nvidia/parakeet-ctc-1.1b-asr

Pull and run nvidia/parakeet-ctc-1_1b-asr 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=parakeet-ctc-1.1b-asr
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=9136dd64-4777-11ef-9f27-37cfd56fa6ee \
  -e NIM_HTTP_API_PORT=9000 \
  -e NIM_GRPC_API_PORT=50051 \
  -p 9000:9000 \
  -p 50051:50051 \
  nvcr.io/nim/nvidia/parakeet-ctc-1.1b-asr: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 Speech to Text inference in streaming modes. Riva ASR supports Mono, 16-bit audio in WAV, OPUS and FLAC formats.

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python3 python-clients/scripts/asr/transcribe_file.py --server 0.0.0.0:50051 --input-file <path_to_speech_file> --language-code en-US

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