fix TorchScript error in frozen builds and update docs for TADA

Remove @torch.jit.script from the DAC shim's snake() function — TorchScript calls inspect.getsource() which fails in PyInstaller binaries (no .py source files). Update all user-facing docs: 4 → 5 TTS engines, add TADA row to every engine comparison table, mark TADA as Shipped in the upcoming engines list, update architecture diagrams and tech stack tables.
fix TADA 3B model name: tada-3b -> tada-3b-ml
2026-09-28 22:55:15 -07:00 · 2026-03-17 03:28:58 -07:00 · 2026-03-17 03:17:53 -07:00 · 2026-03-17 03:15:57 -07:00 · 2026-03-17 02:25:05 -07:00 · 2026-03-17 02:22:26 -07:00
32 changed files with 1179 additions and 76 deletions
@@ -0,0 +1,120 @@
 ---
 name: add-tts-engine
 description: Use this skill to add a new TTS engine to Voicebox. It walks through dependency research, backend implementation, frontend wiring, PyInstaller bundling, and frozen-build testing. Always start with Phase 0 (dependency audit) before writing any code.
 ---
 # Add TTS Engine
 ## Goal
 Integrate a new text-to-speech engine into Voicebox end-to-end: dependency research, backend protocol implementation, frontend UI wiring, PyInstaller bundling, and frozen-build verification. The user should only need to test the final build locally.
 ## Reference Doc
 The full phased guide lives at `docs/content/docs/developer/tts-engines.mdx`. **Read this file in its entirety before starting.** It contains:
 - Phase 0: Dependency research (mandatory before writing code)
 - Phase 1: Backend implementation (`TTSBackend` protocol)
 - Phase 2: Route and service integration (usually zero changes)
 - Phase 3: Frontend integration (5 files)
 - Phase 4: Dependencies (`requirements.txt`, justfile, CI, Docker)
 - Phase 5: PyInstaller bundling (`build_binary.py` + `server.py`)
 - Phase 6: Common upstream workarounds
 - Implementation checklist (gate between phases)
 ## Workflow
 ### 1. Read the guide
 ```bash
 # Read the full TTS engines doc
 cat docs/content/docs/developer/tts-engines.mdx
 ```
 Internalize all phases, especially Phase 0 and Phase 5. The v0.2.3 release was three patch releases because Phase 0 was skipped.
 ### 2. Dependency research (Phase 0)
 Clone the model library into a temporary directory and audit it. Do NOT skip this.
 ```bash
 mkdir /tmp/engine-research && cd /tmp/engine-research
 git clone <model-library-url>
 ```
 Run the grep searches from Phase 0.2 in the guide against the cloned source and its transitive dependencies. Produce a written dependency audit covering:
 1. PyPI vs non-PyPI packages
 2. PyInstaller directives needed (`--collect-all`, `--copy-metadata`, `--hidden-import`)
 3. Runtime data files that must be bundled
 4. Native library paths that need env var overrides in frozen builds
 5. Monkey-patches needed (`torch.load`, float64, MPS, HF token)
 6. Sample rate
 7. Model download method (`from_pretrained` vs `snapshot_download` + `from_local`)
 Test model loading and generation on CPU in the throwaway venv before proceeding.
 ### 3. Implement (Phases 1–4)
 Follow the guide's phases in order. Key files to modify:
 **Backend (Phase 1):**
 - Create `backend/backends/<engine>_backend.py`
 - Register in `backend/backends/__init__.py` (ModelConfig + TTS_ENGINES + factory)
 - Update regex in `backend/models.py`
 **Frontend (Phase 3):**
 - `app/src/lib/api/types.ts` — engine union type
 - `app/src/lib/constants/languages.ts` — ENGINE_LANGUAGES
 - `app/src/components/Generation/EngineModelSelector.tsx` — ENGINE_OPTIONS, ENGINE_DESCRIPTIONS
 - `app/src/lib/hooks/useGenerationForm.ts` — Zod schema, model-name mapping
 - `app/src/components/ServerSettings/ModelManagement.tsx` — MODEL_DESCRIPTIONS
 **Dependencies (Phase 4):**
 - `backend/requirements.txt`
 - `justfile` (setup-python, setup-python-release targets)
 - `.github/workflows/release.yml`
 - `Dockerfile` (if applicable)
 ### 4. PyInstaller bundling (Phase 5)
 Register the engine in `backend/build_binary.py`:
 - `--hidden-import` for the backend module and model package
 - `--collect-all` for packages using `inspect.getsource`, shipping data files, or native libraries
 - `--copy-metadata` for packages using `importlib.metadata`
 If the engine has native data paths, add `os.environ.setdefault()` in `backend/server.py` inside the `if getattr(sys, 'frozen', False):` block.
 ### 5. Verify in dev mode
 ```bash
 just dev
 ```
 Test the full chain: model download → load → generate → voice cloning.
 ### 6. Use the checklist
 Walk through the Implementation Checklist at the bottom of `tts-engines.mdx`. Every item must be checked before handing the build to the user.
 ## Key Lessons (from v0.2.3)
 These are the most common failure modes. Phase 0 research catches all of them:
 | Pattern | Symptom in Frozen Build | Fix |
 |---------|------------------------|-----|
 | `@typechecked` / `inspect.getsource()` | "could not get source code" | `--collect-all <package>` |
 | Package ships pretrained model files | `FileNotFoundError` for `.pth.tar`, `.yaml` | `--collect-all <package>` |
 | C library with hardcoded system paths | `FileNotFoundError` for `/usr/share/...` | `--collect-all` + env var in `server.py` |
 | `importlib.metadata.version()` | "No package metadata found" | `--copy-metadata <package>` |
 | `torch.load` without `map_location` | CUDA device not available on CPU build | Monkey-patch `torch.load` |
 | `torch.from_numpy` on float64 data | dtype mismatch RuntimeError | Cast to `.float()` |
 | `token=True` in HF download calls | Auth failure without stored HF token | Use `snapshot_download(token=None)` + `from_local()` |
 ## Notes
 - The route and service layers have zero per-engine dispatch points. `main.py` requires zero changes.
 - The model config registry in `backends/__init__.py` handles all dispatch automatically.
 - Use `get_torch_device()` and `model_load_progress()` from `backends/base.py` — don't reimplement device detection or progress tracking.
 - Always test with a **clean HuggingFace cache** (no pre-downloaded models from dev).
 - Do NOT push or create a release. Hand the build to the user for local testing.
@@ -1,5 +1,5 @@
 [bumpversion]
-current_version = 0.2.3
+current_version = 0.3.0
 commit = True
 tag = True
 tag_name = v{new_version}
@@ -62,6 +62,7 @@ jobs:
          pip install pyinstaller
          pip install -r backend/requirements.txt
          pip install --no-deps chatterbox-tts
          pip install --no-deps hume-tada
      - name: Install MLX dependencies (Apple Silicon only)
        if: matrix.backend == 'mlx'
@@ -188,6 +189,7 @@ jobs:
          pip install pyinstaller
          pip install -r backend/requirements.txt
          pip install --no-deps chatterbox-tts
          pip install --no-deps hume-tada
      - name: Install PyTorch with CUDA 12.6
        run: |
@@ -51,6 +51,7 @@ app/openapi.json
 tauri/src-tauri/binaries/*
 tauri/src-tauri/gen/Assets.car
 tauri/src-tauri/gen/voicebox.icns
 tauri/src-tauri/gen/partial.plist
 # Temporary
 tmp/
@@ -7,9 +7,25 @@
 ## [Unreleased]
-This release rewrites the backend into a modular architecture, migrates the documentation site to Fumadocs, and ships a batch of bug fixes and UI polish across the stack.
+## [0.3.0] - 2026-03-17
-The backend's 3,000-line monolith `main.py` has been decomposed into domain routers, a services layer, and a proper database package. A style guide and ruff configuration now enforce consistency. On the frontend, model loading status is now visible in the UI, effects presets get a dropdown, and several race conditions and accessibility gaps are closed.
+This release rewrites the backend into a modular architecture, overhauls the settings UI into routed sub-pages, fixes audio player freezing, migrates documentation to Fumadocs, and ships a batch of bug fixes targeting the most-reported issues from the tracker.
 The backend's 3,000-line monolith `main.py` has been decomposed into domain routers, a services layer, and a proper database package. A style guide and ruff configuration now enforce consistency. On the frontend, settings have been split into dedicated routed pages with server logs, a changelog viewer, and an about page. The audio player no longer freezes mid-playback, and model loading status is now visible in the UI. Seven user-reported bugs have been fixed, including server crashes during sample uploads, generation list staleness, cryptic error messages, and CUDA support for RTX 50-series GPUs.
 ### Settings Overhaul ([#294](https://github.com/jamiepine/voicebox/pull/294))
 - Split settings into routed sub-tabs: General, Generation, GPU, Logs, Changelog, About
 - Added live server log viewer with auto-scroll
 - Added in-app changelog page that parses `CHANGELOG.md` at build time
 - Added About page with version info, license, and generation folder quick-open
 - Extracted reusable `SettingRow` component for consistent setting layouts
 ### Audio Player Fix ([#293](https://github.com/jamiepine/voicebox/pull/293))
 - Fixed audio player freezing during playback
 - Improved playback UX with better state management and listener cleanup
 - Fixed restart race condition during regeneration
 - Added stable keys for audio element re-rendering
 - Improved accessibility across player controls
 ### Backend Refactor ([#285](https://github.com/jamiepine/voicebox/pull/285))
 - Extracted all routes from `main.py` into 13 domain routers under `backend/routes/` — `main.py` dropped from ~3,100 lines to ~10
@@ -40,6 +56,17 @@ The backend's 3,000-line monolith `main.py` has been decomposed into domain rout
 - Softened select focus indicator opacity
 - Addressed 4 critical and 12 major issues from CodeRabbit review
 ### Bug Fixes ([#295](https://github.com/jamiepine/voicebox/pull/295))
 - Fixed sample uploads crashing the server — audio decoding now runs in a thread pool instead of blocking the async event loop ([#278](https://github.com/jamiepine/voicebox/issues/278))
 - Fixed generation list not updating when a generation completes — switched to `refetchQueries` for reliable cache busting, added SSE error fallback, and page reset on completion ([#231](https://github.com/jamiepine/voicebox/issues/231))
 - Fixed error toasts showing `[object Object]` instead of the actual error message ([#290](https://github.com/jamiepine/voicebox/issues/290))
 - Added Whisper model selection (`base`, `small`, `medium`, `large`, `turbo`) and expanded language support to the `/transcribe` endpoint ([#233](https://github.com/jamiepine/voicebox/issues/233))
 - Upgraded CUDA backend build from cu121 to cu126 for RTX 50-series (Blackwell) GPU support ([#289](https://github.com/jamiepine/voicebox/issues/289))
 - Handled client disconnects in SSE and streaming endpoints to suppress `[Errno 32] Broken Pipe` errors ([#248](https://github.com/jamiepine/voicebox/issues/248))
 - Fixed Docker build failure from pip hash mismatch on Qwen3-TTS dependencies ([#286](https://github.com/jamiepine/voicebox/issues/286))
 - Added 50 MB upload size limit with chunked reads to prevent unbounded memory allocation on sample uploads
 - Eliminated redundant double audio decode in sample processing pipeline
 ### Platform Fixes
 - Replaced `netstat` with `TcpStream` + PowerShell for Windows port detection ([#277](https://github.com/jamiepine/voicebox/pull/277))
 - Fixed Docker frontend build and cleaned up Docker docs
@@ -35,6 +35,8 @@ RUN pip install --no-cache-dir --upgrade pip
 COPY backend/requirements.txt .
 RUN pip install --no-cache-dir --prefix=/install -r requirements.txt
 RUN pip install --no-cache-dir --prefix=/install --no-deps chatterbox-tts
 RUN pip install --no-cache-dir --prefix=/install --no-deps hume-tada
 RUN pip install --no-cache-dir --prefix=/install \
    git+https://github.com/QwenLM/Qwen3-TTS.git
@@ -59,10 +59,10 @@
 ## What is Voicebox?
-Voicebox is a **local-first voice cloning studio** — a free and open-source alternative to ElevenLabs. Clone voices from a few seconds of audio, generate speech in 23 languages across 4 TTS engines, apply post-processing effects, and compose multi-voice projects with a timeline editor.
+Voicebox is a **local-first voice cloning studio** — a free and open-source alternative to ElevenLabs. Clone voices from a few seconds of audio, generate speech in 23 languages across 5 TTS engines, apply post-processing effects, and compose multi-voice projects with a timeline editor.
 - **Complete privacy** — models and voice data stay on your machine
- **4 TTS engines** — Qwen3-TTS, LuxTTS, Chatterbox Multilingual, and Chatterbox Turbo
+- **5 TTS engines** — Qwen3-TTS, LuxTTS, Chatterbox Multilingual, Chatterbox Turbo, and HumeAI TADA
 - **23 languages** — from English to Arabic, Japanese, Hindi, Swahili, and more
 - **Post-processing effects** — pitch shift, reverb, delay, chorus, compression, and filters
 - **Expressive speech** — paralinguistic tags like `[laugh]`, `[sigh]`, `[gasp]` via Chatterbox Turbo
@@ -93,7 +93,7 @@ Voicebox is a **local-first voice cloning studio** — a free and open-source al
 ### Multi-Engine Voice Cloning
-Four TTS engines with different strengths, switchable per-generation:
+Five TTS engines with different strengths, switchable per-generation:
 | Engine                      | Languages | Strengths                                                                                                                                |
 | --------------------------- | --------- | ---------------------------------------------------------------------------------------------------------------------------------------- |
@@ -101,6 +101,7 @@ Four TTS engines with different strengths, switchable per-generation:
 | **LuxTTS**                  | English   | Lightweight (~1GB VRAM), 48kHz output, 150x realtime on CPU                                                                              |
 | **Chatterbox Multilingual** | 23        | Broadest language coverage — Arabic, Danish, Finnish, Greek, Hebrew, Hindi, Malay, Norwegian, Polish, Swahili, Swedish, Turkish and more |
 | **Chatterbox Turbo**        | English   | Fast 350M model with paralinguistic emotion/sound tags                                                                                   |
 | **TADA** (1B / 3B)          | 10        | HumeAI speech-language model — 700s+ coherent audio, text-acoustic dual alignment                                                        |
 ### Emotions & Paralinguistic Tags
@@ -230,7 +231,7 @@ Full API documentation available at `http://localhost:17493/docs`.
 | Frontend      | React, TypeScript, Tailwind CSS                   |
 | State         | Zustand, React Query                              |
 | Backend       | FastAPI (Python)                                  |
-| TTS Engines   | Qwen3-TTS, LuxTTS, Chatterbox, Chatterbox Turbo   |
+| TTS Engines   | Qwen3-TTS, LuxTTS, Chatterbox, Chatterbox Turbo, TADA |
 | Effects       | Pedalboard (Spotify)                              |
 | Transcription | Whisper / Whisper Turbo (PyTorch or MLX)          |
 | Inference     | MLX (Apple Silicon) / PyTorch (CUDA/ROCm/XPU/CPU) |
@@ -245,7 +246,7 @@ Full API documentation available at `http://localhost:17493/docs`.
 | ----------------------- | ---------------------------------------------- |
 | **Real-time Streaming** | Stream audio as it generates, word by word     |
 | **Voice Design**        | Create new voices from text descriptions       |
-| **More Models**         | XTTS, Bark, and other open-source voice models |
+| **More Models**         | XTTS, Bark, and other open-source voice models  |
 | **Plugin Architecture** | Extend with custom models and effects          |
 | **Mobile Companion**    | Control Voicebox from your phone               |
@@ -276,6 +277,12 @@ just build          # Build CPU server binary + Tauri app
 just build-local    # (Windows) Build CPU + CUDA server binaries + Tauri app
 ```
 ### Adding New Voice Models
 The multi-engine architecture makes adding new TTS engines straightforward. A [step-by-step guide](docs/content/docs/developer/tts-engines.mdx) covers the full process: dependency research, backend protocol implementation, frontend wiring, and PyInstaller bundling.
 The guide is optimized for AI coding agents. An [agent skill](.agents/skills/add-tts-engine/SKILL.md) can pick up a model name and handle the entire integration autonomously — you just test the build locally.
 ### Project Structure
 ```
@@ -1,6 +1,6 @@
 {
  "name": "@voicebox/app",
-  "version": "0.2.3",
+  "version": "0.3.0",
  "private": true,
  "type": "module",
  "scripts": {
@@ -20,6 +20,8 @@ const ENGINE_OPTIONS = [
  { value: 'luxtts', label: 'LuxTTS' },
  { value: 'chatterbox', label: 'Chatterbox' },
  { value: 'chatterbox_turbo', label: 'Chatterbox Turbo' },
  { value: 'tada:1B', label: 'TADA 1B' },
  { value: 'tada:3B', label: 'TADA 3B Multilingual' },
 ] as const;
 const ENGINE_DESCRIPTIONS: Record<string, string> = {
@@ -27,6 +29,7 @@ const ENGINE_DESCRIPTIONS: Record<string, string> = {
  luxtts: 'Fast, English-focused',
  chatterbox: '23 languages, incl. Hebrew',
  chatterbox_turbo: 'English, [laugh] [cough] tags',
  tada: 'HumeAI, 700s+ coherent audio',
 };
 /** Engines that only support English and should force language to 'en' on select. */
@@ -34,6 +37,7 @@ const ENGLISH_ONLY_ENGINES = new Set(['luxtts', 'chatterbox_turbo']);
 function getSelectValue(engine: string, modelSize?: string): string {
  if (engine === 'qwen') return `qwen:${modelSize || '1.7B'}`;
  if (engine === 'tada') return `tada:${modelSize || '1B'}`;
  return engine;
 }
@@ -48,6 +52,20 @@ function handleEngineChange(form: UseFormReturn<GenerationFormValues>, value: st
    if (!available.some((l) => l.value === currentLang)) {
      form.setValue('language', available[0]?.value ?? 'en');
    }
  } else if (value.startsWith('tada:')) {
    const [, modelSize] = value.split(':');
    form.setValue('engine', 'tada');
    form.setValue('modelSize', modelSize as '1B' | '3B');
    // TADA 1B is English-only; 3B is multilingual
    if (modelSize === '1B') {
      form.setValue('language', 'en');
    } else {
      const currentLang = form.getValues('language');
      const available = getLanguageOptionsForEngine('tada');
      if (!available.some((l) => l.value === currentLang)) {
        form.setValue('language', available[0]?.value ?? 'en');
      }
    }
  } else {
    form.setValue('engine', value as GenerationFormValues['engine']);
    form.setValue('modelSize', undefined as unknown as '1.7B' | '0.6B');
@@ -62,6 +62,10 @@ const MODEL_DESCRIPTIONS: Record<string, string> = {
    'Production-grade open source TTS by Resemble AI. Supports 23 languages with voice cloning and emotion exaggeration control.',
  'chatterbox-turbo':
    'Streamlined 350M parameter TTS by Resemble AI. High-quality English speech with less compute and VRAM than larger models.',
  'tada-1b':
    'HumeAI TADA 1B — English speech-language model built on Llama 3.2 1B. Generates 700s+ of coherent audio with synchronized text-acoustic alignment.',
  'tada-3b-ml':
    'HumeAI TADA 3B Multilingual — built on Llama 3.2 3B. Supports 10 languages with high-fidelity voice cloning via text-acoustic dual alignment.',
  'whisper-base':
    'Smallest Whisper model (74M parameters). Fast transcription with moderate accuracy.',
  'whisper-small':
@@ -391,7 +395,8 @@ export function ModelManagement() {
      (m) =>
        m.model_name.startsWith('qwen-tts') ||
        m.model_name.startsWith('luxtts') ||
-        m.model_name.startsWith('chatterbox'),
+        m.model_name.startsWith('chatterbox') ||
        m.model_name.startsWith('tada'),
    ) ?? [];
  const whisperModels = modelStatus?.models.filter((m) => m.model_name.startsWith('whisper')) ?? [];
@@ -42,8 +42,8 @@ export interface GenerationRequest {
  text: string;
  language: LanguageCode;
  seed?: number;
-  model_size?: '1.7B' | '0.6B';
+  model_size?: '1.7B' | '0.6B' | '1B' | '3B';
-  engine?: 'qwen' | 'luxtts' | 'chatterbox' | 'chatterbox_turbo';
+  engine?: 'qwen' | 'luxtts' | 'chatterbox' | 'chatterbox_turbo' | 'tada';
  instruct?: string;
  max_chunk_chars?: number;
  crossfade_ms?: number;
@@ -66,6 +66,7 @@ export const ENGINE_LANGUAGES: Record<string, readonly LanguageCode[]> = {
    'zh',
  ],
  chatterbox_turbo: ['en'],
  tada: ['en', 'ar', 'zh', 'de', 'es', 'fr', 'it', 'ja', 'pl', 'pt'],
 } as const;
 /** Helper: get language options for a given engine. */
@@ -15,9 +15,9 @@ const generationSchema = z.object({
  text: z.string().min(1, '').max(50000),
  language: z.enum(LANGUAGE_CODES as [LanguageCode, ...LanguageCode[]]),
  seed: z.number().int().optional(),
-  modelSize: z.enum(['1.7B', '0.6B']).optional(),
+  modelSize: z.enum(['1.7B', '0.6B', '1B', '3B']).optional(),
  instruct: z.string().max(500).optional(),
-  engine: z.enum(['qwen', 'luxtts', 'chatterbox', 'chatterbox_turbo']).optional(),
+  engine: z.enum(['qwen', 'luxtts', 'chatterbox', 'chatterbox_turbo', 'tada']).optional(),
 });
 export type GenerationFormValues = z.infer<typeof generationSchema>;
@@ -79,7 +79,11 @@ export function useGenerationForm(options: UseGenerationFormOptions = {}) {
            ? 'chatterbox-tts'
            : engine === 'chatterbox_turbo'
              ? 'chatterbox-turbo'
-              : `qwen-tts-${data.modelSize}`;
+              : engine === 'tada'
                ? data.modelSize === '3B'
                  ? 'tada-3b-ml'
                  : 'tada-1b'
                : `qwen-tts-${data.modelSize}`;
      const displayName =
        engine === 'luxtts'
          ? 'LuxTTS'
@@ -87,9 +91,13 @@ export function useGenerationForm(options: UseGenerationFormOptions = {}) {
            ? 'Chatterbox TTS'
            : engine === 'chatterbox_turbo'
              ? 'Chatterbox Turbo'
-              : data.modelSize === '1.7B'
+              : engine === 'tada'
-                ? 'Qwen TTS 1.7B'
+                ? data.modelSize === '3B'
-                : 'Qwen TTS 0.6B';
+                  ? 'TADA 3B Multilingual'
                  : 'TADA 1B'
                : data.modelSize === '1.7B'
                  ? 'Qwen TTS 1.7B'
                  : 'Qwen TTS 0.6B';
      // Check if model needs downloading
      try {
@@ -104,7 +112,7 @@ export function useGenerationForm(options: UseGenerationFormOptions = {}) {
        console.error('Failed to check model status:', error);
      }
-      const isQwen = engine === 'qwen';
+      const hasModelSizes = engine === 'qwen' || engine === 'tada';
      const effectsChain = options.getEffectsChain?.();
      // This now returns immediately with status="generating"
      const result = await generation.mutateAsync({
@@ -112,9 +120,9 @@ export function useGenerationForm(options: UseGenerationFormOptions = {}) {
        text: data.text,
        language: data.language,
        seed: data.seed,
-        model_size: isQwen ? data.modelSize : undefined,
+        model_size: hasModelSizes ? data.modelSize : undefined,
        engine,
-        instruct: isQwen ? data.instruct || undefined : undefined,
+        instruct: engine === 'qwen' ? data.instruct || undefined : undefined,
        max_chunk_chars: maxChunkChars,
        crossfade_ms: crossfadeMs,
        normalize: normalizeAudio,
@@ -1,3 +1,3 @@
 # Backend package
-__version__ = "0.2.3"
+__version__ = "0.3.0"
@@ -166,6 +166,7 @@ TTS_ENGINES = {
    "luxtts": "LuxTTS",
    "chatterbox": "Chatterbox TTS",
    "chatterbox_turbo": "Chatterbox Turbo",
    "tada": "TADA",
 }
@@ -259,6 +260,24 @@ def _get_non_qwen_tts_configs() -> list[ModelConfig]:
            needs_trim=True,
            languages=["en"],
        ),
        ModelConfig(
            model_name="tada-1b",
            display_name="TADA 1B (English)",
            engine="tada",
            hf_repo_id="HumeAI/tada-1b",
            model_size="1B",
            size_mb=4000,
            languages=["en"],
        ),
        ModelConfig(
            model_name="tada-3b-ml",
            display_name="TADA 3B Multilingual",
            engine="tada",
            hf_repo_id="HumeAI/tada-3b-ml",
            model_size="3B",
            size_mb=8000,
            languages=["en", "ar", "zh", "de", "es", "fr", "it", "ja", "pl", "pt"],
        ),
    ]
@@ -339,10 +358,12 @@ def engine_has_model_sizes(engine: str) -> bool:
 async def load_engine_model(engine: str, model_size: str = "default") -> None:
-    """Load a model for the given engine, handling the Qwen model_size special case."""
+    """Load a model for the given engine, handling engines with multiple model sizes."""
    backend = get_tts_backend_for_engine(engine)
    if engine == "qwen":
        await backend.load_model_async(model_size)
    elif engine == "tada":
        await backend.load_model(model_size)
    else:
        await backend.load_model()
@@ -358,7 +379,7 @@ async def ensure_model_cached_or_raise(engine: str, model_size: str = "default")
            cfg = c
            break
-    if engine == "qwen":
+    if engine in ("qwen", "tada"):
        if not backend._is_model_cached(model_size):
            raise HTTPException(
                status_code=400,
@@ -490,6 +511,10 @@ def get_tts_backend_for_engine(engine: str) -> TTSBackend:
            from .chatterbox_turbo_backend import ChatterboxTurboTTSBackend
            backend = ChatterboxTurboTTSBackend()
        elif engine == "tada":
            from .hume_backend import HumeTadaBackend
            backend = HumeTadaBackend()
        else:
            raise ValueError(f"Unknown TTS engine: {engine}. Supported: {list(TTS_ENGINES.keys())}")
@@ -0,0 +1,347 @@
 """
 HumeAI TADA TTS backend implementation.
 Wraps HumeAI's TADA (Text-Acoustic Dual Alignment) model for
 high-quality voice cloning. Two model variants:
  - tada-1b: English-only, ~2B params (Llama 3.2 1B base)
  - tada-3b-ml: Multilingual, ~4B params (Llama 3.2 3B base)
 Both use a shared encoder/codec (HumeAI/tada-codec). The encoder
 produces 1:1 aligned token embeddings from reference audio, and the
 causal LM generates speech via flow-matching diffusion.
 24kHz output, bf16 inference on CUDA, fp32 on CPU.
 """
 import asyncio
 import logging
 import threading
 from typing import ClassVar, List, Optional, Tuple
 import numpy as np
 from . import TTSBackend
 from .base import (
    is_model_cached,
    get_torch_device,
    combine_voice_prompts as _combine_voice_prompts,
    model_load_progress,
 )
 from ..utils.cache import get_cache_key, get_cached_voice_prompt, cache_voice_prompt
 logger = logging.getLogger(__name__)
 # HuggingFace repos
 TADA_CODEC_REPO = "HumeAI/tada-codec"
 TADA_1B_REPO = "HumeAI/tada-1b"
 TADA_3B_ML_REPO = "HumeAI/tada-3b-ml"
 TADA_MODEL_REPOS = {
    "1B": TADA_1B_REPO,
    "3B": TADA_3B_ML_REPO,
 }
 # Key weight files for cache detection
 _TADA_MODEL_WEIGHT_FILES = [
    "model.safetensors",
 ]
 _TADA_CODEC_WEIGHT_FILES = [
    "encoder/model.safetensors",
 ]
 class HumeTadaBackend:
    """HumeAI TADA TTS backend for high-quality voice cloning."""
    _load_lock: ClassVar[threading.Lock] = threading.Lock()
    def __init__(self):
        self.model = None
        self.encoder = None
        self.model_size = "1B"  # default to 1B
        self._device = None
        self._model_load_lock = asyncio.Lock()
    def _get_device(self) -> str:
        # Force CPU on macOS — MPS has issues with flow matching
        # and large vocab lm_head (>65536 output channels)
        return get_torch_device(force_cpu_on_mac=True)
    def is_loaded(self) -> bool:
        return self.model is not None
    def _get_model_path(self, model_size: str = "1B") -> str:
        return TADA_MODEL_REPOS.get(model_size, TADA_1B_REPO)
    def _is_model_cached(self, model_size: str = "1B") -> bool:
        repo = TADA_MODEL_REPOS.get(model_size, TADA_1B_REPO)
        model_cached = is_model_cached(repo, required_files=_TADA_MODEL_WEIGHT_FILES)
        codec_cached = is_model_cached(TADA_CODEC_REPO, required_files=_TADA_CODEC_WEIGHT_FILES)
        return model_cached and codec_cached
    async def load_model(self, model_size: str = "1B") -> None:
        """Load the TADA model and encoder."""
        if self.model is not None and self.model_size == model_size:
            return
        async with self._model_load_lock:
            if self.model is not None and self.model_size == model_size:
                return
            # Unload existing model if switching sizes
            if self.model is not None:
                self.unload_model()
            self.model_size = model_size
            await asyncio.to_thread(self._load_model_sync, model_size)
    def _load_model_sync(self, model_size: str = "1B"):
        """Synchronous model loading with progress tracking."""
        model_name = f"tada-{model_size.lower()}"
        is_cached = self._is_model_cached(model_size)
        repo = TADA_MODEL_REPOS.get(model_size, TADA_1B_REPO)
        with model_load_progress(model_name, is_cached):
            # Install DAC shim before importing tada — tada's encoder/decoder
            # import dac.nn.layers.Snake1d which requires the descript-audio-codec
            # package.  The real package pulls in onnx/tensorboard/matplotlib via
            # descript-audiotools, so we use a lightweight shim instead.
            from ..utils.dac_shim import install_dac_shim
            install_dac_shim()
            import torch
            from huggingface_hub import snapshot_download
            device = self._get_device()
            self._device = device
            logger.info(f"Loading HumeAI TADA {model_size} on {device}...")
            # Download codec (encoder + decoder) if not cached
            logger.info("Downloading TADA codec...")
            snapshot_download(
                repo_id=TADA_CODEC_REPO,
                token=None,
                allow_patterns=["*.safetensors", "*.json", "*.txt", "*.bin"],
            )
            # Download model weights if not cached
            logger.info(f"Downloading TADA {model_size} model...")
            snapshot_download(
                repo_id=repo,
                token=None,
                allow_patterns=["*.safetensors", "*.json", "*.txt", "*.bin", "*.model"],
            )
            # TADA hardcodes "meta-llama/Llama-3.2-1B" as the tokenizer
            # source in its Aligner and TadaForCausalLM.from_pretrained().
            # That repo is gated (requires Meta license acceptance).
            # Download the tokenizer from an ungated mirror and get its
            # local cache path so we can point TADA at it directly.
            logger.info("Downloading Llama tokenizer (ungated mirror)...")
            tokenizer_path = snapshot_download(
                repo_id="unsloth/Llama-3.2-1B",
                token=None,
                allow_patterns=["tokenizer*", "special_tokens*"],
            )
            # Determine dtype — use bf16 on CUDA for ~50% memory savings
            if device == "cuda" and torch.cuda.is_bf16_supported():
                model_dtype = torch.bfloat16
            else:
                model_dtype = torch.float32
            # Patch the Aligner config class to use the local tokenizer
            # path instead of the gated "meta-llama/Llama-3.2-1B" default.
            # This avoids monkey-patching AutoTokenizer.from_pretrained
            # which corrupts the classmethod descriptor for other engines.
            from tada.modules.aligner import AlignerConfig
            AlignerConfig.tokenizer_name = tokenizer_path
            # Load encoder (only needed for voice prompt encoding)
            from tada.modules.encoder import Encoder
            logger.info("Loading TADA encoder...")
            self.encoder = Encoder.from_pretrained(
                TADA_CODEC_REPO, subfolder="encoder"
            ).to(device)
            self.encoder.eval()
            # Load the causal LM (includes decoder for wav generation).
            # TadaForCausalLM.from_pretrained() calls
            #   getattr(config, "tokenizer_name", "meta-llama/Llama-3.2-1B")
            # which hits the gated repo. Pre-load the config from HF,
            # inject the local tokenizer path, then pass it in.
            from tada.modules.tada import TadaForCausalLM, TadaConfig
            logger.info(f"Loading TADA {model_size} model...")
            config = TadaConfig.from_pretrained(repo)
            config.tokenizer_name = tokenizer_path
            self.model = TadaForCausalLM.from_pretrained(
                repo, config=config, torch_dtype=model_dtype
            ).to(device)
            self.model.eval()
        logger.info(f"HumeAI TADA {model_size} loaded successfully on {device}")
    def unload_model(self) -> None:
        """Unload model and encoder to free memory."""
        if self.model is not None:
            del self.model
            self.model = None
        if self.encoder is not None:
            del self.encoder
            self.encoder = None
        self._device = None
        import torch
        if torch.cuda.is_available():
            torch.cuda.empty_cache()
        logger.info("HumeAI TADA unloaded")
    async def create_voice_prompt(
        self,
        audio_path: str,
        reference_text: str,
        use_cache: bool = True,
    ) -> Tuple[dict, bool]:
        """
        Create voice prompt from reference audio using TADA's encoder.
        TADA's encoder performs forced alignment between audio and text tokens,
        producing an EncoderOutput with 1:1 token-audio alignment. If no
        reference_text is provided, the encoder uses built-in ASR (English only).
        We serialize the EncoderOutput to a dict for caching.
        """
        await self.load_model(self.model_size)
        cache_key = (
            "tada_" + get_cache_key(audio_path, reference_text)
        ) if use_cache else None
        if cache_key:
            cached = get_cached_voice_prompt(cache_key)
            if cached is not None and isinstance(cached, dict):
                return cached, True
        def _encode_sync():
            import torch
            import soundfile as sf
            device = self._device
            # Load audio with soundfile (torchaudio 2.10+ requires torchcodec)
            audio_np, sr = sf.read(str(audio_path), dtype="float32")
            audio = torch.from_numpy(audio_np).float()
            if audio.ndim == 1:
                audio = audio.unsqueeze(0)  # (samples,) -> (1, samples)
            else:
                audio = audio.T  # (samples, channels) -> (channels, samples)
            audio = audio.to(device)
            # Encode with forced alignment
            text_arg = [reference_text] if reference_text else None
            prompt = self.encoder(
                audio, text=text_arg, sample_rate=sr
            )
            # Serialize EncoderOutput to a dict of CPU tensors for caching
            prompt_dict = {}
            for field_name in prompt.__dataclass_fields__:
                val = getattr(prompt, field_name)
                if isinstance(val, torch.Tensor):
                    prompt_dict[field_name] = val.detach().cpu()
                elif isinstance(val, list):
                    prompt_dict[field_name] = val
                elif isinstance(val, (int, float)):
                    prompt_dict[field_name] = val
                else:
                    prompt_dict[field_name] = val
            return prompt_dict
        encoded = await asyncio.to_thread(_encode_sync)
        if cache_key:
            cache_voice_prompt(cache_key, encoded)
        return encoded, False
    async def combine_voice_prompts(
        self,
        audio_paths: List[str],
        reference_texts: List[str],
    ) -> Tuple[np.ndarray, str]:
        return await _combine_voice_prompts(audio_paths, reference_texts, sample_rate=24000)
    async def generate(
        self,
        text: str,
        voice_prompt: dict,
        language: str = "en",
        seed: Optional[int] = None,
        instruct: Optional[str] = None,
    ) -> Tuple[np.ndarray, int]:
        """
        Generate audio from text using HumeAI TADA.
        Args:
            text: Text to synthesize
            voice_prompt: Serialized EncoderOutput dict from create_voice_prompt()
            language: Language code (en, ar, de, es, fr, it, ja, pl, pt, zh)
            seed: Random seed for reproducibility
            instruct: Not supported by TADA (ignored)
        Returns:
            Tuple of (audio_array, sample_rate=24000)
        """
        await self.load_model(self.model_size)
        def _generate_sync():
            import torch
            from tada.modules.encoder import EncoderOutput
            if seed is not None:
                torch.manual_seed(seed)
                if torch.cuda.is_available():
                    torch.cuda.manual_seed(seed)
            device = self._device
            # Reconstruct EncoderOutput from the cached dict
            restored = {}
            for k, v in voice_prompt.items():
                if isinstance(v, torch.Tensor):
                    # Move to device and match model dtype for float tensors
                    if v.is_floating_point():
                        model_dtype = next(self.model.parameters()).dtype
                        restored[k] = v.to(device=device, dtype=model_dtype)
                    else:
                        restored[k] = v.to(device=device)
                else:
                    restored[k] = v
            prompt = EncoderOutput(**restored)
            # For non-English with the 3B-ML model, we could reload the
            # encoder with the language-specific aligner. However, the
            # generation itself is language-agnostic — only the encoder's
            # aligner changes. Since we encode at create_voice_prompt time,
            # the language is already baked in. For simplicity, we don't
            # reload the encoder here.
            logger.info(f"[TADA] Generating ({language}), text length: {len(text)}")
            output = self.model.generate(
                prompt=prompt,
                text=text,
            )
            # output.audio is a list of tensors (one per batch item)
            if output.audio and output.audio[0] is not None:
                audio_tensor = output.audio[0]
                audio = audio_tensor.detach().cpu().numpy().squeeze().astype(np.float32)
            else:
                logger.warning("[TADA] Generation produced no audio")
                audio = np.zeros(24000, dtype=np.float32)
            return audio, 24000
        return await asyncio.to_thread(_generate_sync)
@@ -186,6 +186,42 @@ def build_server(cuda=False):
            # needed by LuxTTS for text-to-phoneme conversion
            "--collect-all",
            "piper_phonemize",
            # HumeAI TADA — speech-language model using Llama + flow matching
            "--hidden-import",
            "backend.backends.hume_backend",
            "--hidden-import",
            "tada",
            "--hidden-import",
            "tada.modules",
            "--hidden-import",
            "tada.modules.tada",
            "--hidden-import",
            "tada.modules.encoder",
            "--hidden-import",
            "tada.modules.decoder",
            "--hidden-import",
            "tada.modules.aligner",
            "--hidden-import",
            "tada.modules.acoustic_spkr_verf",
            "--hidden-import",
            "tada.nn",
            "--hidden-import",
            "tada.nn.vibevoice",
            "--hidden-import",
            "tada.utils",
            "--hidden-import",
            "tada.utils.gray_code",
            "--hidden-import",
            "tada.utils.text",
            # DAC shim — provides dac.nn.layers.Snake1d without the real
            # descript-audio-codec package (which pulls onnx/tensorboard via
            # descript-audiotools). The shim is in backend/utils/dac_shim.py.
            "--hidden-import",
            "backend.utils.dac_shim",
            "--hidden-import",
            "torchaudio",
            "--collect-submodules",
            "tada",
        ]
    )
@@ -66,9 +66,9 @@ class GenerationRequest(BaseModel):
    text: str = Field(..., min_length=1, max_length=50000)
    language: str = Field(default="en", pattern="^(zh|en|ja|ko|de|fr|ru|pt|es|it|he|ar|da|el|fi|hi|ms|nl|no|pl|sv|sw|tr)$")
    seed: Optional[int] = Field(None, ge=0)
-    model_size: Optional[str] = Field(default="1.7B", pattern="^(1\\.7B|0\\.6B)$")
+    model_size: Optional[str] = Field(default="1.7B", pattern="^(1\\.7B|0\\.6B|1B|3B)$")
    instruct: Optional[str] = Field(None, max_length=500)
-    engine: Optional[str] = Field(default="qwen", pattern="^(qwen|luxtts|chatterbox|chatterbox_turbo)$")
+    engine: Optional[str] = Field(default="qwen", pattern="^(qwen|luxtts|chatterbox|chatterbox_turbo|tada)$")
    max_chunk_chars: int = Field(
        default=800, ge=100, le=5000, description="Max characters per chunk for long text splitting"
    )
@@ -33,6 +33,13 @@ s3tokenizer
 spacy-pkuseg
 pyloudnorm
 # HumeAI TADA sub-dependencies (hume-tada itself is installed
 # --no-deps in the setup script because it pins torch>=2.7,<2.8.
 # descript-audio-codec is NOT installed — it pulls onnx/tensorboard
 # via descript-audiotools.  A lightweight shim in utils/dac_shim.py
 # provides the only class TADA uses: Snake1d.)
 torchaudio
 # Audio processing
 librosa>=0.10.0
 soundfile>=0.12.0
@@ -0,0 +1,95 @@
 """
 Minimal shim for descript-audio-codec (DAC).
 TADA only imports Snake1d from dac.nn.layers and dac.model.dac.
 The real DAC package pulls in descript-audiotools which depends on
 onnx, tensorboard, protobuf, matplotlib, pystoi, etc. — none of
 which are needed for TADA's runtime use of Snake1d.
 This shim provides the exact Snake1d implementation (MIT-licensed,
 from https://github.com/descriptinc/descript-audio-codec) so we can
 avoid the entire audiotools dependency chain.
 If the real DAC package is installed, this module is never used —
 Python's import system will find the site-packages version first.
 Install this shim only when descript-audio-codec is NOT installed.
 """
 import sys
 import types
 import torch
 import torch.nn as nn
 # ── Snake activation (from dac/nn/layers.py) ────────────────────────
 # NOTE: The original DAC code uses @torch.jit.script here for a 1.4x
 # speedup.  We omit it because TorchScript calls inspect.getsource()
 # which fails inside a PyInstaller frozen binary (no .py source files).
 def snake(x: torch.Tensor, alpha: torch.Tensor) -> torch.Tensor:
    shape = x.shape
    x = x.reshape(shape[0], shape[1], -1)
    x = x + (alpha + 1e-9).reciprocal() * torch.sin(alpha * x).pow(2)
    x = x.reshape(shape)
    return x
 class Snake1d(nn.Module):
    def __init__(self, channels: int):
        super().__init__()
        self.alpha = nn.Parameter(torch.ones(1, channels, 1))
    def forward(self, x: torch.Tensor) -> torch.Tensor:
        return snake(x, self.alpha)
 # ── Register as dac.nn.layers and dac.model.dac ─────────────────────
 def install_dac_shim() -> None:
    """Register fake dac package modules in sys.modules.
    Only installs the shim if 'dac' is not already importable
    (i.e. the real descript-audio-codec is not installed).
    """
    try:
        import dac  # noqa: F401  — real package exists, do nothing
        return
    except ImportError:
        pass
    # Create the module tree: dac -> dac.nn -> dac.nn.layers
    #                              -> dac.model -> dac.model.dac
    dac_pkg = types.ModuleType("dac")
    dac_pkg.__path__ = []  # make it a package
    dac_pkg.__package__ = "dac"
    dac_nn = types.ModuleType("dac.nn")
    dac_nn.__path__ = []
    dac_nn.__package__ = "dac.nn"
    dac_nn_layers = types.ModuleType("dac.nn.layers")
    dac_nn_layers.__package__ = "dac.nn"
    dac_nn_layers.Snake1d = Snake1d
    dac_nn_layers.snake = snake
    dac_model = types.ModuleType("dac.model")
    dac_model.__path__ = []
    dac_model.__package__ = "dac.model"
    dac_model_dac = types.ModuleType("dac.model.dac")
    dac_model_dac.__package__ = "dac.model"
    dac_model_dac.Snake1d = Snake1d
    # Wire up submodules
    dac_pkg.nn = dac_nn
    dac_pkg.model = dac_model
    dac_nn.layers = dac_nn_layers
    dac_model.dac = dac_model_dac
    # Register in sys.modules
    sys.modules["dac"] = dac_pkg
    sys.modules["dac.nn"] = dac_nn
    sys.modules["dac.nn.layers"] = dac_nn_layers
    sys.modules["dac.model"] = dac_model
    sys.modules["dac.model.dac"] = dac_model_dac
@@ -3,8 +3,12 @@ title: "TTS Engines"
 description: "How to add new text-to-speech engines to Voicebox"
 ---
 > **For humans:** This doc is optimized for AI agents to implement new TTS engines autonomously. It's structured as a phased workflow with explicit gates and a checklist so an agent can do the full integration — dependency research, backend, frontend, bundling — and hand you a draft release or prod build to test locally. It's also a useful reference if you're doing it yourself.
 Adding an engine touches ~10 files across 4 layers. The backend protocol work is straightforward — the real time sink is dependency hell, upstream library bugs, and PyInstaller bundling.
 **Do not start writing code until you complete Phase 0.** The v0.2.3 release was three patch releases of PyInstaller fixes because dependency research was skipped. Every issue — `inspect.getsource()` failures, missing native data files, metadata lookups, dtype mismatches — was discoverable by reading the model library's source code before integration began.
 ## Architecture Overview
 The backend is split into layers:
@@ -18,6 +22,124 @@ The backend is split into layers:
 New engines only need to touch `backends/` and `models.py` on the backend side — the route and service layers use a model config registry that handles dispatch automatically.
 ## Phase 0: Dependency Research
 **This phase is mandatory.** Clone the model library and its key dependencies into a temporary directory and inspect them before writing any integration code. The goal is to produce a dependency audit that identifies every PyInstaller-incompatible pattern, every native data file, and every upstream bug you'll need to work around.
 ### 0.1 Clone and Inspect the Model Library
 ```bash
 # Create a throwaway workspace
 mkdir /tmp/engine-research && cd /tmp/engine-research
 # Clone the model library
 git clone https://github.com/org/model-library.git
 cd model-library
 ```
 **Read these files first, in order:**
 1. **`setup.py` / `setup.cfg` / `pyproject.toml`** — Check pinned dependency versions. If the library pins `torch==2.6.0` or `numpy<1.26`, you'll need `--no-deps` installation and manual sub-dependency listing (this is what happened with `chatterbox-tts`).
 2. **`__init__.py` and the main model class** — Trace the import chain. Look for:
   - `from_pretrained()` — does it call `huggingface_hub` internally? Does it pass `token=True` (which crashes without a stored HF token)?
   - `from_local()` — does it exist? You may need manual `snapshot_download()` + `from_local()` to bypass download bugs.
   - Device handling — does it default to CUDA? Does it support MPS? Many libraries crash on MPS with unsupported operators.
 3. **All `import` statements** — Recursively trace what the library imports. You're looking for:
   - `inspect.getsource()` anywhere in the chain (search all `.py` files)
   - `typeguard` / `@typechecked` decorators (these call `inspect.getsource()` at import time)
   - `importlib.metadata.version()` or `pkg_resources.get_distribution()` (need `--copy-metadata`)
   - `lazy_loader` (needs `--collect-all` to bundle `.pyi` stubs)
 ### 0.2 Scan for PyInstaller-Incompatible Patterns
 Run these searches against the cloned library **and** its transitive dependencies:
 ```bash
 # inspect.getsource — will crash in frozen binary without --collect-all
 grep -r "inspect.getsource\|getsource(" .
 # typeguard / @typechecked — calls inspect.getsource at import time
 grep -r "@typechecked\|from typeguard" .
 # importlib.metadata — needs --copy-metadata
 grep -r "importlib.metadata\|pkg_resources.get_distribution\|pkg_resources.require" .
 # Data files loaded at runtime — need --collect-all or --collect-data
 grep -r "Path(__file__).parent\|os.path.dirname(__file__)\|resources_path\|pkg_resources.resource_filename" .
 # Native library paths — may need env var override in frozen builds
 grep -r "/usr/share\|/usr/lib\|/usr/local\|espeak\|phonemize" .
 # torch.load without map_location — will crash on CPU-only builds
 grep -r "torch.load(" . | grep -v "map_location"
 # HuggingFace token bugs
 grep -r 'token=True\|token=os.getenv' .
 # Float64/Float32 assumptions — librosa returns float64, many models assume float32
 grep -r "torch.from_numpy\|\.double()\|float64" .
 ```
 ### 0.3 Install and Trace in a Throwaway Venv
 ```bash
 # Create isolated venv
 python -m venv /tmp/engine-venv
 source /tmp/engine-venv/bin/activate
 # Install the package (try normally first)
 pip install model-package
 # Check if it conflicts with our stack
 pip install model-package torch==2.10 transformers==4.57.3 numpy>=1.26
 # If this fails, you need --no-deps:
 pip install --no-deps model-package
 # Get the full dependency tree
 pip show model-package  # Check Requires: field
 pip show -f model-package  # List all installed files (look for data files)
 # Check for non-PyPI dependencies
 pip install model-package 2>&1 | grep -i "no matching distribution"
 ```
 ### 0.4 Test Model Loading on CPU
 Before writing any integration code, verify the model works on CPU in a plain Python script:
 ```python
 import torch
 # Force CPU to catch map_location bugs early
 model = ModelClass.from_pretrained("org/model", device="cpu")
 # Test with a float32 audio array (not float64)
 import numpy as np
 audio = np.random.randn(16000).astype(np.float32)
 output = model.generate("Hello world", audio)
 print(f"Output shape: {output.shape}, dtype: {output.dtype}, sample rate: {model.sample_rate}")
 ```
 If this crashes, you've found a bug you'll need to monkey-patch. Common ones:
 - `RuntimeError: expected scalar type Float but found Double` → needs float32 cast
 - `RuntimeError: map_location` → needs `torch.load` patch
 - `RuntimeError: Unsupported operator aten::...` → needs MPS skip
 ### 0.5 Produce a Dependency Audit
 Before proceeding to Phase 1, write down:
 1. **PyPI vs non-PyPI deps** — which packages need `--find-links`, `git+https://`, or `--no-deps`?
 2. **PyInstaller directives needed** — which packages need `--collect-all`, `--copy-metadata`, `--hidden-import`?
 3. **Runtime data files** — which packages ship data files (YAML, pretrained weights, phoneme tables, shader libraries) that must be bundled?
 4. **Native library paths** — which packages look for data at system paths that won't exist in a frozen binary?
 5. **Monkey-patches needed** — `torch.load` map_location, float64→float32 casts, MPS skip, HF token bypass, etc.
 6. **Sample rate** — what does the engine output? (24kHz, 44.1kHz, 48kHz)
 7. **Model download method** — `from_pretrained()` with library-managed download, or manual `snapshot_download()` + `from_local()`?
 This audit becomes your implementation plan for Phases 1, 4, and 5.
 ## Phase 1: Backend Implementation
 ### 1.1 Create the Backend File
@@ -155,54 +277,172 @@ In `app/src/components/ServerSettings/ModelManagement.tsx`:
 ## Phase 4: Dependencies
 Use the dependency audit from Phase 0 to drive this phase. You should already know what packages are needed, which conflict, and which require special installation.
 ### 4.1 Python Dependencies
-Add to `backend/requirements.txt`. Watch for:
+Add to `backend/requirements.txt`. There are three installation patterns, depending on what Phase 0 revealed:
-**Pinned dependency conflicts** — If the model package pins old versions, install with `--no-deps`:
+**Normal PyPI packages:**
 ```
 some-model-package>=1.0.0
 ```
 **Pinned dependency conflicts (`--no-deps`)** — If the model package pins old versions of torch/numpy/transformers, install with `--no-deps` and list sub-dependencies manually. This is the pattern used for `chatterbox-tts`:
 ```bash
 # In justfile / CI setup:
 pip install --no-deps chatterbox-tts
 # In requirements.txt — list each actual sub-dependency:
 conformer>=0.3.2
 diffusers>=0.31.0
 omegaconf>=2.3.0
 resemble-perth>=0.0.2
 s3tokenizer>=0.1.6
 ```
-Then list sub-dependencies manually in `requirements.txt`.
+To identify sub-deps: `pip show chatterbox-tts` → `Requires:` field, then cross-reference against existing `requirements.txt` to avoid duplicates.
-**Non-PyPI packages:**
+**Non-PyPI packages** — Some libraries only exist on GitHub or require custom indexes:
 ```
 linacodec @ git+https://github.com/user/repo.git
 ```
 # Git-only packages (no PyPI release)
 linacodec @ git+https://github.com/ysharma3501/LinaCodec.git
 Zipvoice @ git+https://github.com/ysharma3501/LuxTTS.git
-**Custom package indexes:**
+# Custom package indexes (C extensions with platform-specific wheels)
 ```
 --find-links https://k2-fsa.github.io/icefall/piper_phonemize.html
 piper-phonemize>=1.2.0
 ```
-### 4.2 Identifying Hidden Sub-Dependencies
+### 4.2 Dependency Conflict Resolution
-1. Install the package normally in a throwaway venv
+Check for conflicts with the existing stack before adding anything:
 2. Run `pip show <package>` to get its `Requires:` list
 3. Cross-reference against existing requirements.txt
 4. Test that the engine loads and generates
-## Phase 5: PyInstaller Bundling
+```bash
 # Our current stack pins (approximate):
 # Python 3.12+, torch>=2.10, transformers>=4.57, numpy>=1.26
-This is where most of the pain lives. Common issues:
+# Test compatibility
 pip install model-package torch==2.10 transformers==4.57.3 numpy>=1.26
-| Issue | Symptom | Fix |
+# If it fails, check what the package pins:
-|-------|---------|-----|
+pip show model-package | grep Requires
-| `inspect.getsource()` at import | "could not get source code" | `--collect-all <package>` |
+# Look at setup.py/pyproject.toml for version constraints
-| Data files (yaml, .pth.tar) | FileNotFoundError at runtime | `--collect-all <package>` |
+```
 | Native data paths (espeak-ng) | Library looks at `/usr/share/...` | Set env var in frozen builds |
 | `importlib.metadata` lookups | "No package metadata found" | `--copy-metadata <package>` |
 | Dynamic imports | ModuleNotFoundError | `--hidden-import <module>` |
-### Testing Frozen Builds
+**Known incompatible patterns in the wild:**
 - `torch==2.6.0` — many older packages pin this
 - `numpy<1.26` — conflicts with Python 3.12+
 - `transformers==4.46.3` — many packages pin old transformers
 - `onnxruntime` pinned versions — often conflict with torch
-You can't skip this. Models that work in `python -m uvicorn` will break in the PyInstaller binary.
+### 4.3 Update Installation Scripts
 Dependencies must be added in multiple places:
 | File | What to add |
 |------|------------|
 | `backend/requirements.txt` | Package and version constraint |
 | `justfile` | `--no-deps` install line if needed (in `setup-python` and `setup-python-release` targets) |
 | `.github/workflows/release.yml` | Same `--no-deps` line in CI build steps |
 | `Dockerfile` | Same install commands for Docker builds |
 ## Phase 5: PyInstaller Bundling (`build_binary.py`)
 This is where most of the pain lives. **The v0.2.3 release was entirely dedicated to fixing bundling issues** — every new engine that shipped in v0.2.1 (LuxTTS, Chatterbox, Chatterbox Turbo) worked in dev but failed in production builds. Don't skip this phase.
 ### 5.1 Register Your Engine in `build_binary.py`
 Every new engine needs entries in `backend/build_binary.py`. This file drives PyInstaller and is the single most common source of "works in dev, breaks in prod" bugs. You need to decide which PyInstaller directives your engine's dependencies require:
 | Directive | What It Does | When You Need It |
 |-----------|-------------|-----------------|
 | `--hidden-import <module>` | Includes a module PyInstaller can't detect via static analysis | Dynamic imports, lazy imports, plugin architectures |
 | `--collect-all <package>` | Bundles source `.py` files, data files, AND native libraries | Packages that call `inspect.getsource()` at import time (e.g. `inflect` via `typeguard`'s `@typechecked`), or that ship pretrained model files (e.g. `perth` ships `.pth.tar` + `hparams.yaml`) |
 | `--collect-data <package>` | Bundles only data files (not source or native libs) | Packages with YAML configs, vocab files, etc. |
 | `--collect-submodules <package>` | Bundles all submodules | Packages with deep module trees that PyInstaller misses |
 | `--copy-metadata <package>` | Copies `importlib.metadata` info | Packages that call `importlib.metadata.version()` or `pkg_resources.get_distribution()` at runtime. Already required for: `requests`, `transformers`, `huggingface-hub`, `tokenizers`, `safetensors`, `tqdm` |
 **Example: adding hidden imports and collect-all for a new engine:**
 ```python
 # In build_binary.py, inside the args list:
 "--hidden-import",
 "backend.backends.your_engine_backend",
 "--hidden-import",
 "your_engine_package",
 "--hidden-import",
 "your_engine_package.inference",
 "--collect-all",
 "some_dependency_that_uses_inspect_getsource",
 "--copy-metadata",
 "some_dependency_that_checks_its_own_version",
 ```
 ### 5.2 Lessons from v0.2.3 — Real Failures and Their Fixes
 These are actual production failures from shipping new engines. Every one of these passed `python -m uvicorn` in dev:
 | Engine | Failure | Root Cause | Fix |
 |--------|---------|-----------|-----|
 | LuxTTS | `"could not get source code"` on import | `inflect` uses `typeguard`'s `@typechecked` which calls `inspect.getsource()` — needs `.py` source files, not just bytecode | `--collect-all inflect` |
 | LuxTTS | `espeak-ng-data` not found | `piper_phonemize` C library looks for data at `/usr/share/espeak-ng-data/` which doesn't exist in the bundle | `--collect-all piper_phonemize` + set `ESPEAK_DATA_PATH` env var at runtime (see 5.3) |
 | LuxTTS | `inspect.getsource` error in Vocos codec | `linacodec` and `zipvoice` use source introspection | `--collect-all linacodec` + `--collect-all zipvoice` |
 | Chatterbox | `FileNotFoundError` for watermark model | `perth` ships pretrained model files (`hparams.yaml`, `.pth.tar`) that PyInstaller doesn't bundle by default | `--collect-all perth` |
 | All engines | `importlib.metadata` failures | Frozen binary doesn't include package metadata for `huggingface-hub`, `transformers`, etc. | `--copy-metadata` for each affected package |
 | All engines | Download progress bars stuck at 0% | `huggingface_hub` silently disables tqdm progress bars based on logger level in frozen builds — our progress tracker never receives byte updates | Force-enable tqdm's internal counter in `HFProgressTracker` |
 | All engines | `NameError: name 'obj' is not defined` on macOS | Python 3.12.0 has a [CPython bug](https://github.com/pyinstaller/pyinstaller/issues/7992) that corrupts bytecode when PyInstaller rewrites code objects | Upgrade to Python 3.12.13+ |
 | All engines | `resource_tracker` subprocess crash | `multiprocessing` in frozen binaries needs `freeze_support()` called before anything else | Added to `server.py` entry point |
 ### 5.3 Runtime Frozen-Build Handling (`server.py`)
 Some fixes can't live in `build_binary.py` — they need runtime detection. The entry point `backend/server.py` handles these before any heavy imports:
 ```python
 # 1. freeze_support() — MUST be called before any multiprocessing use
 import multiprocessing
 multiprocessing.freeze_support()
 # 2. Native data paths — redirect C libraries to bundled data
 if getattr(sys, 'frozen', False):
    _meipass = getattr(sys, '_MEIPASS', os.path.dirname(sys.executable))
    _espeak_data = os.path.join(_meipass, 'piper_phonemize', 'espeak-ng-data')
    if os.path.isdir(_espeak_data):
        os.environ.setdefault('ESPEAK_DATA_PATH', _espeak_data)
 # 3. stdout/stderr safety — PyInstaller --noconsole on Windows sets these to None
 if not _is_writable(sys.stdout):
    sys.stdout = open(os.devnull, 'w')
 ```
 If your engine's dependencies include native libraries that look for data at system paths (like espeak-ng does), you'll need to add a similar `os.environ.setdefault()` block here.
 ### 5.4 CUDA vs CPU Build Branching
 `build_binary.py` produces two different binaries:
 - **`voicebox-server`** (CPU) — excludes all `nvidia.*` packages to avoid bundling ~3 GB of CUDA DLLs
 - **`voicebox-server-cuda`** — includes `torch.cuda` and `torch.backends.cudnn`
 On Windows, if the build environment has CUDA torch installed but you're building the CPU binary, the script temporarily swaps to CPU-only torch and restores CUDA torch afterward. This prevents PyInstaller from accidentally bundling CUDA libraries into the CPU build.
 New engine imports go in the **common section** (not the CUDA or MLX conditional blocks) unless your engine has platform-specific dependencies.
 ### 5.5 MLX Conditional Inclusion
 Apple Silicon builds conditionally include MLX hidden imports and `--collect-all mlx` / `--collect-all mlx_audio`. If your engine has an MLX-specific backend variant, add its imports inside the `if is_apple_silicon() and not cuda:` block.
 ### 5.6 Testing Frozen Builds
 You can't skip this. Models that work in `python -m uvicorn` will break in the PyInstaller binary. The v0.2.3 release required **three patch releases** (v0.2.1 → v0.2.2 → v0.2.3) to get all engines working in production.
 1. Build: `just build`
-2. Run and try download + load + generate
+2. Launch the binary directly (not via `python -m`)
-3. Check stderr for the actual error
+3. Test the **full chain**: download → load → generate → progress tracking
-4. Fix, rebuild, repeat
+4. Check stderr for the actual error (logs go to stderr for Tauri sidecar capture)
 5. Fix, rebuild, repeat
 **Common gotcha:** testing only generation with a pre-cached model from your dev install. Always test with a clean model cache to verify downloads work too.
 ## Phase 6: Common Upstream Workarounds
@@ -250,8 +490,80 @@ Based on the current model landscape, these are candidates for future integratio
 | **Fish Speech** | 50+ | Medium | Word-level control via inline text | Ready |
 | **Kokoro-82M** | English | 82M | CPU realtime, Apache 2.0 | Ready |
 | **XTTS-v2** | 17+ | Medium | Zero-shot cloning | Ready |
-| **HumeAI TADA** | EN (1B), Multi (3B) | Medium | 700s+ coherent audio, synced transcripts | Needs vetting |
+| **HumeAI TADA** | EN (1B), Multi (3B) | Medium | 700s+ coherent audio, synced transcripts | Shipped |
 | **MOSS-TTS** | Multilingual | Medium | Text-to-voice design, multi-speaker dialogue | Needs vetting |
 | **Pocket TTS** | English | ~100M | CPU-first, >1× realtime | Needs vetting |
-The multi-engine architecture is now in place, making new model integration straightforward (~1 day for a well-documented model with a PyPI package).
+## Implementation Checklist
 Use this as a gate between phases. Do not proceed to the next phase until every item in the current phase is checked.
 ### Phase 0: Dependency Research
 - [ ] Cloned model library source into a temp directory
 - [ ] Read `setup.py` / `pyproject.toml` — noted pinned dependency versions
 - [ ] Traced all imports from the model class through to leaf dependencies
 - [ ] Searched for `inspect.getsource`, `@typechecked`, `typeguard` in the full dependency tree
 - [ ] Searched for `importlib.metadata`, `pkg_resources.get_distribution` in the dependency tree
 - [ ] Searched for `Path(__file__).parent`, `os.path.dirname(__file__)`, hardcoded system paths
 - [ ] Searched for `torch.load` calls missing `map_location`
 - [ ] Searched for `torch.from_numpy` without `.float()` cast
 - [ ] Searched for `token=True` or `token=os.getenv("HF_TOKEN")` in HuggingFace calls
 - [ ] Tested model loading and generation on CPU in a throwaway venv
 - [ ] Tested with a clean HuggingFace cache (no pre-downloaded models)
 - [ ] Produced a written dependency audit documenting all findings
 ### Phase 1: Backend Implementation
 - [ ] Created `backend/backends/<engine>_backend.py` implementing `TTSBackend` protocol
 - [ ] Chose voice prompt pattern (pre-computed tensors vs deferred file paths)
 - [ ] Implemented all monkey-patches identified in Phase 0
 - [ ] Used `get_torch_device()` from `backends/base.py` for device selection
 - [ ] Used `model_load_progress()` from `backends/base.py` for download/load tracking
 - [ ] Tested: model downloads correctly
 - [ ] Tested: model loads on CPU
 - [ ] Tested: generation produces valid audio
 - [ ] Tested: voice cloning from reference audio works
 - [ ] Registered `ModelConfig` in `backends/__init__.py`
 - [ ] Added to `TTS_ENGINES` dict
 - [ ] Added factory branch in `get_tts_backend_for_engine()`
 - [ ] Updated engine regex in `backend/models.py`
 ### Phase 2–3: Route, Service, and Frontend
 - [ ] Confirmed zero changes needed in routes/services (or documented why custom behavior is needed)
 - [ ] Added engine to TypeScript union type in `app/src/lib/api/types.ts`
 - [ ] Added language map entry in `app/src/lib/constants/languages.ts`
 - [ ] Added to `ENGINE_OPTIONS` and `ENGINE_DESCRIPTIONS` in `EngineModelSelector.tsx`
 - [ ] Added to Zod schema and model-name mapping in `useGenerationForm.ts`
 - [ ] Added description in `ModelManagement.tsx`
 ### Phase 4: Dependencies
 - [ ] Added packages to `backend/requirements.txt`
 - [ ] If `--no-deps` needed: listed sub-dependencies explicitly
 - [ ] If git-only packages: added `@ git+https://...` entries
 - [ ] If custom index needed: added `--find-links` line
 - [ ] Updated `justfile` setup targets
 - [ ] Updated `.github/workflows/release.yml` build steps
 - [ ] Updated `Dockerfile` if applicable
 - [ ] Verified `pip install` succeeds in a clean venv with existing requirements
 ### Phase 5: PyInstaller Bundling
 - [ ] Added `--hidden-import` entries in `build_binary.py` for:
  - [ ] `backend.backends.<engine>_backend`
  - [ ] The model package and its key submodules
 - [ ] Added `--collect-all` for any packages that:
  - [ ] Use `inspect.getsource()` / `@typechecked`
  - [ ] Ship pretrained model data files (`.pth.tar`, `.yaml`, etc.)
  - [ ] Ship native data files (phoneme tables, shader libraries, etc.)
 - [ ] Added `--copy-metadata` for any packages that use `importlib.metadata`
 - [ ] If engine has native data paths: added `os.environ.setdefault()` in `server.py`
 - [ ] Built frozen binary with `just build`
 - [ ] Tested in frozen binary with **clean model cache** (not pre-cached from dev):
  - [ ] Model download works with real-time progress
  - [ ] Model loading works
  - [ ] Generation produces valid audio
  - [ ] No errors in stderr logs
 ### Phase 6: Final Verification
 - [ ] Engine works in dev mode (`just dev`)
 - [ ] Engine works in frozen binary (`just build` → run binary directly)
 - [ ] Tested on target platform (macOS for MLX, Windows/Linux for CUDA)
 - [ ] No regressions in existing engines
@@ -3,12 +3,12 @@ title: "Voicebox Documentation"
 description: "Voicebox is a local-first voice cloning studio -- a free and open-source alternative to ElevenLabs."
 ---
-Voicebox is a **local-first voice cloning studio** -- a free and open-source alternative to ElevenLabs. Clone voices from a few seconds of audio, generate speech in 23 languages across 4 TTS engines, apply post-processing effects, and compose multi-voice projects with a timeline editor.
+Voicebox is a **local-first voice cloning studio** -- a free and open-source alternative to ElevenLabs. Clone voices from a few seconds of audio, generate speech in 23 languages across 5 TTS engines, apply post-processing effects, and compose multi-voice projects with a timeline editor.
 ![Voicebox App Screenshot](/images/app-screenshot-1.webp)
 - **Complete privacy** -- models and voice data stay on your machine
- **4 TTS engines** -- Qwen3-TTS, LuxTTS, Chatterbox Multilingual, and Chatterbox Turbo
+- **5 TTS engines** -- Qwen3-TTS, LuxTTS, Chatterbox Multilingual, Chatterbox Turbo, and HumeAI TADA
 - **23 languages** -- from English to Arabic, Japanese, Hindi, Swahili, and more
 - **Post-processing effects** -- pitch shift, reverb, delay, chorus, compression, and filters
 - **Expressive speech** -- paralinguistic tags like `[laugh]`, `[sigh]`, `[gasp]` via Chatterbox Turbo
@@ -5,10 +5,10 @@ description: "Voicebox is a local-first voice cloning studio -- a free and open-
 ## What is Voicebox?
-Voicebox is a **local-first voice cloning studio** -- a free and open-source alternative to ElevenLabs. Clone voices from a few seconds of audio, generate speech in 23 languages across 4 TTS engines, apply post-processing effects, and compose multi-voice projects with a timeline editor.
+Voicebox is a **local-first voice cloning studio** -- a free and open-source alternative to ElevenLabs. Clone voices from a few seconds of audio, generate speech in 23 languages across 5 TTS engines, apply post-processing effects, and compose multi-voice projects with a timeline editor.
 - **Complete privacy** -- models and voice data stay on your machine
- **4 TTS engines** -- Qwen3-TTS, LuxTTS, Chatterbox Multilingual, and Chatterbox Turbo
+- **5 TTS engines** -- Qwen3-TTS, LuxTTS, Chatterbox Multilingual, Chatterbox Turbo, and HumeAI TADA
 - **23 languages** -- from English to Arabic, Japanese, Hindi, Swahili, and more
 - **Post-processing effects** -- pitch shift, reverb, delay, chorus, compression, and filters
 - **Expressive speech** -- paralinguistic tags like `[laugh]`, `[sigh]`, `[gasp]` via Chatterbox Turbo
@@ -20,7 +20,7 @@ Voicebox is a **local-first voice cloning studio** -- a free and open-source alt
 ## TTS Engines
-Four engines with different strengths, switchable per-generation:
+Five engines with different strengths, switchable per-generation:
 | Engine | Languages | Strengths |
 |--------|-----------|-----------|
@@ -28,6 +28,7 @@ Four engines with different strengths, switchable per-generation:
 | **LuxTTS** | English | Lightweight (~1GB VRAM), 48kHz output, 150x realtime on CPU |
 | **Chatterbox Multilingual** | 23 | Broadest language coverage |
 | **Chatterbox Turbo** | English | Fast 350M model with paralinguistic emotion/sound tags |
 | **TADA** (1B / 3B) | 10 | HumeAI speech-language model -- 700s+ coherent audio |
 ## GPU Support
@@ -56,7 +57,7 @@ Four engines with different strengths, switchable per-generation:
 | Frontend | React, TypeScript, Tailwind CSS |
 | State | Zustand, React Query |
 | Backend | FastAPI (Python) |
-| TTS Engines | Qwen3-TTS, LuxTTS, Chatterbox, Chatterbox Turbo |
+| TTS Engines | Qwen3-TTS, LuxTTS, Chatterbox, Chatterbox Turbo, TADA |
 | Effects | Pedalboard (Spotify) |
 | Transcription | Whisper / Whisper Turbo (PyTorch or MLX) |
 | Inference | MLX (Apple Silicon) / PyTorch (CUDA/ROCm/XPU/CPU) |
@@ -36,6 +36,10 @@
 │  │  │  │ Qwen3-TTS│ │LuxTTS │ │Chatterbox │  │  │  │
 │  │  │  │(Py/MLX)  │ │       │ │(MTL+Turbo)│  │  │  │
 │  │  │  └──────────┘ └───────┘ └───────────┘  │  │  │
 │  │  │  ┌──────────┐                           │  │  │
 │  │  │  │ TADA     │                           │  │  │
 │  │  │  │(1B / 3B) │                           │  │  │
 │  │  │  └──────────┘                           │  │  │
 │  │  └─────────────────────────────────────────┘  │  │
 │  │  ┌───────────┐  ┌─────────┐                   │  │
 │  │  │ STTBackend│  │ Profiles│                   │  │
@@ -59,6 +63,7 @@
 | LuxTTS | `backend/backends/luxtts_backend.py` | LuxTTS — fast, CPU-friendly |
 | Chatterbox MTL | `backend/backends/chatterbox_backend.py` | Chatterbox Multilingual — 23 languages |
 | Chatterbox Turbo | `backend/backends/chatterbox_turbo_backend.py` | Chatterbox Turbo — English, paralinguistic tags |
 | TADA | `backend/backends/hume_backend.py` | HumeAI TADA — 1B English + 3B Multilingual |
 | Platform detect | `backend/platform_detect.py` | Apple Silicon → MLX, else → PyTorch |
 | API types | `backend/models.py` | Pydantic request/response models |
 | HF progress | `backend/utils/hf_progress.py` | HFProgressTracker (tqdm patching for download progress) |
@@ -78,7 +83,7 @@
 ```
 POST /generate
  1. Look up voice profile from DB
-  2. Resolve engine from request (qwen | luxtts | chatterbox | chatterbox_turbo)
+  2. Resolve engine from request (qwen | luxtts | chatterbox | chatterbox_turbo | tada)
  3. Get backend: get_tts_backend_for_engine(engine)  # thread-safe singleton per engine
  4. Check model cache → if missing, trigger background download, return HTTP 202
  5. Load model (lazy): tts_backend.load_model(model_size)
@@ -104,7 +109,8 @@ POST /generate
 - LuxTTS integration — fast, CPU-friendly English TTS (PR #254)
 - Chatterbox Multilingual TTS — 23 languages including Hebrew (PR #257)
 - Instruct parameter UI exists but is non-functional across all backends (see #224, Known Limitations)
- Single flat model dropdown (Qwen 1.7B, Qwen 0.6B, LuxTTS, Chatterbox, Chatterbox Turbo)
+- HumeAI TADA integration — 1B English + 3B Multilingual speech-language model (PR #296)
 - Single flat model dropdown (Qwen 1.7B, Qwen 0.6B, LuxTTS, Chatterbox, Chatterbox Turbo, TADA 1B, TADA 3B)
 - Centralized model config registry (`ModelConfig` dataclass) — no per-engine dispatch maps in `main.py`
 - Shared `EngineModelSelector` component — engine/model dropdown defined once, used in both generation forms
@@ -136,6 +142,8 @@ POST /generate
 | LuxTTS | `luxtts` | English | ~300 MB | CPU-friendly, 48 kHz, fast | None |
 | Chatterbox | `chatterbox-tts` | 23 (incl. Hebrew, Arabic, Hindi, etc.) | ~3.2 GB | Zero-shot cloning, multilingual | Partial — `exaggeration` float (0-1) for expressiveness |
 | Chatterbox Turbo | `chatterbox-turbo` | English | ~1.5 GB | Paralinguistic tags ([laugh], [cough]), 350M params, low latency | Partial — inline tags only, no separate instruct param |
 | TADA 1B | `tada-1b` | English | ~4 GB | HumeAI speech-language model, 700s+ coherent audio | None |
 | TADA 3B Multilingual | `tada-3b-ml` | 10 (en, ar, zh, de, es, fr, it, ja, pl, pt) | ~8 GB | Multilingual, text-acoustic dual alignment | None |
 ### Multi-Engine Architecture (Shipped)
@@ -143,7 +151,7 @@ The singleton TTS backend blocker described in the previous version of this doc
 - **Thread-safe backend registry** (`_tts_backends` dict + `_tts_backends_lock`) with double-checked locking
 - **Per-engine backend instances** — each engine gets its own singleton, loaded lazily
- **Engine field on GenerationRequest** — frontend sends `engine: 'qwen' | 'luxtts' | 'chatterbox' | 'chatterbox_turbo'`
+- **Engine field on GenerationRequest** — frontend sends `engine: 'qwen' | 'luxtts' | 'chatterbox' | 'chatterbox_turbo' | 'tada'`
 - **Per-engine language filtering** — `ENGINE_LANGUAGES` map in frontend, backend regex accepts all languages
 - **Per-engine voice prompts** — `create_voice_prompt_for_profile()` dispatches to the correct backend
 - **Trim post-processing** — `trim_tts_output()` for Chatterbox engines (cuts trailing silence/hallucination)
@@ -337,7 +345,7 @@ Notable requests:
 | **CosyVoice2-0.5B** | 3-10s zero-shot | Very fast | 24 kHz | Multilingual | Low | **Yes** — `inference_instruct2()`, works with cloning | Ready | Best instruct candidate |
 | **Fish Speech** | 10-30s few-shot | Real-time | 24-44 kHz | 50+ | Medium | **Yes** — inline text descriptions, word-level control | Ready | Multi-engine arch in place |
 | **MOSS-TTS Family** | Zero-shot | — | — | Multilingual | Medium | **Yes** — text prompts for style + timbre design | Needs vetting | Apache 2.0, multi-speaker dialogue |
-| **HumeAI TADA 1B/3B** | Zero-shot | 5× faster than LLM-TTS | — | EN (1B), Multilingual (3B) | Medium | Partial — automatic prosody from text context | Needs vetting | MIT, 700s+ coherent, synced transcript output |
+| **HumeAI TADA 1B/3B** | Zero-shot | 5× faster than LLM-TTS | 24 kHz | EN (1B), Multilingual (3B) | Medium | Partial — automatic prosody from text context | **Shipped** | PR #296, MIT, 700s+ coherent |
 | **VoxCPM 1.5** | Zero-shot (seconds) | ~0.15 RTF streaming | — | Bilingual (EN/ZH) | Medium | Partial — automatic context-aware prosody | Needs vetting | Apache 2.0, tokenizer-free continuous diffusion |
 | **Kokoro-82M** | 3s instant | CPU realtime | 24 kHz | English | Tiny (82M) | Partial — automatic style inference | Ready | Apache 2.0, multi-engine arch in place |
 | **XTTS-v2** | 6s zero-shot | Mid-GPU | 24 kHz | 17+ | Medium | Partial — style transfer from ref audio only | Ready | Multi-engine arch in place |
@@ -475,7 +483,7 @@ The generation form now uses a flat model dropdown with engine-based routing. Pe
 | `/history/{id}/export` | GET | Export generation ZIP |
 | `/history/{id}/export-audio` | GET | Export audio only |
 | `/transcribe` | POST | Transcribe audio (Whisper) |
-| `/models/status` | GET | All model statuses (Qwen, LuxTTS, Chatterbox, Chatterbox Turbo, Whisper) |
+| `/models/status` | GET | All model statuses (Qwen, LuxTTS, Chatterbox, Chatterbox Turbo, TADA, Whisper) |
 | `/models/download` | POST | Trigger model download |
 | `/models/download/cancel` | POST | Cancel/dismiss download |
 | `/models/{name}` | DELETE | Delete downloaded model |
@@ -46,6 +46,8 @@ setup-python:
    {{ pip }} install -r {{ backend_dir }}/requirements.txt
    # Chatterbox pins numpy<1.26 / torch==2.6 which break on Python 3.12+
    {{ pip }} install --no-deps chatterbox-tts
    # HumeAI TADA pins torch>=2.7,<2.8 which conflicts with our torch>=2.1
    {{ pip }} install --no-deps hume-tada
    # Apple Silicon: install MLX backend
    if [ "$(uname -m)" = "arm64" ] && [ "$(uname)" = "Darwin" ]; then
        echo "Detected Apple Silicon — installing MLX dependencies..."
@@ -74,6 +76,7 @@ setup-python:
    }
    & "{{ pip }}" install -r {{ backend_dir }}/requirements.txt
    & "{{ pip }}" install --no-deps chatterbox-tts
    & "{{ pip }}" install --no-deps hume-tada
    & "{{ pip }}" install git+https://github.com/QwenLM/Qwen3-TTS.git
    & "{{ pip }}" install pyinstaller ruff pytest pytest-asyncio -q
    Write-Host "Python environment ready."
@@ -1,6 +1,6 @@
 {
  "name": "@voicebox/landing",
-  "version": "0.2.3",
+  "version": "0.3.0",
  "description": "Landing page for voicebox.sh",
  "scripts": {
    "dev": "bun --bun next dev --turbo",
@@ -1,6 +1,6 @@
 {
  "name": "voicebox",
-  "version": "0.2.3",
+  "version": "0.3.0",
  "private": true,
  "workspaces": [
    "app",
@@ -1,7 +1,7 @@
 {
  "name": "@voicebox/tauri",
  "private": true,
-  "version": "0.2.3",
+  "version": "0.3.0",
  "type": "module",
  "scripts": {
    "dev": "vite",
@@ -1,6 +1,6 @@
 [package]
 name = "voicebox"
-version = "0.2.3"
+version = "0.3.0"
 description = "A production-quality desktop app for Qwen3-TTS voice cloning and generation"
 authors = ["you"]
 license = ""
@@ -23,15 +23,15 @@ fn main() {
        }
    }
    let project_root = env!("CARGO_MANIFEST_DIR");
    let gen_dir = format!("{}/gen", project_root);
    std::fs::create_dir_all(&gen_dir).expect("Failed to create gen directory");
    // Compile macOS Liquid Glass icon
    #[cfg(target_os = "macos")]
    {
        let project_root = env!("CARGO_MANIFEST_DIR");
        // voicebox.icon is in tauri/assets/voicebox.icon (one level up from src-tauri)
        let icon_source = format!("{}/../assets/voicebox.icon", project_root);
        let gen_dir = format!("{}/gen", project_root);
        std::fs::create_dir_all(&gen_dir).expect("Failed to create gen directory");
        if std::path::Path::new(&icon_source).exists() {
            println!("cargo:rerun-if-changed={}", icon_source);
@@ -76,6 +76,71 @@ fn main() {
                    panic!("Icon compilation failed");
                }
            }
            // Generate voicebox.icns from the source PNG via sips + iconutil
            let icns_path = format!("{}/voicebox.icns", gen_dir);
            if !std::path::Path::new(&icns_path).exists() {
                let source_png = format!("{}/Assets/Voicebox.png", icon_source);
                if std::path::Path::new(&source_png).exists() {
                    let iconset_dir = format!("{}/voicebox.iconset", gen_dir);
                    std::fs::create_dir_all(&iconset_dir).ok();
                    let sizes: &[(u32, &str)] = &[
                        (16, "icon_16x16.png"),
                        (32, "[email protected]"),
                        (32, "icon_32x32.png"),
                        (64, "[email protected]"),
                        (128, "icon_128x128.png"),
                        (256, "[email protected]"),
                        (256, "icon_256x256.png"),
                        (512, "[email protected]"),
                        (512, "icon_512x512.png"),
                        (1024, "[email protected]"),
                    ];
                    for (size, name) in sizes {
                        let dest = format!("{}/{}", iconset_dir, name);
                        let status = Command::new("sips")
                            .args([
                                "-z",
                                &size.to_string(),
                                &size.to_string(),
                                &source_png,
                                "--out",
                                &dest,
                            ])
                            .output();
                        if let Ok(out) = status {
                            if !out.status.success() {
                                eprintln!(
                                    "sips failed for {}: {}",
                                    name,
                                    String::from_utf8_lossy(&out.stderr)
                                );
                            }
                        }
                    }
                    let iconutil_output = Command::new("iconutil")
                        .args(["-c", "icns", "-o", &icns_path, &iconset_dir])
                        .output();
                    match iconutil_output {
                        Ok(out) if out.status.success() => {
                            println!("Generated voicebox.icns");
                        }
                        Ok(out) => {
                            eprintln!("iconutil failed: {}", String::from_utf8_lossy(&out.stderr));
                        }
                        Err(e) => {
                            eprintln!("Failed to run iconutil: {}", e);
                        }
                    }
                    // Clean up iconset directory
                    std::fs::remove_dir_all(&iconset_dir).ok();
                }
            }
        } else {
            println!(
                "cargo:warning=Icon source not found at {}, skipping icon compilation",
@@ -84,5 +149,18 @@ fn main() {
        }
    }
    // Ensure all resource files exist so Tauri's bundler doesn't fail.
    // On non-macOS these are always stubs. On macOS, actool may not produce
    // Assets.car if the Xcode version doesn't support the .icon format.
    {
        let required = ["Assets.car", "voicebox.icns", "partial.plist"];
        for name in required {
            let path = format!("{}/{}", gen_dir, name);
            if !std::path::Path::new(&path).exists() {
                std::fs::write(&path, b"").ok();
            }
        }
    }
    tauri_build::build()
 }
@@ -1,7 +1,7 @@
 {
  "$schema": "https://schema.tauri.app/config/2",
  "productName": "Voicebox",
-  "version": "0.2.3",
+  "version": "0.3.0",
  "identifier": "sh.voicebox.app",
  "build": {
    "beforeDevCommand": "bun run dev",
@@ -1,7 +1,7 @@
 {
  "name": "@voicebox/web",
  "private": true,
-  "version": "0.2.3",
+  "version": "0.3.0",
  "type": "module",
  "scripts": {
    "dev": "vite",
Author	SHA1	Message	Date
James Pine	273483ffcf	fix TorchScript error in frozen builds and update docs for TADA Remove @torch.jit.script from the DAC shim's snake() function — TorchScript calls inspect.getsource() which fails in PyInstaller binaries (no .py source files). Update all user-facing docs: 4 → 5 TTS engines, add TADA row to every engine comparison table, mark TADA as Shipped in the upcoming engines list, update architecture diagrams and tech stack tables.	2026-03-17 03:28:58 -07:00
James Pine	5774a168a9	fix TADA 3B model name: tada-3b -> tada-3b-ml	2026-03-17 03:17:53 -07:00
James Pine	6bf40bd2d0	fix tokenizer patch corrupting AutoTokenizer for other engines Replace the monkey-patch on AutoTokenizer.from_pretrained (which broke the classmethod descriptor and caused 'Tokenizer not loaded' errors when loading Qwen after TADA) with two targeted config patches: - Set AlignerConfig.tokenizer_name to the local ungated tokenizer path - Pre-load TadaConfig, inject tokenizer_name, pass config= to from_pretrained No global state is modified; other engines are unaffected.	2026-03-17 03:15:57 -07:00
James Pine	12cda2e090	fix torchcodec error by using soundfile instead of torchaudio.load torchaudio 2.10+ switched its default audio loading backend to torchcodec, which isn't installed. Replace torchaudio.load() with soundfile.read() in create_voice_prompt(). TADA's internal use of torchaudio.functional.resample() is unaffected (pure PyTorch math, no torchcodec dependency).	2026-03-17 02:25:05 -07:00
James Pine	7a90290a76	fix gated Llama tokenizer error by redirecting to ungated mirror TADA hardcodes 'meta-llama/Llama-3.2-1B' as its tokenizer source in both the Aligner and TadaForCausalLM.from_pretrained(). That repo is gated and requires accepting Meta's license on HuggingFace. Monkey-patch AutoTokenizer.from_pretrained during model loading to redirect Llama tokenizer requests to 'unsloth/Llama-3.2-1B', an ungated mirror with identical tokenizer files. The patch is scoped to model loading only and restored immediately after.	2026-03-17 02:22:26 -07:00
James Pine	b02ce8e2f3	replace descript-audio-codec with lightweight DAC shim The real descript-audio-codec package pulls in descript-audiotools, which transitively requires onnx, tensorboard, protobuf, matplotlib, pystoi, and other heavy dependencies. onnx fails to build from source on macOS due to CMake version incompatibility. TADA only uses Snake1d (a 7-line PyTorch module) from DAC. This commit adds a shim in backend/utils/dac_shim.py that registers fake dac.* modules in sys.modules with just the Snake1d class, completely eliminating the DAC/audiotools dependency chain.	2026-03-17 02:16:33 -07:00
James Pine	4e7772a21d	add HumeAI TADA TTS engine (1B English + 3B Multilingual) Integrates HumeAI's TADA (Text-Acoustic Dual Alignment) speech-language model as a new TTS engine. TADA uses a novel 1:1 token-audio alignment that produces coherent speech over long sequences (700s+). Two model variants: - tada-1b: English-only, ~4GB, built on Llama 3.2 1B - tada-3b-ml: 10 languages, ~8GB, built on Llama 3.2 3B Backend uses the Encoder for voice prompt encoding with caching, and TadaForCausalLM with flow-matching diffusion for generation. Supports bf16 inference on CUDA, forces CPU on macOS (MPS compatibility). Installed with --no-deps due to torch>=2.7 pin conflict; descript-audio-codec and torchaudio added as explicit sub-dependencies.	2026-03-17 01:55:15 -07:00
James Pine	51fb320b8c	readme	2026-03-17 01:24:48 -07:00
James Pine	8ac202aa58	docs for adding new engines	2026-03-17 01:13:30 -07:00
Jamie Pine	ac68052945	create stub resource files when actool output is missing Older Xcode versions don't produce Assets.car from .icon assets. Fall back to empty stubs for all platforms so the bundler succeeds.	2026-03-17 01:07:54 -07:00
Jamie Pine	e601fd2ca4	generate icon assets at build time instead of tracking them build.rs now generates voicebox.icns via sips + iconutil alongside the existing actool Assets.car compilation. On non-macOS, empty stub files are created so Tauri's resource bundler doesn't fail on missing paths.	2026-03-17 00:51:12 -07:00
Jamie Pine	7b25e0ba0b	Bump version: 0.2.3 → 0.3.0	2026-03-17 00:25:56 -07:00
Jamie PineandGitHub	a6817cd082	Merge pull request #295 from jamiepine/fix/misc-bugs fix: batch of bug fixes from issue tracker	2026-03-17 00:08:17 -07:00
`@@ -1,3 +1,3 @@`
	`# Backend package`	`# Backend package`

	`__version__ = "0.2.3"`	`__version__ = "0.3.0"`