---
title: "TTS Engines"
description: "How to add new text-to-speech engines to Voicebox"
---

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.

## Architecture Overview

The backend is split into layers:

| Layer | Purpose | Files Touched |
|-------|---------|---------------|
| `routes/` | Thin HTTP handlers | None (auto-dispatch) |
| `services/` | Business logic | None (auto-dispatch) |
| `backends/` | Engine implementations | `your_engine_backend.py` |
| `utils/` | Shared utilities | As needed |

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 1: Backend Implementation

### 1.1 Create the Backend File

Create `backend/backends/<engine>_backend.py` (~200-300 lines) implementing the `TTSBackend` protocol:

```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 stores tensor dicts; Chatterbox stores paths |
| **Caching** | Use voice prompt cache or skip it | LuxTTS caches with prefix; Chatterbox skips caching |
| **Device selection** | CUDA / MPS / CPU | Chatterbox forces CPU on macOS (MPS bugs) |
| **Model download** | Library handles it vs manual `snapshot_download` | Turbo uses manual download to bypass `token=True` bug |
| **Sample rate** | Engine-specific | LuxTTS outputs 48kHz, everything else is 24kHz |

### 1.2 Voice Prompt Patterns

**Pattern A: Pre-computed tensors** (Qwen, LuxTTS)
```python
encoded = model.encode_prompt(audio_path)
return encoded, False  # (prompt_dict, was_cached)
```

**Pattern B: Deferred file paths** (Chatterbox, MLX)
```python
return {"ref_audio": audio_path, "ref_text": reference_text}, False
```

**Pattern C: Hybrid** (possible for new engines)
```python
embedding = model.extract_speaker(audio_path)
return {"embedding": embedding, "ref_audio": audio_path}, False
```

If caching, prefix your cache keys:
```python
cache_key = "yourengine_" + get_cache_key(audio_path, reference_text)
```

### 1.3 Register the Engine

In `backend/backends/__init__.py`:

**Add a `ModelConfig` entry:**

```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"],
),
```

**Add to `TTS_ENGINES` dict:**

```python
TTS_ENGINES = {
    ...
    "your_engine": "Your Engine",
}
```

**Add factory branch:**

```python
elif engine == "your_engine":
    from .your_backend import YourBackend
    backend = YourBackend()
```

### 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

## Phase 2: Route and Service Integration

With the model config registry, route and service layers have **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 any route or service files** unless your engine needs custom behavior in the generate pipeline.

### Post-Processing

If your model produces trailing silence, set `needs_trim=True` on your `ModelConfig`. The generation service 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

In `app/src/components/Generation/EngineModelSelector.tsx`:
- Add entry to `ENGINE_OPTIONS` and `ENGINE_DESCRIPTIONS`
- Add to `ENGLISH_ONLY_ENGINES` if applicable

### 3.4 Form Hook

In `app/src/lib/hooks/useGenerationForm.ts`:
- Add to Zod schema enum for `engine`
- Add engine-to-model-name mapping
- Update payload construction for engine-specific fields

### 3.5 Model Management

In `app/src/components/ServerSettings/ModelManagement.tsx`:
- Add description to `MODEL_DESCRIPTIONS` record

## Phase 4: Dependencies

### 4.1 Python Dependencies

Add to `backend/requirements.txt`. Watch for:

**Pinned dependency conflicts** — If the model package pins old versions, install with `--no-deps`:
```bash
pip install --no-deps chatterbox-tts
```

Then list sub-dependencies manually in `requirements.txt`.

**Non-PyPI packages:**
```
linacodec @ git+https://github.com/user/repo.git
```

**Custom package indexes:**
```
--find-links https://k2-fsa.github.io/icefall/piper_phonemize.html
```

### 4.2 Identifying Hidden Sub-Dependencies

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

## Phase 5: PyInstaller Bundling

This is where most of the pain lives. Common issues:

| 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>` |

### Testing Frozen Builds

You can't skip this. Models that work in `python -m uvicorn` will break in the PyInstaller binary.

1. Build: `just build`
2. Run and try download + load + generate
3. Check stderr for the actual error
4. Fix, rebuild, repeat

## Phase 6: Common Upstream Workarounds

### torch.load device mismatch
```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
```

### Float64/Float32 dtype mismatch
```python
original_fn = SomeClass.some_method
def patched_fn(self, *args, **kwargs):
    result = original_fn(self, *args, **kwargs)
    return result.float()
SomeClass.some_method = patched_fn
```

### HuggingFace token bug
```python
from huggingface_hub import snapshot_download
local_path = snapshot_download(repo_id=REPO, token=None)
model = ModelClass.from_local(local_path, device=device)
```

### MPS tensor issues
Skip MPS entirely if operators aren't supported:
```python
def _get_device(self):
    if torch.cuda.is_available():
        return "cuda"
    return "cpu"  # Skip MPS
```

## Upcoming Engines

Based on the current model landscape, these are candidates for future integration:

| Model | Languages | Size | Key Features | Status |
|-------|-----------|------|--------------|--------|
| **CosyVoice2-0.5B** | Multilingual | ~500MB | Instruct support (`inference_instruct2()`) | Ready |
| **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).
