# text-to-speech-maya

A small playground for the Maya text-to-speech inference handler and a FastAPI test server.

## Run the FastAPI test server (local)

1. Install dependencies. Follow the official PyTorch instructions for your platform first (CPU/MPS/CUDA), then install the remaining requirements:

```bash
# Example: install torch from https://pytorch.org/ for your system first, then:
pip install -r inference_endpoints/requirements.txt
```

2. Start the server (set `MAYA_MODEL_PATH` to your model path if needed):

```bash
export MAYA_MODEL_PATH="/path/to/maya_model"
uvicorn server.app:app --reload --host 0.0.0.0 --port 8000
```

3. Test the synthesis endpoint (saves `output.wav`):

```bash
curl -X POST "http://localhost:8000/synthesize" -H "Content-Type: application/json" -d '{
  "description": "neutral female voice",
  "text": "Hello world from Maya TTS"
}' --output output.wav
```

Notes

- The `EndpointHandler` expects the Maya model to be compatible with the SNAC decoder flow; you may need to adapt token decoding depending on the actual model outputs.
- `torch` often requires platform-specific wheels; if `pip install -r` fails for `torch`, install the appropriate wheel from pytorch.org first then re-run the requirements install for the remaining packages.

---

## VSCode setup (recommended)

The repo includes helper files to make development in VSCode convenient: a workspace `.venv`, editor settings, tasks, and launch configurations.

1. Create and activate a workspace venv (the Makefile has a helper):

```bash
make venv
source .venv/bin/activate
```

2. Install project requirements into the venv:

```bash
pip install -r inference_endpoints/requirements.txt
# dev tools for formatting & linting
pip install black flake8
```

3. Open this folder in VSCode. The workspace settings point to `${workspaceFolder}/.venv/bin/python` and the Python extension will activate the venv in the terminal.

4. Useful VSCode features included:

- Formatting: `Black` is configured and will run on save (line length 88).
- Linting: `flake8` is enabled; run it using the Tasks panel or via the command palette.
- Tasks: Run `Format: black`, `Lint: flake8`, or `Run server (run.sh)` from the Tasks menu.
- Launch: Use the `Run FastAPI (uvicorn)` launch configuration to start the server with debugging enabled. It will load `server/.env` for environment variables.

5. Running the server from VSCode:

- Use the Run panel and choose `Run FastAPI (uvicorn)` to start with the debugger.
- Or open the integrated terminal, ensure the venv is active, and run:

```bash
./run.sh
```

6. Poetry users

If you prefer Poetry for the server package, there's a `server/pyproject.toml`. From `server/` run:

```bash
poetry install
poetry run uvicorn server.app:app --reload --host 0.0.0.0 --port 8000
```

---

If you want I can add a short checklist or developer guide in `DEVELOPING.md` with these steps and troubleshooting tips.

# text-to-speech-maya

A small playground for the Maya text-to-speech inference handler and a FastAPI test server.

## Run the FastAPI test server (local)

1. Install dependencies. Follow the official PyTorch instructions for your platform first (CPU/MPS/CUDA), then install the remaining requirements:

```bash
# Example: install torch from https://pytorch.org/ for your system first, then:
pip install -r inference_endpoints/requirements.txt
```

2. Start the server (set `MAYA_MODEL_PATH` to your model path if needed):

```bash
export MAYA_MODEL_PATH="/path/to/maya_model"
uvicorn server.app:app --reload --host 0.0.0.0 --port 8000
```

3. Test the synthesis endpoint (saves `output.wav`):

```bash
curl -X POST "http://localhost:8000/synthesize" -H "Content-Type: application/json" -d '{
  "description": "neutral female voice",
  "text": "Hello world from Maya TTS"
}' --output output.wav
```

Notes

- The `EndpointHandler` expects the Maya model to be compatible with the SNAC decoder flow; you may need to adapt token decoding depending on the actual model outputs.
- `torch` often requires platform-specific wheels; if `pip install -r` fails for `torch`, install the appropriate wheel from pytorch.org first then re-run the requirements install for the remaining packages.