nvidia/fastpitch-hifigan-tts

Pull and run nvidia/fastpitch-hifigan-tts using Docker (this will download the full model and run it in your local environment)

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$ docker login nvcr.io
Username: $oauthtoken
Password: <PASTE_API_KEY_HERE>

Pull and run the NVIDIA NIM with the command below.

This command launches NIM container with the generic (non-optimized) model on any of the supported GPUs. GPU specific optimized models are available for select GPUs. For using optimized models, refer the Supported Models and specify NIM_MANIFEST_PROFILE according to your GPU in the Docker run command below.

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export NGC_API_KEY=<PASTE_API_KEY_HERE>

export CONTAINER_NAME=fastpitch-hifigan-tts
docker run -it --rm --name=$CONTAINER_NAME \
  --runtime=nvidia \
  --gpus '"device=0"' \
  --shm-size=8GB \
  -e NGC_API_KEY=$NGC_API_KEY \
  -e NIM_MANIFEST_PROFILE=3c8ee3ee-477f-11ef-aa12-1b4e6406fad5 \
  -e NIM_HTTP_API_PORT=9000 \
  -e NIM_GRPC_API_PORT=50051 \
  -p 9000:9000 \
  -p 50051:50051 \
  nvcr.io/nim/nvidia/fastpitch-hifigan-tts:1.0.0

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It may take a up to 30 minutes depending on your network speed, for the container to be ready and start accepting requests from the time the docker container is started.

Open a new terminal and run following command to check if the service is ready to handle inference requests

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curl -X 'GET' 'http://localhost:9000/v1/health/ready'

If the service is ready, you get a response similar to the following.

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{"ready":true}

Install the Riva Python client package

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sudo apt-get install python3-pip
pip install -r https://raw.githubusercontent.com/nvidia-riva/python-clients/main/requirements.txt
pip install --force-reinstall git+https://github.com/nvidia-riva/python-clients.git

Download Riva sample clients

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git clone https://github.com/nvidia-riva/python-clients.git

Run Text to Speech inference

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python3 python-clients/scripts/tts/talk.py --server 0.0.0.0:50051 --text "Hello, this is a speech synthesizer." --language-code en-US --output output.wav

On running the above command, the synthesized audio file named output.wav will be created.

For more details on getting started with this NIM, visit the NVIDIA NIM Docs.