Welcome to the Hugging Face Audio course!

Dear learner,

Welcome to this course on using transformers for audio. Time and again transformers have proven themselves as one of the most powerful and versatile deep learning architectures, capable of achieving state-of-the-art results in a wide range of tasks, including natural language processing, computer vision, and more recently, audio processing.

In this course, we will explore how transformers can be applied to audio data. You’ll learn how to use them to tackle a range of audio-related tasks. Whether you are interested in speech recognition, audio classification, or generating speech from text, transformers and this course have got you covered.

To give you a taste of what these models can do, say a few words in the demo below and watch the model transcribe it in real-time!

Throughout the course, you will gain an understanding of the specifics of working with audio data, you’ll learn about different transformer architectures, and you’ll train your own audio transformers leveraging powerful pre-trained models.

This course is designed for learners with a background in deep learning, and general familiarity with transformers. No expertise in audio data processing is required. If you need to brush up on your understanding of transformers, check out our NLP Course that goes into much detail on the transformer basics.

Meet the course team

Sanchit Gandhi, Machine Learning Research Engineer at Hugging Face

Vaibhav Srivastav, Machine Learning Developer Advocacy Intern at Hugging Face

Matthijs Hollemans, Machine Learning Engineer at Hugging Face

Maria Khalusova, Documentation Writer at Hugging Face

Course structure

The course is structured into several units that covers various topics in depth:

Each unit includes a theoretical component, where you will gain a deep understanding of the underlying concepts and techniques. Throughout the course, we provide quizzes to help you test your knowledge and reinforce your learning. Some chapters also include hands-on exercises, where you will have the opportunity to apply what you have learned.

By the end of the course, you will have a strong foundation in using transformers for audio data and will be well-equipped to apply these techniques to a wide range of audio-related tasks.

The course units will be released in several consecutive blocks with the following publishing schedule:

Units Publishing date
Unit 0, Unit 1, and Unit 2 TBD
Unit 3, Unit 4 TBD
Unit 5 TBD
Unit 6 TBD
Unit 7, Unit 8 TBD
Bonus Unit TBD

Learning paths and certification

There is no right or wrong way to take this course. All the materials in this course are 100% free, public and open-source. You can take the course at your own pace, however, we recommend going through the units in their order.

If you’d like to get certified upon the course completion, we offer two options:

Certificate type Requirements
Certificate of completion Complete 80% of the hands-on exercises according to instructions before the end of [TBD]
Certificate of honors Complete 100% of the hands-on exercises according to instructions before the end of [TBD]

Each hands-on exercise outlines its completion criteria. Once you have completed enough hands-on exercises to qualify for either of the certificates, refer to the last unit of the course to learn how you can get your certificate. Good luck!

Sign up to the course

The units of this course will be released gradually over the course of a few weeks. We encourage you to sign up to the course updates so that you don’t miss new units when they are released. Learners who sign up to the course updates will also be the first ones to learn about special social events that we plan to host.

Enjoy the course!