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README.md

ASR with NeMo

This example demonstrates how to use the NeMo to perform ASR (Automatic Speech Recognition) in Label Studio.

Use this model if you want to transcribe and fix your audio data.

Before you begin

Before you begin, you must install the Label Studio ML backend.

This tutorial uses the nemo_asr example.

Labeling interface

This example works with the Label Studio's pre-built Audio Transcription template (available under Audio Processing > Audio Transcription).

<View>
  <Audio name="audio" value="$audio" zoom="true" hotkey="ctrl+enter" />
  <Header value="Provide Transcription" />
  <TextArea name="transcription" toName="audio"
            rows="4" editable="true" maxSubmissions="1" />
</View>

But you can use any other labeling interface that combines <Audio> and <TextArea> elements.

Warning: If you use files hosted in Label Studio (meaning they were added using the import action), hosted in cloud storage, or connected through local storage, then you must provide the LABEL_STUDIO_URL and LABEL_STUDIO_API_KEY environment variables to the ML backend. For more information, see Allow the ML backend to access Label Studio data. For information about finding your Label Studio API key, see Access token.

Running with Docker (recommended)

  1. Start the Machine Learning backend on http://localhost:9090 with the prebuilt image:
docker-compose up
  1. Validate that backend is running:
$ curl http://localhost:9090/
{"status":"UP"}
  1. Create a project in Label Studio. Then from the Model page in the project settings, connect the model. The default URL is http://localhost:9090.

Building from source (advanced)

To build the ML backend from source, you have to clone the repository and build the Docker image:

docker-compose build

Running without Docker (advanced)

To run the ML backend without Docker, you have to clone the repository and install all dependencies using pip:

python -m venv ml-backend
source ml-backend/bin/activate
pip install -r requirements.txt

Then you can start the ML backend:

label-studio-ml start ./nemo_asr

Configuration

Parameters can be set in docker-compose.yml before running the container.

The following common parameters are available:

  • MODEL_NAME - Specify the model name for the ASR. (QuartzNet15x5Base-En by default)
  • BASIC_AUTH_USER - Specify the basic auth user for the model server
  • BASIC_AUTH_PASS - Specify the basic auth password for the model server
  • LOG_LEVEL - Set the log level for the model server
  • WORKERS - Specify the number of workers for the model server
  • THREADS - Specify the number of threads for the model server
  • LABEL_STUDIO_HOST: The host of the Label Studio instance. Default is http://localhost:8080.
  • LABEL_STUDIO_API_KEY: The API key for the Label Studio instance.

Customization

The ML backend can be customized by adding your own models and logic inside ./nemo_asr/model.py.