nvidia/parakeet-ctc-1.1b-asr
RUN ANYWHERERecord-setting accuracy and performance for English transcription.
By running the below commands, you accept the NVIDIA AI Enterprise Terms of Use and the NVIDIA Community Models License.
Pull and run nvidia/parakeet-ctc-1_1b-asr
using Docker (this will download the full model and run it in your local environment)
$ docker login nvcr.io Username: $oauthtoken Password: <PASTE_API_KEY_HERE>
Pull and run the NVIDIA NIM with the command below.
This command launches NIM container with the generic (non-optimized) model on any of the supported GPUs. GPU specific optimized models are available for select GPUs. For using optimized models, refer the Supported Models and specify NIM_MANIFEST_PROFILE according to your GPU in the Docker run command below.
export NGC_API_KEY=<PASTE_API_KEY_HERE> export CONTAINER_NAME=parakeet-ctc-1.1b-asr docker run -it --rm --name=$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
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
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 -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
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.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.