docs for adding new engines

2026-09-16 21:30:39 -07:00 · 2026-03-17 01:13:30 -07:00
parent ac68052945
commit 8ac202aa58
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@@ -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.
@@ -3,8 +3,12 @@ title: "TTS Engines"
 description: "How to add new text-to-speech engines to Voicebox"
 ---

+> **For humans:** This doc is optimized for AI agents to implement new TTS engines autonomously. It's structured as a phased workflow with explicit gates and a checklist so an agent can do the full integration — dependency research, backend, frontend, bundling — and hand you a draft release or prod build to test locally. It's also a useful reference if you're doing it yourself.
+
 Adding an engine touches ~10 files across 4 layers. The backend protocol work is straightforward — the real time sink is dependency hell, upstream library bugs, and PyInstaller bundling.

+**Do not start writing code until you complete Phase 0.** The v0.2.3 release was three patch releases of PyInstaller fixes because dependency research was skipped. Every issue — `inspect.getsource()` failures, missing native data files, metadata lookups, dtype mismatches — was discoverable by reading the model library's source code before integration began.
+
 ## Architecture Overview

 The backend is split into layers:
@@ -18,6 +22,124 @@ The backend is split into layers:

 New engines only need to touch `backends/` and `models.py` on the backend side — the route and service layers use a model config registry that handles dispatch automatically.

+## Phase 0: Dependency Research
+
+**This phase is mandatory.** Clone the model library and its key dependencies into a temporary directory and inspect them before writing any integration code. The goal is to produce a dependency audit that identifies every PyInstaller-incompatible pattern, every native data file, and every upstream bug you'll need to work around.
+
+### 0.1 Clone and Inspect the Model Library
+
+```bash
+# Create a throwaway workspace
+mkdir /tmp/engine-research && cd /tmp/engine-research
+
+# Clone the model library
+git clone https://github.com/org/model-library.git
+cd model-library
+```
+
+**Read these files first, in order:**
+
+1. **`setup.py` / `setup.cfg` / `pyproject.toml`** — Check pinned dependency versions. If the library pins `torch==2.6.0` or `numpy<1.26`, you'll need `--no-deps` installation and manual sub-dependency listing (this is what happened with `chatterbox-tts`).
+
+2. **`__init__.py` and the main model class** — Trace the import chain. Look for:
+   - `from_pretrained()` — does it call `huggingface_hub` internally? Does it pass `token=True` (which crashes without a stored HF token)?
+   - `from_local()` — does it exist? You may need manual `snapshot_download()` + `from_local()` to bypass download bugs.
+   - Device handling — does it default to CUDA? Does it support MPS? Many libraries crash on MPS with unsupported operators.
+
+3. **All `import` statements** — Recursively trace what the library imports. You're looking for:
+   - `inspect.getsource()` anywhere in the chain (search all `.py` files)
+   - `typeguard` / `@typechecked` decorators (these call `inspect.getsource()` at import time)
+   - `importlib.metadata.version()` or `pkg_resources.get_distribution()` (need `--copy-metadata`)
+   - `lazy_loader` (needs `--collect-all` to bundle `.pyi` stubs)
+
+### 0.2 Scan for PyInstaller-Incompatible Patterns
+
+Run these searches against the cloned library **and** its transitive dependencies:
+
+```bash
+# inspect.getsource — will crash in frozen binary without --collect-all
+grep -r "inspect.getsource\|getsource(" .
+
+# typeguard / @typechecked — calls inspect.getsource at import time
+grep -r "@typechecked\|from typeguard" .
+
+# importlib.metadata — needs --copy-metadata
+grep -r "importlib.metadata\|pkg_resources.get_distribution\|pkg_resources.require" .
+
+# Data files loaded at runtime — need --collect-all or --collect-data
+grep -r "Path(__file__).parent\|os.path.dirname(__file__)\|resources_path\|pkg_resources.resource_filename" .
+
+# Native library paths — may need env var override in frozen builds
+grep -r "/usr/share\|/usr/lib\|/usr/local\|espeak\|phonemize" .
+
+# torch.load without map_location — will crash on CPU-only builds
+grep -r "torch.load(" . | grep -v "map_location"
+
+# HuggingFace token bugs
+grep -r 'token=True\|token=os.getenv' .
+
+# Float64/Float32 assumptions — librosa returns float64, many models assume float32
+grep -r "torch.from_numpy\|\.double()\|float64" .
+```
+
+### 0.3 Install and Trace in a Throwaway Venv
+
+```bash
+# Create isolated venv
+python -m venv /tmp/engine-venv
+source /tmp/engine-venv/bin/activate
+
+# Install the package (try normally first)
+pip install model-package
+
+# Check if it conflicts with our stack
+pip install model-package torch==2.10 transformers==4.57.3 numpy>=1.26
+# If this fails, you need --no-deps:
+pip install --no-deps model-package
+
+# Get the full dependency tree
+pip show model-package  # Check Requires: field
+pip show -f model-package  # List all installed files (look for data files)
+
+# Check for non-PyPI dependencies
+pip install model-package 2>&1 | grep -i "no matching distribution"
+```
+
+### 0.4 Test Model Loading on CPU
+
+Before writing any integration code, verify the model works on CPU in a plain Python script:
+
+```python
+import torch
+# Force CPU to catch map_location bugs early
+model = ModelClass.from_pretrained("org/model", device="cpu")
+
+# Test with a float32 audio array (not float64)
+import numpy as np
+audio = np.random.randn(16000).astype(np.float32)
+output = model.generate("Hello world", audio)
+print(f"Output shape: {output.shape}, dtype: {output.dtype}, sample rate: {model.sample_rate}")
+```
+
+If this crashes, you've found a bug you'll need to monkey-patch. Common ones:
+- `RuntimeError: expected scalar type Float but found Double` → needs float32 cast
+- `RuntimeError: map_location` → needs `torch.load` patch
+- `RuntimeError: Unsupported operator aten::...` → needs MPS skip
+
+### 0.5 Produce a Dependency Audit
+
+Before proceeding to Phase 1, write down:
+
+1. **PyPI vs non-PyPI deps** — which packages need `--find-links`, `git+https://`, or `--no-deps`?
+2. **PyInstaller directives needed** — which packages need `--collect-all`, `--copy-metadata`, `--hidden-import`?
+3. **Runtime data files** — which packages ship data files (YAML, pretrained weights, phoneme tables, shader libraries) that must be bundled?
+4. **Native library paths** — which packages look for data at system paths that won't exist in a frozen binary?
+5. **Monkey-patches needed** — `torch.load` map_location, float64→float32 casts, MPS skip, HF token bypass, etc.
+6. **Sample rate** — what does the engine output? (24kHz, 44.1kHz, 48kHz)
+7. **Model download method** — `from_pretrained()` with library-managed download, or manual `snapshot_download()` + `from_local()`?
+
+This audit becomes your implementation plan for Phases 1, 4, and 5.
+
 ## Phase 1: Backend Implementation

 ### 1.1 Create the Backend File
@@ -155,54 +277,172 @@ In `app/src/components/ServerSettings/ModelManagement.tsx`:

 ## Phase 4: Dependencies

+Use the dependency audit from Phase 0 to drive this phase. You should already know what packages are needed, which conflict, and which require special installation.
+
 ### 4.1 Python Dependencies

-Add to `backend/requirements.txt`. Watch for:
+Add to `backend/requirements.txt`. There are three installation patterns, depending on what Phase 0 revealed:

-**Pinned dependency conflicts** — If the model package pins old versions, install with `--no-deps`:
+**Normal PyPI packages:**
+```
+some-model-package>=1.0.0
+```
+
+**Pinned dependency conflicts (`--no-deps`)** — If the model package pins old versions of torch/numpy/transformers, install with `--no-deps` and list sub-dependencies manually. This is the pattern used for `chatterbox-tts`:
 ```bash
+# In justfile / CI setup:
 pip install --no-deps chatterbox-tts
+
+# In requirements.txt — list each actual sub-dependency:
+conformer>=0.3.2
+diffusers>=0.31.0
+omegaconf>=2.3.0
+resemble-perth>=0.0.2
+s3tokenizer>=0.1.6
 ```

-Then list sub-dependencies manually in `requirements.txt`.
+To identify sub-deps: `pip show chatterbox-tts` → `Requires:` field, then cross-reference against existing `requirements.txt` to avoid duplicates.

-**Non-PyPI packages:**
-```
-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` |
+| 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

@@ -254,4 +494,76 @@ Based on the current model landscape, these are candidates for future integratio
 | **MOSS-TTS** | Multilingual | Medium | Text-to-voice design, multi-speaker dialogue | Needs vetting |
 | **Pocket TTS** | English | ~100M | CPU-first, >1× realtime | Needs vetting |

-The multi-engine architecture is now in place, making new model integration straightforward (~1 day for a well-documented model with a PyPI package).
+## Implementation Checklist
+
+Use this as a gate between phases. Do not proceed to the next phase until every item in the current phase is checked.
+
+### Phase 0: Dependency Research
+- [ ] Cloned model library source into a temp directory
+- [ ] Read `setup.py` / `pyproject.toml` — noted pinned dependency versions
+- [ ] Traced all imports from the model class through to leaf dependencies
+- [ ] Searched for `inspect.getsource`, `@typechecked`, `typeguard` in the full dependency tree
+- [ ] Searched for `importlib.metadata`, `pkg_resources.get_distribution` in the dependency tree
+- [ ] Searched for `Path(__file__).parent`, `os.path.dirname(__file__)`, hardcoded system paths
+- [ ] Searched for `torch.load` calls missing `map_location`
+- [ ] Searched for `torch.from_numpy` without `.float()` cast
+- [ ] Searched for `token=True` or `token=os.getenv("HF_TOKEN")` in HuggingFace calls
+- [ ] Tested model loading and generation on CPU in a throwaway venv
+- [ ] Tested with a clean HuggingFace cache (no pre-downloaded models)
+- [ ] Produced a written dependency audit documenting all findings
+
+### Phase 1: Backend Implementation
+- [ ] Created `backend/backends/<engine>_backend.py` implementing `TTSBackend` protocol
+- [ ] Chose voice prompt pattern (pre-computed tensors vs deferred file paths)
+- [ ] Implemented all monkey-patches identified in Phase 0
+- [ ] Used `get_torch_device()` from `backends/base.py` for device selection
+- [ ] Used `model_load_progress()` from `backends/base.py` for download/load tracking
+- [ ] Tested: model downloads correctly
+- [ ] Tested: model loads on CPU
+- [ ] Tested: generation produces valid audio
+- [ ] Tested: voice cloning from reference audio works
+- [ ] Registered `ModelConfig` in `backends/__init__.py`
+- [ ] Added to `TTS_ENGINES` dict
+- [ ] Added factory branch in `get_tts_backend_for_engine()`
+- [ ] Updated engine regex in `backend/models.py`
+
+### Phase 2–3: Route, Service, and Frontend
+- [ ] Confirmed zero changes needed in routes/services (or documented why custom behavior is needed)
+- [ ] Added engine to TypeScript union type in `app/src/lib/api/types.ts`
+- [ ] Added language map entry in `app/src/lib/constants/languages.ts`
+- [ ] Added to `ENGINE_OPTIONS` and `ENGINE_DESCRIPTIONS` in `EngineModelSelector.tsx`
+- [ ] Added to Zod schema and model-name mapping in `useGenerationForm.ts`
+- [ ] Added description in `ModelManagement.tsx`
+
+### Phase 4: Dependencies
+- [ ] Added packages to `backend/requirements.txt`
+- [ ] If `--no-deps` needed: listed sub-dependencies explicitly
+- [ ] If git-only packages: added `@ git+https://...` entries
+- [ ] If custom index needed: added `--find-links` line
+- [ ] Updated `justfile` setup targets
+- [ ] Updated `.github/workflows/release.yml` build steps
+- [ ] Updated `Dockerfile` if applicable
+- [ ] Verified `pip install` succeeds in a clean venv with existing requirements
+
+### Phase 5: PyInstaller Bundling
+- [ ] Added `--hidden-import` entries in `build_binary.py` for:
+  - [ ] `backend.backends.<engine>_backend`
+  - [ ] The model package and its key submodules
+- [ ] Added `--collect-all` for any packages that:
+  - [ ] Use `inspect.getsource()` / `@typechecked`
+  - [ ] Ship pretrained model data files (`.pth.tar`, `.yaml`, etc.)
+  - [ ] Ship native data files (phoneme tables, shader libraries, etc.)
+- [ ] Added `--copy-metadata` for any packages that use `importlib.metadata`
+- [ ] If engine has native data paths: added `os.environ.setdefault()` in `server.py`
+- [ ] Built frozen binary with `just build`
+- [ ] Tested in frozen binary with **clean model cache** (not pre-cached from dev):
+  - [ ] Model download works with real-time progress
+  - [ ] Model loading works
+  - [ ] Generation produces valid audio
+  - [ ] No errors in stderr logs
+
+### Phase 6: Final Verification
+- [ ] Engine works in dev mode (`just dev`)
+- [ ] Engine works in frozen binary (`just build` → run binary directly)
+- [ ] Tested on target platform (macOS for MLX, Windows/Linux for CUDA)
+- [ ] No regressions in existing engines