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binhqd/
maya1

endpoints_compatible

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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)

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

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

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.

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

bash

make venv
source .venv/bin/activate

Install project requirements into the venv:

bash

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

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.
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.

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

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)

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

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

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.

binhqd/maya1

text-to-speech-maya

Run the FastAPI test server (local)

VSCode setup (recommended)

text-to-speech-maya

Run the FastAPI test server (local)

binhqd/
maya1