# Speech recognition integration

> Speech recognition integration - Use Whisper to transcribe audio files, process them efficiently,
> and store the transcriptions in a structured format for further analysis or use.

This Markdown file sits beside the HTML page at the same path (with a `.md` suffix). It summarizes the topic and lists links for tools and LLM context.

Companion generated at `2026-05-06T18:17:09.574405+00:00` (UTC).

## Primary page

- [Speech recognition integration](https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/ai-integrations-platforms/speech-rec.html): Full documentation for this topic (HTML).

## Related documentation

- [Developer documentation](https://docs.datarobot.com/en/docs/api/index.html): Linked from this page.
- [Developer learning](https://docs.datarobot.com/en/docs/api/dev-learning/index.html): Linked from this page.
- [AI accelerators](https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/index.html): Linked from this page.
- [AI integrations and platforms](https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/ai-integrations-platforms/index.html): Linked from this page.

## Documentation content

[Access this AI accelerator on GitHub](https://github.com/datarobot/data-science-scripts/blob/master/accelerators_dev/use_cases_and_horizontal_approaches/Speech%20Recognition.ipynb)

This accelerator presents a workflow for transcribing audio files using OpenAI's Whisper model. Whisper is a state-of-the-art speech recognition system designed to handle a wide range of audio types and accents. It is highly effective for converting audio files' spoken language into written text.

The workflow includes steps to use Whisper to transcribe audio files, process them efficiently, and store the transcriptions in a structured format for further analysis or use. This can be particularly useful for tasks such as generating subtitles, transcribing meetings, or converting speech from various audio sources into text for machine learning.

In this example, you take transcribed data and build a classification model with DataRobot. You use DataRobot for model training, selection, deployment, and to evaluate data for insights.

This accelerator demonstrates how to use the Python API client to:

- Set up the environment (install and import necessary libraries including Whisper and dependencies).
- Securely connect to DataRobot.
- Get data (publicly available audio files in this example).
- Transcribe audio with Whisper.
- Use the transcription to create a classification model in DataRobot.
- Retrieve and evaluate model performance and insights.