refactor start

2026-09-20 07:10:40 -07:00 · 2026-03-16 00:52:23 -07:00
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+# Adding a TTS Engine to Voicebox
+
+Guide for adding new TTS model backends. Based on the implementation of LuxTTS (#254), Chatterbox Multilingual (#257), Chatterbox Turbo (#258), and the PyInstaller fixes in v0.2.3.
+
+---
+
+## Overview
+
+Adding an engine touches ~12 files across 4 layers (down from ~19 after the model config registry refactor). The backend protocol work is straightforward — the real time sink is dependency hell, upstream library bugs, and PyInstaller bundling.
+
+---
+
+## Phase 1: Backend Implementation
+
+### 1.1 Create the backend file
+
+`backend/backends/<engine>_backend.py` (~200-300 lines)
+
+Implement the `TTSBackend` protocol from `backend/backends/__init__.py`:
+
+```python
+class YourBackend:
+    """Must satisfy the TTSBackend protocol."""
+
+    async def load_model(self, model_size: str = "default") -> None: ...
+    async def create_voice_prompt(self, audio_path: str, reference_text: str, use_cache: bool = True) -> tuple[dict, bool]: ...
+    async def combine_voice_prompts(self, audio_paths: list[str], ref_texts: list[str]) -> tuple[np.ndarray, str]: ...
+    async def generate(self, text: str, voice_prompt: dict, language: str = "en", seed: int | None = None, instruct: str | None = None) -> tuple[np.ndarray, int]: ...
+    def unload_model(self) -> None: ...
+    def is_loaded(self) -> bool: ...
+    def _get_model_path(self, model_size: str) -> str: ...
+```
+
+Key decisions per engine:
+
+| Decision | Options | Examples |
+|----------|---------|---------|
+| **Voice prompt storage** | Pre-computed tensors vs deferred file paths | Qwen PyTorch stores tensor dicts; Chatterbox stores `{"ref_audio": path, "ref_text": text}` |
+| **Caching** | Use voice prompt cache or skip it | LuxTTS caches with `luxtts_` prefix; Chatterbox skips caching entirely |
+| **Device selection** | CUDA / MPS / CPU | Chatterbox forces CPU on macOS (MPS tensor bugs); LuxTTS supports MPS |
+| **Model download** | Library handles it vs manual `snapshot_download` | Turbo uses manual download to bypass upstream `token=True` bug |
+| **Sample rate** | Engine-specific | LuxTTS outputs 48kHz, everything else is 24kHz |
+
+### 1.2 Voice prompt patterns
+
+There are three patterns in use. Pick the one that fits your model:
+
+**Pattern A: Pre-computed tensors** (Qwen PyTorch, LuxTTS)
+```python
+# create_voice_prompt returns opaque dict of tensors
+# Cached via torch.save(), reused across generations
+encoded = model.encode_prompt(audio_path)
+return encoded, False  # (prompt_dict, was_cached)
+```
+
+**Pattern B: Deferred file paths** (Chatterbox, MLX)
+```python
+# Just store paths, process at generation time
+return {"ref_audio": audio_path, "ref_text": reference_text}, False
+```
+
+**Pattern C: Hybrid** (possible for new engines)
+```python
+# Pre-compute speaker embeddings, store alongside paths
+embedding = model.extract_speaker(audio_path)
+return {"embedding": embedding, "ref_audio": audio_path}, False
+```
+
+If caching, prefix your cache keys to avoid collisions with other engines using the same reference audio:
+```python
+cache_key = "yourengine_" + get_cache_key(audio_path, reference_text)
+```
+
+### 1.3 Register the engine
+
+In `backend/backends/__init__.py`, three things:
+
+**1. Add a `ModelConfig` entry** in `_get_non_qwen_tts_configs()`:
+
+```python
+ModelConfig(
+    model_name="your-engine",
+    display_name="Your Engine",
+    engine="your_engine",
+    hf_repo_id="org/model-repo",
+    size_mb=3200,
+    needs_trim=False,  # set True if output needs trim_tts_output()
+    languages=["en", "fr", "de"],
+),
+```
+
+This single entry replaces what used to be 6+ scattered dicts in `main.py`. The registry helpers (`get_model_config()`, `check_model_loaded()`, `engine_needs_trim()`, etc.) all derive from this config automatically.
+
+**2. Add to `TTS_ENGINES` dict:**
+
+```python
+TTS_ENGINES = {
+    ...
+    "your_engine": "Your Engine",
+}
+```
+
+**3. Add an elif branch in `get_tts_backend_for_engine()`:**
+
+```python
+elif engine == "your_engine":
+    from .your_backend import YourBackend
+    backend = YourBackend()
+```
+
+The import is deferred so platform-specific deps aren't loaded until the engine is first requested.
+
+### 1.4 Update request models
+
+In `backend/models.py`:
+
+- Add engine name to `GenerationRequest.engine` regex pattern
+- Add any new language codes to the language regex on both `GenerationRequest` and `VoiceProfileCreate`
+
+---
+
+## Phase 2: API Integration (`main.py`)
+
+With the model config registry, `main.py` has **zero per-engine dispatch points**. All endpoints use registry helpers like `get_model_config()`, `load_engine_model()`, `engine_needs_trim()`, `check_model_loaded()`, etc.
+
+**You don't need to touch `main.py` at all** unless your engine needs custom behavior in the generate endpoint (e.g. a new post-processing step beyond `trim_tts_output`).
+
+### 2.1 What the registry handles automatically
+
+| Endpoint | Registry function used |
+|----------|----------------------|
+| `POST /generate` | `load_engine_model(engine, size)` + `engine_needs_trim(engine)` |
+| `POST /generate/stream` | `ensure_model_cached_or_raise(engine, size)` + `load_engine_model()` |
+| `GET /models/status` | `get_all_model_configs()` + `check_model_loaded(config)` |
+| `POST /models/download` | `get_model_config(name)` + `get_model_load_func(config)` |
+| `POST /models/{name}/unload` | `get_model_config(name)` + `unload_model_by_config(config)` |
+| `DELETE /models/{name}` | `get_model_config(name)` + `unload_model_by_config(config)` |
+
+### 2.2 Post-processing
+
+If your model produces trailing silence or hallucinated audio, set `needs_trim=True` on your `ModelConfig`. The generate endpoint checks `engine_needs_trim(engine)` and applies `trim_tts_output()` automatically.
+
+---
+
+## Phase 3: Frontend Integration
+
+### 3.1 TypeScript types
+
+In `app/src/lib/api/types.ts`:
+- Add to the `engine` union type on `GenerationRequest`
+
+### 3.2 Language maps
+
+In `app/src/lib/constants/languages.ts`:
+- Add entry to `ENGINE_LANGUAGES` record
+- Add any new language codes to `ALL_LANGUAGES` if needed
+
+### 3.3 Engine/model selector (shared component)
+
+The model selector is a shared component — update one file:
+
+- `app/src/components/Generation/EngineModelSelector.tsx`
+
+Add an entry to `ENGINE_OPTIONS` and `ENGINE_DESCRIPTIONS`. If the engine is English-only, add it to `ENGLISH_ONLY_ENGINES`. The `handleEngineChange()` function handles language validation automatically (resets to first available language if the current one isn't supported).
+
+Both `GenerationForm.tsx` and `FloatingGenerateBox.tsx` use `<EngineModelSelector>` — no changes needed in either.
+
+Handle engine-specific UI conditionals in the form components if needed:
+- Hide instruct field for engines that don't support it
+- Show engine-specific controls (e.g. `ParalinguisticInput` for Turbo)
+
+### 3.4 Form hook
+
+In `app/src/lib/hooks/useGenerationForm.ts`:
+- Add to Zod schema enum for `engine`
+- Add engine-to-model-name mapping (e.g. `"your_engine"` → `"your-engine"`)
+- Update payload construction to conditionally include engine-specific fields
+
+### 3.5 Model management
+
+In `app/src/components/ServerSettings/ModelManagement.tsx`:
+- Add description to `MODEL_DESCRIPTIONS` record
+- The model list auto-renders from `/models/status` data
+
+---
+
+## Phase 4: Dependencies
+
+### 4.1 Python dependencies
+
+Add to `backend/requirements.txt`. Watch for:
+
+**Pinned dependency conflicts** — If the model package pins old versions of numpy, torch, or transformers, install with `--no-deps` and list sub-dependencies manually. This is what Chatterbox requires:
+```
+# In justfile/Makefile (NOT requirements.txt):
+pip install --no-deps chatterbox-tts
+
+# In requirements.txt — list the transitive deps:
+conformer
+diffusers
+omegaconf
+# ... etc
+```
+
+**Non-PyPI packages** — Some deps only exist as git repos:
+```
+linacodec @ git+https://github.com/user/repo.git
+Zipvoice @ git+https://github.com/user/repo.git
+```
+
+**Custom package indexes** — Some packages need `--find-links`:
+```
+--find-links https://k2-fsa.github.io/icefall/piper_phonemize.html
+```
+
+### 4.2 Identifying hidden sub-dependencies
+
+When using `--no-deps`, you need to manually figure out what the package actually imports. There's no shortcut:
+
+1. Install the package normally in a throwaway venv
+2. Run `pip show <package>` to get its `Requires:` list
+3. Cross-reference against what's already in our requirements.txt
+4. Test that the engine loads and generates without import errors
+
+---
+
+## Phase 5: PyInstaller Bundling
+
+This is where most of the pain lives. If your model's Python package or its dependencies use any of the following at runtime, PyInstaller won't bundle them automatically:
+
+### 5.1 Common PyInstaller issues
+
+| Issue | Symptom | Fix |
+|-------|---------|-----|
+| **`inspect.getsource()` at import time** | "could not get source code" | `--collect-all <package>` (bundles `.py` source files, not just bytecode) |
+| **Data files (yaml, .pth.tar, lang dicts)** | FileNotFoundError at runtime | `--collect-all <package>` or `--collect-data <package>` |
+| **Native data paths (espeak-ng, etc.)** | Library looks at `/usr/share/...` | Set env var in frozen builds: `os.environ["ESPEAK_DATA_PATH"] = bundled_path` |
+| **`importlib.metadata` lookups** | "No package metadata found" | `--copy-metadata <package>` |
+| **Dynamic imports** | ModuleNotFoundError | `--hidden-import <module>` |
+| **`typeguard` / `@typechecked`** | Calls `inspect.getsource()` on decorated functions | `--collect-all` for the decorated package |
+
+### 5.2 Testing frozen builds
+
+You can't skip this. Models that work in `python -m uvicorn` will break in the PyInstaller binary. The flow:
+
+1. Build the binary: `just build` or the PyInstaller spec
+2. Run it and try to download + load + generate with the new engine
+3. Check stderr for the actual error (macOS/Linux: stdout/stderr go to Tauri sidecar logs)
+4. Fix, rebuild, repeat
+
+### 5.3 Real examples from v0.2.3
+
+These were all models that worked perfectly in dev:
+
+- **LuxTTS**: `typeguard`'s `@typechecked` calls `inspect.getsource()` at import → needed `--collect-all inflect`. `piper_phonemize` bundles `espeak-ng-data/` → needed `--collect-all piper_phonemize` + `ESPEAK_DATA_PATH` env var
+- **Chatterbox**: `resemble-perth` bundles a pretrained watermark model (`.pth.tar`, `hparams.yaml`) → needed `--collect-all perth`
+- **Both**: `huggingface_hub` silently disables tqdm based on logger level → progress bars showed 0% in frozen builds until we force-enabled the internal counter
+
+---
+
+## Phase 6: Common Upstream Workarounds
+
+Almost every model library has bugs you'll need to work around. Here's the catalog:
+
+### 6.1 torch.load device mismatch
+
+If model weights were saved on CUDA but you're loading on CPU/MPS:
+```python
+_original_torch_load = torch.load
+def _patched_torch_load(*args, **kwargs):
+    kwargs.setdefault("map_location", "cpu")
+    return _original_torch_load(*args, **kwargs)
+torch.load = _patched_torch_load
+```
+Used by both Chatterbox backends. Use a threading lock if patching globally.
+
+### 6.2 Float64/Float32 dtype mismatch
+
+`librosa` returns float64, model weights are float32. Patch the offending methods:
+```python
+original_fn = SomeClass.some_method
+def patched_fn(self, *args, **kwargs):
+    result = original_fn(self, *args, **kwargs)
+    return result.float()  # float64 → float32
+SomeClass.some_method = patched_fn
+```
+Used by Chatterbox for `S3Tokenizer.log_mel_spectrogram` and `VoiceEncoder.forward`.
+
+### 6.3 Transformers attention implementation
+
+If the model uses `output_attentions=True` with transformers >= 4.36:
+```python
+for module in model.modules():
+    if hasattr(module, '_attn_implementation'):
+        module._attn_implementation = "eager"
+```
+SDPA (the new default) doesn't support `output_attentions`. Force eager attention.
+
+### 6.4 HuggingFace token bug
+
+Some models' `from_pretrained()` passes `token=True` which requires a stored HF token even for public repos:
+```python
+from huggingface_hub import snapshot_download
+local_path = snapshot_download(repo_id=REPO, token=None)
+model = ModelClass.from_local(local_path, device=device)
+```
+Used by Chatterbox Turbo.
+
+### 6.5 MPS tensor issues
+
+MPS (Apple Silicon GPU) has incomplete operator coverage. If generation crashes on MPS:
+```python
+def _get_device(self):
+    if torch.cuda.is_available():
+        return "cuda"
+    return "cpu"  # Skip MPS entirely
+```
+Used by both Chatterbox backends. LuxTTS works fine on MPS.
+
+### 6.6 HuggingFace progress tracking
+
+To get download progress bars in the UI, wrap model loading with `HFProgressTracker`:
+```python
+from backend.utils.hf_progress import HFProgressTracker
+tracker = HFProgressTracker(model_name, progress_manager)
+with tracker.patch_download():
+    model = ModelClass.from_pretrained(repo_id)
+```
+The tracker monkey-patches tqdm to intercept HuggingFace's internal progress bars. Must be set up BEFORE importing the model library if it imports HF at module level.
+
+---
+
+## Checklist
+
+### Backend
+- [ ] `backend/backends/<engine>_backend.py` — implements TTSBackend protocol
+- [ ] `backend/backends/__init__.py` — `ModelConfig` entry + `TTS_ENGINES` + `get_tts_backend_for_engine()` elif
+- [ ] `backend/models.py` — engine name in regex, any new language codes
+- [ ] `backend/requirements.txt` — dependencies added (check for `--no-deps` needs)
+- [ ] `justfile` / `Makefile` — `--no-deps` install step if needed
+
+### API (`backend/main.py`)
+No changes needed — the model config registry handles all dispatch automatically.
+
+### Frontend
+- [ ] `app/src/lib/api/types.ts` — engine union type
+- [ ] `app/src/lib/constants/languages.ts` — `ENGINE_LANGUAGES` entry
+- [ ] `app/src/components/Generation/EngineModelSelector.tsx` — `ENGINE_OPTIONS` + `ENGINE_DESCRIPTIONS` + `ENGLISH_ONLY_ENGINES`
+- [ ] `app/src/lib/hooks/useGenerationForm.ts` — Zod schema + model mapping
+- [ ] `app/src/components/ServerSettings/ModelManagement.tsx` — model description
+
+### Production
+- [ ] PyInstaller spec — `--collect-all`, `--hidden-import`, `--copy-metadata` as needed
+- [ ] Test in frozen binary — download, load, generate all work
+- [ ] Download progress — `HFProgressTracker` wired up, progress shows in UI
+
+### Upstream workarounds (check which apply)
+- [ ] torch.load device mapping (CUDA weights on CPU)
+- [ ] Float64→Float32 patches (librosa interaction)
+- [ ] Eager attention forcing (transformers >= 4.36)
+- [ ] HF token bypass (snapshot_download + from_local)
+- [ ] MPS skip (if operators not supported)
+- [ ] espeak-ng / native data path env vars