Nemo ASR Parakeet RNNT 1.1B Pipeline Tuning

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This example demonstrates how to use NVIDIA AITune to tune the Nemo ASR with Parakeet RNNT 1.1B model.

Environment Setup

You can use either of the following options to set up the environment:

Option 1 - virtual environment managed by you

Activate your virtual environment and install the dependencies:

$pip install --extra-index-url https://pypi.nvidia.com .

Option 2 - virtual environment managed by uv

Install dependencies:

$uv sync

Usage

Sample audio file

The example uses a sample audio file that is downloaded automatically when you run the commands below without an explicit --audio_path. You can also download it manually:

$wget https://dldata-public.s3.us-east-2.amazonaws.com/2086-149220-0033.wav

Tuning and inference the model

To tune the ASR model, run:

$tune

To infer the ASR model, run:

$inference

Logging hardware metrics

If you would like to log hardware metrics during tuning or inference, export AITUNE_HARDWARE_METRICS=True environment variable, e.g.

$AITUNE_HARDWARE_METRICS=True uv run inference

AI Dynamo ParakeetRNNT Deployment

To run ParakeetRNNT as an AI Dynamo service, you need to first tune your model and then launch a test run using the provided script.

1uv pip install ".[dynamo]"
2tune
3./run_dynamo.sh

This script starts the frontend and backend services, waits for them to be ready, then runs a test client to send sample requests. Once the test completes, all services are automatically shut down. This is meant as a functional check, not to provide a permanent server.

Dynamic batching

The service uses dynamic batching — requests are grouped and processed together for efficiency. Currently, there is one frontend and one worker. To support multiple workers, move batching to a separate service that handles request grouping.

Model Details

Can be found in following pages: