README.md

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:

# 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):

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):

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):

make venv
source .venv/bin/activate

2. Install project requirements into the venv:

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:

./run.sh

6. Poetry users

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

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:

# 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):

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):

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.