nvidia

canary-1b-asr

Run Anywhere

Multi-lingual model supporting speech-to-text recognition and translation.

asrastmultilingualnvidia nimnvidia rivaspanishbatchstreamingspeech-to-text
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API Reference
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Follow the steps below to download and run the NVIDIA NIM inference microservice for this model on your infrastructure of choice.

Generate API Key

Pull and Run the NIM

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

Refer to Supported Models for full list of models.

export NGC_API_KEY=<PASTE_API_KEY_HERE>

docker run -it --rm --name=riva-asr \
   --runtime=nvidia \
   --gpus '"device=0"' \
   --shm-size=8GB \
   -e NGC_API_KEY \
   -e NIM_HTTP_API_PORT=9000 \
   -e NIM_GRPC_API_PORT=50051 \
   -p 9000:9000 \
   -p 50051:50051 \
   -e NIM_TAGS_SELECTOR=name=canary-1b \
   nvcr.io/nim/nvidia/riva-asr:1.3.0

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.

Test the NIM

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

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

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

{"ready":true}

Install the Riva Python client package

sudo apt-get install python3-pip
pip install nvidia-riva-client

Download Riva sample clients

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

Run Speech to Text inference in offline modes. Riva ASR supports Mono, 16-bit audio in WAV, OPUS and FLAC formats.

python3 python-clients/scripts/asr/transcribe_file_offline.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 Riva ASR NIM Docs.