fix: always clean up temp archive on failure and fix justfile data dir path

- Wrap download/verify/extract in try/finally so .download-*.tmp is always deleted, even on mid-download or extraction failures - Fix justfile build-server-cuda to use sh.voicebox.app (production path)
Bump version: 0.3.0 → 0.3.1
2026-09-27 14:15:16 -07:00 · 2026-03-17 09:15:23 -07:00 · 2026-03-17 07:50:12 -07:00 · 2026-03-17 06:53:54 -07:00 · 2026-03-17 06:12:30 -07:00 · 2026-03-17 04:58:13 -07:00
36 changed files with 1893 additions and 302 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 = 0.3.0
+current_version = 0.3.1
 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: |
@@ -198,33 +200,37 @@ jobs:
        run: |
          python -c "import torch; print(f'CUDA available in build: {torch.cuda.is_available()}'); print(f'CUDA version: {torch.version.cuda}')"

-      - name: Build CUDA server binary
+      - name: Build CUDA server binary (onedir)
        shell: bash
        working-directory: backend
        run: python build_binary.py --cuda

-      - name: Split binary for GitHub Releases
+      - name: Package into server core + CUDA libs archives
        shell: bash
        run: |
-          python scripts/split_binary.py \
-            backend/dist/voicebox-server-cuda.exe \
-            --output release-assets/
+          python scripts/package_cuda.py \
+            backend/dist/voicebox-server-cuda/ \
+            --output release-assets/ \
+            --cuda-libs-version cu126-v1 \
+            --torch-compat ">=2.6.0,<2.11.0"

-      - name: Upload split parts to GitHub Release
+      - name: Upload archives to GitHub Release
        if: startsWith(github.ref, 'refs/tags/')
-        uses: softprops/action-gh-release@v1
+        uses: softprops/action-gh-release@v2
        with:
          files: |
-            release-assets/voicebox-server-cuda.part*.exe
-            release-assets/voicebox-server-cuda.sha256
-            release-assets/voicebox-server-cuda.manifest
+            release-assets/voicebox-server-cuda.tar.gz
+            release-assets/voicebox-server-cuda.tar.gz.sha256
+            release-assets/cuda-libs-cu126-v1.tar.gz
+            release-assets/cuda-libs-cu126-v1.tar.gz.sha256
+            release-assets/cuda-libs.json
          draft: true
        env:
          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}

-      - name: Upload binary as workflow artifact
+      - name: Upload onedir as workflow artifact
        uses: actions/upload-artifact@v4
        with:
          name: voicebox-server-cuda-windows
-          path: backend/dist/voicebox-server-cuda.exe
+          path: backend/dist/voicebox-server-cuda/
          retention-days: 7
@@ -53,6 +53,12 @@ tauri/src-tauri/gen/Assets.car
 tauri/src-tauri/gen/voicebox.icns
 tauri/src-tauri/gen/partial.plist

+# PyInstaller
+*.spec
+
+# Windows artifacts
+nul
+
 # Temporary
 tmp/
 temp/
@@ -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": "0.3.0",
+  "version": "0.3.1",
  "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';
-  engine?: 'qwen' | 'luxtts' | 'chatterbox' | 'chatterbox_turbo';
+  model_size?: '1.7B' | '0.6B' | '1B' | '3B';
+  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'
-                ? 'Qwen TTS 1.7B'
-                : 'Qwen TTS 0.6B';
+              : engine === 'tada'
+                ? data.modelSize === '3B'
+                  ? '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__ = "0.3.0"
+__version__ = "0.3.1"
@@ -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)
@@ -34,9 +34,15 @@ def build_server(cuda=False):
    binary_name = "voicebox-server-cuda" if cuda else "voicebox-server"

    # PyInstaller arguments
+    # CUDA builds use --onedir so we can split the output into two archives:
+    #   1. Server core (~200-400MB) — versioned with the app
+    #   2. CUDA libs (~2GB) — versioned independently (only redownloaded on
+    #      CUDA toolkit / torch major version changes)
+    # CPU builds remain --onefile for simplicity.
+    pack_mode = "--onedir" if cuda else "--onefile"
    args = [
        "server.py",  # Use server.py as entry point instead of main.py
-        "--onefile",
+        pack_mode,
        "--name",
        binary_name,
    ]
@@ -186,6 +192,42 @@ 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
@@ -1,16 +1,22 @@
 """
-CUDA backend binary download, assembly, and verification.
+CUDA backend download, assembly, and verification.

-Downloads split parts of the CUDA-enabled voicebox-server binary from
-GitHub Releases, reassembles them, verifies integrity via SHA-256,
-and places the binary in the app's data directory for use on next
-backend restart.
+Downloads two archives from GitHub Releases:
+  1. Server core (voicebox-server-cuda.tar.gz) — the exe + non-NVIDIA deps,
+     versioned with the app.
+  2. CUDA libs (cuda-libs-{version}.tar.gz) — NVIDIA runtime libraries,
+     versioned independently (only redownloaded on CUDA toolkit bump).
+
+Both archives are extracted into {data_dir}/backends/cuda/ which forms the
+complete PyInstaller --onedir directory structure that torch expects.
 """

 import hashlib
+import json
 import logging
 import os
 import sys
+import tarfile
 from pathlib import Path
 from typing import Optional

@@ -24,6 +30,10 @@ GITHUB_RELEASES_URL = "https://github.com/jamiepine/voicebox/releases/download"

 PROGRESS_KEY = "cuda-backend"

+# The current expected CUDA libs version.  Bump this when we change the
+# CUDA toolkit version or torch's CUDA dependency changes (e.g. cu126 -> cu128).
+CUDA_LIBS_VERSION = "cu126-v1"
+

 def get_backends_dir() -> Path:
    """Directory where downloaded backend binaries are stored."""
@@ -32,21 +42,46 @@ def get_backends_dir() -> Path:
    return d


-def get_cuda_binary_name() -> str:
-    """Platform-specific CUDA binary filename."""
+def get_cuda_dir() -> Path:
+    """Directory where the CUDA backend (onedir) is extracted."""
+    d = get_backends_dir() / "cuda"
+    d.mkdir(parents=True, exist_ok=True)
+    return d
+
+
+def get_cuda_exe_name() -> str:
+    """Platform-specific CUDA executable filename."""
    if sys.platform == "win32":
        return "voicebox-server-cuda.exe"
    return "voicebox-server-cuda"


 def get_cuda_binary_path() -> Optional[Path]:
-    """Return path to CUDA binary if it exists."""
-    p = get_backends_dir() / get_cuda_binary_name()
+    """Return path to the CUDA executable if it exists inside the onedir."""
+    p = get_cuda_dir() / get_cuda_exe_name()
    if p.exists():
        return p
    return None


+def get_cuda_libs_manifest_path() -> Path:
+    """Path to the cuda-libs.json manifest inside the CUDA dir."""
+    return get_cuda_dir() / "cuda-libs.json"
+
+
+def get_installed_cuda_libs_version() -> Optional[str]:
+    """Read the installed CUDA libs version from cuda-libs.json, or None."""
+    manifest_path = get_cuda_libs_manifest_path()
+    if not manifest_path.exists():
+        return None
+    try:
+        data = json.loads(manifest_path.read_text())
+        return data.get("version")
+    except Exception as e:
+        logger.warning(f"Could not read cuda-libs.json: {e}")
+        return None
+
+
 def is_cuda_active() -> bool:
    """Check if the current process is the CUDA binary.

@@ -60,25 +95,151 @@ def get_cuda_status() -> dict:
    progress_manager = get_progress_manager()
    cuda_path = get_cuda_binary_path()
    progress = progress_manager.get_progress(PROGRESS_KEY)
+    cuda_libs_version = get_installed_cuda_libs_version()

    return {
        "available": cuda_path is not None,
        "active": is_cuda_active(),
        "binary_path": str(cuda_path) if cuda_path else None,
+        "cuda_libs_version": cuda_libs_version,
        "downloading": progress is not None and progress.get("status") == "downloading",
        "download_progress": progress,
    }


-async def download_cuda_binary(version: Optional[str] = None):
-    """Download the CUDA backend binary from GitHub Releases.
+def _needs_server_download(version: Optional[str] = None) -> bool:
+    """Check if the server core archive needs to be (re)downloaded."""
+    cuda_path = get_cuda_binary_path()
+    if not cuda_path:
+        return True
+    # Check if the binary version matches the expected app version
+    installed = get_cuda_binary_version()
+    expected = version or __version__
+    if expected.startswith("v"):
+        expected = expected[1:]
+    return installed != expected

-    Downloads split parts listed in a manifest file, concatenates them,
-    and verifies the SHA-256 checksum for integrity. Atomic write
-    (temp file -> rename).
+
+def _needs_cuda_libs_download() -> bool:
+    """Check if the CUDA libs archive needs to be (re)downloaded."""
+    installed = get_installed_cuda_libs_version()
+    if installed is None:
+        return True
+    return installed != CUDA_LIBS_VERSION
+
+
+async def _download_and_extract_archive(
+    client,
+    url: str,
+    sha256_url: Optional[str],
+    dest_dir: Path,
+    label: str,
+    progress_offset: int,
+    total_size: int,
+):
+    """Download a .tar.gz archive and extract it into dest_dir.

    Args:
-        version: Version tag (e.g. "v0.2.0"). Defaults to current app version.
+        client: httpx.AsyncClient
+        url: URL of the .tar.gz archive
+        sha256_url: URL of the .sha256 checksum file (optional)
+        dest_dir: Directory to extract into
+        label: Human-readable label for progress updates
+        progress_offset: Byte offset for progress reporting (when downloading
+            multiple archives sequentially)
+        total_size: Total bytes across all downloads (for progress bar)
+    """
+    progress = get_progress_manager()
+    temp_path = dest_dir / f".download-{label.replace(' ', '-')}.tmp"
+
+    # Clean up leftover partial download
+    if temp_path.exists():
+        temp_path.unlink()
+
+    # Fetch expected checksum (fail-fast: never extract an unverified archive)
+    expected_sha = None
+    if sha256_url:
+        try:
+            sha_resp = await client.get(sha256_url)
+            sha_resp.raise_for_status()
+            expected_sha = sha_resp.text.strip().split()[0]
+            logger.info(f"{label}: expected SHA-256: {expected_sha[:16]}...")
+        except Exception as e:
+            raise RuntimeError(f"{label}: failed to fetch checksum from {sha256_url}") from e
+
+    # Stream download, verify, and extract — always clean up temp file
+    downloaded = 0
+    try:
+        async with client.stream("GET", url) as response:
+            response.raise_for_status()
+            with open(temp_path, "wb") as f:
+                async for chunk in response.aiter_bytes(chunk_size=1024 * 1024):
+                    f.write(chunk)
+                    downloaded += len(chunk)
+                    progress.update_progress(
+                        PROGRESS_KEY,
+                        current=progress_offset + downloaded,
+                        total=total_size,
+                        filename=f"Downloading {label}",
+                        status="downloading",
+                    )
+
+        # Verify integrity
+        if expected_sha:
+            progress.update_progress(
+                PROGRESS_KEY,
+                current=progress_offset + downloaded,
+                total=total_size,
+                filename=f"Verifying {label}...",
+                status="downloading",
+            )
+            sha256 = hashlib.sha256()
+            with open(temp_path, "rb") as f:
+                while True:
+                    data = f.read(1024 * 1024)
+                    if not data:
+                        break
+                    sha256.update(data)
+            actual = sha256.hexdigest()
+            if actual != expected_sha:
+                raise ValueError(
+                    f"{label} integrity check failed: expected {expected_sha[:16]}..., got {actual[:16]}..."
+                )
+            logger.info(f"{label}: integrity verified")
+
+        # Extract (use data filter for path traversal protection on Python 3.12+)
+        progress.update_progress(
+            PROGRESS_KEY,
+            current=progress_offset + downloaded,
+            total=total_size,
+            filename=f"Extracting {label}...",
+            status="downloading",
+        )
+        with tarfile.open(temp_path, "r:gz") as tar:
+            if sys.version_info >= (3, 12):
+                tar.extractall(path=dest_dir, filter="data")
+            else:
+                tar.extractall(path=dest_dir)
+
+        logger.info(f"{label}: extracted to {dest_dir}")
+    finally:
+        if temp_path.exists():
+            temp_path.unlink()
+    return downloaded
+
+
+async def download_cuda_binary(version: Optional[str] = None):
+    """Download the CUDA backend (server core + CUDA libs if needed).
+
+    Downloads both archives from GitHub Releases, extracts them into
+    {data_dir}/backends/cuda/, and writes the cuda-libs.json manifest.
+
+    Only downloads what's needed:
+    - Server core: always redownloaded (versioned with app)
+    - CUDA libs: only if missing or version mismatch
+
+    Args:
+        version: Version tag (e.g. "v0.3.0"). Defaults to current app version.
    """
    import httpx

@@ -86,114 +247,91 @@ async def download_cuda_binary(version: Optional[str] = None):
        version = f"v{__version__}"

    progress = get_progress_manager()
-    binary_name = get_cuda_binary_name()
-    dest_dir = get_backends_dir()
-    final_path = dest_dir / binary_name
-    temp_path = dest_dir / f"{binary_name}.download"
+    cuda_dir = get_cuda_dir()

-    # Clean up any leftover partial download
-    if temp_path.exists():
-        temp_path.unlink()
+    need_server = _needs_server_download(version)
+    need_libs = _needs_cuda_libs_download()

-    logger.info(f"Starting CUDA backend download for {version}")
+    if not need_server and not need_libs:
+        logger.info("CUDA backend is up to date, nothing to download")
+        return
+
+    logger.info(
+        f"Starting CUDA backend download for {version} "
+        f"(server={'yes' if need_server else 'cached'}, "
+        f"libs={'yes' if need_libs else 'cached'})"
+    )
    progress.update_progress(
-        PROGRESS_KEY, current=0, total=0,
-        filename="Fetching manifest...", status="downloading",
+        PROGRESS_KEY,
+        current=0,
+        total=0,
+        filename="Preparing download...",
+        status="downloading",
    )

    base_url = f"{GITHUB_RELEASES_URL}/{version}"
-    stem = Path(binary_name).stem  # voicebox-server-cuda
+    server_archive = "voicebox-server-cuda.tar.gz"
+    libs_archive = f"cuda-libs-{CUDA_LIBS_VERSION}.tar.gz"

    try:
        async with httpx.AsyncClient(follow_redirects=True, timeout=30.0) as client:
-            # Fetch the manifest (list of split part filenames)
-            manifest_url = f"{base_url}/{stem}.manifest"
-            manifest_resp = await client.get(manifest_url)
-            manifest_resp.raise_for_status()
-            parts = [p.strip() for p in manifest_resp.text.strip().splitlines() if p.strip()]
-
-            if not parts:
-                raise ValueError("Empty manifest — no split parts found")
-
-            logger.info(f"Found {len(parts)} split parts to download")
-
-            # Fetch expected checksum (optional — for integrity verification)
-            expected_sha = None
-            try:
-                sha_url = f"{base_url}/{stem}.sha256"
-                sha_resp = await client.get(sha_url)
-                if sha_resp.status_code == 200:
-                    # Format: "sha256hex  filename\n"
-                    expected_sha = sha_resp.text.strip().split()[0]
-                    logger.info(f"Expected SHA-256: {expected_sha[:16]}...")
-            except Exception as e:
-                logger.warning(f"Could not fetch checksum file — skipping verification: {e}")
-
-            # Get total size across all parts by issuing HEAD requests
+            # Estimate total download size
            total_size = 0
-            for part_name in parts:
+            if need_server:
                try:
-                    head_resp = await client.head(f"{base_url}/{part_name}")
-                    content_length = int(head_resp.headers.get("content-length", 0))
-                    total_size += content_length
+                    head = await client.head(f"{base_url}/{server_archive}")
+                    total_size += int(head.headers.get("content-length", 0))
                except Exception:
                    pass
+            if need_libs:
+                try:
+                    head = await client.head(f"{base_url}/{libs_archive}")
+                    total_size += int(head.headers.get("content-length", 0))
+                except Exception:
+                    pass
+
            logger.info(f"Total download size: {total_size / 1024 / 1024:.1f} MB")

-            # Download and concatenate parts
-            total_downloaded = 0
-            with open(temp_path, "wb") as f:
-                for i, part_name in enumerate(parts):
-                    part_url = f"{base_url}/{part_name}"
-                    logger.info(f"Downloading part {i + 1}/{len(parts)}: {part_name}")
+            offset = 0

-                    async with client.stream("GET", part_url) as response:
-                        response.raise_for_status()
-                        async for chunk in response.aiter_bytes(chunk_size=1024 * 1024):
-                            f.write(chunk)
-                            total_downloaded += len(chunk)
-                            progress.update_progress(
-                                PROGRESS_KEY, current=total_downloaded, total=total_size,
-                                filename=f"Downloading CUDA backend ({i + 1}/{len(parts)})",
-                                status="downloading",
-                            )
-
-        # Verify integrity if checksum was available
-        if expected_sha:
-            progress.update_progress(
-                PROGRESS_KEY, current=total_downloaded, total=total_downloaded,
-                filename="Verifying integrity...", status="downloading",
-            )
-            sha256 = hashlib.sha256()
-            with open(temp_path, "rb") as f:
-                while True:
-                    chunk = f.read(1024 * 1024)
-                    if not chunk:
-                        break
-                    sha256.update(chunk)
-
-            actual = sha256.hexdigest()
-            if actual != expected_sha:
-                raise ValueError(
-                    f"Integrity check failed: expected {expected_sha[:16]}..., "
-                    f"got {actual[:16]}..."
+            # Download server core
+            if need_server:
+                server_downloaded = await _download_and_extract_archive(
+                    client,
+                    url=f"{base_url}/{server_archive}",
+                    sha256_url=f"{base_url}/{server_archive}.sha256",
+                    dest_dir=cuda_dir,
+                    label="CUDA server",
+                    progress_offset=offset,
+                    total_size=total_size,
                )
-            logger.info(f"Integrity verified: {actual[:16]}...")
+                offset += server_downloaded

-        # Atomic move into place (replace handles existing target on all platforms)
-        temp_path.replace(final_path)
+                # Make executable on Unix
+                exe_path = cuda_dir / get_cuda_exe_name()
+                if sys.platform != "win32" and exe_path.exists():
+                    exe_path.chmod(0o755)

-        # Make executable on Unix
-        if sys.platform != "win32":
-            final_path.chmod(0o755)
+            # Download CUDA libs
+            if need_libs:
+                await _download_and_extract_archive(
+                    client,
+                    url=f"{base_url}/{libs_archive}",
+                    sha256_url=f"{base_url}/{libs_archive}.sha256",
+                    dest_dir=cuda_dir,
+                    label="CUDA libraries",
+                    progress_offset=offset,
+                    total_size=total_size,
+                )

-        logger.info(f"CUDA backend downloaded to {final_path}")
+                # Write local cuda-libs.json manifest
+                manifest = {"version": CUDA_LIBS_VERSION}
+                get_cuda_libs_manifest_path().write_text(json.dumps(manifest, indent=2) + "\n")
+
+        logger.info(f"CUDA backend ready at {cuda_dir}")
        progress.mark_complete(PROGRESS_KEY)

    except Exception as e:
-        # Clean up on failure
-        if temp_path.exists():
-            temp_path.unlink()
        logger.error(f"CUDA backend download failed: {e}")
        progress.mark_error(PROGRESS_KEY, str(e))
        raise
@@ -202,15 +340,19 @@ async def download_cuda_binary(version: Optional[str] = None):
 def get_cuda_binary_version() -> Optional[str]:
    """Get the version of the installed CUDA binary, or None if not installed."""
    import subprocess
+
    cuda_path = get_cuda_binary_path()
    if not cuda_path:
        return None
    try:
        result = subprocess.run(
            [str(cuda_path), "--version"],
-            capture_output=True, text=True, timeout=30,
+            capture_output=True,
+            text=True,
+            timeout=30,
+            cwd=str(cuda_path.parent),  # Run from the onedir directory
        )
-        # Output format: "voicebox-server 0.2.0"
+        # Output format: "voicebox-server 0.3.0"
        for line in result.stdout.strip().splitlines():
            if "voicebox-server" in line:
                return line.split()[-1]
@@ -222,26 +364,29 @@ def get_cuda_binary_version() -> Optional[str]:
 async def check_and_update_cuda_binary():
    """Check if the CUDA binary is outdated and auto-download if so.

-    Called on server startup. If a CUDA binary exists but its version
-    doesn't match the current app version, triggers a background download
-    of the updated CUDA binary. The download progress is visible to the
-    frontend via the existing SSE progress endpoint.
+    Called on server startup. Checks both server version and CUDA libs
+    version. Downloads only what's needed.
    """
    cuda_path = get_cuda_binary_path()
    if not cuda_path:
        return  # No CUDA binary installed, nothing to update

-    cuda_version = get_cuda_binary_version()
-    current_version = __version__
+    need_server = _needs_server_download()
+    need_libs = _needs_cuda_libs_download()

-    if cuda_version == current_version:
-        logger.info(f"CUDA binary is up to date (v{current_version})")
+    if not need_server and not need_libs:
+        logger.info(f"CUDA binary is up to date (server=v{__version__}, libs={get_installed_cuda_libs_version()})")
        return

-    logger.info(
-        f"CUDA binary version mismatch: binary=v{cuda_version}, app=v{current_version}. "
-        f"Auto-downloading updated CUDA backend..."
-    )
+    reasons = []
+    if need_server:
+        cuda_version = get_cuda_binary_version()
+        reasons.append(f"server v{cuda_version} != v{__version__}")
+    if need_libs:
+        installed_libs = get_installed_cuda_libs_version()
+        reasons.append(f"libs {installed_libs} != {CUDA_LIBS_VERSION}")
+
+    logger.info(f"CUDA backend needs update ({', '.join(reasons)}). Auto-downloading...")

    try:
        await download_cuda_binary()
@@ -250,10 +395,12 @@ async def check_and_update_cuda_binary():


 async def delete_cuda_binary() -> bool:
-    """Delete the downloaded CUDA binary. Returns True if deleted."""
-    path = get_cuda_binary_path()
-    if path and path.exists():
-        path.unlink()
-        logger.info(f"Deleted CUDA binary: {path}")
+    """Delete the downloaded CUDA backend directory. Returns True if deleted."""
+    import shutil
+
+    cuda_dir = get_cuda_dir()
+    if cuda_dir.exists() and any(cuda_dir.iterdir()):
+        shutil.rmtree(cuda_dir)
+        logger.info(f"Deleted CUDA backend directory: {cuda_dir}")
        return True
    return False
@@ -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
@@ -159,12 +159,14 @@ Tauri looks for `voicebox-server-${PLATFORM}` in `src-tauri/binaries/` and bundl

 The `build-cuda-windows` job runs separately:

-1. Install PyTorch with CUDA 12.1
-2. Build with `build_binary.py --cuda`
-3. Split binary with `scripts/split_binary.py`
-4. Upload parts as release artifacts
+1. Install PyTorch with CUDA 12.6
+2. Build with `build_binary.py --cuda` (produces `--onedir` output)
+3. Package with `scripts/package_cuda.py` into two archives:
+   - `voicebox-server-cuda.tar.gz` — server core (~945 MB)
+   - `cuda-libs-cu126-v1.tar.gz` — NVIDIA runtime libraries (~1.7 GB, cached independently)
+4. Upload archives as release artifacts

-This binary is downloaded on-demand by users who enable CUDA in settings.
+This binary is downloaded on-demand by users who enable CUDA in settings. The CUDA libs archive is only re-downloaded when the CUDA toolkit version changes, not on every app update.

 ## Troubleshooting

@@ -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 +22,133 @@ 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" .
+
+# @torch.jit.script — calls inspect.getsource(), crashes in frozen builds
+grep -r "@torch.jit.script\|torch.jit.script" .
+
+# torchaudio.load — requires torchcodec in torchaudio 2.10+, use soundfile.read() instead
+grep -r "torchaudio.load\|torchaudio.save" .
+
+# Gated HuggingFace repos — models that hardcode gated repos as tokenizer/config sources
+grep -r "from_pretrained\|tokenizer_name\|AutoTokenizer" . | grep -i "llama\|meta-llama\|gated"
+```
+
+### 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
@@ -148,6 +279,8 @@ In `app/src/lib/hooks/useGenerationForm.ts`:
 - Add engine-to-model-name mapping
 - Update payload construction for engine-specific fields

+**Watch out for model naming inconsistencies.** The HuggingFace repo name, the model size label, and the API model name don't always follow predictable patterns. For example, TADA's 3B model is named `tada-3b-ml` (not `tada-3b`), because it's a multilingual variant. Always check the actual repo names and build the frontend model name mapping from those, not from assumptions like `{engine}-{size}`.
+
 ### 3.5 Model Management

 In `app/src/components/ServerSettings/ModelManagement.tsx`:
@@ -155,54 +288,173 @@ 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:**
-```
-linacodec @ git+https://github.com/user/repo.git
+**Non-PyPI packages** — Some libraries only exist on GitHub or require custom indexes:
 ```
+# 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
-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
+Check for conflicts with the existing stack before adding anything:

-## 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 |
-|-------|---------|-----|
-| `inspect.getsource()` at import | "could not get source code" | `--collect-all <package>` |
-| 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>` |
+# If it fails, check what the package pins:
+pip show model-package | grep Requires
+# Look at setup.py/pyproject.toml for version constraints
+```

-### 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` |
+| TADA | `inspect.getsource` error in DAC's `Snake1d` | `@torch.jit.script` calls `inspect.getsource()` which fails without `.py` source files | Wrote a lightweight shim (`dac_shim.py`) reimplementing `Snake1d` without `@torch.jit.script`, registered fake `dac.*` modules in `sys.modules` |
+| 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
-3. Check stderr for the actual error
-4. Fix, rebuild, repeat
+2. Launch the binary directly (not via `python -m`)
+3. Test the **full chain**: download → load → generate → progress tracking
+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

@@ -240,6 +492,90 @@ def _get_device(self):
    return "cpu"  # Skip MPS
 ```

+### Gated HuggingFace repos as hardcoded config sources
+
+Some models hardcode a gated HuggingFace repo as their tokenizer or config source (e.g., TADA hardcodes `"meta-llama/Llama-3.2-1B"` in both its `AlignerConfig` and `TadaConfig`). This silently fails without HF authentication.
+
+**Fix:** Download from an ungated mirror and patch the config objects directly:
+
+```python
+# Download tokenizer from ungated mirror
+UNGATED_TOKENIZER = "unsloth/Llama-3.2-1B"
+tokenizer_path = snapshot_download(UNGATED_TOKENIZER, token=None)
+
+# Patch the model config to use the local path instead of the gated repo
+config = ModelConfig.from_pretrained(model_path)
+config.tokenizer_name = tokenizer_path
+model = ModelClass.from_pretrained(model_path, config=config)
+```
+
+**Do NOT monkey-patch `AutoTokenizer.from_pretrained`** — it's a classmethod, and replacing it corrupts the descriptor, which breaks other engines that use different tokenizers (e.g., Qwen uses a Qwen tokenizer via `AutoTokenizer`). Always patch at the config level, not the class method level.
+
+### `torchaudio.load()` requires `torchcodec` in 2.10+
+
+As of `torchaudio>=2.10`, `torchaudio.load()` requires the `torchcodec` package for audio I/O. If your engine or backend code uses `torchaudio.load()`, replace it with `soundfile`:
+
+```python
+# Before (breaks without torchcodec):
+import torchaudio
+waveform, sr = torchaudio.load("audio.wav")
+
+# After:
+import soundfile as sf
+import torch
+data, sr = sf.read("audio.wav", dtype="float32")
+waveform = torch.from_numpy(data).unsqueeze(0)
+```
+
+Note: `torchaudio.functional.resample()` and other pure-PyTorch math functions work fine without `torchcodec` — only the I/O functions are affected.
+
+### `@torch.jit.script` breaks in frozen builds
+
+`torch.jit.script` calls `inspect.getsource()` to parse the decorated function's source code. In a PyInstaller binary, `.py` source files aren't available, so this crashes at import time.
+
+**Fix:** Remove or avoid `@torch.jit.script` decorators. If the decorated function comes from an upstream dependency, write a shim that reimplements the function without the decorator (see "Toxic dependency chains" below).
+
+### Toxic dependency chains — the shim pattern
+
+Sometimes a model library depends on a package with a massive, hostile transitive dependency tree, but only uses a tiny piece of it. When the dependency chain is unbuildable or would pull in dozens of unwanted packages, the right move is to write a lightweight shim.
+
+**Example:** TADA depends on `descript-audio-codec` (DAC), which pulls in `descript-audiotools` -> `onnx`, `tensorboard`, `protobuf`, `matplotlib`, `pystoi`, etc. The `onnx` package fails to build from source on macOS. But TADA only uses `Snake1d` from DAC — a 7-line PyTorch module.
+
+**Solution:** Create a shim at `backend/utils/dac_shim.py` that registers fake modules in `sys.modules`:
+
+```python
+import sys
+import types
+import torch
+from torch import nn
+
+def snake(x, alpha):
+    """Snake activation — reimplemented without @torch.jit.script."""
+    return x + (1.0 / (alpha + 1e-9)) * torch.sin(alpha * x).pow(2)
+
+class Snake1d(nn.Module):
+    def __init__(self, channels):
+        super().__init__()
+        self.alpha = nn.Parameter(torch.ones(1, channels, 1))
+    def forward(self, x):
+        return snake(x, self.alpha)
+
+# Register fake dac.* modules so "from dac.nn.layers import Snake1d" works
+_nn = types.ModuleType("dac.nn")
+_layers = types.ModuleType("dac.nn.layers")
+_layers.Snake1d = Snake1d
+_nn.layers = _layers
+
+for name, mod in [("dac", types.ModuleType("dac")),
+                   ("dac.nn", _nn), ("dac.nn.layers", _layers)]:
+    sys.modules[name] = mod
+```
+
+**Key rules for shims:**
+- Import the shim **before** importing the model library (so it finds the fake modules first)
+- Do NOT use `@torch.jit.script` in the shim (see above)
+- Only reimplement what the model actually uses — check the import chain carefully
+
 ## Upcoming Engines

 Based on the current model landscape, these are candidates for future integration:
@@ -250,8 +586,83 @@ 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 |
 | **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
+- [ ] Searched for `@torch.jit.script` / `torch.jit.script` (crashes in frozen builds)
+- [ ] Searched for `torchaudio.load` / `torchaudio.save` (requires `torchcodec` in 2.10+)
+- [ ] Searched for hardcoded gated HuggingFace repo names (e.g., `meta-llama/*`)
+- [ ] Evaluated whether any dependency is used minimally enough to shim instead of install
+- [ ] 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 |
@@ -0,0 +1,173 @@
+# CUDA Libs as a Bolt-On Addon
+
+## Problem
+
+Every time we bump `__version__` (even for a UI tweak or bugfix), the exact-match version check in both `main.rs:222` and `cuda.py:237` invalidates the user's ~2.4GB CUDA binary, forcing a full redownload. The CUDA binary is the entire server rebuilt with NVIDIA libs included -- there's no separation between app logic and the CUDA runtime.
+
+## Why This Is Hard With `--onefile`
+
+The core tension is PyInstaller `--onefile` mode (`build_binary.py:39`). In onefile mode, everything -- Python code, all dependencies, torch, the NVIDIA `.dll`/`.so` files -- gets packed into a single self-extracting archive. There's no concept of "swap out one part." The binary IS the server.
+
+## Options
+
+### Option A: Switch to `--onedir` for the CUDA Build (Recommended)
+
+Instead of `--onefile`, build the CUDA variant as a directory (a folder with the exe + all the shared libs alongside it). Then split the distribution into two archives:
+
+1. **`voicebox-server-cuda` executable + non-NVIDIA deps** (~200-400MB) -- versioned with the app, redownloaded on every app update.
+2. **`cuda-libs-cu126.tar.gz`** (~2GB) -- the `nvidia.*` packages (cublas, cudnn, cuda_runtime, etc.), versioned independently (e.g., `cuda-libs-cu126-v1`). Only redownloaded when we bump the CUDA toolkit version or torch's CUDA dependency changes.
+
+#### How it would work at runtime
+
+- Tauri downloads the server binary archive and extracts it to `{data_dir}/backends/cuda/`
+- On first CUDA setup (or when cuda-libs version bumps), downloads and extracts the libs archive into the same directory
+- The CUDA server exe finds the `.dll`/`.so` files next to it (standard PyInstaller onedir behavior)
+- Version check becomes two checks: server version + cuda-libs version
+
+#### Independent versioning
+
+Add a `cuda-libs.json` manifest:
+
+```json
+{"version": "cu126-v1", "torch_compat": ">=2.6.0,<2.8.0"}
+```
+
+The server checks this on startup. The Tauri side checks it before launching. Only bump `cu126-v1` -> `cu126-v2` when we actually change the CUDA toolkit or torch major version.
+
+#### Build pipeline changes
+
+The CI `build-cuda-windows` job would build with `--onedir`, then separate the output into two archives. The CUDA libs archive could be built less frequently (only when torch/CUDA version changes) and stored as a pinned release asset.
+
+#### Download experience
+
+- First-time CUDA setup: ~2.4GB total (same as today)
+- Subsequent app updates: ~200-400MB for the server, CUDA libs stay cached
+- CUDA toolkit bump: ~2GB for just the libs
+
+#### Pros
+
+- PyInstaller `--onedir` natively produces this structure -- NVIDIA DLLs end up as discrete files in the output directory
+- The separation is natural: PyInstaller puts torch's NVIDIA deps in predictable paths (`nvidia/cublas/lib/`, etc.)
+- CUDA libs are highly stable -- only rebundle when changing CUDA toolkit version (e.g., cu126 -> cu128) or major torch version
+- Server updates become ~200-400MB instead of ~2.4GB
+- No library path hacking needed -- torch finds NVIDIA DLLs because they're in the same directory tree
+
+#### Cons
+
+- Onedir means a folder with hundreds of files instead of a single exe -- more complex to manage, extract, and clean up
+- Need to modify download/assembly logic in `cuda.py` to handle two separate archives
+- The Tauri side (`main.rs`) needs to point at an exe inside a directory rather than a standalone binary
+- Users who manually manage the file may find the folder structure confusing
+
+#### TTS engine compatibility
+
+No issues. The TTS engines are pure Python + torch. They don't care whether NVIDIA libs are inside the binary or sitting next to it -- torch's dynamic loader finds them either way.
+
+---
+
+### Option B: Keep `--onefile` but Externalize CUDA Libs via Library Path
+
+Keep the server as a single `--onefile` binary (with NVIDIA packages excluded, same as the CPU build). Ship the CUDA libs as a separate download that gets extracted to `{data_dir}/backends/cuda-libs/`. Before launching, set the library search path to include that directory.
+
+**Important caveat:** The CPU torch wheel (`whl/cpu`) doesn't have CUDA kernels compiled in -- it's a fundamentally different build. So the binary would need to be built with CUDA-compiled torch but with the NVIDIA runtime libraries excluded. The runtime libs (cublas, cudnn, etc.) would be provided externally.
+
+#### How it would work
+
+- Build ONE "CUDA-ready" server binary with CUDA-compiled torch but NVIDIA runtime packages excluded
+- Ship `cuda-libs-cu126-v1.tar.gz` separately (~2GB of `.dll`/`.so` files)
+- When launching, Tauri sets `PATH` (Windows) or `LD_LIBRARY_PATH` (Linux) to include the cuda-libs directory
+
+#### Pros
+
+- Single server binary for both CPU and CUDA users -- simplifies build pipeline enormously
+- True bolt-on CUDA libs with fully independent versioning
+- Server updates are always small (~150MB for the onefile binary)
+
+#### Cons
+
+- **Fragile on Windows.** PyInstaller `--onefile` extracts to a temp directory at runtime and the internal torch may not find externally-placed NVIDIA libs. DLL resolution on Windows is notoriously unreliable in this scenario.
+- `os.add_dll_directory()` only affects `LoadLibraryEx` with `LOAD_LIBRARY_SEARCH_USER_DIRS` flag -- not all DLL loads go through this path
+- PyInstaller's onefile bootloader may configure DLL search paths before Python code runs
+- Could work on Linux but is fragile on Windows
+
+---
+
+### Option C: Hybrid -- `--onefile` Server + Dynamic CUDA Lib Loading at Runtime
+
+Build the server as `--onefile` with CUDA-compiled torch but with NVIDIA packages excluded. At startup, before torch initializes CUDA, explicitly load the NVIDIA shared libraries using `ctypes.CDLL` or `os.add_dll_directory()`.
+
+In `server.py`, before any torch imports:
+
+```python
+cuda_libs_dir = os.environ.get("VOICEBOX_CUDA_LIBS")
+if cuda_libs_dir and os.path.isdir(cuda_libs_dir):
+    if sys.platform == "win32":
+        os.add_dll_directory(cuda_libs_dir)
+        os.environ["PATH"] = cuda_libs_dir + os.pathsep + os.environ.get("PATH", "")
+    else:
+        os.environ["LD_LIBRARY_PATH"] = cuda_libs_dir + ":" + os.environ.get("LD_LIBRARY_PATH", "")
+```
+
+#### Pros
+
+- Single server binary, true bolt-on CUDA libs
+- Clean separation of concerns
+- Independent versioning
+
+#### Cons
+
+- Needs careful testing with each torch version -- CUDA initialization happens deep in C++ extension layer
+- On Windows, `os.add_dll_directory()` may not cover all DLL load paths
+- PyInstaller's onefile bootloader may have already configured DLL search paths before Python code runs
+- Most complex to get right and maintain
+
+## Recommendation
+
+**Option A (`--onedir` with split archives)** is the most reliable path:
+
+1. **It actually works.** `--onedir` puts all files on disk as regular files. Torch finds NVIDIA DLLs because they're in the same directory tree, exactly as they would be in a normal pip install.
+2. **Natural separation.** PyInstaller's `--onedir` output already separates the NVIDIA `.dll`/`.so` files into `nvidia/` subdirectories. We can split the output directory into "core" and "nvidia-libs" archives after building.
+3. **Independent versioning is straightforward.** A `cuda-libs.json` manifest controls when redownloads are needed.
+4. **Build pipeline simplification.** Build CUDA libs archive less frequently, store as a pinned release asset.
+
+The main cost is managing a directory instead of a single file, but we already have sophisticated download/assembly infrastructure in `cuda.py` with manifests and split parts. Extending that to handle two archives is incremental work.
+
+## Tauri Compatibility (Validated)
+
+Tauri handles PyInstaller `--onedir` with no issues. The key insight is that we're **not** using a static sidecar for CUDA -- we're downloading and extracting at runtime (the existing `cuda.py` + `main.rs` flow). For runtime-launched processes, Tauri's `tauri::shell::Command` supports arbitrary directories natively.
+
+### The critical change in `main.rs`
+
+The only Tauri-side change needed is adding `.current_dir()` when spawning the CUDA backend:
+
+```rust
+let cuda_dir = data_dir.join("backends/cuda");
+let exe_path = cuda_dir.join("voicebox-server-cuda.exe");
+
+let mut cmd = app.shell().command(exe_path.to_str().unwrap());
+cmd = cmd.current_dir(&cuda_dir);  // PyInstaller finds all DLLs relative to exe
+cmd = cmd.args(["--data-dir", &data_dir_str, "--port", &port_str, "--parent-pid", &parent_pid_str]);
+```
+
+`.current_dir()` tells the PyInstaller bootloader that everything (DLLs, `nvidia/cublas/lib/`, `_internal/`, torch extensions, etc.) lives relative to the exe. Torch finds the NVIDIA libs exactly as it does in a normal `pip install` or dev environment -- no `LD_LIBRARY_PATH` hacks, no `os.add_dll_directory` gymnastics.
+
+### Community evidence
+
+- Multiple Tauri users run this exact pattern: Nuitka folders (exe + pythonXX.dll + supporting files), multi-file .NET apps, and PyInstaller onedir backends (GitHub issues #5719, discussion #5206).
+- The shell plugin explicitly supports `cwd` in both Rust and JS APIs.
+- No reports of torch/CUDA-specific breakage -- the onedir layout is identical to what PyInstaller produces in normal usage.
+
+### Known gotcha: process termination on Windows
+
+PyInstaller onedir creates a parent bootloader + child Python process on Windows. `child.kill()` only hits the outer process in some cases (Tauri issue #11686). Mitigation: keep a reference to the parent PID or use `taskkill /F /T` for clean shutdown. This is not a blocker -- our existing `--parent-pid` watchdog mechanism in `server.py` already handles orphan cleanup.
+
+## Next Steps
+
+1. Prototype: Build the current CUDA binary with `--onedir` and verify torch CUDA works from the output directory
+2. Measure the size split: how much is NVIDIA libs vs everything else
+3. Design the two-archive download flow and dual version checking
+4. Update `cuda.py` for dual-archive extraction (server core + cuda-libs)
+5. Update `main.rs`: change launch path to `backends/cuda/` dir + add `.current_dir()`
+6. Add `ensure_cuda_structure()` helper in Rust to verify exe + nvidia/ subdirs exist before spawning
+7. Update CI pipeline: `build-cuda-windows` produces two archives instead of split parts
+8. ~~Update `split_binary.py` or replace with archive-based distribution~~ Done: replaced with `package_cuda.py`
@@ -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."
@@ -205,10 +208,11 @@ build-server-cuda: _ensure-venv
    $env:PATH = "{{ venv_bin }};$env:PATH"; \
    & "{{ python }}" backend/build_binary.py --cuda; \
    if ($LASTEXITCODE -ne 0) { throw "build_binary.py --cuda failed with exit code $LASTEXITCODE" }; \
-    $dest = "$env:APPDATA/com.voicebox.app/backends"; \
+    $dest = "$env:APPDATA/sh.voicebox.app/backends/cuda"; \
+    if (Test-Path $dest) { Remove-Item -Recurse -Force $dest }; \
    New-Item -ItemType Directory -Path $dest -Force | Out-Null; \
-    Copy-Item "backend/dist/voicebox-server-cuda.exe" "$dest/voicebox-server-cuda.exe" -Force; \
-    Write-Host "Copied CUDA binary to $dest"
+    Copy-Item "backend/dist/voicebox-server-cuda/*" $dest -Recurse -Force; \
+    Write-Host "Copied CUDA backend to $dest"

 # Build everything locally: CPU server + CUDA server + installable Tauri app
 [windows]
@@ -1,6 +1,6 @@
 {
  "name": "@voicebox/landing",
-  "version": "0.3.0",
+  "version": "0.3.1",
  "description": "Landing page for voicebox.sh",
  "scripts": {
    "dev": "bun --bun next dev --turbo",
@@ -1,6 +1,6 @@
 {
  "name": "voicebox",
-  "version": "0.3.0",
+  "version": "0.3.1",
  "private": true,
  "workspaces": [
    "app",
@@ -0,0 +1,232 @@
+"""
+Package the PyInstaller --onedir CUDA build into two archives.
+
+Takes the PyInstaller --onedir output directory and splits it into:
+  1. voicebox-server-cuda.tar.gz  — server core (exe + non-NVIDIA deps)
+  2. cuda-libs-cu126.tar.gz       — NVIDIA runtime libraries only
+  3. cuda-libs.json                — version manifest for the CUDA libs
+
+Usage:
+    python scripts/package_cuda.py backend/dist/voicebox-server-cuda/
+    python scripts/package_cuda.py backend/dist/voicebox-server-cuda/ --output release-assets/
+    python scripts/package_cuda.py backend/dist/voicebox-server-cuda/ --cuda-libs-version cu126-v1
+"""
+
+import argparse
+import hashlib
+import json
+import sys
+import tarfile
+from pathlib import Path
+
+# DLL name prefixes that identify NVIDIA CUDA runtime libraries.
+# These DLLs may appear in different locations depending on the torch
+# and PyInstaller version:
+#   - nvidia/ subdirectories (older torch with separate nvidia-* packages)
+#   - _internal/torch/lib/ (torch 2.10+ bundles NVIDIA DLLs directly)
+#   - Top-level directory (some PyInstaller versions)
+NVIDIA_DLL_PREFIXES = (
+    "cublas",
+    "cublaslt",
+    "cudart",
+    "cudnn",
+    "cufft",
+    "cufftw",
+    "curand",
+    "cusolver",
+    "cusolvermg",
+    "cusparse",
+    "nvjitlink",
+    "nvrtc",
+    "nccl",
+    "caffe2_nvrtc",
+)
+
+# Files to keep in the server core even if they match NVIDIA prefixes.
+# These are small Python modules or stubs, not the large runtime DLLs.
+NVIDIA_KEEP_IN_CORE = {
+    "torch/cuda/nccl.py",
+    "torch/_inductor/codegen/cuda/cutlass_lib_extensions/cutlass_mock_imports/cuda/cudart.py",
+}
+
+
+def is_nvidia_file(rel_path: str) -> bool:
+    """Check if a relative path belongs to the NVIDIA CUDA libs.
+
+    Identifies large NVIDIA runtime DLLs (.dll/.so) regardless of where
+    PyInstaller placed them. Excludes small Python stubs that happen to
+    share NVIDIA-related names.
+    """
+    rel_lower = rel_path.lower().replace("\\", "/")
+
+    # Never split out Python source files or small stubs
+    if rel_lower in NVIDIA_KEEP_IN_CORE:
+        return False
+
+    # Files under nvidia/ subdirectory tree (older torch layout)
+    if rel_lower.startswith("nvidia/") or "/nvidia/" in rel_lower:
+        # Only DLLs/shared objects — not .py, .dist-info, etc.
+        if rel_lower.endswith((".dll", ".so")):
+            return True
+        # Include entire nvidia/ namespace package tree
+        for part in rel_lower.split("/"):
+            if part == "nvidia":
+                return True
+
+    # NVIDIA DLLs anywhere in the tree (e.g. _internal/torch/lib/cublas64_12.dll)
+    name = rel_lower.rsplit("/", 1)[-1]
+    if name.endswith(".dll") or name.endswith(".so"):
+        name_no_ext = name.rsplit(".", 1)[0]
+        for prefix in NVIDIA_DLL_PREFIXES:
+            if name_no_ext.startswith(prefix):
+                return True
+
+    return False
+
+
+def sha256_file(path: Path) -> str:
+    """Compute SHA-256 hex digest of a file."""
+    h = hashlib.sha256()
+    with open(path, "rb") as f:
+        while True:
+            chunk = f.read(1024 * 1024)
+            if not chunk:
+                break
+            h.update(chunk)
+    return h.hexdigest()
+
+
+def package(
+    onedir_path: Path,
+    output_dir: Path,
+    cuda_libs_version: str,
+    torch_compat: str,
+):
+    output_dir.mkdir(parents=True, exist_ok=True)
+
+    # Collect all files in the onedir output, split into core vs nvidia
+    core_files = []
+    nvidia_files = []
+
+    for item in sorted(onedir_path.rglob("*")):
+        if item.is_dir():
+            continue
+        rel = item.relative_to(onedir_path)
+        rel_str = str(rel)
+        if is_nvidia_file(rel_str):
+            nvidia_files.append((rel_str, item))
+        else:
+            core_files.append((rel_str, item))
+
+    core_size = sum(f.stat().st_size for _, f in core_files)
+    nvidia_size = sum(f.stat().st_size for _, f in nvidia_files)
+
+    print(f"Input directory: {onedir_path}")
+    print(f"Core files:  {len(core_files)} ({core_size / (1024**2):.1f} MB)")
+    print(f"NVIDIA files: {len(nvidia_files)} ({nvidia_size / (1024**2):.1f} MB)")
+
+    if not nvidia_files:
+        print(
+            f"ERROR: No NVIDIA files found in {onedir_path}. "
+            "Refusing to create an empty CUDA libs archive.",
+            file=sys.stderr,
+        )
+        print(
+            "Make sure you built with --cuda and the NVIDIA packages are present.",
+            file=sys.stderr,
+        )
+        sys.exit(1)
+
+    # Create server core archive
+    # Files are stored relative to the archive root (no parent directory prefix)
+    # so extracting to backends/cuda/ puts everything at the right level.
+    server_archive = output_dir / "voicebox-server-cuda.tar.gz"
+    print(f"\nCreating server core archive: {server_archive.name}")
+    with tarfile.open(server_archive, "w:gz") as tar:
+        for rel_str, full_path in core_files:
+            tar.add(full_path, arcname=rel_str)
+    server_sha = sha256_file(server_archive)
+    (output_dir / "voicebox-server-cuda.tar.gz.sha256").write_text(
+        f"{server_sha}  voicebox-server-cuda.tar.gz\n"
+    )
+    print(f"  Size: {server_archive.stat().st_size / (1024**2):.1f} MB")
+    print(f"  SHA-256: {server_sha[:16]}...")
+
+    # Create CUDA libs archive
+    cuda_libs_archive = output_dir / f"cuda-libs-{cuda_libs_version}.tar.gz"
+    print(f"\nCreating CUDA libs archive: {cuda_libs_archive.name}")
+    with tarfile.open(cuda_libs_archive, "w:gz") as tar:
+        for rel_str, full_path in nvidia_files:
+            tar.add(full_path, arcname=rel_str)
+    cuda_sha = sha256_file(cuda_libs_archive)
+    (output_dir / f"cuda-libs-{cuda_libs_version}.tar.gz.sha256").write_text(
+        f"{cuda_sha}  cuda-libs-{cuda_libs_version}.tar.gz\n"
+    )
+    print(f"  Size: {cuda_libs_archive.stat().st_size / (1024**2):.1f} MB")
+    print(f"  SHA-256: {cuda_sha[:16]}...")
+
+    # Write cuda-libs.json manifest
+    manifest = {
+        "version": cuda_libs_version,
+        "torch_compat": torch_compat,
+        "archive": cuda_libs_archive.name,
+        "sha256": cuda_sha,
+    }
+    manifest_path = output_dir / "cuda-libs.json"
+    manifest_path.write_text(json.dumps(manifest, indent=2) + "\n")
+    print(f"\nManifest: {manifest_path.name}")
+    print(json.dumps(manifest, indent=2))
+
+    # Summary
+    total_input = core_size + nvidia_size
+    total_output = server_archive.stat().st_size + cuda_libs_archive.stat().st_size
+    print(f"\nTotal input:  {total_input / (1024**3):.2f} GB")
+    print(f"Total output: {total_output / (1024**3):.2f} GB (compressed)")
+    print(
+        f"Server core:  {server_archive.stat().st_size / (1024**2):.1f} MB (redownloaded on app update)"
+    )
+    print(
+        f"CUDA libs:    {cuda_libs_archive.stat().st_size / (1024**2):.1f} MB (cached until CUDA toolkit bump)"
+    )
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        description="Package PyInstaller --onedir CUDA build into server + CUDA libs archives"
+    )
+    parser.add_argument(
+        "input",
+        type=Path,
+        help="Path to PyInstaller --onedir output directory (e.g. backend/dist/voicebox-server-cuda/)",
+    )
+    parser.add_argument(
+        "--output",
+        type=Path,
+        default=None,
+        help="Output directory for archives (default: same as input parent)",
+    )
+    parser.add_argument(
+        "--cuda-libs-version",
+        type=str,
+        default="cu126-v1",
+        help="Version string for the CUDA libs archive (default: cu126-v1)",
+    )
+    parser.add_argument(
+        "--torch-compat",
+        type=str,
+        default=">=2.6.0,<2.11.0",
+        help="Torch version compatibility range (default: >=2.6.0,<2.11.0)",
+    )
+    args = parser.parse_args()
+
+    if not args.input.is_dir():
+        print(f"Error: {args.input} is not a directory", file=sys.stderr)
+        print("Expected a PyInstaller --onedir output directory.", file=sys.stderr)
+        sys.exit(1)
+
+    output_dir = args.output or args.input.parent
+    package(args.input, output_dir, args.cuda_libs_version, args.torch_compat)
+
+
+if __name__ == "__main__":
+    main()
@@ -1,82 +0,0 @@
-"""
-Split a large binary into chunks for GitHub Releases (<2 GB each).
-
-Usage:
-    python scripts/split_binary.py backend/dist/voicebox-server-cuda.exe
-    python scripts/split_binary.py backend/dist/voicebox-server-cuda.exe --chunk-size 1900000000
-    python scripts/split_binary.py backend/dist/voicebox-server-cuda.exe --output release-assets/
-
-The script produces:
-    - voicebox-server-cuda.part00.exe, .part01.exe, ...  (binary chunks)
-    - voicebox-server-cuda.sha256      (SHA-256 checksum of the complete file)
-    - voicebox-server-cuda.manifest    (ordered list of part filenames)
-"""
-
-import argparse
-import hashlib
-import sys
-from pathlib import Path
-
-
-def split(input_path: Path, chunk_size: int, output_dir: Path):
-    output_dir.mkdir(parents=True, exist_ok=True)
-    data = input_path.read_bytes()
-    total_size = len(data)
-
-    # Write SHA-256 of the complete file
-    sha256 = hashlib.sha256(data).hexdigest()
-    checksum_file = output_dir / f"{input_path.stem}.sha256"
-    checksum_file.write_text(f"{sha256}  {input_path.name}\n")
-
-    # Split into chunks
-    parts = []
-    for i in range(0, total_size, chunk_size):
-        part_index = len(parts)
-        part_name = f"{input_path.stem}.part{part_index:02d}{input_path.suffix}"
-        part_path = output_dir / part_name
-        part_path.write_bytes(data[i:i + chunk_size])
-        parts.append(part_name)
-
-    # Write manifest (ordered list of part filenames)
-    manifest_file = output_dir / f"{input_path.stem}.manifest"
-    manifest_file.write_text("\n".join(parts) + "\n")
-
-    print(f"Input:    {input_path} ({total_size / (1024**3):.2f} GB)")
-    print(f"Output:   {output_dir}/")
-    print(f"Parts:    {len(parts)} (chunk size: {chunk_size / (1024**3):.2f} GB)")
-    print(f"SHA-256:  {sha256}")
-    print(f"Manifest: {manifest_file.name}")
-    for p in parts:
-        size = (output_dir / p).stat().st_size
-        print(f"  {p}  ({size / (1024**3):.2f} GB)")
-
-
-def main():
-    parser = argparse.ArgumentParser(
-        description="Split a large binary into chunks for GitHub Releases"
-    )
-    parser.add_argument("input", type=Path, help="Path to the binary file to split")
-    parser.add_argument(
-        "--chunk-size",
-        type=int,
-        default=1_900_000_000,  # 1.9 GB — safely under 2 GB GitHub limit
-        help="Maximum chunk size in bytes (default: 1.9 GB)",
-    )
-    parser.add_argument(
-        "--output",
-        type=Path,
-        default=None,
-        help="Output directory (default: same directory as input)",
-    )
-    args = parser.parse_args()
-
-    if not args.input.exists():
-        print(f"Error: {args.input} does not exist", file=sys.stderr)
-        sys.exit(1)
-
-    output_dir = args.output or args.input.parent
-    split(args.input, args.chunk_size, output_dir)
-
-
-if __name__ == "__main__":
-    main()
@@ -1,7 +1,7 @@
 {
  "name": "@voicebox/tauri",
  "private": true,
-  "version": "0.3.0",
+  "version": "0.3.1",
  "type": "module",
  "scripts": {
    "dev": "vite",
@@ -1,6 +1,6 @@
 [package]
 name = "voicebox"
-version = "0.3.0"
+version = "0.3.1"
 description = "A production-quality desktop app for Qwen3-TTS voice cloning and generation"
 authors = ["you"]
 license = ""
@@ -197,22 +197,24 @@ async fn start_server(
    println!("Data directory: {:?}", data_dir);
    println!("Remote mode: {}", remote.unwrap_or(false));

-    // Check for CUDA backend binary in data directory
+    // Check for CUDA backend in data directory (onedir layout: backends/cuda/)
    let cuda_binary = {
-        let backends_dir = data_dir.join("backends");
+        let cuda_dir = data_dir.join("backends").join("cuda");
        let cuda_name = if cfg!(windows) {
            "voicebox-server-cuda.exe"
        } else {
            "voicebox-server-cuda"
        };
-        let path = backends_dir.join(cuda_name);
-        if path.exists() {
-            println!("Found CUDA backend binary at {:?}", path);
+        let exe_path = cuda_dir.join(cuda_name);
+        if exe_path.exists() {
+            println!("Found CUDA backend at {:?}", cuda_dir);

-            // Version check: run --version and compare to app version
+            // Version check: run --version from the onedir directory so
+            // PyInstaller can find its support files for the fast --version path
            let app_version = app.config().version.clone().unwrap_or_default();
-            let version_ok = match std::process::Command::new(&path)
+            let version_ok = match std::process::Command::new(&exe_path)
                .arg("--version")
+                .current_dir(&cuda_dir)
                .output()
            {
                Ok(output) => {
@@ -237,7 +239,7 @@ async fn start_server(
            };

            if version_ok {
-                Some(path)
+                Some(exe_path)
            } else {
                None
            }
@@ -300,10 +302,14 @@ async fn start_server(
        println!("Custom models directory: {}", dir);
    }

-    // If CUDA binary exists, launch it directly instead of the bundled sidecar
+    // If CUDA binary exists, launch it from the onedir directory.
+    // .current_dir() is critical: PyInstaller onedir expects all DLLs and
+    // support files (nvidia/, _internal/, etc.) relative to the exe.
    let spawn_result = if let Some(ref cuda_path) = cuda_binary {
-        println!("Launching CUDA backend: {:?}", cuda_path);
+        let cuda_dir = cuda_path.parent().unwrap();
+        println!("Launching CUDA backend: {:?} (cwd: {:?})", cuda_path, cuda_dir);
        let mut cmd = app.shell().command(cuda_path.to_str().unwrap());
+        cmd = cmd.current_dir(cuda_dir);
        cmd = cmd.args(["--data-dir", &data_dir_str, "--port", &port_str, "--parent-pid", &parent_pid_str]);
        if is_remote {
            cmd = cmd.args(["--host", "0.0.0.0"]);
@@ -1,7 +1,7 @@
 {
  "$schema": "https://schema.tauri.app/config/2",
  "productName": "Voicebox",
-  "version": "0.3.0",
+  "version": "0.3.1",
  "identifier": "sh.voicebox.app",
  "build": {
    "beforeDevCommand": "bun run dev",
@@ -1,7 +1,7 @@
 {
  "name": "@voicebox/web",
  "private": true,
-  "version": "0.3.0",
+  "version": "0.3.1",
  "type": "module",
  "scripts": {
    "dev": "vite",
Author	SHA1	Message	Date
Jamie Pine	81864e831a	fix: always clean up temp archive on failure and fix justfile data dir path - Wrap download/verify/extract in try/finally so .download-*.tmp is always deleted, even on mid-download or extraction failures - Fix justfile build-server-cuda to use sh.voicebox.app (production path)	2026-03-17 09:15:23 -07:00
Jamie Pine	7bd72ea9f7	Bump version: 0.3.0 → 0.3.1	2026-03-17 07:50:12 -07:00
Jamie Pine	f96eae2567	fix: address PR review feedback from CodeRabbit - Upgrade softprops/action-gh-release@v1 to @v2 (Node 16 EOL) - Fail-fast on checksum fetch failure instead of extracting unverified archives - Abort packaging if no NVIDIA files found (prevents empty cuda-libs archive) - Fix nvidia/ path detection bug (list membership vs substring check) - Fix justfile Copy-Item nesting (copy contents, not the directory itself)	2026-03-17 06:53:54 -07:00
Jamie Pine	7d53699c96	fix: update build-server-cuda to copy onedir folder instead of single exe	2026-03-17 06:12:30 -07:00
Jamie Pine	28e91ce2c1	chore: add .spec and nul to .gitignore	2026-03-17 04:58:13 -07:00
Jamie Pine	88be097b62	fix: update package_cuda.py for PyInstaller 6.18 layout and remove split_binary.py - Fix is_nvidia_file() to match NVIDIA DLLs in _internal/torch/lib/ (PyInstaller 6.18 + torch 2.10 no longer uses nvidia/ subdirectories) - Remove deprecated split_binary.py (both archives are under 2GB) - Update torch_compat range to >=2.6.0,<2.11.0 - Update build docs for new dual-archive packaging flow	2026-03-17 04:57:05 -07:00
James Pine	564d787927	feat: split CUDA backend into independently versioned server + libs archives Switch CUDA builds from PyInstaller --onefile to --onedir and split the output into two separately versioned archives: 1. Server core (~200-400MB) — versioned with the app, redownloaded on every app update 2. CUDA libs (~2GB) — versioned independently (cu126-v1), only redownloaded when the CUDA toolkit or torch version changes This eliminates the ~2.4GB full redownload on every version bump. After initial setup, most app updates only need ~200-400MB. Closes #297	2026-03-17 04:04:17 -07:00
James Pine	2c1ee94891	docs: add TADA learnings to TTS engine guide and CUDA libs addon plan Enrich tts-engines.mdx with patterns discovered during TADA integration: - Phase 0.2: new greps for @torch.jit.script, torchaudio.load, gated repos - Phase 3.4: model naming inconsistency warning - Phase 5.2: TADA shim failure added to lessons table - Phase 6: four new workaround sections (gated repos, torchcodec, torch.jit.script, toxic dependency shim pattern) - Checklist: four new items matching the new scan patterns - Remove TADA from upcoming engines (now shipped) Add CUDA_LIBS_ADDON.md exploring --onedir split to avoid 2.4GB redownloads on every version bump.	2026-03-17 03:53:40 -07:00
Jamie PineandGitHub	e789c937ad	Merge pull request #296 from jamiepine/feat/add-tada-tts-engine Add HumeAI TADA TTS engine (1B English + 3B Multilingual)	2026-03-17 03:47:47 -07:00
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