huggingface/audio-transformers-course

What it solves

Learning to apply Transformer models to audio and speech can be challenging because the field is technical and fast-moving. This course provides a structured, free, and open-source path to understanding how Transformers are used for tasks like speech recognition, audio classification, and more. It removes the barrier of expensive or closed materials by making all content freely available.

How it works

The course is built from chapter files written in MDX (a mix of Markdown and JSX) that live in the repository. These chapters contain explanatory text and code snippets. The code snippets can be automatically extracted into Jupyter notebooks, so learners can run and experiment with the examples. The content is hosted on the Hugging Face website, and the repository includes a table of contents file (_toctree.yml) that defines the course structure. The project also supports community contributions, especially translations into other languages, with a clear workflow: open an issue, join the Discord, fork the repo, copy the English files, translate, build locally, and submit a pull request.

Who it's for

This is for anyone who wants to learn how to apply Transformer models to audio and speech data, from beginners to practitioners. It's also for people who want to help make such education accessible to non-English speakers by contributing translations. The course is free, so it's open to all learners.

Highlights

• Completely free and open-source
• Covers Transformers applied to audio and speech tasks
• Available in multiple languages (Bengali, English, Spanish, French, Korean, Russian, Turkish, and simplified Chinese) with more possible via community translation
• Includes Jupyter notebooks generated from the course code snippets
• Provides clear guidelines for contributing translations, including building the course locally and submitting pull requests
• The course content is structured into chapters and sections, with a table of contents for easy navigation