metadata

title: Seamless Streaming
emoji: 📞
colorFrom: blue
colorTo: yellow
sdk: docker
pinned: false
suggested_hardware: t4-small

Seamless Streaming demo

Running on HF spaces

You can simply duplicate the space to run it. Make sure to unset the environment variable LOCK_SERVER_COMPLETELY.

Running locally

Install backend seamless_server dependencies

Please note: we do not recommend running the model on CPU. CPU inference will be slow and introduce noticable delays in the simultaneous translation.

The example below is for PyTorch stable (2.1.1) and variant cu118. Check here to find the torch/torchaudio command for your variant. Check here to find the fairseq2 command for your variant.

If running for the first time, create conda environment and install the desired torch version. Then install the rest of the requirements:

cd seamless_server
conda create --yes --name smlss_server python=3.8 libsndfile==1.0.31
conda activate smlss_server
conda install --yes pytorch torchvision torchaudio pytorch-cuda=11.8 -c pytorch -c nvidia
pip install fairseq2 --pre --extra-index-url https://fair.pkg.atmeta.com/fairseq2/whl/nightly/pt2.1.1/cu118
pip install -r requirements.txt

Install frontend streaming-react-app dependencies

conda install -c conda-forge nodejs
cd streaming-react-app
npm install --global yarn
yarn
yarn build  # this will create the dist/ folder

Running the server

The server can be run locally with uvicorn below. Run the server in dev mode:

cd seamless_server
uvicorn app_pubsub:app --reload --host localhost

Run the server in prod mode:

cd seamless_server
uvicorn app_pubsub:app --host 0.0.0.0

To enable additional logging from uvicorn pass --log-level debug or --log-level trace.

Debuging

If you enable "Server Debug Flag" when starting streaming from the client, this enables extensive debug logging and it saves audio files in /debug folder.