movie.mp4
CUDA 12.1:
$ docker run --gpus=all -e COQUI_TOS_AGREED=1 --rm -p 8000:80 ghcr.io/coqui-ai/xtts-streaming-server:latest-cuda121
CUDA 11.8 (for older cards):
$ docker run --gpus=all -e COQUI_TOS_AGREED=1 --rm -p 8000:80 ghcr.io/coqui-ai/xtts-streaming-server:latest
CPU (not recommended):
$ docker run -e COQUI_TOS_AGREED=1 --rm -p 8000:80 ghcr.io/coqui-ai/xtts-streaming-server:latest-cpu
Run with a fine-tuned model:
Make sure the model folder /path/to/model/folder
contains the following files:
config.json
model.pth
vocab.json
$ docker run -v /path/to/model/folder:/app/tts_models --gpus=all -e COQUI_TOS_AGREED=1 --rm -p 8000:80 ghcr.io/coqui-ai/xtts-streaming-server:latest`
Setting the COQUI_TOS_AGREED
environment variable to 1
indicates you have read and agreed to
the terms of the CPML license. (Fine-tuned XTTS models also are under the CPML license)
To build the Docker container Pytorch 2.1 and CUDA 11.8 :
DOCKERFILE
may be Dockerfile
, Dockerfile.cpu
, Dockerfile.cuda121
, or your own custom Dockerfile.
$ git clone [email protected]:coqui-ai/xtts-streaming-server.git
$ cd xtts-streaming-server/server
$ docker build -t xtts-stream . -f DOCKERFILE
$ docker run --gpus all -e COQUI_TOS_AGREED=1 --rm -p 8000:80 xtts-stream
Setting the COQUI_TOS_AGREED
environment variable to 1
indicates you have read and agreed to
the terms of the CPML license. (Fine-tuned XTTS models also are under the CPML license)
Once your Docker container is running, you can test that it's working properly. You will need to run the following code from a fresh terminal.
$ git clone [email protected]:coqui-ai/xtts-streaming-server.git
$ cd xtts-streaming-server
$ python -m pip install -r test/requirements.txt
$ python demo.py
$ cd xtts-streaming-server/test
$ python -m pip install -r requirements.txt
$ python test_streaming.py