Removes unwanted noises from audio improving speech intelligibility.
Follow the steps below to download and run the NVIDIA NIM inference microservice for this model on your infrastructure of choice.
$ docker login nvcr.io
Username: $oauthtoken
Password: <PASTE_API_KEY_HERE>
NVIDIA BNR NIM uses gRPC APIs for inferencing requests.
A NGC API KEY is required to download the appropriate models and resources when starting the NIM.
If you are not familiar with how to create the NGC_API_KEY environment variable, the simplest way is to export it in your terminal:
export NGC_API_KEY=<PASTE_API_KEY_HERE>
Run one of the following commands to make the key available at startup:
# If using bash
echo "export NGC_API_KEY=<value>" >> ~/.bashrc
# If using zsh
echo "export NGC_API_KEY=<value>" >> ~/.zshrc
Other, more secure options include saving the value in a file, so that you can retrieve with cat $NGC_API_KEY_FILE, or using a password manager.
The following command launches the BNR NIM container with the gRPC service. Find reference to runtime parameters for the container here.
docker run -it --rm --name=bnr \
--runtime=nvidia \
--gpus all \
--shm-size=8GB \
-e NGC_API_KEY=$NGC_API_KEY \
-e NIM_MAX_CONCURRENCY_PER_GPU=8 \
-e NIM_MODEL_PROFILE=<nim_model_profile> \
-e FILE_SIZE_LIMIT=36700160 \
-p 8000:8000 \
-p 8001:8001 \
nvcr.io/nim/nvidia/bnr:latest
Ensure you use the appropriate NIM_MODEL_PROFILE for your GPU. For more information about NIM_MODEL_PROFILE, refer to the the NIM Model Profile Table.
Please note, the flag --gpus all is used to assign all available GPUs to the docker container. This fails on multiple GPU unless all GPUs are same. To assign specific GPU to the docker container (in case of different multiple GPUs available in your machine) use --gpus '"device=0,1,2..."'
If the command runs successfully, you will get an output ending similar to the following:
I0415 05:25:07.343449 100 grpc_server.cc:2560] "Started GRPCInferenceService at 127.0.0.1:9001"
I0415 05:25:07.343632 100 http_server.cc:4822] "Started HTTPService at 127.0.0.1:9000"
I0415 05:25:07.384715 100 http_server.cc:358] "Started Metrics Service at 127.0.0.1:9002"
[INFO AI4M BASE LOGGER 2026-04-15 05:25:09.012 PID:176] Listening to 0.0.0.0:8001
By default BNR gRPC service is hosted on port 8001. You will have to use this port for inferencing requests.
We have provided a sample client script file in our GitHub repo. The script could be used to invoke the Docker container using the following instructions.
Download the BNR Python client code by cloning the NIM gRPC client Repository:
git clone https://github.com/NVIDIA-Maxine/nim-clients
cd nim-clients/bnr
Install the dependencies for the NVIDIA BNR Python client:
sudo apt-get install python3-pip
pip install -r requirements.txt
Go to scripts directory
cd scripts
Run the command to send a gRPC request (By Default streaming mode):
python bnr.py --target <target_ip:port> --input <input_file_path> --output <output_file_path>
For transactional mode:
python bnr.py --target <target_ip:port> --input <input_file_path> --output <output_file_path> --streaming False --sample-rate 48000
Ensure that the selected --sample-rate (48000, 16000) aligns with the NIM_MODEL_PROFILE model type configuration to maintain compatibility.
eg: if --sample-rate is 48000, then NIM_MODEL_PROFILE with model type v1-48k, v2-48k needs to be selected.
To view details of command line arguments run this command
python bnr.py -h
You will get a response similar to the following.
usage: bnr.py [-h] [--ssl-mode {MTLS,TLS}] [--ssl-key SSL_KEY] [--ssl-cert SSL_CERT] [--ssl-root-cert SSL_ROOT_CERT] [--target TARGET]
[--input INPUT] [--output OUTPUT] [--api-key API_KEY] [--function-id FUNCTION_ID] [--streaming] [--sample-rate {48000, 16000}]
Process wav audio files using gRPC and apply bnr.
options:
-h, --help show this help message and exit
--preview-mode Flag to send request to preview NVCF server on https://build.nvidia.com/nvidia/bnr/api.
--ssl-mode {MTLS,TLS}
Flag to set SSL mode, default is None
--ssl-key SSL_KEY The path to ssl private key.
--ssl-cert SSL_CERT The path to ssl certificate chain.
--ssl-root-cert SSL_ROOT_CERT
The path to ssl root certificate.
--target TARGET IP:port of gRPC service, when hosted locally. Use grpc.nvcf.nvidia.com:443 when hosted on NVCF.
--input INPUT The path to the input audio file.
--output OUTPUT The path for the output audio file.
--api-key API_KEY NGC API key required for authentication, utilized when using TRY API ignored otherwise
--function-id FUNCTION_ID
NVCF function ID for the service, utilized when using TRY API ignored otherwise
--streaming Streaming mode is enabled by default. Set --streaming False to enable transactional mode.
--sample-rate {48000, 16000}
Sample rate of input audio file in Hz, default is 48000.
--intensity-ratio Intensity ratio value between 0 and 1 to control denoising intensity. Default is 1.0 (maximum denoising).
For more details on getting started with this NIM including configuring using parameters, visit the NVIDIA BNR NIM Docs.