Building an XTTS Streaming API with BentoML

This is a BentoML example project, demonstrating how to build a text-to-speech inference API with streaming capability using the XTTS model.

See here for a full list of BentoML example projects.

Install dependencies

git clone https://github.com/bentoml/BentoXTTSStreaming
cd BentoXTTSSTreaming

# Recommend Python 3.11 in a virtual environment
pip install -r requirements.txt

Import XTTS Model

We need to import xtts model to local BentoML model store first. You may also set the environment variable COQUI_TTS_AGREED=1 to agree to the terms of Coqui TTS.

$ COQUI_TOS_AGREED=1 python import_model.py

We can list imported model by running:

$ bentoml models list

Tag                                                                   Module  Size        Creation Time
coqui--xtts-v2:xhbbjpeiqsveicf7                                               1.95 GiB    2024-10-12 18:28:30

Run the BentoML Service

We have defined a BentoML Service in service.py. Run bentoml serve in your project directory to start the Service.

$ COQUI_TOS_AGREED=1 bentoml serve .

2024-01-18T11:13:54+0800 [INFO] [cli] Starting production HTTP BentoServer from "service:XTTSStreaming" listening on http://localhost:3000 (Press CTRL+C to quit)

The server is now active at http://localhost:3000. You can interact with it using the Swagger UI or in other different ways.

CURL

curl --header "Content-Type: application/json" \
  --request POST \
  --data '{
  "text":"It took me quite a long time to develop a voice and now that I have it I am not going to be silent.",
  "language":"en",
  "stream_chunk_size": 20,
  "add_wav_header": true}' \
  http://localhost:3000/tts/stream -o output.wav

curl -X 'POST' \
  'http://localhost:3000/synthesize' \
  -H 'accept: */*' \
  -H 'Content-Type: application/json' \
  -d '{
  "text": "It took me quite a long time to develop a voice and now that I have it I am not going to be silent.",
  "lang": "en"
}' -o output.wav

Deploy to BentoCloud

After the Service is ready, you can deploy the application to BentoCloud for better management and scalability. Sign up if you haven't got a BentoCloud account.

Make sure you have logged in to BentoCloud, then run the following command to deploy it.

bentoml deploy .

Once the application is up and running on BentoCloud, you can access it via the exposed URL.

Note: For custom deployment in your own infrastructure, use BentoML to generate an OCI-compliant image.