Nemo ASR Parakeet CTC 0.6B Pipeline Tuning

View as Markdown

This example demonstrates how to use NVIDIA AITune to tune the Nemo ASR with Parakeet-CTC-0.6B 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 ParakeetCTC Deployment

To run ParakeetCTC 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: