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Running Inference#

Verify Server Health#

Perform a health check on the gRPC endpoint.

  1. Install grpcurl from the fullstorydev/grpcurl repo.

    Example commands to run on Ubuntu:

    wget https://github.com/fullstorydev/grpcurl/releases/download/v1.9.1/grpcurl_1.9.1_linux_amd64.deb
sudo dpkg -i grpcurl_1.9.1_linux_amd64.deb

  2. Download the health checking proto:

    wget https://raw.githubusercontent.com/grpc/grpc/master/src/proto/grpc/health/v1/health.proto

  3. Run the health check on <server_ip> or localhost:

    grpcurl --plaintext --proto health.proto <server_ip>:8001 grpc.health.v1.Health/Check

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

    { "status": "SERVING" }

Note

To use grpcurl with an SSL enabled server, avoid using the --plaintext argument. Instead, use --cacert with a CA certificate, --key with a private key, or --cert with a certificate file. For more details, refer to grpcurl --help.

Running Inference via Script#

1. Clone the Repository#

Download the BNR Python client code by cloning the NVIDIA Maxine NIM Clients repository:

git clone https://github.com/NVIDIA-Maxine/nim-clients.git

# Go to the 'bnr' folder
cd nim-clients/bnr/

2. Install Dependencies#

sudo apt-get install python3-pip
pip install -r requirements.txt

3. Run the Python Client#

You can use the sample client script in the BNR GitHub repo to send a gRPC request to the hosted NIM server:

  1. Go to the scripts directory.

    cd scripts

  2. Run the command to send the gRPC request.

    python bnr.py --target <server_ip:port> --input <input_audio_file_path> --output <output_audio_file_path>

Streaming mode (recommended): The following example command streams the raw audio data from the sample audio file in 10 ms chunks and saves streamed output from the NIM to bnr_48k_output.wav file in the current folder.

python bnr.py --target 127.0.0.1:8001 --input ../assets/bnr_48k_input.wav --output bnr_48k_output.wav --sample-rate 48000

Note

Ensure that the selected --sample-rate aligns with the NIM_MODEL_PROFILE model type configuration to maintain compatibility.

Transactional mode: The following example command processes the packaged sample audio file and generates a bnr_48k_output.wav file in the current folder.

python bnr.py --target 127.0.0.1:8001 --input ../assets/bnr_48k_input.wav --output bnr_48k_output.wav --streaming False

Note

To use the client in Streaming mode, launch the NIM in Streaming mode. Similarly, to use the client in Transactional mode, launch the NIM in Transactional mode.

Note

Only WAV files are supported.

Note

Please note that the first inference isn’t indicative of the model’s performance.

For more details on modes, refer to Overview.

Usage for Preview API Request#

The Preview API provides a hosted version of BNR, which can be used for testing.

To use the hosted version of the API, use the following command:

python bnr.py --preview-mode \
    --ssl-mode TLS \
    --target grpc.nvcf.nvidia.com:443 \
    --function-id <function_id> \
    --api-key $API_KEY_REQUIRED_IF_EXECUTING_OUTSIDE_NGC \
    --input <input_file_path> \
    --output <output_file_path> \
    --streaming False

For further details, refer to the Try API documentation.

Command-Line Arguments#

To view the details of command-line arguments, run this command:

python bnr.py -h

  • --target <ip:port> - URI of NIM’s gRPC service. Use grpc.nvcf.nvidia.com:443 when hosted on NVCF. Default value is 127.0.0.1:8001.

  • --preview-mode - Flag to send request to preview NVCF server on Try API.

  • --ssl-mode - Set the SSL mode to TLS or MTLS. Defaults to no SSL. When running preview, TLS mode must be used with the default root certificate.

  • --ssl-key - The path to client’s PEM encoded private key. Only required for MTLS mode.

  • --ssl-cert - The path to client’s PEM encoded public certificate. Only required for MTLS mode.

  • --ssl-root-cert - The path to PEM encoded root certificate. Used only for TLS or MTLS modes.

  • --api-key <NGC_API_KEY> - NGC API key required for authentication. Utilized when using TRY API, ignored otherwise.

  • --function-id - NVCF function ID for the service. Utilized when using TRY API, ignored otherwise.

  • --input - The path to the input audio file. Default value is ../assets/bnr_48k_input.wav.

  • --output - The path for the output audio file. Default is ./bnr_48k_output.wav.

  • --streaming - Flag to control whether streaming or transactional mode should be used. The default is streaming mode; to enable transactional mode, set to False.

  • --sample-rate - Sample rate of input audio file in Hz. It can be set to 48000 or 16000. Default value is 48000.

  • --intensity-ratio - Intensity ratio value between 0 and 1 to control denoising intensity. Default is 1.0 (maximum denoising).

Python Notebook#

For an interactive experience and to explore all feature parameters, we provide a comprehensive Python notebook that demonstrates the Background Noise Removal service capabilities.

The Python notebook is located at notebook/ in the bnr client folder.