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State-of-the-art accuracy and speed for English transcriptions.
Follow the steps below to download and run the NVIDIA NIM inference microservice for this model on your infrastructure of choice.
Install WSL2. For additional instructions refer to the documentation.
Once installed, open the NVIDIA-Workbench WSL2 distro using the following command in the Windows terminal.
wsl -d NVIDIA-Workbench
$ podman login nvcr.io
Username: $oauthtoken
Password: <PASTE_API_KEY_HERE>
Pull and run the NVIDIA NIM with the command below.
export NGC_API_KEY=<PASTE_API_KEY_HERE>
export LOCAL_NIM_CACHE=~/.cache/nim
mkdir -p "$LOCAL_NIM_CACHE"
chmod -R a+w "$LOCAL_NIM_CACHE"
podman run -it --rm \
--device nvidia.com/gpu=all \
--shm-size=16GB \
-e NGC_API_KEY=$NGC_API_KEY \
-e NIM_TAGS_SELECTOR=name=parakeet-0-6b-ctc-en-us,mode=ofl,bs=1 \
-e NIM_HTTP_API_PORT=9000 \
-e NIM_GRPC_API_PORT=50051 \
-e NIM_RELAX_MEM_CONSTRAINTS=1 \
-v "$LOCAL_NIM_CACHE:/opt/nim/.cache" \
-u $(id -u) \
-p 9000:9000 \
-p 50051:50051 \
nvcr.io/nim/nvidia/parakeet-0-6b-ctc-en-us:latest
It may take 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 Distro instance 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 streaming 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 NVIDIA NIM Docs.