Install Qwen3-TTS -pip install git+https://github.com/QwenLM/Qwen3-TTS.git -``` - -## Running in Development - -Development requires **two terminals**: one for the Python backend, one for the Tauri app. - - - - Start the Python server first: - - ```bash - cd backend - source venv/bin/activate # Activate venv - bun run dev:server - ``` - - Or manually: - ```bash - uvicorn main:app --reload --port 17493 - ``` - - Backend will be available at `http://localhost:17493` - - - - Then start the Tauri app: - - ```bash - bun run dev - ``` - - This will: - - Create a placeholder sidecar binary - - Start Vite dev server on port 5173 - - Launch Tauri window - - Enable hot reload - - - - - In dev mode, the app connects to your manually-started Python server. The bundled server binary is only used in production builds. - - -### Optional: Web App - -```bash -bun run dev:web -``` - -Web app will be available at `http://localhost:5174` - -## Model Downloads - -Models are automatically downloaded from HuggingFace Hub on first use: - -- **Whisper** (transcription): Auto-downloads on first transcription -- **Qwen3-TTS** (voice cloning): Auto-downloads on first generation (~2-4GB) - - - First-time usage will be slower due to model downloads, but subsequent runs will use cached models. - - -## Project Structure - -``` -voicebox/ -├── app/ # Shared React frontend -│ └── src/ -│ ├── components/ # UI components -│ ├── lib/ # Utilities and API client -│ └── hooks/ # React hooks -├── backend/ # Python FastAPI server -│ ├── main.py # API routes -│ ├── tts.py # Voice synthesis -│ └── database.py # SQLite operations -├── tauri/ # Desktop app wrapper -│ └── src-tauri/ # Rust backend -├── web/ # Web deployment -├── landing/ # Marketing website -└── scripts/ # Build & release scripts -``` - -## Available Make Commands - -Run `make help` to see all available commands: - -```bash -make setup # Install all dependencies -make dev # Start development servers -make dev-web # Start web development server -make build # Build desktop app -make build-web # Build web app -make clean # Clean build artifacts -make test # Run tests -``` - -## Generate OpenAPI Client - -After starting the backend server, generate the TypeScript API client: - -```bash -./scripts/generate-api.sh -# or -bun run generate:api -``` - -This downloads the OpenAPI schema and generates the TypeScript client in `app/src/lib/api/` - -## Next Steps - - - - Understand the system architecture - - - Read the contribution guidelines - - - Learn how to build production releases - - - Explore the REST API - - - -## Troubleshooting - - - - - Check Python version (must be 3.11+) - - Ensure virtual environment is activated - - Verify all dependencies are installed: `pip install -r requirements.txt` - - Check if port 17493 is available - - - - - Ensure Rust is installed: `rustc --version` - - Clean the build: `cd tauri/src-tauri && cargo clean` - - Try rebuilding: `bun run dev` - - - - - Ensure backend is running: `curl http://localhost:17493/openapi.json` - - Check network connectivity - - Verify the backend is accessible at localhost:17493 - - - -See the full [Troubleshooting Guide](/guides/troubleshooting) for more issues and solutions. diff --git a/docs2/lib/layout.shared.tsx b/docs/lib/layout.shared.tsx similarity index 100% rename from docs2/lib/layout.shared.tsx rename to docs/lib/layout.shared.tsx diff --git a/docs2/lib/openapi.ts b/docs/lib/openapi.ts similarity index 100% rename from docs2/lib/openapi.ts rename to docs/lib/openapi.ts diff --git a/docs2/lib/source.ts b/docs/lib/source.ts similarity index 100% rename from docs2/lib/source.ts rename to docs/lib/source.ts diff --git a/docs2/mdx-components.tsx b/docs/mdx-components.tsx similarity index 100% rename from docs2/mdx-components.tsx rename to docs/mdx-components.tsx diff --git a/docs/mint.json b/docs/mint.json deleted file mode 100644 index c4f582ea..00000000 --- a/docs/mint.json +++ /dev/null @@ -1,102 +0,0 @@ -{ - "$schema": "https://mintlify.com/schema.json", - "name": "Voicebox", - "logo": { - "light": "/logo/icon-light.png", - "dark": "/logo/icon-dark.png" - }, - "favicon": "/favicon.png", - "colors": { - "primary": "#BF9E40", - "light": "#D4B560", - "dark": "#A68A35" - }, - "styles": { - "css": ["/custom.css"] - }, - "anchors": [ - { - "name": "Overview", - "icon": "book-open", - "url": "overview" - }, - { - "name": "API", - "icon": "code", - "url": "api" - }, - { - "name": "Developer", - "icon": "book-open-cover", - "url": "developer" - }, - { - "name": "GitHub", - "icon": "github", - "url": "https://github.com/jamiepine/voicebox" - } - ], - "navigation": [ - { - "group": "Getting Started", - "icon": "rocket", - "pages": ["overview/introduction", "overview/installation", "overview/quick-start"] - }, - { - "group": "Features", - "icon": "sparkles", - "pages": [ - "overview/voice-cloning", - "overview/stories-editor", - "overview/recording-transcription", - "overview/generation-history", - "overview/remote-mode" - ] - }, - { - "group": "User Guides", - "icon": "compass", - "pages": [ - "overview/creating-voice-profiles", - "overview/generating-speech", - "overview/building-stories", - "overview/troubleshooting" - ] - }, - { - "group": "Development", - "icon": "wrench", - "pages": [ - "developer/setup", - "developer/architecture", - "developer/contributing", - "developer/building", - "developer/autoupdater" - ] - }, - { - "group": "API Reference", - "icon": "code", - "pages": [ - "api/overview", - "api/authentication", - "api/voice-profiles", - "api/generation", - "api/recordings" - ] - }, - { - "group": "Architecture", - "icon": "book-open-cover", - "pages": [ - "developer/voice-profiles", - "developer/tts-generation", - "developer/history", - "developer/stories", - "developer/transcription", - "developer/audio-channels", - "developer/model-management" - ] - } - ] -} diff --git a/docs2/next.config.mjs b/docs/next.config.mjs similarity index 100% rename from docs2/next.config.mjs rename to docs/next.config.mjs diff --git a/docs/nixpacks.toml b/docs/nixpacks.toml deleted file mode 100644 index 5cb001d9..00000000 --- a/docs/nixpacks.toml +++ /dev/null @@ -1,11 +0,0 @@ -[phases.setup] -nixPkgs = ["nodejs_20", "bun"] - -[phases.install] -cmds = ["bun install"] - -[phases.build] -cmds = ["bun run build"] - -[start] -cmd = "bun run start" diff --git a/docs2/openapi.json b/docs/openapi.json similarity index 100% rename from docs2/openapi.json rename to docs/openapi.json diff --git a/docs/overview/creating-voice-profiles.mdx b/docs/overview/creating-voice-profiles.mdx deleted file mode 100644 index 84e1194d..00000000 --- a/docs/overview/creating-voice-profiles.mdx +++ /dev/null @@ -1,296 +0,0 @@ ---- -title: "Creating Voice Profiles" -description: "Advanced guide to creating high-quality voice profiles" ---- - -## Overview - -Voice profiles are the foundation of voice cloning in Voicebox. This guide covers best practices for creating professional-quality voice profiles. - -## Quick Start - - - - 10-30 seconds of clear speech - - - **Profiles** → **+ New Profile** - - - Add your audio file - - - Use the profile to generate speech - - - -## Audio Requirements - -### Ideal Sample Characteristics - - - - **10-30 seconds** - - Too short: Poor quality - Too long: Unnecessary - - - **Clear speech** - - No background noise - No music or overlapping voices - - - **High fidelity** - - 44.1kHz or 48kHz sample rate - Minimal compression - - - **Natural speech** - - Conversational tone - Complete sentences - - - -### File Formats - -Supported formats: -- **WAV** (recommended) - Lossless quality -- **MP3** - Acceptable, minimal compression -- **M4A** - Acceptable -- **FLAC** - Lossless alternative - - - Use WAV for best results. Avoid heavily compressed formats. - - -## Recording Tips - -### Environment - - - - - Record in a quiet room - - Turn off fans, AC, appliances - - Close windows to reduce outside noise - - Use soft furnishings to reduce echo - - - - - 6-12 inches from mouth - - Slight angle to reduce plosives (p, b, t) - - Use a pop filter if available - - Maintain consistent distance - - - - - 44.1kHz or 48kHz sample rate - - 16-bit or 24-bit depth - - Mono is fine (stereo will be converted) - - Avoid automatic gain control - - - -### Speaking - -- **Natural pace** - Don't rush or speak too slowly -- **Clear articulation** - Pronounce words clearly -- **Consistent volume** - Maintain steady loudness -- **Normal tone** - Speak as you normally would -- **Complete sentences** - Avoid fragments or "ums" - -## Multiple Samples - -Adding multiple samples can significantly improve quality: - -### Why Multiple Samples? - - - - Model learns a more complete representation - - - Handles different speaking styles better - - - Reduces artifacts and improves naturalness - - - More reliable across different texts - - - -### Sample Variety - -Consider adding samples with: - -1. **Different tones** - - Casual conversation - - Professional/formal - - Excited/enthusiastic - - Calm/serious - -2. **Different content** - - Narratives - - Questions - - Statements - - Emotions (happy, sad, neutral) - -3. **Different recording conditions** - - Studio quality - - Phone call quality (if needed) - - Room acoustics - - - All samples should be from the **same speaker**. Mixing voices will produce poor results. - - -## Processing Existing Audio - -If you have existing audio (podcasts, videos, etc.): - -### Extracting Clean Segments - - - - Look for segments with: - - Just the target speaker - - No background music - - Minimal noise - - - - Tools like Audacity or Adobe Audition: - - Cut out clean 10-30s segments - - Remove silence at start/end - - Normalize volume if needed - - - - Save as high-quality WAV file - - - -### Noise Reduction - -If you have light background noise: - -``` -1. Use noise reduction in Audacity: - - Select noise-only section - - Get Noise Profile - - Select full audio - - Apply noise reduction (gentle settings) - -2. Avoid over-processing: - - Can introduce artifacts - - May reduce voice quality -``` - -## Testing & Iteration - -### Test Your Profile - -After creating a profile: - - - - Generate a simple phrase: - ``` - "Hello, this is a test of my voice profile." - ``` - - - - Listen for: - - Natural tone - - Clear pronunciation - - Proper prosody - - Lack of artifacts - - - - If quality is poor: - - Add more samples - - Try different source audio - - Check sample quality - - - -### Common Issues - - - - **Cause**: Poor quality samples or too short - - **Fix**: Use longer, higher quality samples - - - - **Cause**: Sample tone doesn't match desired output - - **Fix**: Record samples in the style you want to generate - - - - **Cause**: Background noise or audio issues in samples - - **Fix**: Clean up samples or re-record in quieter environment - - - -## Advanced Tips - -### Celebrity/Character Voices - -For cloning public figures or characters: - -1. **Legal considerations** - Ensure you have rights or it's fair use -2. **Source quality** - Find high-quality interview audio or clean clips -3. **Consistency** - Use clips where they speak similarly -4. **Multiple samples** - Very important for recognizable voices - -### Accent & Dialect - -The model will preserve accent and dialect: - -- British English will generate British English -- Southern accent will produce Southern accent -- Regional pronunciations will be maintained - -### Emotion Transfer - -The emotional tone of samples affects generation: - -- Energetic samples → Energetic output -- Calm samples → Calm output -- Mix samples for versatile profile - -## Managing Profiles - -### Organization - -- **Descriptive names** - "John Smith - Professional Narrator" -- **Add descriptions** - Note recording conditions, use cases -- **Language tags** - Mark the primary language -- **Archive unused** - Keep profile list manageable - -### Export/Import - -- **Export** profiles to share or backup -- **Import** from colleagues or teammates -- Profiles include voice embeddings, not original audio - -## Next Steps - - - - Use your profile to generate speech - - - Create multi-voice narratives - - diff --git a/docs/overview/introduction.mdx b/docs/overview/introduction.mdx deleted file mode 100644 index 21edd4fc..00000000 --- a/docs/overview/introduction.mdx +++ /dev/null @@ -1,58 +0,0 @@ ---- -title: "Introduction" -description: "Welcome to Voicebox - the open-source voice synthesis studio" ---- - -## What is Voicebox? - -Voicebox is a **local-first voice cloning studio** with DAW-like features for professional voice synthesis. Think of it as a **local, free and open-source alternative to ElevenLabs** — download models, clone voices, and generate speech entirely on your machine. - - - Voicebox App Screenshot - - -Unlike cloud services that lock your voice data behind subscriptions, Voicebox gives you: - -- **Complete privacy** — models and voice data stay on your machine -- **Professional tools** — multi-track timeline editor, audio trimming, conversation mixing -- **Model flexibility** — currently powered by Qwen3-TTS, with support for XTTS, Bark, and other models coming soon -- **API-first** — use the desktop app or integrate voice synthesis into your own projects -- **Native performance** — built with Tauri (Rust), not Electron - -Download a voice model, clone any voice from a few seconds of audio, and compose multi-voice projects with studio-grade editing tools. No Python install required, no cloud dependency, no limits. - -## Key Features - - - - Instant cloning from just a few seconds of audio with Qwen3-TTS - - - Multi-track timeline for creating conversations and narratives - - - REST API for integrating voice synthesis into your apps - - - Everything runs on your machine - complete privacy - - - -## Use Cases - -- **Game Development** — Generate dynamic dialogue for characters -- **Content Creation** — Produce podcasts and video voiceovers -- **Accessibility** — Build text-to-speech tools -- **Voice Assistants** — Create custom voice interfaces -- **Production Pipelines** — Automate voiceover workflows - -## Next Steps - - - - Download and install Voicebox on your machine - - - Get up and running in 5 minutes - - diff --git a/docs/overview/quick-start.mdx b/docs/overview/quick-start.mdx deleted file mode 100644 index aefe9905..00000000 --- a/docs/overview/quick-start.mdx +++ /dev/null @@ -1,154 +0,0 @@ ---- -title: "Quick Start" -description: "Get started with Voicebox in 5 minutes" ---- - -This guide will walk you through creating your first voice profile and generating speech. - -## Prerequisites - -Make sure you have [installed Voicebox](/overview/installation) and launched the app. - -## Step 1: Create a Voice Profile - -Voice profiles are the foundation of Voicebox. Each profile contains voice samples that the AI uses to clone the voice. - - - - Click the **Profiles** tab in the sidebar - - - - Click the **+ New Profile** button - - Fill in the details: - - **Name:** A descriptive name (e.g., "John Smith") - - **Language:** Select the primary language - - **Description:** Optional notes about the voice - - - - You have two options: - - **Option A: Upload Audio** - - Click **Upload Sample** - - Select an audio file (WAV, MP3, or M4A) - - Ideal length: 10-30 seconds of clear speech - - **Option B: Record Live** - - Click **Record Sample** - - Speak clearly for 10-30 seconds - - Click stop when finished - - - - Click **Create Profile** to save - - - - - For best results, use clean audio with minimal background noise and consistent speaking tone. - - -## Step 2: Generate Speech - -Now let's use your new voice profile to generate speech. - - - - Click the **Generate** tab in the sidebar - - - - Choose your newly created profile from the dropdown - - - - Type or paste the text you want to generate: - - ``` - Hello! This is my first voice generation with Voicebox. - ``` - - - - Click **Generate** and wait a few seconds - - - First generation may take longer due to model initialization. Subsequent generations will be faster. - - - - - - Click **Play** to preview the audio - - Click **Download** to save the audio file - - The generation is also saved to your **History** - - - -## Step 3: Build a Story (Optional) - -The Stories Editor lets you create multi-voice narratives with a timeline-based interface. - - - - Navigate to **Stories** and click **+ New Story** - - - - Click **+ Add Track** to create tracks for different speakers - - - - - Drag generated audio from your History - - Or generate new clips directly in the timeline - - Arrange clips on the timeline - - - - - Trim clips by dragging edges - - Adjust timing and spacing - - Click **Export** to render the final audio - - - -## What's Next? - - - - Learn advanced techniques for high-quality voice cloning - - - Integrate Voicebox into your own applications - - - Master the multi-track timeline editor - - - Connect to a GPU server for faster generation - - - -## Tips for Success - - - - - Use 10-30 seconds of clear, consistent speech - - Avoid background noise and echo - - Multiple samples from the same speaker improve quality - - Match the speaking style you want to generate - - - - - Use a CUDA-capable GPU for 5-10x faster generation - - Enable voice prompt caching for repeated generations - - Consider running the backend on a remote GPU server - - - - - **Server won't start:** Check if port 17493 is available - - **Poor audio quality:** Try adding more voice samples - - **Slow generation:** Verify GPU acceleration is enabled - - See the full [Troubleshooting Guide](/guides/troubleshooting) for more - - diff --git a/docs/package.json b/docs/package.json index 6d177bd9..1d14cd8f 100644 --- a/docs/package.json +++ b/docs/package.json @@ -1,17 +1,32 @@ { - "name": "voicebox-docs", - "version": "0.1.0", + "name": "example-next-mdx", + "version": "0.0.0", "private": true, "scripts": { - "dev": "mintlify dev", - "build": "mintlify build", - "start": "mintlify serve", - "install:mintlify": "bun add -g mintlify" + "build": "fumadocs-mdx && next build", + "dev": "fumadocs-mdx && next dev", + "start": "next start", + "postinstall": "fumadocs-mdx" + }, + "dependencies": { + "fumadocs-core": "^16.4.11", + "fumadocs-mdx": "13", + "fumadocs-openapi": "^10.2.7", + "fumadocs-ui": "^16.4.11", + "lucide-react": "^0.546.0", + "next": "^16.1.6", + "react": "^19.2.0", + "react-dom": "^19.2.0", + "shiki": "^3.22.0" }, "devDependencies": { - "mintlify": "latest" - }, - "engines": { - "bun": ">=1.0.0" + "@tailwindcss/postcss": "^4.1.15", + "@types/mdx": "^2.0.13", + "@types/node": "^24.9.1", + "@types/react": "^19.2.2", + "@types/react-dom": "^19.2.2", + "postcss": "^8.5.6", + "tailwindcss": "^4.1.15", + "typescript": "^5.9.3" } } diff --git a/docs/plans/DOCKER_DEPLOYMENT.md b/docs/plans/DOCKER_DEPLOYMENT.md deleted file mode 100644 index 6af65ed7..00000000 --- a/docs/plans/DOCKER_DEPLOYMENT.md +++ /dev/null @@ -1,758 +0,0 @@ -# Docker Deployment Guide - -**Status:** In Development for v0.2.0 -**Requested By:** Reddit community ([thread](https://reddit.com/r/LocalLLaMA/...)) - -## Overview - -Docker support makes Voicebox easier to deploy, especially for: - -- **Consistent Environments**: Same setup across dev/staging/prod -- **GPU Passthrough**: Easy NVIDIA/AMD GPU access -- **Server Deployments**: Run on headless Linux servers -- **Multi-User Setups**: Isolate instances per user/team -- **Cloud Platforms**: Deploy to AWS, GCP, Azure, DigitalOcean - -## Quick Start - -### Using Pre-Built Images (Recommended) - -```bash -# CPU-only version -docker run -p 8000:8000 -v voicebox-data:/app/data \ - ghcr.io/jamiepine/voicebox:latest - -# NVIDIA GPU version -docker run --gpus all -p 8000:8000 -v voicebox-data:/app/data \ - ghcr.io/jamiepine/voicebox:latest-cuda - -# AMD GPU version (experimental) -docker run --device=/dev/kfd --device=/dev/dri -p 8000:8000 \ - -v voicebox-data:/app/data \ - ghcr.io/jamiepine/voicebox:latest-rocm -``` - -Then open: `http://localhost:8000` - -### Using Docker Compose (Easiest) - -Create `docker-compose.yml`: - -```yaml -version: '3.8' - -services: - voicebox: - image: ghcr.io/jamiepine/voicebox:latest-cuda - ports: - - "8000:8000" - volumes: - - voicebox-data:/app/data - - huggingface-cache:/root/.cache/huggingface - environment: - - GPU_MEMORY_FRACTION=0.8 # Use 80% of GPU memory - - TTS_MODE=local - - WHISPER_MODE=local - deploy: - resources: - reservations: - devices: - - driver: nvidia - count: 1 - capabilities: [gpu] - -volumes: - voicebox-data: - huggingface-cache: -``` - -Run: -```bash -docker compose up -d -``` - -## Building From Source - -### Basic Dockerfile - -```dockerfile -# Dockerfile -FROM python:3.11-slim - -WORKDIR /app - -# Install system dependencies -RUN apt-get update && apt-get install -y \ - git \ - build-essential \ - ffmpeg \ - && rm -rf /var/lib/apt/lists/* - -# Copy application -COPY backend/ /app/backend/ -COPY requirements.txt /app/ - -# Install Python dependencies -RUN pip install --no-cache-dir -r requirements.txt -RUN pip install --no-cache-dir git+https://github.com/QwenLM/Qwen3-TTS.git - -# Create data directory -RUN mkdir -p /app/data - -# Expose port -EXPOSE 8000 - -# Run server -CMD ["uvicorn", "backend.main:app", "--host", "0.0.0.0", "--port", "8000"] -``` - -Build and run: -```bash -docker build -t voicebox . -docker run -p 8000:8000 -v $(pwd)/data:/app/data voicebox -``` - -### Multi-Stage Build (Optimized) - -Smaller image size by separating build and runtime: - -```dockerfile -# Dockerfile.optimized -# Stage 1: Build dependencies -FROM python:3.11-slim AS builder - -WORKDIR /build - -RUN apt-get update && apt-get install -y \ - git build-essential && \ - rm -rf /var/lib/apt/lists/* - -COPY backend/requirements.txt . -RUN pip install --no-cache-dir --target=/build/packages \ - -r requirements.txt - -RUN pip install --no-cache-dir --target=/build/packages \ - git+https://github.com/QwenLM/Qwen3-TTS.git - -# Stage 2: Runtime -FROM python:3.11-slim - -WORKDIR /app - -# Install only runtime dependencies -RUN apt-get update && apt-get install -y \ - ffmpeg \ - && rm -rf /var/lib/apt/lists/* - -# Copy installed packages from builder -COPY --from=builder /build/packages /usr/local/lib/python3.11/site-packages/ - -# Copy application code -COPY backend/ /app/backend/ - -# Create data directory -RUN mkdir -p /app/data - -EXPOSE 8000 - -CMD ["uvicorn", "backend.main:app", "--host", "0.0.0.0", "--port", "8000"] -``` - -Build: -```bash -docker build -f Dockerfile.optimized -t voicebox:slim . -``` - -## GPU Support - -### NVIDIA GPUs (CUDA) - -**Dockerfile:** -```dockerfile -FROM nvidia/cuda:12.1.0-runtime-ubuntu22.04 - -# Install Python -RUN apt-get update && apt-get install -y \ - python3.11 python3-pip git ffmpeg && \ - rm -rf /var/lib/apt/lists/* - -WORKDIR /app - -# Install PyTorch with CUDA support -COPY backend/requirements.txt . -RUN pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121 - -# Install other dependencies -RUN pip3 install -r requirements.txt -RUN pip3 install git+https://github.com/QwenLM/Qwen3-TTS.git - -COPY backend/ /app/backend/ - -EXPOSE 8000 -CMD ["uvicorn", "backend.main:app", "--host", "0.0.0.0", "--port", "8000"] -``` - -**Run with GPU:** -```bash -docker run --gpus all -p 8000:8000 \ - -v voicebox-data:/app/data \ - voicebox:cuda -``` - -**Docker Compose with GPU:** -```yaml -services: - voicebox: - image: voicebox:cuda - deploy: - resources: - reservations: - devices: - - driver: nvidia - count: all - capabilities: [gpu] -``` - -### AMD GPUs (ROCm) - Experimental - -**Dockerfile:** -```dockerfile -FROM rocm/dev-ubuntu-22.04:6.0 - -# Install Python -RUN apt-get update && apt-get install -y \ - python3.11 python3-pip git ffmpeg && \ - rm -rf /var/lib/apt/lists/* - -WORKDIR /app - -# Install PyTorch with ROCm support -COPY backend/requirements.txt . -RUN pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm6.0 - -# Install other dependencies -RUN pip3 install -r requirements.txt -RUN pip3 install git+https://github.com/QwenLM/Qwen3-TTS.git - -# Set ROCm environment variables -ENV HSA_OVERRIDE_GFX_VERSION=10.3.0 -ENV ROCM_PATH=/opt/rocm - -COPY backend/ /app/backend/ - -EXPOSE 8000 -CMD ["uvicorn", "backend.main:app", "--host", "0.0.0.0", "--port", "8000"] -``` - -**Run with AMD GPU:** -```bash -docker run --device=/dev/kfd --device=/dev/dri \ - --group-add video --ipc=host --cap-add=SYS_PTRACE \ - --security-opt seccomp=unconfined \ - -p 8000:8000 -v voicebox-data:/app/data \ - voicebox:rocm -``` - -**Note:** ROCm support varies by GPU model. Works best on Linux. See [AMD ROCm docs](https://rocm.docs.amd.com) for compatibility. - -## Volume Mounts - -### Essential Volumes - -```bash -docker run -v voicebox-data:/app/data \ # Profiles, generations, history - -v huggingface-cache:/root/.cache/huggingface \ # Downloaded models - -p 8000:8000 voicebox -``` - -### Development Volume Mounts - -For development with hot-reload: - -```bash -docker run -v $(pwd)/backend:/app/backend \ # Live code changes - -v voicebox-data:/app/data \ - -e RELOAD=true \ - -p 8000:8000 voicebox -``` - -### Custom Model Storage - -Use external model directory: - -```bash -docker run -v /path/to/models:/models \ - -e MODELS_DIR=/models \ - -v voicebox-data:/app/data \ - -p 8000:8000 voicebox -``` - -## Environment Variables - -Configure Voicebox via environment variables: - -```bash -docker run -e TTS_MODE=local \ - -e WHISPER_MODE=openai-api \ - -e OPENAI_API_KEY=sk-... \ - -e GPU_MEMORY_FRACTION=0.8 \ - -e LOG_LEVEL=info \ - -p 8000:8000 voicebox -``` - -### Available Variables - -| Variable | Default | Description | -|----------|---------|-------------| -| `TTS_MODE` | `local` | TTS provider: `local`, `remote` | -| `TTS_REMOTE_URL` | - | URL for remote TTS server | -| `WHISPER_MODE` | `local` | Whisper provider: `local`, `openai-api`, `remote` | -| `WHISPER_REMOTE_URL` | - | URL for remote Whisper server | -| `OPENAI_API_KEY` | - | OpenAI API key (if using OpenAI Whisper) | -| `GPU_MEMORY_FRACTION` | `0.9` | Fraction of GPU memory to use (0.0-1.0) | -| `DATA_DIR` | `/app/data` | Directory for profiles/generations | -| `MODELS_DIR` | `/app/models` | Directory for local models | -| `LOG_LEVEL` | `info` | Logging level: `debug`, `info`, `warning`, `error` | -| `RELOAD` | `false` | Enable hot-reload for development | - -## Complete Docker Compose Examples - -### Production Deployment - -```yaml -# docker-compose.prod.yml -version: '3.8' - -services: - voicebox: - image: ghcr.io/jamiepine/voicebox:latest-cuda - container_name: voicebox - restart: unless-stopped - ports: - - "8000:8000" - volumes: - - voicebox-data:/app/data - - huggingface-cache:/root/.cache/huggingface - environment: - - TTS_MODE=local - - WHISPER_MODE=local - - GPU_MEMORY_FRACTION=0.8 - - LOG_LEVEL=info - deploy: - resources: - reservations: - devices: - - driver: nvidia - count: 1 - capabilities: [gpu] - healthcheck: - test: ["CMD", "curl", "-f", "http://localhost:8000/health"] - interval: 30s - timeout: 10s - retries: 3 - start_period: 40s - -volumes: - voicebox-data: - driver: local - huggingface-cache: - driver: local -``` - -Run: -```bash -docker compose -f docker-compose.prod.yml up -d -``` - -### Development Setup - -```yaml -# docker-compose.dev.yml -version: '3.8' - -services: - voicebox: - build: - context: . - dockerfile: Dockerfile - ports: - - "8000:8000" - volumes: - - ./backend:/app/backend:ro - - voicebox-data:/app/data - - huggingface-cache:/root/.cache/huggingface - environment: - - RELOAD=true - - LOG_LEVEL=debug - - TTS_MODE=local - command: uvicorn backend.main:app --host 0.0.0.0 --port 8000 --reload - -volumes: - voicebox-data: - huggingface-cache: -``` - -### Multi-Service Stack - -Full stack with reverse proxy and monitoring: - -```yaml -# docker-compose.stack.yml -version: '3.8' - -services: - # Main Voicebox app - voicebox: - image: ghcr.io/jamiepine/voicebox:latest-cuda - restart: unless-stopped - volumes: - - voicebox-data:/app/data - - huggingface-cache:/root/.cache/huggingface - environment: - - TTS_MODE=local - - WHISPER_MODE=local - deploy: - resources: - reservations: - devices: - - driver: nvidia - count: 1 - capabilities: [gpu] - - # Nginx reverse proxy - nginx: - image: nginx:alpine - ports: - - "80:80" - - "443:443" - volumes: - - ./nginx.conf:/etc/nginx/nginx.conf:ro - - ./ssl:/etc/nginx/ssl:ro - depends_on: - - voicebox - - # Prometheus monitoring (optional) - prometheus: - image: prom/prometheus - ports: - - "9090:9090" - volumes: - - ./prometheus.yml:/etc/prometheus/prometheus.yml - - prometheus-data:/prometheus - -volumes: - voicebox-data: - huggingface-cache: - prometheus-data: -``` - -## Cloud Deployment - -### AWS EC2 - -1. **Launch GPU Instance** (g4dn.xlarge or p3.2xlarge) -2. **Install Docker + nvidia-docker:** - ```bash - # Amazon Linux 2 - sudo yum install -y docker - sudo systemctl start docker - distribution=$(. /etc/os-release;echo $ID$VERSION_ID) - curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add - - curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | \ - sudo tee /etc/apt/sources.list.d/nvidia-docker.list - sudo apt-get update && sudo apt-get install -y nvidia-docker2 - sudo systemctl restart docker - ``` -3. **Deploy:** - ```bash - docker run --gpus all -d -p 80:8000 \ - -v voicebox-data:/app/data \ - --restart unless-stopped \ - ghcr.io/jamiepine/voicebox:latest-cuda - ``` - -### DigitalOcean - -Use GPU Droplet + Docker: - -```bash -# Create droplet via CLI -doctl compute droplet create voicebox \ - --size gpu-h100x1-80gb \ - --image ubuntu-22-04-x64 \ - --region nyc3 - -# SSH and deploy -ssh root@ -curl -fsSL https://get.docker.com -o get-docker.sh -sh get-docker.sh -docker run --gpus all -d -p 80:8000 voicebox:cuda -``` - -### Google Cloud Run (CPU-only) - -```bash -# Build and push -docker build -t gcr.io/your-project/voicebox . -docker push gcr.io/your-project/voicebox - -# Deploy to Cloud Run -gcloud run deploy voicebox \ - --image gcr.io/your-project/voicebox \ - --platform managed \ - --region us-central1 \ - --memory 4Gi \ - --cpu 2 \ - --port 8000 -``` - -### Fly.io - -Create `fly.toml`: -```toml -app = "voicebox" - -[build] - image = "ghcr.io/jamiepine/voicebox:latest" - -[[services]] - http_checks = [] - internal_port = 8000 - protocol = "tcp" - - [[services.ports]] - port = 80 - handlers = ["http"] - - [[services.ports]] - port = 443 - handlers = ["tls", "http"] - -[mounts] - source = "voicebox_data" - destination = "/app/data" -``` - -Deploy: -```bash -fly launch -fly deploy -``` - -## Troubleshooting - -### GPU Not Detected - -**Check NVIDIA Docker:** -```bash -docker run --rm --gpus all nvidia/cuda:12.1.0-base-ubuntu22.04 nvidia-smi -``` - -If this fails, reinstall nvidia-docker2. - -**Check AMD ROCm:** -```bash -docker run --rm --device=/dev/kfd --device=/dev/dri rocm/dev-ubuntu-22.04:6.0 rocminfo -``` - -### Permission Errors - -Container can't write to volumes: -```bash -# Fix permissions -docker run --user $(id -u):$(id -g) -v $(pwd)/data:/app/data voicebox -``` - -### Out of Memory - -Reduce GPU memory usage: -```bash -docker run -e GPU_MEMORY_FRACTION=0.5 voicebox -``` - -Or use CPU-only: -```bash -docker run -e DEVICE=cpu voicebox -``` - -### Model Download Fails - -Ensure HuggingFace cache is writable: -```bash -docker run -v huggingface-cache:/root/.cache/huggingface voicebox -``` - -Or use host cache: -```bash -docker run -v ~/.cache/huggingface:/root/.cache/huggingface voicebox -``` - -### Port Already in Use - -Change host port: -```bash -docker run -p 8080:8000 voicebox # Use port 8080 instead -``` - -## Security Best Practices - -### 1. Don't Run as Root - -Create non-root user in Dockerfile: -```dockerfile -RUN useradd -m -u 1000 voicebox -USER voicebox -``` - -### 2. Use Secrets for API Keys - -Don't put API keys in docker-compose.yml: - -```bash -# Use Docker secrets -echo "sk-your-key" | docker secret create openai_key - - -docker service create \ - --secret openai_key \ - -e OPENAI_API_KEY_FILE=/run/secrets/openai_key \ - voicebox -``` - -### 3. Network Isolation - -Use internal networks for multi-container setups: - -```yaml -services: - voicebox: - networks: - - internal - nginx: - networks: - - internal - - external - ports: - - "80:80" - -networks: - internal: - internal: true - external: -``` - -### 4. Resource Limits - -Prevent resource exhaustion: - -```yaml -services: - voicebox: - deploy: - resources: - limits: - cpus: '4' - memory: 8G - reservations: - cpus: '2' - memory: 4G -``` - -## Performance Tuning - -### GPU Memory Management - -```bash -# Use 80% of GPU (default 90%) -docker run -e GPU_MEMORY_FRACTION=0.8 voicebox - -# Allow GPU memory growth (prevents OOM) -docker run -e TF_FORCE_GPU_ALLOW_GROWTH=true voicebox -``` - -### Model Caching - -Pre-download models to volume: - -```bash -# Download models first -docker run --rm -v huggingface-cache:/root/.cache/huggingface \ - voicebox python -c " -from transformers import WhisperProcessor, WhisperForConditionalGeneration -WhisperProcessor.from_pretrained('openai/whisper-base') -WhisperForConditionalGeneration.from_pretrained('openai/whisper-base') -" - -# Then run normally -docker run -v huggingface-cache:/root/.cache/huggingface voicebox -``` - -### Multi-Worker Setup - -Use uvicorn workers for better throughput: - -```dockerfile -CMD ["uvicorn", "backend.main:app", "--host", "0.0.0.0", "--port", "8000", "--workers", "4"] -``` - -## Monitoring - -### Health Checks - -Built-in health endpoint: -```bash -curl http://localhost:8000/health -``` - -Docker health check: -```yaml -healthcheck: - test: ["CMD", "curl", "-f", "http://localhost:8000/health"] - interval: 30s - timeout: 10s - retries: 3 -``` - -### Prometheus Metrics - -Add metrics exporter: -```python -# backend/main.py -from prometheus_fastapi_instrumentator import Instrumentator - -Instrumentator().instrument(app).expose(app) -``` - -Then scrape `/metrics` with Prometheus. - -### Logs - -View container logs: -```bash -docker logs -f voicebox - -# Or with compose -docker compose logs -f voicebox -``` - -## Next Steps - -- [ ] Publish official images to GitHub Container Registry -- [ ] Add Kubernetes Helm charts -- [ ] Create Docker Desktop extension -- [ ] Add automated vulnerability scanning -- [ ] Support ARM64 builds for Raspberry Pi / Apple Silicon - -## Contributing - -Help improve Docker support: -1. Test on different platforms (AMD GPU, ARM64, etc.) -2. Submit Dockerfile optimizations -3. Share deployment configurations -4. Report issues: [GitHub Issues](https://github.com/jamiepine/voicebox/issues) - -## Resources - -- [Docker Documentation](https://docs.docker.com) -- [NVIDIA Container Toolkit](https://github.com/NVIDIA/nvidia-docker) -- [AMD ROCm Docker](https://rocm.docs.amd.com/projects/install-on-linux/en/latest/how-to/docker.html) -- [Docker Compose Reference](https://docs.docker.com/compose/compose-file/) diff --git a/docs/plans/EXTERNAL_PROVIDERS.md b/docs/plans/EXTERNAL_PROVIDERS.md deleted file mode 100644 index 3b1e7e21..00000000 --- a/docs/plans/EXTERNAL_PROVIDERS.md +++ /dev/null @@ -1,435 +0,0 @@ -# External Provider Support - -**Status:** Planned for v0.2.0 -**Discussion:** [Reddit Thread](https://reddit.com/r/LocalLLaMA/...) - -## Overview - -External provider support allows you to connect Voicebox to remotely-hosted TTS and Whisper services instead of running models locally. This is useful for: - -- **Existing GPU Infrastructure**: You already have Qwen3-TTS running on a GPU server -- **AMD GPU Users**: Run models on your AMD hardware, use Voicebox as the UI -- **Cloud Deployments**: Host models on Modal, Replicate, RunPod, etc. -- **Team Sharing**: Multiple users share one GPU server running models -- **Mixed Deployments**: Local Whisper + remote TTS, or vice versa - -## Architecture - -``` -┌─────────────────┐ HTTP/API ┌──────────────────┐ -│ Voicebox UI │ ───────────────────────> │ Your TTS Server │ -│ + Backend │ │ (Qwen3-TTS on │ -│ │ <─────────────────────── │ AMD/NVIDIA GPU)│ -│ - Profiles │ Audio + Metadata └──────────────────┘ -│ - History │ -│ - Audio Edit │ HTTP/API ┌──────────────────┐ -│ - UI │ ───────────────────────> │ Whisper Service │ -└─────────────────┘ │ (OpenAI API or │ - │ self-hosted) │ - └──────────────────┘ -``` - -**What Voicebox Still Handles:** -- Voice profile management -- Generation history -- Audio trimming/editing -- Multi-track story editor -- UI/UX layer - -**What External Providers Handle:** -- Model inference (TTS generation, transcription) -- GPU allocation -- Model loading/caching - -## Configuration - -### Environment Variables - -```bash -# TTS Provider -TTS_MODE=remote # local | remote -TTS_REMOTE_URL=http://192.168.1.100:8000 # Your TTS server URL -TTS_API_KEY=your-api-key # Optional authentication - -# Whisper Provider -WHISPER_MODE=openai-api # local | openai-api | remote -WHISPER_REMOTE_URL=http://localhost:9000 # For self-hosted Whisper -OPENAI_API_KEY=sk-... # For OpenAI Whisper API -``` - -### Voicebox Config UI (Planned) - -Settings page will include: -- Provider selection dropdowns -- URL/API key inputs -- Connection test button -- Latency/status indicators - -## Hosting External Services - -### Option 1: Simple FastAPI Server (Recommended) - -Create a lightweight server to expose your local Qwen3-TTS model: - -```python -# tts_server.py -from fastapi import FastAPI, UploadFile, File -from qwen_tts import Qwen3TTSModel -import numpy as np -import base64 - -app = FastAPI() -model = Qwen3TTSModel.from_pretrained( - "Qwen/Qwen3-TTS-12Hz-1.7B-Base", - device_map="cuda" # or "cpu" for AMD ROCm: use torch+rocm -) - -@app.post("/v1/generate") -async def generate( - text: str, - voice_prompt: dict, - language: str = "en", - seed: int = None -): - """Generate speech from text using voice prompt.""" - audio, sample_rate = model.generate_voice_clone( - text=text, - voice_clone_prompt=voice_prompt, - ) - - # Return as base64 for transport - audio_bytes = audio.tobytes() - return { - "audio": base64.b64encode(audio_bytes).decode(), - "sample_rate": sample_rate, - "dtype": str(audio.dtype) - } - -@app.post("/v1/create_voice_prompt") -async def create_voice_prompt( - audio: UploadFile = File(...), - reference_text: str = "" -): - """Create voice prompt from reference audio.""" - # Save uploaded audio temporarily - audio_path = f"/tmp/{audio.filename}" - with open(audio_path, "wb") as f: - f.write(await audio.read()) - - # Create voice prompt - voice_prompt = model.create_voice_clone_prompt( - ref_audio=audio_path, - ref_text=reference_text, - ) - - return {"voice_prompt": voice_prompt} - -@app.get("/health") -async def health(): - return { - "status": "healthy", - "model": "Qwen3-TTS-12Hz-1.7B-Base", - "device": str(model.device) - } - -if __name__ == "__main__": - import uvicorn - uvicorn.run(app, host="0.0.0.0", port=8000) -``` - -**Run it:** -```bash -# Install dependencies -pip install fastapi uvicorn qwen-tts torch - -# For AMD GPUs, use ROCm PyTorch: -pip install torch --index-url https://download.pytorch.org/whl/rocm6.4 - -# Start server -python tts_server.py -``` - -### Option 2: vLLM (If Supported) - -```bash -vllm serve Qwen/Qwen3-TTS-12Hz-1.7B-Base \ - --host 0.0.0.0 \ - --port 8000 \ - --gpu-memory-utilization 0.9 -``` - -### Option 3: Cloud Platforms - -**Modal.com Example:** -```python -import modal - -app = modal.App("qwen-tts") -image = modal.Image.debian_slim().pip_install("qwen-tts", "torch") - -@app.function(gpu="A10G", image=image) -@modal.web_endpoint(method="POST") -def generate(text: str, voice_prompt: dict): - from qwen_tts import Qwen3TTSModel - model = Qwen3TTSModel.from_pretrained("Qwen/Qwen3-TTS-12Hz-1.7B-Base") - audio, sr = model.generate_voice_clone(text, voice_prompt) - return {"audio": audio.tolist(), "sample_rate": sr} -``` - -Deploy: `modal deploy tts_server.py` -Get URL: `https://yourapp--generate.modal.run` - -## API Specification - -External TTS providers must implement these endpoints: - -### `POST /v1/generate` - -Generate speech from text. - -**Request:** -```json -{ - "text": "Hello, this is a test.", - "voice_prompt": { /* voice prompt object */ }, - "language": "en", - "seed": 12345 -} -``` - -**Response:** -```json -{ - "audio": "base64-encoded-audio-bytes", - "sample_rate": 24000, - "dtype": "float32" -} -``` - -### `POST /v1/create_voice_prompt` - -Create a voice prompt from reference audio. - -**Request:** (multipart/form-data) -- `audio`: Audio file upload -- `reference_text`: Transcript of the audio - -**Response:** -```json -{ - "voice_prompt": { /* voice prompt object */ } -} -``` - -### `GET /health` - -Health check endpoint. - -**Response:** -```json -{ - "status": "healthy", - "model": "Qwen3-TTS-12Hz-1.7B-Base", - "device": "cuda:0" -} -``` - -## Whisper External Providers - -### OpenAI Whisper API - -Simply set: -```bash -WHISPER_MODE=openai-api -OPENAI_API_KEY=sk-... -``` - -Voicebox will use OpenAI's Whisper API automatically. - -### Self-Hosted Whisper - -Run your own Whisper server: - -```python -# whisper_server.py -from fastapi import FastAPI, UploadFile, File -from transformers import WhisperProcessor, WhisperForConditionalGeneration -import librosa - -app = FastAPI() -processor = WhisperProcessor.from_pretrained("openai/whisper-base") -model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-base") - -@app.post("/v1/transcribe") -async def transcribe(audio: UploadFile = File(...), language: str = None): - # Load audio - audio_path = f"/tmp/{audio.filename}" - with open(audio_path, "wb") as f: - f.write(await audio.read()) - - audio_data, sr = librosa.load(audio_path, sr=16000) - - # Process - inputs = processor(audio_data, sampling_rate=16000, return_tensors="pt") - predicted_ids = model.generate(inputs["input_features"]) - transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)[0] - - return {"text": transcription} -``` - -Configure Voicebox: -```bash -WHISPER_MODE=remote -WHISPER_REMOTE_URL=http://localhost:9000 -``` - -## Use Cases - -### 1. AMD GPU User with Existing Setup - -**Scenario:** You have a Radeon 7900 XTX running Qwen3-TTS on Linux. - -**Setup:** -1. Run `tts_server.py` on your AMD box (ROCm PyTorch) -2. Configure Voicebox: `TTS_MODE=remote`, `TTS_REMOTE_URL=http://amd-box:8000` -3. Use Voicebox UI for profiles, generation, editing -4. TTS happens on your AMD GPU - -### 2. Team Deployment - -**Scenario:** 5 team members, 1 GPU server. - -**Setup:** -1. Deploy TTS server on shared GPU box -2. Each person runs Voicebox desktop app locally -3. All point to same `TTS_REMOTE_URL` -4. Profiles and history stay local per user -5. GPU usage is shared - -### 3. Hybrid Local/Remote - -**Scenario:** Fast local Whisper, heavy TTS on cloud. - -**Setup:** -```bash -TTS_MODE=remote -TTS_REMOTE_URL=https://your-modal-app.modal.run - -WHISPER_MODE=local # Fast transcription on your CPU -``` - -### 4. OpenAI Whisper + Self-Hosted TTS - -**Scenario:** Use OpenAI's API for transcription, run TTS locally. - -**Setup:** -```bash -TTS_MODE=local - -WHISPER_MODE=openai-api -OPENAI_API_KEY=sk-... -``` - -## Security Considerations - -### Authentication - -Add API key authentication to your external server: - -```python -from fastapi import Header, HTTPException - -API_KEY = "your-secret-key" - -async def verify_api_key(x_api_key: str = Header(...)): - if x_api_key != API_KEY: - raise HTTPException(status_code=401, detail="Invalid API key") - -@app.post("/v1/generate", dependencies=[Depends(verify_api_key)]) -async def generate(...): - ... -``` - -Configure Voicebox: -```bash -TTS_API_KEY=your-secret-key -``` - -### Network Security - -- **VPN/Tailscale**: Use private network for remote servers -- **HTTPS**: Use reverse proxy (nginx/Caddy) with SSL certificates -- **Firewall**: Restrict access to known IPs - -### Rate Limiting - -Protect your external server: - -```python -from slowapi import Limiter -from slowapi.util import get_remote_address - -limiter = Limiter(key_func=get_remote_address) -app.state.limiter = limiter - -@app.post("/v1/generate") -@limiter.limit("10/minute") -async def generate(...): - ... -``` - -## Performance Considerations - -### Latency - -External providers add network latency: -- **Local network**: ~10-50ms overhead (negligible) -- **Same datacenter**: ~1-5ms overhead -- **Cross-region cloud**: 50-200ms+ overhead - -For real-time applications, keep TTS server on local network or same cloud region. - -### Caching - -Implement response caching on external server: - -```python -from functools import lru_cache - -@lru_cache(maxsize=1000) -def get_cached_generation(text, voice_prompt_hash, language, seed): - return model.generate_voice_clone(text, voice_prompt) -``` - -### Load Balancing - -For high-traffic deployments, run multiple TTS servers behind a load balancer: - -``` -Voicebox ──> Load Balancer ──> TTS Server 1 (GPU 1) - ├──> TTS Server 2 (GPU 2) - └──> TTS Server 3 (GPU 3) -``` - -## Future Enhancements - -- [ ] **Provider Marketplace**: Built-in directory of compatible providers -- [ ] **Automatic Fallback**: If remote fails, fallback to local -- [ ] **Cost Tracking**: Monitor API usage and costs -- [ ] **Performance Metrics**: Latency, throughput dashboards -- [ ] **Multi-Provider**: Use different providers for different voices/languages - -## Contributing - -If you build an external provider, please share: -1. Server implementation -2. Performance benchmarks -3. Deployment guide - -Submit to: [GitHub Discussions](https://github.com/jamiepine/voicebox/discussions) - -## Questions? - -- **Discord**: [Join the community](https://discord.gg/...) -- **GitHub**: [Open an issue](https://github.com/jamiepine/voicebox/issues) -- **Docs**: [Full documentation](https://voicebox.sh/docs) diff --git a/docs/plans/MLX_AUDIO.md b/docs/plans/MLX_AUDIO.md deleted file mode 100644 index 9c2a100a..00000000 --- a/docs/plans/MLX_AUDIO.md +++ /dev/null @@ -1,396 +0,0 @@ -# MLX Audio Integration - -**Status:** Validated ✅ -**Context:** [mlx-audio v0.3.1 release](https://github.com/Blaizzy/mlx-audio) - -## Validation Results - -We validated mlx-audio in an isolated environment (`mlx-test/`). Key findings: - -| Metric | Result | -|--------|--------| -| MLX Version | 0.30.4 | -| Model Load Time | ~1s (after initial download) | -| Generation RTF | **0.5-0.6x** (1.7-2x faster than real-time) | -| Test Hardware | Apple Silicon Mac | - -### Model Mapping - -| voicebox (PyTorch) | mlx-audio (MLX) | -|--------------------|-----------------| -| `Qwen/Qwen3-TTS-12Hz-1.7B-Base` | `mlx-community/Qwen3-TTS-12Hz-1.7B-Base-bf16` | -| `Qwen/Qwen3-TTS-12Hz-0.6B-Base` | (not yet converted) | - -### mlx-audio API - -The API uses a **generator-based streaming pattern**: - -```python -from mlx_audio.tts import load - -model = load("mlx-community/Qwen3-TTS-12Hz-1.7B-Base-bf16") - -# generate() yields GenerationResult objects -for result in model.generate("Hello world"): - audio = result.audio # numpy array of samples - sample_rate = result.sample_rate # 24000 - rtf = result.real_time_factor # e.g., 0.55 -``` - -### Known Warnings (harmless) - -``` -You are using a model of type qwen3_tts to instantiate a model of type . -The tokenizer you are loading... with an incorrect regex pattern... -``` - -These warnings appear but don't affect functionality or output quality. - -### Demo Script - -Run `mlx-test/demo.py` to test: -```bash -cd mlx-test && source venv/bin/activate && python demo.py "Your text here" -``` - -## Problem - -Apple Silicon users are stuck on CPU inference while Windows and Linux users get CUDA acceleration. The current PyTorch MPS backend has stability issues (lines 34-36 in `backend/tts.py` and `backend/transcribe.py`), forcing a CPU fallback that makes voicebox significantly slower on M1/M2/M3 Macs. - -This creates a poor experience for a large portion of users who bought Apple Silicon specifically for ML workloads. - -## Solution - -Integrate [mlx-audio](https://github.com/Blaizzy/mlx-audio) as the inference engine for macOS Apple Silicon builds. MLX is Apple's native ML framework, optimized for Metal and the unified memory architecture. It's fast, stable, and already supports the same Qwen3-TTS models we use. - -**Key wins:** -- Native GPU acceleration on Apple Silicon (no more CPU fallback) -- Streaming TTS support (faster perceived latency) -- Memory optimizations (run larger models on less RAM) -- Fixed 0.6B silence bug that we currently ship -- Same Qwen3-TTS models (zero migration cost for users) - -## Architecture - -### Current Stack -``` -┌─────────────────────────┐ -│ PyTorch + Qwen3-TTS │ -│ (CPU only on macOS) │ -└─────────────────────────┘ -``` - -### Proposed Stack -``` -┌─────────────────────────────────────────┐ -│ Platform Detection at Runtime │ -└─────────────────────────────────────────┘ - │ - ├─── Apple Silicon (aarch64-darwin) - │ ┌─────────────────────────┐ - │ │ MLX Audio Backend │ - │ │ - Qwen3-TTS (mlx) │ - │ │ - Whisper (mlx) │ - │ │ - Streaming support │ - │ └─────────────────────────┘ - │ - └─── Other (x86_64, Windows, Linux) - ┌─────────────────────────┐ - │ PyTorch Backend │ - │ - Qwen3-TTS (pytorch) │ - │ - Whisper (pytorch) │ - │ - CUDA if available │ - └─────────────────────────┘ -``` - -## Implementation Phases - -### Phase 1: Platform Detection & Dependency Management - -Create a backend that switches between PyTorch and MLX based on runtime platform detection. - -**New files:** -- `backend/platform.py` - Detect Apple Silicon, return backend type -- `backend/backends/__init__.py` - Backend factory pattern -- `backend/requirements-mlx.txt` - MLX-specific deps (macOS only) - -**Modified files:** -- `backend/requirements.txt` - Keep PyTorch as default -- `backend/main.py` - Import from backend factory instead of direct imports - -**Platform detection logic:** -```python -def get_backend_type() -> str: - """Detect best backend for current platform.""" - if platform.system() == "Darwin" and platform.machine() == "arm64": - # Apple Silicon detected - try: - import mlx - return "mlx" - except ImportError: - return "pytorch" # Fallback if mlx not installed - return "pytorch" -``` - -### Phase 2: MLX Backend Implementation - -Create parallel implementations of TTS and STT using mlx-audio. - -**New files:** -- `backend/backends/mlx_backend.py` - MLX inference engine -- `backend/backends/pytorch_backend.py` - Refactor current code into backend - -**Interface both backends must implement:** -```python -class TTSBackend(Protocol): - async def load_model(self, model_size: str) -> None: ... - async def create_voice_prompt(self, audio_path: str, reference_text: str) -> dict: ... - async def generate(self, text: str, voice_prompt: dict, **kwargs) -> Tuple[np.ndarray, int]: ... - async def generate_streaming(self, text: str, voice_prompt: dict, **kwargs) -> AsyncIterator[bytes]: ... - def unload_model(self) -> None: ... - -class STTBackend(Protocol): - async def load_model(self, model_size: str) -> None: ... - async def transcribe(self, audio_path: str, language: Optional[str]) -> str: ... - def unload_model(self) -> None: ... -``` - -**MLX backend implementation notes:** - -mlx-audio's `generate()` returns a generator by default (streaming is built-in): - -```python -# MLX backend wrapper -from mlx_audio.tts import load - -class MLXTTSBackend: - def __init__(self): - self.model = None - - async def load_model(self, model_size: str) -> None: - model_map = { - "1.7B": "mlx-community/Qwen3-TTS-12Hz-1.7B-Base-bf16", - # "0.6B": needs conversion to mlx format - } - self.model = load(model_map[model_size]) - - async def generate(self, text: str, voice_prompt: dict, **kwargs) -> Tuple[np.ndarray, int]: - # Collect all chunks from generator - chunks = [] - for result in self.model.generate(text): # TODO: add voice_prompt support - chunks.append(np.array(result.audio)) - return np.concatenate(chunks), 24000 -``` - -**MLX-specific features to expose:** -- Streaming TTS (new endpoint: `/api/generate/stream`) -- Memory-optimized model loading -- Qwen3-ASR for transcription (in addition to Whisper) - -### Phase 3: API Layer Updates - -Update FastAPI endpoints to support new streaming capabilities and maintain backward compatibility. - -**Modified files:** -- `backend/main.py` - Add streaming endpoints -- `backend/tts.py` - Refactor to use backend abstraction -- `backend/transcribe.py` - Refactor to use backend abstraction - -**New endpoints:** -```python -@app.post("/api/generate/stream") -async def generate_stream(...) -> StreamingResponse: - """Stream TTS chunks as they're generated (MLX only).""" - backend = get_backend() - if not hasattr(backend, 'generate_streaming'): - raise HTTPException(501, "Streaming not supported on this backend") - return StreamingResponse(backend.generate_streaming(...), media_type="audio/wav") -``` - -**Backward compatibility:** -- Keep all existing `/api/generate` endpoints unchanged -- PyTorch backend users see no behavior change -- MLX users automatically get faster inference, streaming is opt-in - -### Phase 4: Frontend Integration - -Add UI indicators for backend type and streaming progress. - -**Modified files:** -- `app/src/hooks/useGenerationForm.tsx` - Add streaming support -- `app/src/components/GenerationForm.tsx` - Show backend badge, streaming toggle -- `app/src/lib/api.ts` - Add streaming API client - -**UI additions:** -- Badge showing current backend ("MLX" or "PyTorch") -- Toggle for streaming mode (disabled if PyTorch) -- Real-time streaming playback (WaveSurfer progressive loading) - -### Phase 5: Build & Distribution - -Create separate installers for MLX (Apple Silicon) and PyTorch (Universal). - -**Modified files:** -- `tauri/src-tauri/tauri.conf.json` - Add target-specific builds -- `.github/workflows/release.yml` - Build both variants - -**Build matrix:** -```yaml -- target: aarch64-apple-darwin - backend: mlx - installer: voicebox-macos-silicon-{version}.dmg - -- target: x86_64-apple-darwin - backend: pytorch - installer: voicebox-macos-intel-{version}.dmg - -- target: x86_64-pc-windows-msvc - backend: pytorch - installer: voicebox-windows-{version}.exe -``` - -**Installation flow:** -- Auto-detect architecture, recommend correct installer -- MLX installer includes `mlx-audio` in embedded Python -- PyTorch installer includes `torch` in embedded Python -- Both can coexist (different backend, same profile format) - -### Phase 6: Testing & Validation - -Ensure both backends produce compatible outputs. - -**New files:** -- `backend/tests/test_backend_parity.py` - Verify both backends produce similar audio -- `backend/tests/test_streaming.py` - Streaming-specific tests - -**Test scenarios:** -- Same voice prompt on both backends → similar (not identical) audio output -- Profile created on MLX → loads on PyTorch (and vice versa) -- Streaming chunks assemble into valid WAV file -- Model downloads work on both backends -- Memory usage stays within bounds - -### Phase 7: Documentation - -Update user-facing docs and developer guides. - -**New files:** -- `docs/developer/BACKENDS.md` - Guide for adding new backends -- `docs/overview/performance.md` - Backend comparison benchmarks - -**Modified files:** -- `README.md` - Note Apple Silicon acceleration -- `docs/TROUBLESHOOTING.md` - Add MLX-specific issues - -**Key docs to write:** -- Which installer to download (architecture detection) -- Performance comparison (MLX vs PyTorch on same M2 hardware) -- How streaming mode works -- How to force PyTorch on Apple Silicon (for debugging) - -## Technical Decisions - -### Why Dual Backend Instead of MLX-Only? - -**Pros of dual backend:** -- Windows and Intel Mac users unaffected -- Easier testing (can compare outputs) -- Fallback if MLX has issues - -**Cons of dual backend:** -- More code to maintain -- Two dependency trees -- Build complexity (separate installers) - -**Decision:** Dual backend. The maintenance cost is worth it to avoid breaking existing users and to have a fallback. - -### Why Separate Installers Instead of Runtime Detection? - -**Pros of separate installers:** -- Smaller bundle size (don't ship both PyTorch and MLX) -- Clearer to users which version they have -- Easier to debug (no "which backend am I running?" confusion) -- Can optimize each build for its target - -**Cons:** -- More installers to build and test -- Users might download the wrong one - -**Decision:** Separate installers. Bundle size matters (PyTorch + MLX would be huge), and we can auto-detect architecture on the download page. - -### Streaming vs Batch Generation - -MLX supports streaming, PyTorch doesn't (without significant work). Should streaming be: -1. MLX-only feature (✅ chosen) -2. Implemented for both (lots of work) -3. Not exposed at all (wasted opportunity) - -**Decision:** MLX-only. Expose as opt-in feature with graceful degradation (button disabled on PyTorch backend). - -## Migration Path - -Nothing needs migrating, macos users will just notice a speed-boost in inference - -**Data format compatibility:** -- Profiles (SQLite) → no schema changes needed -- Voice prompts (cached) → backend-agnostic (just numpy arrays) -- Audio files → unchanged - -## Performance Expectations - -### Measured Results (from validation) - -| Metric | MLX (measured) | PyTorch CPU (estimated) | -|--------|----------------|-------------------------| -| **6s audio generation** | ~3-4s | ~10-15s | -| **Real-time factor** | 0.5-0.6x | 2-3x | -| **Model load (cached)** | ~1s | ~3-5s | - -### TTS Generation (1.7B model, ~20s output) -- **PyTorch CPU (M2 Max):** ~45-60s (slower than real-time) -- **MLX (M2 Max):** ~8-12s (faster than real-time) -- **Improvement:** ~4-5x faster - -### Whisper Transcription (10s audio clip) -- **PyTorch CPU:** ~5-8s -- **MLX:** ~1-2s -- **Improvement:** ~3-4x faster - -### Memory Usage (1.7B model) -- **PyTorch:** ~8-10GB (no GPU offload, so CPU RAM) -- **MLX:** ~4-6GB (unified memory, better optimization) -- **Improvement:** ~40% less RAM - -Full benchmarks will be in `docs/overview/performance.md` after Phase 6. - -## Open Questions - -- **Should we support Qwen3-ASR (MLX-only) in addition to Whisper?** Adds another model option but increases complexity. Probably phase 8+. - Sure -- **Should we backport streaming to PyTorch?** Would require chunking and callback-based generation. Probably not worth it given mlx-audio already has it. - No -- **What's the auto-update UX for migrating PyTorch→MLX users?** Needs design. Don't want to force reinstall, but also want to make upgrade obvious. - it just updates, users see nothing -- **Do we expose backend selection in settings or hide it?** Leaning toward auto-detect only, with env var override for power users. - -## Success Metrics - -How we'll know this worked: - -1. **Performance:** Apple Silicon users report generation faster than real-time -2. **Adoption:** >80% of macOS downloads are MLX build within 1 month -3. **Stability:** <5% increase in bug reports (backend abstraction doesn't introduce regressions) -4. **Feedback:** Positive sentiment in Discord/GitHub about macOS performance - -## Related Work - -- [PyTorch MPS tracking issue](https://github.com/pytorch/pytorch/issues/77764) - Why we can't use MPS directly -- [mlx-audio server implementation](https://github.com/Blaizzy/mlx-audio/blob/main/examples/server.py) - Reference for streaming API -- [MLX Whisper benchmarks](https://github.com/ml-explore/mlx-examples/tree/main/whisper) - Performance data - -## Next Steps - -1. ~~Validate mlx-audio can load Qwen3-TTS models (quick test)~~ ✅ Done - see `mlx-test/` -2. Get approval on dual-backend architecture -3. Start Phase 1 (platform detection) - -## Questions? - -Feedback welcome in GitHub discussions or Discord. diff --git a/docs/plans/OPENAI_SUPPORT.md b/docs/plans/OPENAI_SUPPORT.md deleted file mode 100644 index 5409e55a..00000000 --- a/docs/plans/OPENAI_SUPPORT.md +++ /dev/null @@ -1,235 +0,0 @@ -# OpenAI API Compatibility - -**Status:** Planned for v0.2.0 - -**Issue:** [#10 OpenAI API compatibility](https://github.com/jamiepine/voicebox/issues/10) - -## Overview - -This feature exposes OpenAI-compatible endpoints from Voicebox, allowing any tool, library, or application that speaks the OpenAI Audio API to use Voicebox as a drop-in local replacement. - -```mermaid -flowchart LR - subgraph clients [External Clients] - SDK[OpenAI SDK] - Curl[curl / HTTP] - Apps[Third-party Apps] - end - - subgraph voicebox [Voicebox Server] - OpenAI["/v1/audio/* endpoints"] - TTS[TTSModel] - Whisper[WhisperModel] - Profiles[Voice Profiles] - end - - SDK --> OpenAI - Curl --> OpenAI - Apps --> OpenAI - OpenAI --> TTS - OpenAI --> Whisper - OpenAI --> Profiles -``` - -## Use Cases - -- **OpenAI SDK users**: `openai.audio.speech.create()` works with Voicebox -- **LLM frameworks**: LangChain, AutoGen, etc. can use Voicebox for TTS -- **Shell scripts**: `curl` commands copy-pasted from OpenAI docs work -- **Existing integrations**: Any tool expecting OpenAI's API works without code changes - -## Endpoints to Implement - -### 1. `POST /v1/audio/speech` (TTS) - -OpenAI spec: https://platform.openai.com/docs/api-reference/audio/createSpeech - -**Request:** - -```json -{ - "model": "tts-1", - "input": "Hello world!", - "voice": "alloy", - "response_format": "mp3", - "speed": 1.0 -} -``` - -**Response:** Audio file (mp3, wav, opus, aac, flac, pcm) - -**Voice Mapping Strategy:** - -- `voice` parameter maps to Voicebox profile names (case-insensitive) -- If no match, use a configurable default profile -- Support special syntax: `voice: "profile:uuid"` for explicit profile ID - -### 2. `POST /v1/audio/transcriptions` (Whisper) - -OpenAI spec: https://platform.openai.com/docs/api-reference/audio/createTranscription - -**Request:** (multipart/form-data) - -- `file`: Audio file -- `model`: "whisper-1" -- `language`: Optional language hint -- `response_format`: json, text, srt, verbose_json, vtt - -**Response:** - -```json -{ - "text": "Hello world!" -} -``` - -## Implementation Details - -### New File: `backend/openai_compat.py` - -Create a dedicated module with an APIRouter for OpenAI-compatible endpoints: - -```python -from fastapi import APIRouter, UploadFile, File, Form, HTTPException -from fastapi.responses import StreamingResponse -from pydantic import BaseModel -from typing import Literal, Optional - -router = APIRouter(prefix="/v1/audio", tags=["OpenAI Compatible"]) - -class SpeechRequest(BaseModel): - model: str = "tts-1" - input: str - voice: str = "alloy" - response_format: Literal["mp3", "wav", "opus", "aac", "flac", "pcm"] = "mp3" - speed: float = 1.0 - -@router.post("/speech") -async def create_speech(request: SpeechRequest, db: Session = Depends(get_db)): - # 1. Map voice name to profile - # 2. Generate audio using existing TTSModel - # 3. Convert to requested format - # 4. Return audio stream - ... - -@router.post("/transcriptions") -async def create_transcription( - file: UploadFile = File(...), - model: str = Form("whisper-1"), - language: Optional[str] = Form(None), - response_format: str = Form("json"), -): - # 1. Save uploaded file - # 2. Transcribe using existing WhisperModel - # 3. Return in requested format - ... -``` - -### Voice Profile Resolution - -Add helper in [backend/profiles.py](backend/profiles.py): - -```python -async def resolve_voice_for_openai(voice: str, db: Session) -> Optional[VoiceProfile]: - """ - Resolve OpenAI voice parameter to a Voicebox profile. - - Priority: - 1. Exact profile name match (case-insensitive) - 2. Profile ID match (if voice starts with "profile:") - 3. Default profile from config - 4. First available profile - """ - ... -``` - -### Audio Format Conversion - -Add conversion utilities in [backend/utils/audio.py](backend/utils/audio.py): - -```python -def convert_audio_format( - audio: np.ndarray, - sample_rate: int, - target_format: str, # mp3, wav, opus, aac, flac, pcm -) -> bytes: - """Convert audio to target format using ffmpeg or pydub.""" - ... -``` - -### Configuration - -Add to [backend/config.py](backend/config.py): - -```python -# OpenAI API Compatibility -OPENAI_COMPAT_ENABLED = True -OPENAI_COMPAT_DEFAULT_VOICE = None # Profile ID or name for default voice -OPENAI_COMPAT_REQUIRE_AUTH = False # Require API key validation -OPENAI_COMPAT_API_KEY = None # If set, validate against this -``` - -### Integration with main.py - -In [backend/main.py](backend/main.py), include the router: - -```python -from . import openai_compat - -# Add OpenAI-compatible routes -if config.OPENAI_COMPAT_ENABLED: - app.include_router(openai_compat.router) -``` - -## Streaming Support (Future Enhancement) - -Initial implementation returns complete audio. Streaming can be added later: - -```python -@router.post("/speech") -async def create_speech(request: SpeechRequest): - if request.stream: - return StreamingResponse( - generate_audio_chunks(request), - media_type=f"audio/{request.response_format}" - ) - ... -``` - -## Testing - -Example usage after implementation: - -```bash -# TTS with curl -curl http://localhost:8000/v1/audio/speech \ - -H "Content-Type: application/json" \ - -d '{"model": "tts-1", "input": "Hello!", "voice": "MyProfile"}' \ - --output speech.mp3 - -# With OpenAI Python SDK -from openai import OpenAI -client = OpenAI(base_url="http://localhost:8000/v1", api_key="unused") -response = client.audio.speech.create( - model="tts-1", - voice="MyProfile", - input="Hello world!" -) -response.stream_to_file("output.mp3") - -# Transcription -curl http://localhost:8000/v1/audio/transcriptions \ - -F file=@audio.mp3 \ - -F model="whisper-1" -``` - -## Security Considerations - -- Optional API key validation (for shared deployments) -- Rate limiting on endpoints -- Input length limits (same as existing `/generate` endpoint) - -## Dependencies - -- `pydub` or `ffmpeg-python` for audio format conversion (mp3, opus, etc.) -- No changes to existing TTS/Whisper model code \ No newline at end of file diff --git a/docs/plans/TTS_PROVIDER_ARCHITECTURE.md b/docs/plans/TTS_PROVIDER_ARCHITECTURE.md deleted file mode 100644 index 8d35a7e5..00000000 --- a/docs/plans/TTS_PROVIDER_ARCHITECTURE.md +++ /dev/null @@ -1,964 +0,0 @@ -# TTS Provider Architecture - -**Status:** Planned for v0.1.13 -**Created:** 2025-01-31 -**Problem:** GitHub 2GB release limit + poor UX for frequent updates requiring 2.4GB re-downloads - ---- - -## Overview - -Split the monolithic backend into modular components: - -1. **Main App** (~150-200MB): Tauri + FastAPI backend + Whisper + UI/profiles/history -2. **TTS Providers** (downloadable plugins): Separate executables for model inference - -This architecture solves: - -- ✅ GitHub 2GB release artifact limit -- ✅ Frequent app updates without re-downloading large python binaries -- ✅ User choice of compute backend (CPU/GPU/Cloud) -- ✅ External provider support (OpenAI, custom servers) -- ✅ Future extensibility - ---- - -## Architecture Diagram - -``` -┌─────────────────────────────────────────────────────────┐ -│ Voicebox App (Tauri + Backend) ~150MB │ -│ ├─ UI Layer (React) │ -│ ├─ Backend (FastAPI) │ -│ │ ├─ Voice Profiles │ -│ │ ├─ Generation History │ -│ │ ├─ Audio Editing / Stories │ -│ │ └─ Provider Manager ◄──────────────┐ │ -│ └─ Whisper (bundled, tiny ~50MB) │ │ -└─────────────────────────────────────────┼────────────────┘ - │ - HTTP/IPC │ - │ - ┌────────────────────────────────┼─────────────────┐ - │ │ │ - ▼ ▼ ▼ -┌─────────────────┐ ┌─────────────────┐ ┌──────────────────┐ -│ TTS Provider: │ │ TTS Provider: │ │ TTS Provider: │ -│ PyTorch CPU │ │ PyTorch CUDA │ │ MLX (Apple) │ -│ │ │ │ │ │ -│ ~300MB │ │ ~2.4GB │ │ ~800MB │ -│ │ │ │ │ │ -│ Local inference │ │ GPU inference │ │ Metal inference │ -└─────────────────┘ └─────────────────┘ └──────────────────┘ - │ │ │ - └────────────────────────┴─────────────────────┘ - │ - ┌─────────────▼──────────────┐ - │ Future Providers: │ - │ • Remote Server │ - │ • OpenAI API │ - │ • ElevenLabs │ - │ • Custom Docker Container │ - └────────────────────────────┘ -``` - ---- - -## Problem Statement - -### Current Architecture Issues - -**Monolithic Binary:** - -- CPU version: ~295MB -- CUDA version: ~2.37GB -- GitHub releases: 2GB file size limit (BLOCKED) -- Updates require re-downloading entire binary -- Poor UX: update app → restart → download CUDA update → restart again - -**User Pain Points:** - -1. Cannot release CUDA version on GitHub (over 2GB) -2. Every app update forces 2.4GB re-download for GPU users -3. No flexibility (can't use OpenAI, remote servers, etc.) -4. Wastes bandwidth for small bug fixes - ---- - -## Solution: Pluggable TTS Providers - -### Component Breakdown - -#### 1. Main App (voicebox.exe / .app / .AppImage) - -**Size:** ~100-150MB - -**Includes:** - -- Tauri runtime + React UI -- FastAPI backend (pure Python, no PyTorch) -- Whisper model (tiny, ~50MB) -- SQLite database -- Profile/history/audio editing logic -- Provider management system - -**Does NOT include:** - -- PyTorch (CPU or CUDA) -- TTS models (Qwen3-TTS) -- Heavy ML dependencies - -**Updates frequently:** UI fixes, feature additions, non-ML changes - ---- - -#### 2. TTS Provider: PyTorch CPU - -**Binary:** `tts-provider-pytorch-cpu.exe` -**Size:** ~200MB - -**Includes:** - -- PyTorch CPU build -- Qwen3-TTS package -- Transformers -- No CUDA libraries - -**Download source:** Cloudflare R2 -**Updates rarely:** Only when model code changes - ---- - -#### 3. TTS Provider: PyTorch CUDA - -**Binary:** `tts-provider-pytorch-cuda.exe` -**Size:** ~2.4GB - -**Includes:** - -- PyTorch CUDA build (cu121) -- Qwen3-TTS package -- CUDA runtime, cuDNN, cuBLAS -- Transformers - -**Download source:** Cloudflare R2 -**Platform:** Windows + Linux (NVIDIA GPU) -**Updates rarely:** Only when model code or CUDA version changes - ---- - -#### 4. TTS Provider: MLX - -**Binary:** `tts-provider-mlx` -**Size:** ~150MB - -**Includes:** - -- MLX framework -- MLX-optimized Qwen3-TTS -- Metal acceleration - -**Platform:** macOS only (Apple Silicon) -**Download source:** Cloudflare R2 - ---- - -#### 5. TTS Provider: Remote - -**Binary:** None (built-in config) -**Size:** 0MB - -**How it works:** - -- User provides URL to their own TTS server -- Backend proxies requests to that server -- Implements API spec from `EXTERNAL_PROVIDERS.md` - -**Use cases:** - -- AMD GPU users running their own server -- Team deployments with shared GPU server -- Cloud hosting (Modal, RunPod, Replicate) - ---- - -#### 6. TTS Provider: OpenAI - -**Binary:** None (API wrapper) -**Size:** 0MB - -**How it works:** - -- User provides OpenAI API key -- Backend wraps OpenAI Audio API -- Voice profiles map to OpenAI voices - -**Benefits:** - -- Zero local compute -- Pay-per-use -- Instant setup - ---- - -## Communication Protocol - -### Provider API Specification - -All TTS providers must implement these endpoints: - -#### POST /tts/generate - -Generate speech from text. - -**Request:** - -```json -{ - "text": "Hello world!", - "voice_prompt": { - /* voice prompt object */ - }, - "language": "en", - "seed": 12345, - "model_size": "1.7B" -} -``` - -**Response:** - -```json -{ - "audio": "base64-encoded-audio", - "sample_rate": 24000, - "duration": 2.5 -} -``` - -#### POST /tts/create_voice_prompt - -Create voice prompt from reference audio. - -**Request:** (multipart/form-data) - -- `audio`: Audio file -- `reference_text`: Transcript - -**Response:** - -```json -{ - "voice_prompt": { - /* serialized prompt */ - } -} -``` - -#### GET /tts/health - -Health check. - -**Response:** - -```json -{ - "status": "healthy", - "provider": "pytorch-cuda", - "version": "1.0.0", - "model": "Qwen3-TTS-12Hz-1.7B-Base", - "device": "cuda:0" -} -``` - -#### GET /tts/status - -Model status. - -**Response:** - -```json -{ - "model_loaded": true, - "model_size": "1.7B", - "available_sizes": ["0.6B", "1.7B"], - "gpu_available": true, - "vram_used_mb": 1234 -} -``` - ---- - -## Backend Implementation - -### Provider Manager - -**File:** `backend/providers/__init__.py` - -```python -class ProviderManager: - """Manages TTS provider lifecycle.""" - - def __init__(self): - self.active_provider: Optional[Provider] = None - self.config = load_provider_config() - - async def start_provider(self, provider_type: str) -> str: - """Start a TTS provider process.""" - if provider_type == "pytorch-cpu": - return await self._start_local_provider("tts-provider-pytorch-cpu.exe") - elif provider_type == "pytorch-cuda": - return await self._start_local_provider("tts-provider-pytorch-cuda.exe") - elif provider_type == "mlx": - return await self._start_local_provider("tts-provider-mlx") - elif provider_type == "remote": - return self.config["remote_url"] - elif provider_type == "openai": - return None # No subprocess, API wrapper - - async def _start_local_provider(self, binary_name: str) -> str: - """Start local provider subprocess.""" - provider_path = get_provider_binary_path(binary_name) - - if not provider_path.exists(): - raise ProviderNotInstalledException(binary_name) - - # Start subprocess on random port - port = get_free_port() - process = subprocess.Popen([ - str(provider_path), - "--port", str(port), - "--data-dir", str(config.get_data_dir()) - ]) - - # Wait for provider to be ready - await wait_for_provider_health(f"http://localhost:{port}") - - self.active_provider = Provider(process, port) - return f"http://localhost:{port}" - - async def stop_provider(self): - """Stop active provider.""" - if self.active_provider: - self.active_provider.process.terminate() - self.active_provider = None -``` - ---- - -### Provider Abstraction - -**File:** `backend/providers/base.py` - -```python -class TTSProvider(ABC): - """Abstract base for TTS providers.""" - - @abstractmethod - async def generate( - self, - text: str, - voice_prompt: dict, - language: str, - seed: Optional[int] - ) -> tuple[np.ndarray, int]: - """Generate speech audio.""" - pass - - @abstractmethod - async def create_voice_prompt( - self, - audio_path: str, - reference_text: str - ) -> dict: - """Create voice prompt from reference audio.""" - pass -``` - -**File:** `backend/providers/local.py` - -```python -class LocalProvider(TTSProvider): - """Provider that communicates with local subprocess via HTTP.""" - - def __init__(self, base_url: str): - self.base_url = base_url - self.client = httpx.AsyncClient() - - async def generate(self, text, voice_prompt, language, seed): - response = await self.client.post( - f"{self.base_url}/tts/generate", - json={ - "text": text, - "voice_prompt": voice_prompt, - "language": language, - "seed": seed - } - ) - data = response.json() - audio = np.frombuffer(base64.b64decode(data["audio"]), dtype=np.float32) - return audio, data["sample_rate"] -``` - -**File:** `backend/providers/openai.py` - -```python -class OpenAIProvider(TTSProvider): - """Provider that wraps OpenAI Audio API.""" - - def __init__(self, api_key: str): - self.client = OpenAI(api_key=api_key) - - async def generate(self, text, voice_prompt, language, seed): - # Map voice_prompt to OpenAI voice name - voice = map_profile_to_openai_voice(voice_prompt) - - response = await self.client.audio.speech.create( - model="tts-1", - voice=voice, - input=text - ) - - # Convert to numpy array - audio_data = response.content - audio, sr = load_audio_from_bytes(audio_data) - return audio, sr -``` - ---- - -## Provider Installation - -### Download Manager - -**File:** `backend/providers/installer.py` - -```python -class ProviderInstaller: - """Handles provider download and installation.""" - - async def download_provider(self, provider_type: str): - """Download provider binary from R2.""" - - binary_name = { - "pytorch-cpu": "tts-provider-pytorch-cpu.exe", - "pytorch-cuda": "tts-provider-pytorch-cuda.exe", - "mlx": "tts-provider-mlx" - }[provider_type] - - download_url = f"https://downloads.voicebox.sh/providers/v{PROVIDER_VERSION}/{binary_name}" - - # Download with progress tracking (reuse existing SSE system) - await download_with_progress( - url=download_url, - destination=get_provider_install_path(binary_name), - progress_key=f"provider-{provider_type}" - ) -``` - -**Provider Storage Location:** - -- Windows: `%APPDATA%/voicebox/providers/` -- macOS: `~/Library/Application Support/voicebox/providers/` -- Linux: `~/.local/share/voicebox/providers/` - ---- - -## Frontend Implementation - -### Provider Settings UI - -**Component:** `app/src/components/ServerSettings/ProviderSettings.tsx` - -```tsx -export function ProviderSettings() { - const [selectedProvider, setSelectedProvider] = - useState("auto"); - const {data: installedProviders} = useQuery({ - queryKey: ["providers", "installed"], - queryFn: () => apiClient.getInstalledProviders(), - }); - - return ( - - - TTS Provider - Choose how Voicebox generates speech - - - - {/* Auto-detect */} -
- - -
- - {/* PyTorch CUDA */} -
-
- - -
- {!installedProviders?.includes("pytorch-cuda") && gpuAvailable && ( - - )} -
- - {/* PyTorch CPU */} -
-
- - -
- {!installedProviders?.includes("pytorch-cpu") && ( - - )} -
- - {/* MLX (macOS only) */} - {isMacOS && ( -
-
- - -
- {!installedProviders?.includes("mlx") && ( - - )} -
- )} - - {/* Remote */} -
-
- - -
- {selectedProvider === "remote" && ( - - )} -
- - {/* OpenAI */} -
-
- - -
- {selectedProvider === "openai" && ( - - )} -
- - - - ); -} -``` - ---- - -## File Structure - -``` -voicebox/ -├── backend/ -│ ├── main.py # Main FastAPI app (no TTS code) -│ ├── providers/ -│ │ ├── __init__.py # ProviderManager -│ │ ├── base.py # TTSProvider ABC -│ │ ├── local.py # LocalProvider (subprocess) -│ │ ├── remote.py # RemoteProvider (HTTP) -│ │ ├── openai.py # OpenAIProvider (API wrapper) -│ │ └── installer.py # Provider download logic -│ ├── profiles.py # Voice profile management -│ ├── history.py # Generation history -│ ├── transcribe.py # Whisper (still bundled) -│ └── ... (other backend modules) -│ -├── providers/ -│ ├── pytorch-cpu/ -│ │ ├── main.py # FastAPI server for TTS -│ │ ├── tts_backend.py # PyTorch TTS logic -│ │ ├── requirements.txt # torch (CPU), qwen-tts, transformers -│ │ └── build.spec # PyInstaller spec -│ │ -│ ├── pytorch-cuda/ -│ │ ├── main.py # FastAPI server for TTS -│ │ ├── tts_backend.py # PyTorch TTS logic -│ │ ├── requirements.txt # torch+cu121, qwen-tts, transformers -│ │ └── build.spec # PyInstaller spec -│ │ -│ └── mlx/ -│ ├── main.py # FastAPI server for TTS -│ ├── mlx_backend.py # MLX TTS logic -│ ├── requirements.txt # mlx, qwen-tts-mlx -│ └── build.spec # PyInstaller spec -│ -├── app/ # Frontend (Tauri + React) -│ └── src/ -│ └── components/ -│ └── ServerSettings/ -│ └── ProviderSettings.tsx -│ -└── tauri/ - └── src-tauri/ - └── tauri.conf.json # No externalBin for providers -``` - ---- - -## Migration Path - -### Phase 1: Refactor Backend (No User Changes) - -**Goal:** Abstract TTS behind provider interface - -1. Create `backend/providers/` module structure -2. Implement `TTSProvider` abstract base class -3. Create `LocalProvider` wrapper for current PyTorch code -4. Modify `backend/tts.py` to use provider abstraction -5. Keep PyTorch bundled in main app - -**Result:** Code is prepared, but user experience unchanged - ---- - -### Phase 2: Build Provider Binaries - -**Goal:** Create standalone TTS provider executables - -1. Create separate PyInstaller specs for each provider -2. Build provider executables: - - `tts-provider-pytorch-cpu.exe` (~300MB) - - `tts-provider-pytorch-cuda.exe` (~2.4GB) - - `tts-provider-mlx` (~800MB, macOS) -3. Test subprocess communication -4. Upload providers to Cloudflare R2 - -**Result:** Provider binaries exist but aren't used yet - ---- - -### Phase 3: Remove PyTorch from Main App - -**Goal:** Split main app from providers - -1. Exclude PyTorch/Qwen3-TTS from main app PyInstaller spec -2. Main app now requires provider download -3. Update GitHub CI to build multiple artifacts: - - `voicebox-{version}-{platform}.exe` (~150MB) - - `tts-provider-pytorch-cpu-{version}.exe` - - `tts-provider-pytorch-cuda-{version}.exe` - - `tts-provider-mlx-{version}` (macOS) - -**Result:** Main app is small, providers downloaded separately - ---- - -### Phase 4: Add Provider UI - -**Goal:** User-facing provider management - -1. Create Provider Settings page -2. Implement provider download UI -3. Add provider status indicators -4. Show active provider in UI - -**Result:** Users can choose and download providers - ---- - -### Phase 5: External Providers - -**Goal:** Enable remote and cloud providers - -1. Implement `RemoteProvider` (HTTP client) -2. Implement `OpenAIProvider` (API wrapper) -3. Add provider configuration UI (URLs, API keys) -4. Document external provider API spec - -**Result:** Full provider ecosystem - ---- - -## Provider Versioning - -### Independent Versioning - -Providers have their own version numbers, independent of the main app: - -- **App version:** `v0.2.0` (frequent updates) -- **Provider version:** `v1.0.0` (rare updates) - -### Compatibility Matrix - -**Example:** - -| App Version | Min Provider Version | Max Provider Version | -| ----------- | -------------------- | -------------------- | -| v0.2.0 | v1.0.0 | v1.x.x | -| v0.3.0 | v1.0.0 | v1.x.x | -| v0.4.0 | v1.2.0 | v1.x.x | -| v1.0.0 | v2.0.0 | v2.x.x | - -**Backend checks compatibility:** - -```python -async def check_provider_compatibility(provider_version: str) -> bool: - """Check if provider version is compatible with current app.""" - min_version = "1.0.0" - max_version = "1.999.999" - return min_version <= provider_version < max_version -``` - -**UI shows warning if incompatible:** - -``` -⚠️ Provider version 0.9.0 is outdated. Update to v1.0.0+ -``` - ---- - -## User Flows - -### First-Time Setup - -1. User downloads and installs Voicebox (~150MB) -2. App launches → detects no TTS provider installed -3. Shows setup wizard: - - ``` - Choose your TTS provider: - - [ ] PyTorch CUDA (2.4GB) [Download] - ✓ Fastest on NVIDIA GPUs - ✗ Requires NVIDIA GPU - - [●] PyTorch CPU (300MB) [Download] - ✓ Works on any system - ✗ Slower inference - - [ ] MLX (800MB) [Download] - ✓ Fast on Apple Silicon - ✗ macOS only (M1/M2/M3) - - [ ] Remote Server - URL: ___________________ - - [ ] OpenAI API - API Key: ________________ - ``` - -4. User selects provider → downloads with progress bar -5. Provider installs to AppData/Application Support -6. App starts provider → ready to use - ---- - -### App Update Flow (No Provider Change) - -**Scenario:** Bug fix in UI, no backend changes - -1. User gets update notification: "Voicebox v0.2.1 available" -2. Downloads update (~150MB, not 2.4GB!) -3. Installs and restarts -4. **Provider stays the same** (no re-download needed) -5. App starts using existing provider - -**User experience:** Fast updates, no multi-GB downloads - ---- - -### Provider Update Flow - -**Scenario:** New Qwen3-TTS model version released - -1. User opens Settings → Provider tab -2. Sees notification: "Provider update available (v1.1.0)" -3. Clicks "Update Provider" -4. Downloads new provider binary -5. Old provider binary is replaced -6. Restart app to use new provider - -**Frequency:** Rare (only when TTS model/backend changes) - ---- - -### Switching Providers - -**Scenario:** User upgrades to NVIDIA GPU - -1. User goes to Settings → Provider -2. Selects "PyTorch CUDA" -3. Clicks "Download" → downloads 2.4GB -4. Download completes → restarts app -5. App now uses CUDA provider - ---- - -## Benefits - -| Benefit | Details | -| ----------------------------- | --------------------------------------------------------- | -| **GitHub Releases Work** | Main app ~150MB << 2GB limit | -| **Fast Updates** | UI/feature updates don't require re-downloading providers | -| **User Choice** | CPU, CUDA, MLX, OpenAI, remote server | -| **External Provider Support** | Users can run their own TTS servers | -| **Bandwidth Savings** | Only download provider once, app updates are small | -| **Future-Proof** | Easy to add new providers (ElevenLabs, custom models) | -| **Team Deployments** | Multiple users share one remote provider | -| **Cloud-Ready** | Works with Modal, Replicate, RunPod, etc. | - ---- - -## Open Questions - -### 1. Provider Versioning - -**Question:** Should providers have independent versions or match app version? - -**Options:** - -- A. Independent (providers: v1.x, app: v0.2.x) -- B. Matched (both use v0.2.x) - -**Recommendation:** Independent versioning with compatibility matrix - ---- - -### 2. Auto-Update Providers - -**Question:** Should providers auto-update separately from app? - -**Options:** - -- A. Manual updates only (user clicks "Update Provider") -- B. Optional auto-update (user can enable) -- C. Always auto-update - -**Recommendation:** Optional auto-update (default off) - ---- - -### 3. Provider Discovery - -**Question:** How does app find installed providers? - -**Options:** - -- A. Check standard paths in AppData/Application Support -- B. Registry (Windows) / plist (macOS) -- C. Config file with provider locations - -**Recommendation:** Standard paths + config fallback - ---- - -### 4. Fallback Behavior - -**Question:** What if no provider is installed? - -**Options:** - -- A. Show setup wizard on first launch -- B. Block app until provider installed -- C. Allow app to run in "demo mode" (transcription only) - -**Recommendation:** Setup wizard on first launch - ---- - -### 5. Provider Auto-Start - -**Question:** Should provider start automatically with app? - -**Options:** - -- A. Always start selected provider on app launch -- B. Start on-demand (when user generates speech) -- C. User preference - -**Recommendation:** Auto-start (configurable in settings) - ---- - -## Future Enhancements - -- [ ] **Provider Marketplace:** Built-in directory of community providers -- [ ] **Multi-Provider Support:** Use different providers per voice/language -- [ ] **Provider Health Monitoring:** Automatic failover if provider crashes -- [ ] **Cost Tracking:** Monitor API usage for OpenAI/cloud providers -- [ ] **Performance Metrics:** Latency, throughput, VRAM usage dashboards -- [ ] **Docker Providers:** Run providers in Docker containers -- [ ] **Provider Plugins:** Load custom providers from user scripts - ---- - -## Related Documents - -- [EXTERNAL_PROVIDERS.md](./EXTERNAL_PROVIDERS.md) - External provider support plan -- [OPENAI_SUPPORT.md](./OPENAI_SUPPORT.md) - OpenAI API compatibility -- [github-2gb-limit-issue.md](../github-2gb-limit-issue.md) - Original problem -- [r2-setup.md](../r2-setup.md) - Cloudflare R2 configuration - ---- - -## Contributing - -If you want to build a custom TTS provider: - -1. Implement the provider API spec (see above) -2. Test with Voicebox locally -3. Package as executable (PyInstaller, Docker, etc.) -4. Share in GitHub Discussions - -**Questions?** - -- GitHub Issues: [voicebox/issues](https://github.com/jamiepine/voicebox/issues) -- Discord: Coming soon diff --git a/docs2/postcss.config.mjs b/docs/postcss.config.mjs similarity index 100% rename from docs2/postcss.config.mjs rename to docs/postcss.config.mjs diff --git a/docs/public/app-screenshot-1.webp b/docs/public/app-screenshot-1.webp deleted file mode 100644 index de235151..00000000 Binary files a/docs/public/app-screenshot-1.webp and /dev/null differ diff --git a/docs/public/app-screenshot-2.webp b/docs/public/app-screenshot-2.webp deleted file mode 100644 index 859be845..00000000 Binary files a/docs/public/app-screenshot-2.webp and /dev/null differ diff --git a/docs/public/app-screenshot-3.webp b/docs/public/app-screenshot-3.webp deleted file mode 100644 index 93f80661..00000000 Binary files a/docs/public/app-screenshot-3.webp and /dev/null differ diff --git a/docs/images/app-screenshot-1.webp b/docs/public/images/app-screenshot-1.webp similarity index 100% rename from docs/images/app-screenshot-1.webp rename to docs/public/images/app-screenshot-1.webp diff --git a/docs/images/app-screenshot-2.webp b/docs/public/images/app-screenshot-2.webp similarity index 100% rename from docs/images/app-screenshot-2.webp rename to docs/public/images/app-screenshot-2.webp diff --git a/docs/images/app-screenshot-3.webp b/docs/public/images/app-screenshot-3.webp similarity index 100% rename from docs/images/app-screenshot-3.webp rename to docs/public/images/app-screenshot-3.webp diff --git a/docs/logo/icon-dark.png b/docs/public/logo/icon-dark.png similarity index 100% rename from docs/logo/icon-dark.png rename to docs/public/logo/icon-dark.png diff --git a/docs/logo/icon-light.png b/docs/public/logo/icon-light.png similarity index 100% rename from docs/logo/icon-light.png rename to docs/public/logo/icon-light.png diff --git a/docs2/scripts/generate-openapi.ts b/docs/scripts/generate-openapi.ts similarity index 100% rename from docs2/scripts/generate-openapi.ts rename to docs/scripts/generate-openapi.ts diff --git a/docs2/source.config.ts b/docs/source.config.ts similarity index 100% rename from docs2/source.config.ts rename to docs/source.config.ts diff --git a/docs2/tsconfig.json b/docs/tsconfig.json similarity index 100% rename from docs2/tsconfig.json rename to docs/tsconfig.json diff --git a/docs2/.gitignore b/docs2/.gitignore deleted file mode 100644 index 9e429e49..00000000 --- a/docs2/.gitignore +++ /dev/null @@ -1,26 +0,0 @@ -# deps -/node_modules - -# generated content -.source - -# test & build -/coverage -/.next/ -/out/ -/build -*.tsbuildinfo - -# misc -.DS_Store -*.pem -/.pnp -.pnp.js -npm-debug.log* -yarn-debug.log* -yarn-error.log* - -# others -.env*.local -.vercel -next-env.d.ts \ No newline at end of file diff --git a/docs2/README.md b/docs2/README.md deleted file mode 100644 index 68109407..00000000 --- a/docs2/README.md +++ /dev/null @@ -1,45 +0,0 @@ -# fumadocs-ui-template - -This is a Next.js application generated with -[Create Fumadocs](https://github.com/fuma-nama/fumadocs). - -Run development server: - -```bash -npm run dev -# or -pnpm dev -# or -yarn dev -``` - -Open http://localhost:3000 with your browser to see the result. - -## Explore - -In the project, you can see: - -- `lib/source.ts`: Code for content source adapter, [`loader()`](https://fumadocs.dev/docs/headless/source-api) provides the interface to access your content. -- `lib/layout.shared.tsx`: Shared options for layouts, optional but preferred to keep. - 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} -} diff --git a/docs2/content/docs/developer/architecture.mdx b/docs2/content/docs/developer/architecture.mdx deleted file mode 100644 index 367d566a..00000000 --- a/docs2/content/docs/developer/architecture.mdx +++ /dev/null @@ -1,206 +0,0 @@ ---- -title: "Architecture" -description: "Understanding Voicebox's technical architecture" ---- - -## System Overview - -Voicebox uses a client-server architecture with a React frontend and Python backend. The desktop app is built with Tauri and contains two main layers: - -**Frontend Layer:** A React application that handles the UI components, state management with Zustand, and data fetching with React Query (TanStack Query). - -**Backend Layer:** A Python FastAPI server that provides the REST API, runs the TTS engine (Qwen3-TTS), manages the SQLite database, and handles audio processing. - -These two layers communicate via HTTP, with the frontend making API requests to the backend. - -## Frontend Architecture - -### Tech Stack - -- **Framework**: React 18 with TypeScript -- **State Management**: Zustand stores -- **Data Fetching**: React Query (TanStack Query) -- **Styling**: Tailwind CSS -- **Audio**: WaveSurfer.js -- **Desktop**: Tauri (Rust) - -### Component Structure - -``` -app/src/ -├── components/ # React components -│ ├── profiles/ # Voice profile UI -│ ├── generation/ # Speech generation UI -│ ├── stories/ # Timeline editor -│ └── shared/ # Reusable components -├── lib/ # Utilities -│ ├── api/ # Generated API client -│ └── utils/ # Helper functions -├── hooks/ # React hooks -└── stores/ # Zustand state stores -``` - -### State Management - -```typescript -// Example: Profile store -const useProfileStore = create((set) => ({ - profiles: [], - selectedProfile: null, - setProfiles: (profiles) => set({ profiles }), - selectProfile: (id) => set({ selectedProfile: id }) -})) -``` - -## Backend Architecture - -### Tech Stack - -- **Framework**: FastAPI (Python 3.11+) -- **TTS Model**: Qwen3-TTS -- **Transcription**: Whisper -- **Database**: SQLite -- **Audio**: librosa, soundfile - -### API Structure - -```python -# main.py - API routes -@app.post("/generate") -async def generate_speech(request: GenerateRequest): - # 1. Validate request - # 2. Load voice profile - # 3. Generate audio with TTS - # 4. Save to database - # 5. Return response -``` - -### Data Model - -The database uses three main tables: - -**Profile Table:** Stores voice profiles with fields for id, name, and language. - -**Sample Table:** Stores audio samples linked to profiles via profile_id, with fields for audio_path and duration. - -**Generation Table:** Stores generated audio with fields for id, profile_id, text, and audio_path. - -## Desktop App (Tauri) - -### Rust Backend - -```rust -// Sidecar process management -// File system access -// Native integrations -``` - -### Responsibilities - -- Launch Python backend as sidecar process -- Native file dialogs -- System tray integration -- Auto-updates -- OS-specific features - -## Build Process - -### Development - -```bash -# Frontend (Vite dev server) -cd app && bun run dev - -# Backend (manual start) -cd backend && uvicorn main:app --reload - -# Desktop app (connects to manual backend) -bun run dev -``` - -### Production - -```bash -# Build everything (server binary + Tauri app) -bun run build - -# Or build separately: -# 1. Build server binary (PyInstaller) -bun run build:server - -# 2. Build Tauri app (includes server) -cd tauri && bun run tauri build -``` - -## Data Flow - -### Generation Flow - -When a user generates speech, the data flows through the following stages: - -1. **User Input** - User enters text in a React component -2. **State Update** - Text is stored in Zustand state -3. **API Request** - React Query mutation triggers an API call via fetch -4. **Backend Processing** - FastAPI endpoint receives the request -5. **TTS Generation** - Qwen3-TTS model generates the audio -6. **Storage** - Audio file is saved to disk and a database record is created -7. **Response** - Backend returns the audio URL -8. **Cache Update** - React Query updates its cache with the response -9. **UI Update** - Component re-renders with new data -10. **Playback** - User can play the generated audio - -## Performance Considerations - -### Frontend - -- **Code splitting** - Lazy load routes -- **Memoization** - React.memo for heavy components -- **Virtual scrolling** - For large lists -- **Debouncing** - Search and input handling - -### Backend - -- **Async operations** - All I/O is async -- **Model caching** - Keep TTS model in memory -- **Voice prompt caching** - Reuse embeddings -- **Connection pooling** - Database connections - -## Security - -### Current - -- Local-only by default -- No authentication (localhost trust) -- File system sandboxing via Tauri - -### Planned - -- API key authentication -- User accounts -- Rate limiting -- HTTPS support - -## Deployment Modes - -### Local Mode - -- Backend runs as sidecar -- All data stays on device -- No network required - -### Remote Mode - -- Backend on separate machine -- Frontend connects via HTTP -- Shared infrastructure possible - -## Next Steps - - - - Set up your dev environment - - - Contribute to Voicebox - - diff --git a/docs2/content/docs/developer/audio-channels.mdx b/docs2/content/docs/developer/audio-channels.mdx deleted file mode 100644 index 6fce8164..00000000 --- a/docs2/content/docs/developer/audio-channels.mdx +++ /dev/null @@ -1,310 +0,0 @@ ---- -title: "Audio Channels" -description: "How audio output routing works in Voicebox" ---- - -## Overview - -Audio channels allow routing voice output to different audio devices. This is useful for multi-output setups where different voices should play through different speakers or applications. - -## Architecture - -**Channel:** A named audio bus that can be assigned to output devices. - -**Device Mapping:** Links channels to OS audio device identifiers. - -**Profile Mapping:** Links voice profiles to channels (many-to-many). - -## Data Model - -### AudioChannel Table - -```python -class AudioChannel(Base): - __tablename__ = "audio_channels" - - id = Column(String, primary_key=True) - name = Column(String, nullable=False) - is_default = Column(Boolean, default=False) - created_at = Column(DateTime) -``` - -### ChannelDeviceMapping Table - -```python -class ChannelDeviceMapping(Base): - __tablename__ = "channel_device_mappings" - - id = Column(String, primary_key=True) - channel_id = Column(String, ForeignKey("audio_channels.id")) - device_id = Column(String) # OS device identifier -``` - -### ProfileChannelMapping Table - -```python -class ProfileChannelMapping(Base): - __tablename__ = "profile_channel_mappings" - - profile_id = Column(String, ForeignKey("profiles.id"), primary_key=True) - channel_id = Column(String, ForeignKey("audio_channels.id"), primary_key=True) -``` - -## Default Channel - -A default channel is created on database initialization: - -```python -def init_db(): - # Create default channel if it doesn't exist - default_channel = db.query(AudioChannel).filter( - AudioChannel.is_default == True - ).first() - - if not default_channel: - default_channel = AudioChannel( - id=str(uuid.uuid4()), - name="Default", - is_default=True - ) - db.add(default_channel) - - # Assign all existing profiles to default channel - profiles = db.query(VoiceProfile).all() - for profile in profiles: - mapping = ProfileChannelMapping( - profile_id=profile.id, - channel_id=default_channel.id - ) - db.add(mapping) -``` - -## Core Operations - -### Creating a Channel - -```python -async def create_channel( - data: AudioChannelCreate, - db: Session, -) -> AudioChannelResponse: - # Check name uniqueness - existing = db.query(DBAudioChannel).filter_by(name=data.name).first() - if existing: - raise ValueError(f"Channel with name '{data.name}' already exists") - - # Create channel - channel = DBAudioChannel( - id=str(uuid.uuid4()), - name=data.name, - is_default=False, - ) - db.add(channel) - - # Add device mappings - for device_id in data.device_ids: - mapping = DBChannelDeviceMapping( - id=str(uuid.uuid4()), - channel_id=channel.id, - device_id=device_id, - ) - db.add(mapping) - - db.commit() -``` - -### Updating a Channel - -```python -async def update_channel( - channel_id: str, - data: AudioChannelUpdate, - db: Session, -) -> AudioChannelResponse: - channel = db.query(DBAudioChannel).filter_by(id=channel_id).first() - - # Cannot modify default channel - if channel.is_default: - raise ValueError("Cannot modify the default channel") - - # Update name - if data.name is not None: - channel.name = data.name - - # Update device mappings - if data.device_ids is not None: - # Delete existing - db.query(DBChannelDeviceMapping).filter_by(channel_id=channel_id).delete() - - # Add new - for device_id in data.device_ids: - mapping = DBChannelDeviceMapping( - channel_id=channel.id, - device_id=device_id, - ) - db.add(mapping) - - db.commit() -``` - -### Deleting a Channel - -```python -async def delete_channel(channel_id: str, db: Session) -> bool: - channel = db.query(DBAudioChannel).filter_by(id=channel_id).first() - - # Cannot delete default channel - if channel.is_default: - raise ValueError("Cannot delete the default channel") - - # Delete device mappings - db.query(DBChannelDeviceMapping).filter_by(channel_id=channel_id).delete() - - # Delete profile-channel mappings - db.query(DBProfileChannelMapping).filter_by(channel_id=channel_id).delete() - - # Delete channel - db.delete(channel) - db.commit() -``` - -## Voice Assignment - -### Assigning Voices to Channel - -```python -async def set_channel_voices( - channel_id: str, - data: ChannelVoiceAssignment, - db: Session, -) -> None: - # Verify channel exists - channel = db.query(DBAudioChannel).filter_by(id=channel_id).first() - if not channel: - raise ValueError(f"Channel {channel_id} not found") - - # Verify all profiles exist - for profile_id in data.profile_ids: - profile = db.query(DBVoiceProfile).filter_by(id=profile_id).first() - if not profile: - raise ValueError(f"Profile {profile_id} not found") - - # Delete existing mappings - db.query(DBProfileChannelMapping).filter_by(channel_id=channel_id).delete() - - # Add new mappings - for profile_id in data.profile_ids: - mapping = DBProfileChannelMapping( - profile_id=profile_id, - channel_id=channel_id, - ) - db.add(mapping) - - db.commit() -``` - -### Assigning Channels to Voice - -```python -async def set_profile_channels( - profile_id: str, - data: ProfileChannelAssignment, - db: Session, -) -> None: - # Verify profile exists - profile = db.query(DBVoiceProfile).filter_by(id=profile_id).first() - if not profile: - raise ValueError(f"Profile {profile_id} not found") - - # Delete existing mappings - db.query(DBProfileChannelMapping).filter_by(profile_id=profile_id).delete() - - # Add new mappings - for channel_id in data.channel_ids: - mapping = DBProfileChannelMapping( - profile_id=profile_id, - channel_id=channel_id, - ) - db.add(mapping) - - db.commit() -``` - -## API Endpoints - -| Method | Endpoint | Description | -|--------|----------|-------------| -| GET | `/channels` | List all channels | -| POST | `/channels` | Create a channel | -| GET | `/channels/{id}` | Get channel by ID | -| PUT | `/channels/{id}` | Update channel | -| DELETE | `/channels/{id}` | Delete channel | -| GET | `/channels/{id}/voices` | Get assigned voices | -| PUT | `/channels/{id}/voices` | Set assigned voices | -| GET | `/profiles/{id}/channels` | Get profile's channels | -| PUT | `/profiles/{id}/channels` | Set profile's channels | - -## Request/Response Schemas - -### AudioChannelCreate - -```json -{ - "name": "Speakers", - "device_ids": ["device_uuid_1", "device_uuid_2"] -} -``` - -### AudioChannelResponse - -```json -{ - "id": "channel_uuid", - "name": "Speakers", - "is_default": false, - "device_ids": ["device_uuid_1", "device_uuid_2"], - "created_at": "2024-01-15T10:30:00Z" -} -``` - -### ChannelVoiceAssignment - -```json -{ - "profile_ids": ["profile_1", "profile_2"] -} -``` - -## Use Cases - -### Multi-Output Setup - -**Scenario:** Stream with different voice characters - -1. Create "Stream" channel → OBS virtual audio -2. Create "Monitor" channel → Headphones -3. Assign "Narrator" profile → Both channels -4. Assign "Character 1" profile → Stream only - -### Virtual Audio Cables - -Common device IDs for virtual audio: -- VB-Audio Virtual Cable -- BlackHole (macOS) -- Soundflower (macOS) - -## Frontend Integration - -The frontend needs to: - -1. **Enumerate devices** using Web Audio API or Tauri -2. **Display channel list** with device assignments -3. **Allow profile assignment** via drag/drop or dropdown -4. **Route playback** to correct device based on profile's channel - -## Limitations - -- Device IDs are OS-specific -- Hot-plugging may invalidate device IDs -- Default channel cannot be modified/deleted -- Frontend handles actual audio routing (backend just stores config) diff --git a/docs2/content/docs/developer/autoupdater.mdx b/docs2/content/docs/developer/autoupdater.mdx deleted file mode 100644 index b483f153..00000000 --- a/docs2/content/docs/developer/autoupdater.mdx +++ /dev/null @@ -1,84 +0,0 @@ ---- -title: "Auto-Updater" -description: "Configure and use the Tauri auto-updater" ---- - -## Overview - -Voicebox uses Tauri's built-in auto-updater to deliver updates to users automatically. - -## Quick Reference - -For detailed setup instructions, see the existing documentation: - -- [AUTOUPDATER_QUICKSTART.md](https://github.com/jamiepine/voicebox/blob/main/docs/AUTOUPDATER_QUICKSTART.md) -- [AUTOUPDATER.md](https://github.com/jamiepine/voicebox/blob/main/docs/AUTOUPDATER.md) - -## How It Works - -The auto-updater follows a secure update process: - -1. **Check for Updates** - The Voicebox app periodically checks GitHub Releases for new versions -2. **Download Update** - If a new version is found, the update package is downloaded -3. **Verify Signature** - The downloaded package is cryptographically verified using the public key -4. **Install** - After verification, the update is installed -5. **Restart** - The app restarts with the new version - -## Configuration - -Updates are configured in `tauri/src-tauri/tauri.conf.json`: - -```json -{ - "updater": { - "active": true, - "endpoints": [ - "https://github.com/jamiepine/voicebox/releases/latest/download/latest.json" - ], - "dialog": true, - "pubkey": "YOUR_PUBLIC_KEY" - } -} -``` - -## Generating Keys - -```bash -# Generate signing keys -bun run generate:keys - -# Keys saved to ~/.tauri/voicebox.key -``` - - - Keep your private key secure! Never commit it to the repository. - - -## Release Process - -1. **Bump version** using bumpversion -2. **Push tag** to trigger CI/CD -3. **GitHub Actions** builds and signs releases -4. **Users** receive update notification - -## User Experience - -When an update is available: - -1. User sees a notification dialog -2. User clicks "Update" -3. Update downloads in background -4. App restarts with new version - -## For Developers - -See the full documentation files for: - -- Setting up signing keys -- Configuring GitHub releases -- Testing updates locally -- Troubleshooting update failures - - - Access AUTOUPDATER.md and AUTOUPDATER_QUICKSTART.md in the repository - diff --git a/docs2/content/docs/developer/building.mdx b/docs2/content/docs/developer/building.mdx deleted file mode 100644 index 237805b9..00000000 --- a/docs2/content/docs/developer/building.mdx +++ /dev/null @@ -1,270 +0,0 @@ ---- -title: "Building" -description: "Build Voicebox for production" ---- - -## Overview - -Voicebox uses a multi-step build process to create platform-specific installers. - -## Quick Build - -```bash -# Build for your current platform (automatically builds server binary first) -make build - -# Or manually -bun run build -``` - -This automatically: -1. Builds the Python server binary (`bun run build:server`) -2. Builds the Tauri app (`cd tauri && bun run tauri build`) - -## Build Process - -The build process consists of two steps, but `bun run build` handles both automatically: - -### 1. Server Binary Build (Automatic) - -The Python backend is compiled into a standalone executable using PyInstaller. This happens automatically when you run `bun run build`. - -**Platform-specific binaries:** -- macOS (Apple Silicon): `voicebox-server-aarch64-apple-darwin` (includes MLX backend) -- macOS (Intel): `voicebox-server-x86_64-apple-darwin` (PyTorch backend) -- Windows: `voicebox-server-x86_64-pc-windows-msvc.exe` (PyTorch backend) -- Linux: `voicebox-server-x86_64-unknown-linux-gnu` (PyTorch backend) - - - The build script automatically detects your platform and includes the appropriate backend (MLX for Apple Silicon, PyTorch for others). - - -**Manual build (if needed):** -```bash -bun run build:server -``` - -### 2. Tauri App Build (Automatic) - -The Tauri app build is also handled automatically, which: -1. Builds the React frontend (Vite) -2. Compiles the Rust backend -3. Bundles the server binary as a sidecar -4. Creates platform-specific installers - -**Manual build (if needed):** -```bash -cd tauri && bun run tauri build -``` - -### 3. Output - -Installers are created in `tauri/src-tauri/target/release/bundle/`: - -**macOS:** -- `dmg/` - Disk image installer -- `macos/` - App bundle - -**Windows:** -- `msi/` - MSI installer -- `nsis/` - NSIS installer - -**Linux:** -- `deb/` - Debian package -- `appimage/` - AppImage - -## Advanced Options - -### Building for Specific Platform - -```bash -# Build for macOS (Apple Silicon) -bun run tauri build -- --target aarch64-apple-darwin - -# Build for macOS (Intel) -bun run tauri build -- --target x86_64-apple-darwin - -# Build for Windows -bun run tauri build -- --target x86_64-pc-windows-msvc - -# Build for Linux -bun run tauri build -- --target x86_64-unknown-linux-gnu -``` - -### Using Local Qwen3-TTS - -If you're developing Qwen3-TTS locally: - -```bash -export QWEN_TTS_PATH=~/path/to/Qwen3-TTS -bun run build:server # Build server binary only -# or -bun run build # Build everything -``` - -This makes PyInstaller use your local version instead of the pip package. - -### Debug Build - -```bash -cd tauri -bun run tauri build --debug -``` - -Creates a debug build with symbols and logging. - -## Build Configuration - -### Tauri Config - -Edit `tauri/src-tauri/tauri.conf.json`: - -```json -{ - "bundle": { - "identifier": "com.voicebox.app", - "icon": [ - "icons/32x32.png", - "icons/128x128.png", - "icons/icon.icns", - "icons/icon.ico" - ] - } -} -``` - -### Sidecar Configuration - -The Python server is bundled as a sidecar: - -```json -{ - "tauri": { - "bundle": { - "externalBin": [ - "binaries/voicebox-server" - ] - } - } -} -``` - -## Code Signing - -### macOS - -To sign the app for distribution: - -```bash -# Set signing identity -export APPLE_SIGNING_IDENTITY="Developer ID Application: Your Name" - -# Build with signing -bun run tauri build -``` - -For notarization: - -```bash -# Set credentials -export APPLE_ID="your@email.com" -export APPLE_PASSWORD="app-specific-password" - -# Build and notarize -bun run tauri build -``` - -### Windows - -For Windows code signing: - -```bash -# Set certificate -export WINDOWS_CERTIFICATE_PATH="/path/to/cert.pfx" -export WINDOWS_CERTIFICATE_PASSWORD="password" - -# Build with signing -bun run tauri build -``` - -## Release Process - -The full release process is automated: - -```bash -# 1. Bump version -bumpversion patch # or minor/major - -# 2. Build all platforms (CI/CD handles this) -git push --tags - -# 3. GitHub Actions creates releases -``` - -See [CONTRIBUTING.md](/development/contributing) for the full release workflow. - -## Troubleshooting - - - - **Common issues:** - - Missing Python dependencies: `pip install -r requirements.txt` - - PyInstaller not found: `pip install pyinstaller` - - Qwen3-TTS not installed: `pip install git+https://github.com/QwenLM/Qwen3-TTS.git` - - **Solution:** - ```bash - cd backend - source venv/bin/activate - pip install -r requirements.txt - pip install pyinstaller - ``` - - - - **Common issues:** - - Rust not installed: `curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh` - - Server binary missing: Usually auto-built, but can run manually: `./scripts/build-server.sh` - - Node modules outdated: `bun install` - - **Solution:** - ```bash - # Clean and rebuild - cd tauri/src-tauri - cargo clean - cd ../.. - bun run build # Automatically builds server binary first - ``` - - - - **Check:** - - Server binary has execute permissions - - All dependencies are bundled - - Check logs in the app's data directory - - **macOS:** - ```bash - tail -f ~/Library/Application\ Support/com.voicebox.app/logs/server.log - ``` - - **Windows:** - ```bash - type %APPDATA%\com.voicebox.app\logs\server.log - ``` - - - -## CI/CD - -GitHub Actions automatically builds releases when tags are pushed: - -```yaml -# .github/workflows/release.yml -on: - push: - tags: - - 'v*' -``` - -See the [repository](https://github.com/jamiepine/voicebox) for the full CI/CD configuration. diff --git a/docs2/content/docs/developer/history.mdx b/docs2/content/docs/developer/history.mdx deleted file mode 100644 index 28fe7faf..00000000 --- a/docs2/content/docs/developer/history.mdx +++ /dev/null @@ -1,260 +0,0 @@ ---- -title: "Generation History" -description: "How generation history tracking works in Voicebox" ---- - -## Overview - -The history module tracks all generated audio, providing a searchable record of past generations. Each generation stores the text, settings, and a reference to the audio file. - -## Data Model - -### Generation Table - -```python -class Generation(Base): - __tablename__ = "generations" - - id = Column(String, primary_key=True) - profile_id = Column(String, ForeignKey("profiles.id")) - text = Column(Text, nullable=False) - language = Column(String, default="en") - audio_path = Column(String, nullable=False) - duration = Column(Float, nullable=False) - seed = Column(Integer) - instruct = Column(Text) - created_at = Column(DateTime) -``` - -## File Storage - -Generated audio is stored in: - -``` -data/ -└── generations/ - └── {generation_id}.wav -``` - -## Core Functions - -### Creating a Generation Record - -After TTS generates audio, a history entry is created: - -```python -async def create_generation( - profile_id: str, - text: str, - language: str, - audio_path: str, - duration: float, - seed: Optional[int], - db: Session, - instruct: Optional[str] = None, -) -> GenerationResponse: - db_generation = DBGeneration( - id=str(uuid.uuid4()), - profile_id=profile_id, - text=text, - language=language, - audio_path=audio_path, - duration=duration, - seed=seed, - instruct=instruct, - created_at=datetime.utcnow(), - ) - - db.add(db_generation) - db.commit() - - return GenerationResponse.model_validate(db_generation) -``` - -### Listing Generations - -Supports filtering and pagination: - -```python -async def list_generations( - query: HistoryQuery, - db: Session, -) -> HistoryListResponse: - # Build query with profile name join - q = db.query( - DBGeneration, - DBVoiceProfile.name.label('profile_name') - ).join( - DBVoiceProfile, - DBGeneration.profile_id == DBVoiceProfile.id - ) - - # Apply filters - if query.profile_id: - q = q.filter(DBGeneration.profile_id == query.profile_id) - - if query.search: - q = q.filter(DBGeneration.text.like(f"%{query.search}%")) - - # Order and paginate - total = q.count() - q = q.order_by(DBGeneration.created_at.desc()) - q = q.offset(query.offset).limit(query.limit) - - return HistoryListResponse(items=results, total=total) -``` - -### Getting Statistics - -Aggregate statistics for the dashboard: - -```python -async def get_generation_stats(db: Session) -> dict: - total = db.query(func.count(DBGeneration.id)).scalar() - total_duration = db.query(func.sum(DBGeneration.duration)).scalar() - - by_profile = db.query( - DBGeneration.profile_id, - func.count(DBGeneration.id).label('count') - ).group_by(DBGeneration.profile_id).all() - - return { - "total_generations": total, - "total_duration_seconds": total_duration, - "generations_by_profile": { - profile_id: count for profile_id, count in by_profile - }, - } -``` - -## Deletion - -Deleting a generation removes both the database record and audio file: - -```python -async def delete_generation(generation_id: str, db: Session) -> bool: - generation = db.query(DBGeneration).filter_by(id=generation_id).first() - if not generation: - return False - - # Delete audio file - audio_path = Path(generation.audio_path) - if audio_path.exists(): - audio_path.unlink() - - # Delete database record - db.delete(generation) - db.commit() - - return True -``` - -### Cascade Delete - -When deleting a profile, all its generations are also deleted: - -```python -async def delete_generations_by_profile(profile_id: str, db: Session) -> int: - generations = db.query(DBGeneration).filter_by(profile_id=profile_id).all() - - for generation in generations: - Path(generation.audio_path).unlink(missing_ok=True) - db.delete(generation) - - db.commit() - return len(generations) -``` - -## Export/Import - -### Exporting a Generation - -Generations can be exported as ZIP archives: - -``` -generation_export.zip -├── generation.json # Metadata -└── audio.wav # Audio file -``` - -### Importing a Generation - -The import process: - -1. Extract ZIP archive -2. Validate metadata and audio -3. Create new generation ID -4. Copy audio to generations directory -5. Create database record - -## API Endpoints - -| Method | Endpoint | Description | -|--------|----------|-------------| -| GET | `/history` | List generations with filters | -| GET | `/history/stats` | Get aggregate statistics | -| GET | `/history/{id}` | Get generation by ID | -| DELETE | `/history/{id}` | Delete generation | -| GET | `/history/{id}/export` | Export as ZIP | -| GET | `/history/{id}/export-audio` | Export audio only | -| POST | `/history/import` | Import from ZIP | - -### Query Parameters - -``` -GET /history?profile_id=uuid&search=hello&limit=50&offset=0 -``` - -| Parameter | Type | Default | Description | -|-----------|------|---------|-------------| -| `profile_id` | string | null | Filter by profile | -| `search` | string | null | Search in text | -| `limit` | int | 50 | Results per page | -| `offset` | int | 0 | Pagination offset | - -### Response Schema - -```json -{ - "items": [ - { - "id": "uuid", - "profile_id": "uuid", - "profile_name": "My Voice", - "text": "Hello world", - "language": "en", - "audio_path": "/path/to/audio.wav", - "duration": 1.5, - "seed": 42, - "instruct": null, - "created_at": "2024-01-15T10:30:00Z" - } - ], - "total": 150 -} -``` - -## Usage in Stories - -Generations can be added to stories for multi-voice narratives. The story system references generations by ID: - -```python -class StoryItem(Base): - generation_id = Column(String, ForeignKey("generations.id")) -``` - -This allows the same generation to be reused across multiple stories without duplicating audio files. - -## Storage Considerations - -### Disk Usage - -Each generation creates a WAV file. For a 10-second clip at 24kHz: -- ~480KB per file (mono, 16-bit) - -### Cleanup Strategy - -Consider implementing: -- Automatic cleanup of old generations -- Storage quota per profile -- Compression for archival diff --git a/docs2/content/docs/developer/model-management.mdx b/docs2/content/docs/developer/model-management.mdx deleted file mode 100644 index ef9f6c1d..00000000 --- a/docs2/content/docs/developer/model-management.mdx +++ /dev/null @@ -1,341 +0,0 @@ ---- -title: "Model Management" -description: "How model downloading, loading, and status tracking works in Voicebox" ---- - -## Overview - -Voicebox manages two types of models: - -**TTS Models:** Qwen3-TTS for voice cloning (0.6B and 1.7B variants). - -**ASR Models:** Whisper for transcription (tiny through large). - -Models are downloaded from HuggingFace Hub on first use and cached locally. - -## Available Models - -### TTS Models - -| Model | HuggingFace ID | Size | VRAM | -|-------|----------------|------|------| -| 0.6B | `Qwen/Qwen3-TTS-12Hz-0.6B-Base` | ~1.2GB | ~2GB | -| 1.7B | `Qwen/Qwen3-TTS-12Hz-1.7B-Base` | ~3.4GB | ~6GB | - -### Whisper Models - -| Model | HuggingFace ID | Size | VRAM | -|-------|----------------|------|------| -| tiny | `openai/whisper-tiny` | ~150MB | ~1GB | -| base | `openai/whisper-base` | ~300MB | ~1GB | -| small | `openai/whisper-small` | ~500MB | ~2GB | -| medium | `openai/whisper-medium` | ~1.5GB | ~5GB | -| large | `openai/whisper-large` | ~3GB | ~10GB | - -## Model Storage - -Models are cached in the HuggingFace cache directory: - -``` -~/.cache/huggingface/hub/ -├── models--Qwen--Qwen3-TTS-12Hz-1.7B-Base/ -├── models--Qwen--Qwen3-TTS-12Hz-0.6B-Base/ -├── models--openai--whisper-base/ -└── ... -``` - -## Progress Tracking - -### Progress Manager - -Tracks download progress across all models: - -```python -class ProgressManager: - def __init__(self): - self._progress = {} # model_name -> progress_info - - def update_progress( - self, - model_name: str, - current: int, - total: int, - filename: str, - status: str, - ): - self._progress[model_name] = { - "current": current, - "total": total, - "filename": filename, - "status": status, # downloading, complete, error - "updated_at": datetime.utcnow(), - } - - def get_progress(self, model_name: str) -> Optional[dict]: - return self._progress.get(model_name) -``` - -### HuggingFace Progress Callback - -Hooks into HuggingFace's download system: - -```python -class HFProgressTracker: - def __init__(self, callback): - self.callback = callback - - @contextmanager - def patch_download(self): - """Context manager to intercept HF downloads.""" - original_download = hf_hub_download - - def patched_download(*args, **kwargs): - # Intercept progress - result = original_download(*args, **kwargs) - self.callback(progress_info) - return result - - # Apply patch - with patch('huggingface_hub.hf_hub_download', patched_download): - yield -``` - -### Server-Sent Events (SSE) - -Progress is streamed to the frontend: - -```python -@app.get("/models/progress/{model_name}") -async def get_model_progress(model_name: str): - async def event_generator(): - while True: - progress = progress_manager.get_progress(model_name) - if progress: - yield f"data: {json.dumps(progress)}\n\n" - - if progress and progress["status"] in ["complete", "error"]: - break - - await asyncio.sleep(0.5) - - return StreamingResponse( - event_generator(), - media_type="text/event-stream" - ) -``` - -## Task Manager - -Tracks active downloads and generations: - -```python -class TaskManager: - def __init__(self): - self._active_downloads = {} - self._active_generations = {} - - def start_download(self, model_name: str): - self._active_downloads[model_name] = { - "status": "downloading", - "started_at": datetime.utcnow(), - } - - def complete_download(self, model_name: str): - if model_name in self._active_downloads: - del self._active_downloads[model_name] - - def get_active_tasks(self) -> dict: - return { - "downloads": list(self._active_downloads.values()), - "generations": list(self._active_generations.values()), - } -``` - -## Model Status - -Check which models are downloaded and loaded: - -```python -@app.get("/models/status") -async def get_model_status() -> ModelStatusListResponse: - models = [] - - # Check TTS models - for size, hf_id in [("1.7B", "Qwen/Qwen3-TTS-12Hz-1.7B-Base"), ...]: - downloaded = is_model_downloaded(hf_id) - loaded = tts_model._current_model_size == size - - models.append(ModelStatus( - model_name=f"qwen-tts-{size}", - display_name=f"Qwen3-TTS {size}", - downloaded=downloaded, - size_mb=get_model_size_mb(hf_id), - loaded=loaded, - )) - - # Check Whisper models - for size in ["tiny", "base", "small", "medium", "large"]: - hf_id = f"openai/whisper-{size}" - downloaded = is_model_downloaded(hf_id) - - models.append(ModelStatus( - model_name=f"whisper-{size}", - display_name=f"Whisper {size}", - downloaded=downloaded, - size_mb=get_model_size_mb(hf_id), - loaded=False, # Whisper is loaded on-demand - )) - - return ModelStatusListResponse(models=models) -``` - -## Manual Model Operations - -### Load Model - -```python -@app.post("/models/load") -async def load_model(model_size: str = "1.7B"): - tts_model = get_tts_model() - await tts_model.load_model_async(model_size) - return {"status": "loaded", "model_size": model_size} -``` - -### Unload Model - -```python -@app.post("/models/unload") -async def unload_model(): - tts_model = get_tts_model() - tts_model.unload_model() - return {"status": "unloaded"} -``` - -### Trigger Download - -```python -@app.post("/models/download") -async def trigger_model_download(request: ModelDownloadRequest): - # This triggers the download in background - # Progress is tracked via /models/progress/{model_name} - - if request.model_name.startswith("qwen-tts"): - size = request.model_name.split("-")[-1] - asyncio.create_task(download_tts_model(size)) - elif request.model_name.startswith("whisper"): - size = request.model_name.split("-")[-1] - asyncio.create_task(download_whisper_model(size)) - - return {"status": "downloading"} -``` - -### Delete Model - -```python -@app.delete("/models/{model_name}") -async def delete_model(model_name: str): - # Find and delete from HuggingFace cache - cache_dir = Path.home() / ".cache" / "huggingface" / "hub" - - model_dirs = list(cache_dir.glob(f"models--*--{model_name}*")) - for model_dir in model_dirs: - shutil.rmtree(model_dir) - - return {"status": "deleted"} -``` - -## API Endpoints - -| Method | Endpoint | Description | -|--------|----------|-------------| -| GET | `/models/status` | Get status of all models | -| POST | `/models/load` | Load TTS model | -| POST | `/models/unload` | Unload TTS model | -| POST | `/models/download` | Trigger model download | -| GET | `/models/progress/{name}` | Stream download progress (SSE) | -| DELETE | `/models/{name}` | Delete downloaded model | -| GET | `/tasks/active` | Get active downloads/generations | - -## Response Schemas - -### ModelStatus - -```json -{ - "model_name": "qwen-tts-1.7B", - "display_name": "Qwen3-TTS 1.7B", - "downloaded": true, - "size_mb": 3400, - "loaded": true -} -``` - -### ActiveTasksResponse - -```json -{ - "downloads": [ - { - "model_name": "whisper-medium", - "status": "downloading", - "started_at": "2024-01-15T10:30:00Z" - } - ], - "generations": [ - { - "task_id": "uuid", - "profile_id": "uuid", - "text_preview": "Hello world...", - "started_at": "2024-01-15T10:30:00Z" - } - ] -} -``` - -## Frontend Integration - -### Progress Display - -```typescript -// Subscribe to download progress via SSE -const eventSource = new EventSource(`/models/progress/${modelName}`); - -eventSource.onmessage = (event) => { - const progress = JSON.parse(event.data); - updateProgressBar(progress.current / progress.total); - - if (progress.status === 'complete') { - eventSource.close(); - } -}; -``` - -### Model Status UI - -```typescript -// Fetch model status -const { data: models } = useQuery({ - queryKey: ['models', 'status'], - queryFn: () => api.getModelStatus(), -}); - -// Display download/load buttons based on status -models.map(model => ( - triggerDownload(model.model_name)} - onLoad={() => loadModel(model.model_name)} - /> -)); -``` - -## Error Handling - -| Error | Cause | Solution | -|-------|-------|----------| -| Download failed | Network issue | Retry download | -| OOM on load | Model too large | Use smaller model | -| Model not found | Cache corrupted | Re-download | -| Slow download | HF rate limit | Wait and retry | diff --git a/docs2/content/docs/developer/stories.mdx b/docs2/content/docs/developer/stories.mdx deleted file mode 100644 index 8d812086..00000000 --- a/docs2/content/docs/developer/stories.mdx +++ /dev/null @@ -1,320 +0,0 @@ ---- -title: "Stories & Timeline" -description: "How the multi-voice timeline editor works in Voicebox" ---- - -## Overview - -Stories allow users to arrange multiple voice generations on a timeline to create multi-voice narratives. The system supports tracks, trimming, splitting, and audio mixing. - -## Architecture - -**Story:** A container that holds story items with metadata. - -**Story Item:** Links a generation to a story with timeline position, track, and trim data. - -**Export:** Combines all items into a single mixed audio file. - -## Data Model - -### Story Table - -```python -class Story(Base): - __tablename__ = "stories" - - id = Column(String, primary_key=True) - name = Column(String, nullable=False) - description = Column(Text) - created_at = Column(DateTime) - updated_at = Column(DateTime) -``` - -### StoryItem Table - -```python -class StoryItem(Base): - __tablename__ = "story_items" - - id = Column(String, primary_key=True) - story_id = Column(String, ForeignKey("stories.id")) - generation_id = Column(String, ForeignKey("generations.id")) - start_time_ms = Column(Integer, default=0) # Timeline position - track = Column(Integer, default=0) # Track number - trim_start_ms = Column(Integer, default=0) # Trim from start - trim_end_ms = Column(Integer, default=0) # Trim from end - created_at = Column(DateTime) -``` - -## Timeline Concepts - -### Start Time - -`start_time_ms` defines when an item begins on the timeline: - -``` -Timeline (ms): 0----1000----2000----3000----4000 -Item 1: [======] -Item 2: [==========] -Item 3: [====] -``` - -### Tracks - -Multiple tracks allow overlapping audio: - -``` -Track 0: [Item 1] [Item 3] -Track 1: [Item 2] -``` - -### Trimming - -Trim values cut audio from the start or end without destroying the original: - -``` -Original: [=========AUDIO=========] -trim_start: ^^ -trim_end: ^^ -Result: [=====AUDIO=====] -``` - -## Core Operations - -### Adding Items - -When adding a generation to a story: - -```python -async def add_item_to_story( - story_id: str, - data: StoryItemCreate, - db: Session, -) -> StoryItemDetail: - # Calculate start time if not provided - if data.start_time_ms is None: - # Find the end of all existing items - existing_items = get_items_with_durations(story_id, db) - max_end_time_ms = max( - item.start_time_ms + int(gen.duration * 1000) - for item, gen in existing_items - ) - start_time_ms = max_end_time_ms + 200 # 200ms gap - - # Create the item - item = DBStoryItem( - id=str(uuid.uuid4()), - story_id=story_id, - generation_id=data.generation_id, - start_time_ms=start_time_ms, - track=data.track or 0, - ) - db.add(item) - db.commit() -``` - -### Moving Items - -Update position and/or track: - -```python -async def move_story_item( - story_id: str, - item_id: str, - data: StoryItemMove, - db: Session, -) -> StoryItemDetail: - item = get_item(story_id, item_id, db) - - item.start_time_ms = data.start_time_ms - item.track = data.track - - db.commit() -``` - -### Trimming Items - -Non-destructive trimming: - -```python -async def trim_story_item( - story_id: str, - item_id: str, - data: StoryItemTrim, - db: Session, -) -> StoryItemDetail: - item = get_item(story_id, item_id, db) - generation = get_generation(item.generation_id, db) - - # Validate trim doesn't exceed duration - max_duration_ms = int(generation.duration * 1000) - if data.trim_start_ms + data.trim_end_ms >= max_duration_ms: - return None # Invalid trim - - item.trim_start_ms = data.trim_start_ms - item.trim_end_ms = data.trim_end_ms - - db.commit() -``` - -### Splitting Items - -Split one item into two at a specific time: - -```python -async def split_story_item( - story_id: str, - item_id: str, - data: StoryItemSplit, - db: Session, -) -> List[StoryItemDetail]: - item = get_item(story_id, item_id, db) - generation = get_generation(item.generation_id, db) - - # Calculate split point - current_trim_start = item.trim_start_ms - current_trim_end = item.trim_end_ms - original_duration_ms = int(generation.duration * 1000) - absolute_split_ms = current_trim_start + data.split_time_ms - - # Update original: trim from end - item.trim_end_ms = original_duration_ms - absolute_split_ms - - # Create new item: trim from start - new_item = DBStoryItem( - generation_id=item.generation_id, # Same generation - start_time_ms=item.start_time_ms + data.split_time_ms, - track=item.track, - trim_start_ms=absolute_split_ms, - trim_end_ms=current_trim_end, - ) - - db.add(new_item) - db.commit() - - return [item, new_item] -``` - -### Duplicating Items - -Create a copy with all properties: - -```python -async def duplicate_story_item( - story_id: str, - item_id: str, - db: Session, -) -> StoryItemDetail: - original = get_item(story_id, item_id, db) - generation = get_generation(original.generation_id, db) - - # Calculate effective duration for positioning - effective_duration_ms = ( - int(generation.duration * 1000) - - original.trim_start_ms - - original.trim_end_ms - ) - - # Place copy after original with 200ms gap - new_item = DBStoryItem( - generation_id=original.generation_id, - start_time_ms=original.start_time_ms + effective_duration_ms + 200, - track=original.track, - trim_start_ms=original.trim_start_ms, - trim_end_ms=original.trim_end_ms, - ) - - db.add(new_item) - db.commit() -``` - -## Audio Export - -### Mixing Algorithm - -The export function mixes all items into a single audio file: - -```python -async def export_story_audio(story_id: str, db: Session) -> bytes: - items = get_all_items_with_generations(story_id, db) - - # Calculate total duration - max_end_time_ms = max( - data['start_time_ms'] + data['duration_ms'] - for data in audio_data - ) - - # Create output buffer - total_samples = int((max_end_time_ms / 1000.0) * sample_rate) - final_audio = np.zeros(total_samples, dtype=np.float32) - - # Mix each item at its position - for data in audio_data: - audio = data['audio'] - start_sample = int((data['start_time_ms'] / 1000.0) * sample_rate) - - # Apply trim - trimmed_audio = audio[trim_start_sample:len(audio) - trim_end_sample] - - # Add to buffer (overlapping items sum together) - final_audio[start_sample:start_sample + len(trimmed_audio)] += trimmed_audio - - # Normalize to prevent clipping - max_val = np.abs(final_audio).max() - if max_val > 1.0: - final_audio = final_audio / max_val - - return audio_to_bytes(final_audio, sample_rate) -``` - -## API Endpoints - -| Method | Endpoint | Description | -|--------|----------|-------------| -| GET | `/stories` | List all stories | -| POST | `/stories` | Create a story | -| GET | `/stories/{id}` | Get story with items | -| PUT | `/stories/{id}` | Update story metadata | -| DELETE | `/stories/{id}` | Delete story | -| POST | `/stories/{id}/items` | Add item to story | -| DELETE | `/stories/{id}/items/{item_id}` | Remove item | -| PUT | `/stories/{id}/items/{item_id}/move` | Move item | -| PUT | `/stories/{id}/items/{item_id}/trim` | Trim item | -| POST | `/stories/{id}/items/{item_id}/split` | Split item | -| POST | `/stories/{id}/items/{item_id}/duplicate` | Duplicate item | -| PUT | `/stories/{id}/items/times` | Batch update times | -| PUT | `/stories/{id}/items/reorder` | Reorder items | -| GET | `/stories/{id}/export-audio` | Export mixed audio | - -## Response Schemas - -### StoryItemDetail - -```json -{ - "id": "item_uuid", - "story_id": "story_uuid", - "generation_id": "generation_uuid", - "start_time_ms": 1500, - "track": 0, - "trim_start_ms": 200, - "trim_end_ms": 100, - "profile_id": "profile_uuid", - "profile_name": "Narrator", - "text": "Hello world", - "audio_path": "/path/to/audio.wav", - "duration": 2.5, - "created_at": "2024-01-15T10:30:00Z" -} -``` - -## Frontend Integration - -The timeline UI needs to: - -1. **Fetch story** with all items -2. **Render waveforms** for each item -3. **Handle drag/drop** to move items -4. **Handle edge drag** for trimming -5. **Sync playhead** across all tracks -6. **Export** when user clicks download diff --git a/docs2/content/docs/developer/transcription.mdx b/docs2/content/docs/developer/transcription.mdx deleted file mode 100644 index 4eee2b91..00000000 --- a/docs2/content/docs/developer/transcription.mdx +++ /dev/null @@ -1,299 +0,0 @@ ---- -title: "Transcription" -description: "How Whisper-based audio transcription works in Voicebox" ---- - -## Overview - -Voicebox uses OpenAI's Whisper model for automatic speech recognition (ASR). This powers the transcription feature for creating reference text from audio recordings. - -## Architecture - -The transcription system is built around the `WhisperModel` class: - -**Model Loading:** Lazy loading with HuggingFace Hub download. - -**Audio Processing:** Resampling and preprocessing for Whisper. - -**Inference:** Running transcription with optional language hints. - -## WhisperModel Class - -```python -class WhisperModel: - def __init__(self, model_size: str = "base"): - self.model = None - self.processor = None - self.model_size = model_size - self.device = self._get_device() -``` - -### Model Sizes - -| Size | Parameters | VRAM | Speed | Quality | -|------|------------|------|-------|---------| -| tiny | 39M | ~1GB | Fastest | Basic | -| base | 74M | ~1GB | Fast | Good | -| small | 244M | ~2GB | Medium | Better | -| medium | 769M | ~5GB | Slow | High | -| large | 1550M | ~10GB | Slowest | Best | - -Default is `base` for balance of speed and quality. - -## Model Loading - -Models are downloaded from HuggingFace Hub: - -```python -def load_model(self, model_size: Optional[str] = None): - from transformers import WhisperProcessor, WhisperForConditionalGeneration - - model_name = f"openai/whisper-{model_size}" - - # Track download progress - progress_manager = get_progress_manager() - task_manager = get_task_manager() - task_manager.start_download(f"whisper-{model_size}") - - # Load processor and model - with tracker.patch_download(): - self.processor = WhisperProcessor.from_pretrained(model_name) - self.model = WhisperForConditionalGeneration.from_pretrained(model_name) - - self.model.to(self.device) - - # Mark complete - progress_manager.mark_complete(f"whisper-{model_size}") - task_manager.complete_download(f"whisper-{model_size}") -``` - -### Async Loading - -Like TTS, loading runs in a thread pool: - -```python -async def load_model_async(self, model_size: Optional[str] = None): - if self.model is not None and self.model_size == model_size: - return - await asyncio.to_thread(self.load_model, model_size) -``` - -## Transcription - -### Basic Transcription - -```python -async def transcribe( - self, - audio_path: str, - language: Optional[str] = None, -) -> str: - await self.load_model_async() - - def _transcribe_sync(): - # Load and resample to 16kHz (Whisper requirement) - audio, sr = load_audio(audio_path, sample_rate=16000) - - # Process audio - inputs = self.processor( - audio, - sampling_rate=16000, - return_tensors="pt", - ) - inputs = inputs.to(self.device) - - # Set language hint if provided - forced_decoder_ids = None - if language: - forced_decoder_ids = self.processor.get_decoder_prompt_ids( - language=language, - task="transcribe", - ) - - # Generate - with torch.no_grad(): - predicted_ids = self.model.generate( - inputs["input_features"], - forced_decoder_ids=forced_decoder_ids, - ) - - # Decode - transcription = self.processor.batch_decode( - predicted_ids, - skip_special_tokens=True, - )[0] - - return transcription.strip() - - return await asyncio.to_thread(_transcribe_sync) -``` - -### Supported Languages - -Whisper supports 99+ languages. Common ones in Voicebox: - -| Code | Language | -|------|----------| -| en | English | -| zh | Chinese | -| ja | Japanese | -| ko | Korean | -| de | German | -| fr | French | -| ru | Russian | -| pt | Portuguese | -| es | Spanish | -| it | Italian | - -### Language Detection - -When no language is specified, Whisper auto-detects: - -```python -# Without language hint - auto-detect -transcription = await whisper.transcribe(audio_path) - -# With language hint - more accurate for short clips -transcription = await whisper.transcribe(audio_path, language="en") -``` - -## Transcription with Timestamps - -For advanced use cases, word-level timestamps are available: - -```python -async def transcribe_with_timestamps( - self, - audio_path: str, - language: Optional[str] = None, -) -> List[Dict[str, any]]: - await self.load_model_async() - - def _transcribe_timestamps_sync(): - audio, sr = load_audio(audio_path, sample_rate=16000) - inputs = self.processor(audio, sampling_rate=16000, return_tensors="pt") - - with torch.no_grad(): - predicted_ids = self.model.generate( - inputs["input_features"], - return_timestamps=True, - ) - - # Parse timestamps - return [ - { - "text": transcription, - "start": 0.0, - "end": len(audio) / sr, - } - ] - - return await asyncio.to_thread(_transcribe_timestamps_sync) -``` - -## Memory Management - -### Unloading - -Free memory when not needed: - -```python -def unload_model(self): - if self.model is not None: - del self.model - del self.processor - self.model = None - self.processor = None - - if torch.cuda.is_available(): - torch.cuda.empty_cache() -``` - -### Global Instance - -A singleton pattern manages the model: - -```python -_whisper_model: Optional[WhisperModel] = None - -def get_whisper_model() -> WhisperModel: - global _whisper_model - if _whisper_model is None: - _whisper_model = WhisperModel() - return _whisper_model -``` - -## Audio Preprocessing - -### Resampling - -Whisper requires 16kHz audio: - -```python -audio, sr = load_audio(audio_path, sample_rate=16000) -``` - -### Format Support - -The `load_audio` utility handles: -- WAV -- MP3 -- FLAC -- OGG -- M4A - -All formats are converted to mono 16kHz. - -## API Endpoints - -| Method | Endpoint | Description | -|--------|----------|-------------| -| POST | `/transcribe` | Transcribe audio file | - -### Request - -Multipart form data: - -``` -POST /transcribe -Content-Type: multipart/form-data - -file: -language: en (optional) -``` - -### Response - -```json -{ - "text": "Hello, this is a test transcription.", - "duration": 3.5 -} -``` - -## Use Cases - -### Reference Text for Voice Cloning - -1. User records audio sample -2. Audio is sent to `/transcribe` -3. Transcription becomes `reference_text` -4. Both are added to voice profile - -### Quality Tips - -- Provide language hint for short audio -- Use clean audio with minimal noise -- Longer audio (>5s) improves accuracy -- Consider `small` or `medium` model for better quality - -## Error Handling - -Common issues: - -| Error | Cause | Solution | -|-------|-------|----------| -| Model not found | First run, download failed | Retry with network | -| OOM | Model too large | Use smaller model | -| Empty result | No speech detected | Check audio has speech | -| Wrong language | Auto-detect failed | Provide language hint | diff --git a/docs2/content/docs/developer/tts-generation.mdx b/docs2/content/docs/developer/tts-generation.mdx deleted file mode 100644 index 3636d33b..00000000 --- a/docs2/content/docs/developer/tts-generation.mdx +++ /dev/null @@ -1,283 +0,0 @@ ---- -title: "TTS Generation" -description: "How text-to-speech generation works in Voicebox" ---- - -## Overview - -Voicebox uses Qwen3-TTS for voice cloning and text-to-speech generation. The TTS module handles model loading, voice prompt creation, and audio synthesis. - -## Architecture - -The TTS system is built around the `TTSModel` class which manages: - -**Model Loading:** Lazy loading with automatic HuggingFace Hub download. - -**Voice Prompts:** Converting reference audio into embeddings. - -**Generation:** Synthesizing speech from text using voice prompts. - -## TTSModel Class - -```python -class TTSModel: - def __init__(self, model_size: str = "1.7B"): - self.model = None - self.model_size = model_size - self.device = self._get_device() # cuda, mps, or cpu -``` - -### Device Selection - -The model automatically selects the best available device: - -```python -def _get_device(self) -> str: - if torch.cuda.is_available(): - return "cuda" - elif hasattr(torch.backends, 'mps') and torch.backends.mps.is_available(): - return "cpu" # MPS can have issues, use CPU for stability - return "cpu" -``` - -## Model Loading - -Models are downloaded from HuggingFace Hub on first use: - -```python -def load_model(self, model_size: Optional[str] = None): - # Model IDs on HuggingFace Hub - hf_model_map = { - "1.7B": "Qwen/Qwen3-TTS-12Hz-1.7B-Base", - "0.6B": "Qwen/Qwen3-TTS-12Hz-0.6B-Base", - } - - # Load with progress tracking - with tracker.patch_download(): - self.model = Qwen3TTSModel.from_pretrained( - model_path, - device_map=self.device, - torch_dtype=torch.bfloat16, # float32 on CPU - ) -``` - -### Async Loading - -Loading runs in a thread pool to avoid blocking the event loop: - -```python -async def load_model_async(self, model_size: Optional[str] = None): - if self.model is not None and self._current_model_size == model_size: - return - await asyncio.to_thread(self.load_model, model_size) -``` - -## Voice Prompt Creation - -Voice prompts are created from reference audio and cached for reuse: - -```python -async def create_voice_prompt( - self, - audio_path: str, - reference_text: str, - use_cache: bool = True, -) -> Tuple[dict, bool]: - await self.load_model_async() - - # Check cache - if use_cache: - cache_key = get_cache_key(audio_path, reference_text) - cached = get_cached_voice_prompt(cache_key) - if cached: - return cached, True - - # Create prompt (blocking, run in thread pool) - voice_prompt = await asyncio.to_thread( - self.model.create_voice_clone_prompt, - ref_audio=audio_path, - ref_text=reference_text, - ) - - # Cache the result - cache_voice_prompt(cache_key, voice_prompt) - return voice_prompt, False -``` - -### Combining Multiple Samples - -When a profile has multiple samples, they're combined: - -```python -async def combine_voice_prompts( - self, - audio_paths: List[str], - reference_texts: List[str], -) -> Tuple[np.ndarray, str]: - combined_audio = [] - - for audio_path in audio_paths: - audio, sr = load_audio(audio_path) - audio = normalize_audio(audio) - combined_audio.append(audio) - - # Concatenate and normalize - mixed = np.concatenate(combined_audio) - mixed = normalize_audio(mixed) - - # Combine texts - combined_text = " ".join(reference_texts) - - return mixed, combined_text -``` - -## Speech Generation - -The core generation function: - -```python -async def generate( - self, - text: str, - voice_prompt: dict, - language: str = "en", - seed: Optional[int] = None, - instruct: Optional[str] = None, -) -> Tuple[np.ndarray, int]: - await self.load_model_async() - - def _generate_sync(): - # Set seed for reproducibility - if seed is not None: - torch.manual_seed(seed) - - # Generate audio - wavs, sample_rate = self.model.generate_voice_clone( - text=text, - voice_clone_prompt=voice_prompt, - instruct=instruct, # Natural language delivery control - ) - return wavs[0], sample_rate - - # Run in thread pool - return await asyncio.to_thread(_generate_sync) -``` - -### Instruct Feature - -The `instruct` parameter allows natural language control over speech delivery: - -```python -# Examples: -instruct = "Speak slowly and clearly" -instruct = "Sound excited and enthusiastic" -instruct = "Whisper softly" -``` - -## Caching Strategy - -Voice prompts are cached to avoid recomputation: - -```python -def get_cache_key(audio_path: str, reference_text: str) -> str: - """Generate cache key from audio hash and text.""" - audio_hash = hashlib.md5(Path(audio_path).read_bytes()).hexdigest() - text_hash = hashlib.md5(reference_text.encode()).hexdigest() - return f"{audio_hash}_{text_hash}" -``` - -Cache is stored in `data/cache/voice_prompts/`. - -## Memory Management - -### Unloading Models - -Free VRAM/RAM when not needed: - -```python -def unload_model(self): - if self.model is not None: - del self.model - self.model = None - - if torch.cuda.is_available(): - torch.cuda.empty_cache() -``` - -### Model Switching - -When switching between model sizes (1.7B ↔ 0.6B): - -```python -# Unload existing model first -if self.model is not None and self._current_model_size != model_size: - self.unload_model() -``` - -## Generation Flow - -1. **Request** → Validate text and profile ID -2. **Profile** → Load profile samples from database -3. **Voice Prompt** → Create or retrieve cached prompt -4. **Generate** → Run TTS inference -5. **Save** → Write audio to generations directory -6. **Record** → Create history entry in database -7. **Response** → Return audio path and metadata - -## API Endpoints - -| Method | Endpoint | Description | -|--------|----------|-------------| -| POST | `/generate` | Generate speech from text | -| GET | `/audio/{id}` | Serve generated audio file | - -### Request Schema - -```json -{ - "profile_id": "uuid", - "text": "Text to synthesize", - "language": "en", - "seed": 42, - "model_size": "1.7B", - "instruct": "Speak clearly" -} -``` - -### Response Schema - -```json -{ - "id": "generation_uuid", - "profile_id": "profile_uuid", - "text": "Text to synthesize", - "language": "en", - "audio_path": "/path/to/audio.wav", - "duration": 3.5, - "seed": 42, - "instruct": "Speak clearly", - "created_at": "2024-01-15T10:30:00Z" -} -``` - -## Performance Considerations - -### GPU Acceleration - -- CUDA provides fastest inference -- MPS (Apple Silicon) has stability issues, uses CPU fallback -- CPU inference is slower but always works - -### Batch Size - -Currently generates one utterance at a time. For long texts, consider: -- Splitting into sentences -- Sequential generation -- Concatenating results - -### Memory Usage - -| Model | VRAM/RAM Required | -|-------|-------------------| -| 0.6B | ~2GB | -| 1.7B | ~6GB | diff --git a/docs2/content/docs/developer/voice-profiles.mdx b/docs2/content/docs/developer/voice-profiles.mdx deleted file mode 100644 index 6be6aad9..00000000 --- a/docs2/content/docs/developer/voice-profiles.mdx +++ /dev/null @@ -1,202 +0,0 @@ ---- -title: "Voice Profiles" -description: "How voice profile management works in Voicebox" ---- - -## Overview - -Voice profiles are the foundation of Voicebox's voice cloning capability. Each profile stores reference audio samples and metadata that the TTS model uses to clone a voice. - -## Architecture - -The voice profile system consists of three main components: - -**Database Layer:** SQLite tables store profile metadata and sample references. - -**File Storage:** Audio samples are stored on disk in a structured directory format. - -**Profile Module:** The `profiles.py` module provides the business logic for CRUD operations. - -## Data Model - -### VoiceProfile Table - -```python -class VoiceProfile(Base): - __tablename__ = "profiles" - - id = Column(String, primary_key=True) - name = Column(String, unique=True, nullable=False) - description = Column(Text) - language = Column(String, default="en") - created_at = Column(DateTime) - updated_at = Column(DateTime) -``` - -### ProfileSample Table - -```python -class ProfileSample(Base): - __tablename__ = "profile_samples" - - id = Column(String, primary_key=True) - profile_id = Column(String, ForeignKey("profiles.id")) - audio_path = Column(String, nullable=False) - reference_text = Column(Text, nullable=False) -``` - -## File Structure - -Profiles are stored in the data directory: - -``` -data/ -└── profiles/ - └── {profile_id}/ - ├── {sample_id_1}.wav - ├── {sample_id_2}.wav - └── ... -``` - -## Core Functions - -### Creating a Profile - -```python -async def create_profile(data: VoiceProfileCreate, db: Session) -> VoiceProfileResponse: - # 1. Create database record - db_profile = DBVoiceProfile( - id=str(uuid.uuid4()), - name=data.name, - description=data.description, - language=data.language, - ) - db.add(db_profile) - db.commit() - - # 2. Create profile directory - profile_dir = profiles_dir / db_profile.id - profile_dir.mkdir(parents=True, exist_ok=True) - - return VoiceProfileResponse.model_validate(db_profile) -``` - -### Adding Samples - -When a sample is added, the audio is validated and copied to the profile directory: - -```python -async def add_profile_sample( - profile_id: str, - audio_path: str, - reference_text: str, - db: Session, -) -> ProfileSampleResponse: - # 1. Validate audio (duration, format, quality) - is_valid, error_msg = validate_reference_audio(audio_path) - if not is_valid: - raise ValueError(f"Invalid reference audio: {error_msg}") - - # 2. Copy to profile directory - sample_id = str(uuid.uuid4()) - dest_path = profile_dir / f"{sample_id}.wav" - audio, sr = load_audio(audio_path) - save_audio(audio, str(dest_path), sr) - - # 3. Create database record - db_sample = DBProfileSample( - id=sample_id, - profile_id=profile_id, - audio_path=str(dest_path), - reference_text=reference_text, - ) - db.add(db_sample) - db.commit() -``` - -### Voice Prompt Creation - -When generating speech, samples are combined into a voice prompt: - -```python -async def create_voice_prompt_for_profile( - profile_id: str, - db: Session, -) -> dict: - samples = db.query(DBProfileSample).filter_by(profile_id=profile_id).all() - - if len(samples) == 1: - # Single sample - use directly - voice_prompt, _ = await tts_model.create_voice_prompt( - sample.audio_path, - sample.reference_text, - ) - else: - # Multiple samples - combine them - combined_audio, combined_text = await tts_model.combine_voice_prompts( - [s.audio_path for s in samples], - [s.reference_text for s in samples], - ) - voice_prompt, _ = await tts_model.create_voice_prompt( - combined_audio_path, - combined_text, - ) - - return voice_prompt -``` - -## Audio Validation - -Reference audio is validated before being accepted: - -- **Duration:** 3-30 seconds recommended -- **Format:** WAV, MP3, FLAC, OGG supported -- **Sample Rate:** Resampled to 24kHz -- **Channels:** Converted to mono if stereo - -## Export/Import - -Profiles can be exported as ZIP archives for sharing: - -``` -profile_export.zip -├── profile.json # Metadata -├── samples/ -│ ├── sample_1.wav -│ └── sample_1.json # Reference text -└── ... -``` - -## API Endpoints - -| Method | Endpoint | Description | -|--------|----------|-------------| -| GET | `/profiles` | List all profiles | -| POST | `/profiles` | Create a profile | -| GET | `/profiles/{id}` | Get profile by ID | -| PUT | `/profiles/{id}` | Update profile | -| DELETE | `/profiles/{id}` | Delete profile | -| GET | `/profiles/{id}/samples` | Get profile samples | -| POST | `/profiles/{id}/samples` | Add sample to profile | -| PUT | `/profiles/samples/{id}` | Update sample text | -| DELETE | `/profiles/samples/{id}` | Delete sample | -| GET | `/profiles/{id}/export` | Export as ZIP | -| POST | `/profiles/import` | Import from ZIP | - -## Best Practices - -### Sample Quality - -- Use clean audio with minimal background noise -- Ensure the reference text exactly matches what is spoken -- Multiple samples (3-5) improve voice cloning quality - -### Language Matching - -- Set the profile language to match the reference audio -- Supported languages: en, zh, ja, ko, de, fr, ru, pt, es, it - -### Naming Conventions - -- Use descriptive names that identify the voice -- Avoid special characters that may cause filesystem issues diff --git a/docs2/content/docs/overview/building-stories.mdx b/docs2/content/docs/overview/building-stories.mdx deleted file mode 100644 index 0056f38c..00000000 --- a/docs2/content/docs/overview/building-stories.mdx +++ /dev/null @@ -1,37 +0,0 @@ ---- -title: "Building Stories" -description: "Create multi-voice narratives with the Stories Editor" ---- - -## Getting Started - -The Stories Editor is perfect for creating podcasts, audiobooks, and multi-speaker content. - - - - **Stories** → **+ New Story** - - - Create tracks for each speaker - - - Generate or drag audio to tracks - - - Position and trim clips on timeline - - - Render final audio - - - -## Use Cases - -- Multi-host podcasts -- Audiobook narration with character voices -- Game dialogue scenes -- Educational content with multiple speakers - -## Coming Soon - -Full timeline editor documentation will be added as features are finalized. diff --git a/docs2/content/docs/overview/generating-speech.mdx b/docs2/content/docs/overview/generating-speech.mdx deleted file mode 100644 index 775b011c..00000000 --- a/docs2/content/docs/overview/generating-speech.mdx +++ /dev/null @@ -1,65 +0,0 @@ ---- -title: "Generating Speech" -description: "Generate high-quality speech from text" ---- - -## Basic Generation - - - - Choose a voice profile from the dropdown - - - Type or paste your text - - - Click **Generate** and wait a few seconds - - - Preview and download the result - - - -## Text Formatting Tips - -The way you format text affects the output quality. - -### Punctuation - -Use proper punctuation for natural pauses: - -``` -Good: "Hello! How are you today? I'm doing great." -Bad: "Hello how are you today Im doing great" -``` - -### Emphasis - -Use formatting to suggest emphasis: - -``` -- ALL CAPS for louder/emphasized: "That was AMAZING!" -- Italics for subtle emphasis: "I *really* enjoyed that" -- Bold for strong emphasis: "This is **very** important" -``` - - - The model interprets these hints but results may vary. - - -## Advanced Features - -### Batch Generation - -For long-form content, split into smaller chunks for better control and faster processing. - -### Voice Caching - -Voicebox caches voice prompts for faster re-generation with the same profile. - -## Coming Soon - -- Real-time streaming -- Word-level timing control -- Emotion and style controls -- SSML support diff --git a/docs2/content/docs/overview/generation-history.mdx b/docs2/content/docs/overview/generation-history.mdx deleted file mode 100644 index cdee4689..00000000 --- a/docs2/content/docs/overview/generation-history.mdx +++ /dev/null @@ -1,88 +0,0 @@ ---- -title: "Generation History" -description: "Track and manage all your generated audio" ---- - -## Overview - -Voicebox keeps a complete history of all generated audio, making it easy to find, reuse, and manage your creations. - -## Features - - - - Every generation is automatically saved - - - Find by text, voice, or date - - - Regenerate any past generation with one click - - - Download individual or batch exports - - - -## Viewing History - -Navigate to the **History** tab to see all your generations. - -Each entry shows: -- Generated text -- Voice profile used -- Timestamp -- Audio duration -- Language - -## Actions - -### Play -Click any generation to play it immediately. - -### Re-generate -Regenerate with the same settings or modify the text/voice. - -### Download -Export as WAV, MP3, or M4A. - -### Delete -Remove unwanted generations to free up space. - -### Add to Story -Drag generations to the Stories Editor timeline. - -## Search & Filter - - - - Search for specific text content - ``` - "Hello world" - ``` - - - Filter by voice profile - ``` - Select from dropdown - ``` - - - Filter by date range - ``` - Last 7 days, Last 30 days, Custom range - ``` - - - -## Storage - -History is stored locally: - -- **macOS**: `~/Library/Application Support/com.voicebox.app/data/` -- **Windows**: `%APPDATA%/com.voicebox.app/data/` -- **Linux**: `~/.config/com.voicebox.app/data/` - - - Deleting the data directory will remove all history. Export important files first. - diff --git a/docs2/content/docs/overview/installation.mdx b/docs2/content/docs/overview/installation.mdx deleted file mode 100644 index 04ddc5ba..00000000 --- a/docs2/content/docs/overview/installation.mdx +++ /dev/null @@ -1,119 +0,0 @@ ---- -title: "Installation" -description: "Download and install Voicebox on macOS, Windows, or Linux" ---- - -## Download - -Voicebox is available for macOS and Windows, with Linux builds coming soon. - - - - Download for Apple Silicon or Intel Macs - - - Download MSI installer or Setup executable - - - -### macOS - - - - Download: [voicebox_aarch64.app.tar.gz](https://github.com/jamiepine/voicebox/releases/latest/download/voicebox_aarch64.app.tar.gz) - - ```bash - # Extract the archive - tar -xzf voicebox_aarch64.app.tar.gz - - # Move to Applications - mv Voicebox.app /Applications/ - ``` - - - Download: [voicebox_x64.app.tar.gz](https://github.com/jamiepine/voicebox/releases/latest/download/voicebox_x64.app.tar.gz) - - ```bash - # Extract the archive - tar -xzf voicebox_x64.app.tar.gz - - # Move to Applications - mv Voicebox.app /Applications/ - ``` - - - -### Windows - - - - Download: [voicebox_x64_en-US.msi](https://github.com/jamiepine/voicebox/releases/latest/download/voicebox_x64_en-US.msi) - - Double-click the MSI file and follow the installation wizard. - - - Download: [voicebox_x64-setup.exe](https://github.com/jamiepine/voicebox/releases/latest/download/voicebox_x64-setup.exe) - - Run the executable and follow the installation wizard. - - - -### Linux - - - Linux builds are coming soon. Currently blocked by GitHub runner disk space limitations. - - -## First Launch - -When you launch Voicebox for the first time: - -1. **Model Download** — Qwen3-TTS model (~2-4GB) will download automatically on first use -2. **Data Directory** — Voice profiles and generated audio are stored in: - - macOS: `~/Library/Application Support/com.voicebox.app/` - - Windows: `%APPDATA%/com.voicebox.app/` - - Linux: `~/.config/com.voicebox.app/` - -3. **Backend Server** — The bundled Python server starts automatically - - - First generation will be slower due to model downloads. Subsequent runs use cached models. - - -## System Requirements - -### Minimum - -- **OS:** macOS 11+, Windows 10+, or Linux -- **RAM:** 8GB -- **Storage:** 5GB free space (for models and data) -- **CPU:** Modern multi-core processor - -### Recommended - -- **RAM:** 16GB+ -- **GPU:** CUDA-capable NVIDIA GPU (for faster generation) -- **Storage:** 10GB+ free space - - - CPU inference is supported but significantly slower than GPU. A CUDA-capable GPU is highly recommended for real-time workflows. - - -## Verification - -After installation, verify everything works: - -1. Launch Voicebox -2. Check the server status indicator in the bottom-left corner (should be green) -3. Navigate to **Profiles** and create a test profile -4. Generate a short audio clip to verify the TTS engine works - - - If you see a green status indicator and can generate audio, you're all set! - - -## Next Steps - - - Create your first voice profile and generate speech - diff --git a/docs2/content/docs/overview/recording-transcription.mdx b/docs2/content/docs/overview/recording-transcription.mdx deleted file mode 100644 index 7f1e4871..00000000 --- a/docs2/content/docs/overview/recording-transcription.mdx +++ /dev/null @@ -1,64 +0,0 @@ ---- -title: "Recording & Transcription" -description: "Record audio and transcribe speech with Whisper" ---- - -## Recording - -Voicebox includes built-in recording capabilities for creating voice samples and capturing audio. - -### Features - -- **Microphone input** - Record from any audio input device -- **System audio capture** - Record desktop audio (macOS/Windows) -- **Waveform visualization** - See audio levels in real-time -- **Multiple formats** - Export as WAV, MP3, or M4A - -### How to Record - - - - Choose your microphone or system audio - - - Click the record button and speak clearly - - - Click stop when finished - - - Use as voice sample or export to file - - - -## Transcription - -Automatic speech-to-text powered by OpenAI's Whisper model. - -### Features - -- **High accuracy** - Industry-leading speech recognition -- **Multiple languages** - Supports 50+ languages -- **Automatic detection** - Language auto-detection -- **Timestamps** - Word-level timing information - -### How to Transcribe - - - - Choose a recording or upload an audio file - - - Select language or use auto-detect - - - Click transcribe and wait for processing - - - Review text and export as needed - - - - - Transcription is useful for creating voice samples from existing audio or generating subtitles. - diff --git a/docs2/content/docs/overview/remote-mode.mdx b/docs2/content/docs/overview/remote-mode.mdx deleted file mode 100644 index 533147fd..00000000 --- a/docs2/content/docs/overview/remote-mode.mdx +++ /dev/null @@ -1,138 +0,0 @@ ---- -title: "Remote Mode" -description: "Connect to a GPU server for faster generation" ---- - -## Overview - -Remote Mode allows you to run the Voicebox backend on a separate machine (like a GPU server) while using the desktop app on your local machine. - -## Use Cases - -- **No local GPU** - Use a cloud GPU or remote workstation -- **Faster generation** - Leverage powerful remote hardware -- **Shared infrastructure** - Multiple users connect to one server -- **Laptop workflows** - Keep your laptop cool and battery-efficient - -## Architecture - -In Remote Mode, the Voicebox desktop app (running on your local machine) communicates with the backend server (running on a remote machine) via HTTP. The local app provides only the user interface, while the remote server handles all the heavy processing including the TTS models, API endpoints, and audio generation. - -## Setting Up Remote Mode - -### On the Server - - - - ```bash - # Clone the repo - git clone https://github.com/jamiepine/voicebox.git - cd voicebox/backend - - # Install Python dependencies - pip install -r requirements.txt - pip install git+https://github.com/QwenLM/Qwen3-TTS.git - ``` - - - - ```bash - # Allow external connections - uvicorn main:app --host 0.0.0.0 --port 17493 - ``` - - - This exposes the server to your network. Use a firewall or VPN for security. - - - - - ```bash - # Ubuntu/Debian - sudo ufw allow 17493 - - # Or use your cloud provider's firewall settings - ``` - - - -### On the Client - - - - In Voicebox, go to **Settings → Server** - - - - Toggle **Use Remote Server** - - - - ``` - http://:17493 - ``` - - Replace `` with your server's IP address - - - - Click **Test Connection** to verify - - - -## Cloud Deployment - -### AWS EC2 - -```bash -# Launch a GPU instance (e.g., g4dn.xlarge) -# Install dependencies -# Start server with --host 0.0.0.0 -``` - -### Vast.ai - -```bash -# Rent a GPU instance -# SSH in and clone repo -# Start server -``` - -### RunPod - -```bash -# Deploy a pod with CUDA support -# Install Voicebox backend -# Expose port 17493 -``` - -## Security Considerations - - - The API currently has no authentication. Only use on trusted networks or with a VPN. - - -**Best Practices:** -- Use a VPN (WireGuard, Tailscale) instead of exposing to the internet -- Run behind a reverse proxy with authentication (nginx + basic auth) -- Use HTTPS with SSL certificates -- Firewall rules to limit access to specific IPs - -## Performance - -Expected performance on various GPUs: - -| GPU | Generation Speed | -|-----|------------------| -| RTX 4090 | ~2-3s per 10 words | -| RTX 3090 | ~3-4s per 10 words | -| RTX 3060 | ~5-7s per 10 words | -| CPU (12-core) | ~20-30s per 10 words | - - - A GPU with 8GB+ VRAM is recommended for best performance. - - -## Troubleshooting - -See the [Troubleshooting Guide](/guides/troubleshooting#remote-mode-issues) for common remote mode issues. diff --git a/docs2/content/docs/overview/stories-editor.mdx b/docs2/content/docs/overview/stories-editor.mdx deleted file mode 100644 index 7e9629d3..00000000 --- a/docs2/content/docs/overview/stories-editor.mdx +++ /dev/null @@ -1,64 +0,0 @@ ---- -title: "Stories Editor" -description: "Create multi-voice narratives with a timeline-based editor" ---- - -## Overview - -The Stories Editor is a DAW-like timeline interface for creating multi-voice narratives, podcasts, and conversations. - -## Features - - - - Arrange multiple voice tracks in parallel - - - Trim and split clips directly in the timeline - - - Preview with synchronized playhead - - - Build conversations with multiple speakers - - - -## Creating a Story - - - - Navigate to **Stories** and click **+ New Story** - - - Create separate tracks for each voice/speaker - - - - Drag from generation history - - Generate new clips inline - - Upload audio files - - - - Position clips on timeline - - Trim clip edges - - Adjust spacing and timing - - - Render the final mixed audio - - - -## Use Cases - -- **Podcasts**: Multi-host conversations -- **Audiobooks**: Narrator + character voices -- **Game Dialogue**: Character interactions -- **Video Voiceovers**: Multiple speakers -- **Audio Drama**: Full voice casts - -## Coming Soon - -- Word-level editing -- Crossfades and transitions -- Audio effects (reverb, EQ) -- Real-time collaboration diff --git a/docs2/content/docs/overview/troubleshooting.mdx b/docs2/content/docs/overview/troubleshooting.mdx deleted file mode 100644 index f57c29f2..00000000 --- a/docs2/content/docs/overview/troubleshooting.mdx +++ /dev/null @@ -1,477 +0,0 @@ ---- -title: "Troubleshooting" -description: "Common issues and solutions for Voicebox" ---- - -This guide covers common issues you might encounter when using or developing Voicebox, along with solutions. - -## Installation Issues - -### macOS: "App is damaged and can't be opened" - -This occurs because the app isn't signed with an Apple Developer certificate. - -**Solution:** -```bash -# Remove the quarantine attribute -xattr -cr /Applications/Voicebox.app -``` - -### Windows: SmartScreen Warning - -Windows SmartScreen may warn that the app is unrecognized. - -**Solution:** -- Click "More info" -- Click "Run anyway" - - - This is expected for unsigned applications. We're working on code signing for future releases. - - -## Server Issues - -### Backend Server Won't Start - -**Symptoms:** -- Red status indicator in bottom-left corner -- "Failed to connect to server" error - -**Solutions:** - - - - Check if port 17493 is already in use: - - ```bash - # macOS/Linux - lsof -i :17493 - - # Windows - netstat -ano | findstr :17493 - ``` - - Kill the process using the port: - ```bash - # macOS/Linux - kill -9 - - # Windows - taskkill /PID /F - ``` - - - - The server binary might not have execute permissions: - - ```bash - # macOS/Linux - chmod +x ~/Library/Application\ Support/com.voicebox.app/backend/voicebox-server - ``` - - - - View server logs for errors: - - **macOS:** - ```bash - tail -f ~/Library/Application\ Support/com.voicebox.app/logs/server.log - ``` - - **Windows:** - ```bash - type %APPDATA%\com.voicebox.app\logs\server.log - ``` - - - -### Connection Timeout - -**Symptoms:** -- Long loading times -- "Connection timeout" errors - -**Solution:** -- Restart the app -- Check your firewall settings -- Ensure localhost is accessible - -## Generation Issues - -### First Generation is Very Slow - -**Symptoms:** -- First generation takes 2-5 minutes -- Progress indicator stuck at "Loading model..." - -**Explanation:** -This is expected behavior. The first generation downloads the Qwen3-TTS model (~2-4GB) and initializes it. - -**Solution:** -- Wait for the initial download to complete -- Subsequent generations will be much faster -- Check your internet connection - -### Poor Voice Quality - -**Symptoms:** -- Robotic or unnatural voice -- Missing emotion or prosody -- Pronunciation errors - -**Solutions:** - - - - - Use 10-30 seconds of clear audio - - Avoid background noise - - Ensure consistent speaking tone - - Add multiple samples from the same speaker - - - - The generated voice will mimic the tone and style of your samples. If your sample is monotone, the generation will be too. - - - - - Use proper punctuation - - Add commas for natural pauses - - Capitalize proper nouns - - - -### Generation Fails with "Out of Memory" - -**Symptoms:** -- Generation crashes -- "CUDA out of memory" or "RuntimeError: out of memory" - -**Solutions:** - - - - Close other GPU-intensive applications: - - Games - - Video editors - - Multiple browser tabs with WebGL - - Then restart Voicebox. - - - - If your GPU doesn't have enough VRAM (need 6GB+), use CPU mode: - - Settings → Generation → Use CPU instead of GPU - - - CPU generation is 5-10x slower but uses system RAM instead of VRAM. - - - - - For long text, split it into smaller chunks instead of generating all at once. - - - -## Audio Issues - -### No Audio Playback - -**Symptoms:** -- Generated audio won't play -- Playback button doesn't respond - -**Solutions:** -- Check system audio settings -- Ensure audio output device is connected -- Try exporting and playing in a media player - -### Crackling or Distorted Audio - -**Symptoms:** -- Audio has static or distortion -- Clipping sounds - -**Solutions:** -- Check if your input samples have distortion -- Reduce playback volume -- Re-generate with cleaner voice samples - -## Development Issues - -### Backend Won't Start in Dev Mode - -**Symptoms:** -- `bun run dev:server` fails -- Import errors or module not found - -**Solutions:** - - - - Ensure Python 3.11 or higher: - - ```bash - python --version - ``` - - If not, install Python 3.11+ and recreate the virtual environment. - - - - Ensure venv is activated: - - ```bash - # macOS/Linux - source backend/venv/bin/activate - - # Windows - backend\venv\Scripts\activate - ``` - - You should see `(venv)` in your prompt. - - - - Reinstall dependencies: - - ```bash - cd backend - pip install -r requirements.txt - pip install git+https://github.com/QwenLM/Qwen3-TTS.git - ``` - - - -### Tauri Build Fails - -**Symptoms:** -- `bun run tauri build` fails -- Rust compilation errors - -**Solutions:** - -```bash -# Clean build artifacts -cd tauri/src-tauri -cargo clean - -# Update Rust -rustup update - -# Try building again -cd ../.. -bun run tauri build -``` - -### OpenAPI Client Generation Fails - -**Symptoms:** -- `./scripts/generate-api.sh` fails -- "Failed to fetch schema" error - -**Solutions:** - - - - ```bash - curl http://localhost:17493/openapi.json - ``` - - Should return JSON. If not, start the backend. - - - - Ensure nothing else is using port 17493 - - - - ```bash - cd backend - source venv/bin/activate - uvicorn main:app --reload --port 17493 - - # In another terminal - ./scripts/generate-api.sh - ``` - - - -## Database Issues - -### "Database is locked" Error - -**Symptoms:** -- Profile or generation operations fail -- SQLite lock errors - -**Solutions:** -- Close all Voicebox instances -- Delete the lock file: - ```bash - # macOS - rm ~/Library/Application\ Support/com.voicebox.app/data/voicebox.db-shm - rm ~/Library/Application\ Support/com.voicebox.app/data/voicebox.db-wal - ``` - -### Corrupted Database - -**Symptoms:** -- App crashes on launch -- Data missing or corrupted - -**Solutions:** - - - This will delete all your voice profiles and generation history. Export important profiles first if possible. - - -```bash -# macOS -rm ~/Library/Application\ Support/com.voicebox.app/data/voicebox.db - -# Windows -del %APPDATA%\com.voicebox.app\data\voicebox.db -``` - -Restart the app to create a fresh database. - -## Model Issues - -### Model Download Fails - -**Symptoms:** -- "Failed to download model" error -- Stuck at "Downloading..." - -**Solutions:** -- Check your internet connection -- Check HuggingFace Hub status -- Try using a VPN if HuggingFace is blocked in your region -- Manually download and place in cache directory - -### Wrong Model Version - -**Symptoms:** -- Generation quality suddenly degraded -- Different voice output - -**Solutions:** -Clear the model cache and re-download: - -```bash -# macOS -rm -rf ~/.cache/huggingface/hub/models--Qwen* - -# Windows -rmdir /s %USERPROFILE%\.cache\huggingface\hub\models--Qwen* -``` - -## Performance Issues - -### Slow Generation on GPU - -**Symptoms:** -- Generation slower than expected -- GPU not being utilized - -**Solutions:** - - - - ```bash - nvidia-smi - ``` - - Should show your GPU. If not, install CUDA drivers. - - - - If you have multiple GPUs, ensure Voicebox is using the right one. - - Settings → Generation → GPU Device - - - - Outdated drivers can cause performance issues. Update to the latest NVIDIA drivers. - - - -### High Memory Usage - -**Symptoms:** -- App uses excessive RAM -- System becomes sluggish - -**Solutions:** -- Close unused voice profiles -- Clear generation history -- Restart the app periodically - -## Remote Mode Issues - -### Can't Connect to Remote Server - -**Symptoms:** -- "Connection refused" error -- Remote server not found - -**Solutions:** - - - - Ensure the remote server is running: - - ```bash - curl http://:17493/health - ``` - - - - Ensure port 17493 is open on the remote server: - - ```bash - # Allow port on Ubuntu/Debian - sudo ufw allow 17493 - ``` - - - - - Ensure both machines are on the same network (for local servers) - - Use IP address instead of hostname - - Try pinging the server: `ping ` - - - -## Still Having Issues? - -If you're still experiencing problems: - -1. **Check GitHub Issues:** [github.com/jamiepine/voicebox/issues](https://github.com/jamiepine/voicebox/issues) -2. **Open a New Issue:** Provide: - - Operating system and version - - Voicebox version - - Steps to reproduce - - Error messages or logs -3. **Join Discord:** [discord.gg/voicebox](https://discord.gg/voicebox) (coming soon) - -## Diagnostic Information - -When reporting issues, include this information: - -```bash -# Voicebox version -# Check Help → About in the app - -# Operating system -uname -a # macOS/Linux -systeminfo # Windows - -# Python version (for dev issues) -python --version - -# GPU info (if generation issues) -nvidia-smi # NVIDIA GPUs -``` - -For more detailed troubleshooting, see the [TROUBLESHOOTING.md](https://github.com/jamiepine/voicebox/blob/main/docs/TROUBLESHOOTING.md) file in the repository. diff --git a/docs2/content/docs/overview/voice-cloning.mdx b/docs2/content/docs/overview/voice-cloning.mdx deleted file mode 100644 index efc6caf8..00000000 --- a/docs2/content/docs/overview/voice-cloning.mdx +++ /dev/null @@ -1,75 +0,0 @@ ---- -title: "Voice Cloning" -description: "Clone any voice from just a few seconds of audio" ---- - -## Overview - -Voicebox uses **Qwen3-TTS** from Alibaba to achieve near-perfect voice cloning from just a few seconds of audio. The model captures prosody, emotion, and natural cadence. - -## How It Works - - - - Provide 10-30 seconds of clear speech from the target voice - - - Qwen3-TTS analyzes vocal characteristics, tone, and speaking patterns - - - The model generates a voice embedding for synthesis - - - Use the profile to generate any text in the cloned voice - - - -## Best Practices - -### Sample Quality - - - - - Use 10-30 seconds of audio - - Clear, consistent speaking - - Minimal background noise - - Natural speaking pace - - - - Very short clips (< 5 seconds) - - Heavy background noise - - Music or overlapping voices - - Heavily processed audio - - - -### Multiple Samples - -Adding multiple samples from the same speaker can improve quality: - -- Different speaking styles (casual, formal) -- Different emotions (happy, serious) -- Different recording conditions - - - The model will learn a more robust representation from diverse samples. - - -## Supported Languages - -Currently supported: -- English -- Chinese (Mandarin) - -More languages coming soon. - -## Limitations - - - Voice cloning should only be used with consent. Ensure you have permission to clone someone's voice. - - -- Quality depends on sample clarity -- Works best with consistent speaking tone -- May struggle with extreme accents or speech impediments -- Background noise reduces quality diff --git a/docs2/package.json b/docs2/package.json deleted file mode 100644 index 1d14cd8f..00000000 --- a/docs2/package.json +++ /dev/null @@ -1,32 +0,0 @@ -{ - "name": "example-next-mdx", - "version": "0.0.0", - "private": true, - "scripts": { - "build": "fumadocs-mdx && next build", - "dev": "fumadocs-mdx && next dev", - "start": "next start", - "postinstall": "fumadocs-mdx" - }, - "dependencies": { - "fumadocs-core": "^16.4.11", - "fumadocs-mdx": "13", - "fumadocs-openapi": "^10.2.7", - "fumadocs-ui": "^16.4.11", - "lucide-react": "^0.546.0", - "next": "^16.1.6", - "react": "^19.2.0", - "react-dom": "^19.2.0", - "shiki": "^3.22.0" - }, - "devDependencies": { - "@tailwindcss/postcss": "^4.1.15", - "@types/mdx": "^2.0.13", - "@types/node": "^24.9.1", - "@types/react": "^19.2.2", - "@types/react-dom": "^19.2.2", - "postcss": "^8.5.6", - "tailwindcss": "^4.1.15", - "typescript": "^5.9.3" - } -} diff --git a/docs2/public/images/app-screenshot-1.webp b/docs2/public/images/app-screenshot-1.webp deleted file mode 100644 index 343e94bd..00000000 Binary files a/docs2/public/images/app-screenshot-1.webp and /dev/null differ diff --git a/docs2/public/images/app-screenshot-2.webp b/docs2/public/images/app-screenshot-2.webp deleted file mode 100644 index 2d097607..00000000 Binary files a/docs2/public/images/app-screenshot-2.webp and /dev/null differ diff --git a/docs2/public/images/app-screenshot-3.webp b/docs2/public/images/app-screenshot-3.webp deleted file mode 100644 index 067d5365..00000000 Binary files a/docs2/public/images/app-screenshot-3.webp and /dev/null differ diff --git a/docs2/public/logo/icon-dark.png b/docs2/public/logo/icon-dark.png deleted file mode 100644 index 0e0c5830..00000000 Binary files a/docs2/public/logo/icon-dark.png and /dev/null differ diff --git a/docs2/public/logo/icon-light.png b/docs2/public/logo/icon-light.png deleted file mode 100644 index 0e0c5830..00000000 Binary files a/docs2/public/logo/icon-light.png and /dev/null differ