Hugging Face Audio Course Detailed Introduction

Course Overview

The Hugging Face Audio Course is a comprehensive program focused on processing audio data using Transformers. This course demonstrates how Transformers, as one of the most powerful and versatile deep learning architectures, achieve state-of-the-art results in the field of audio processing.

Course Objectives

This course will teach learners how to apply Transformers to audio data, covering a variety of audio-related tasks:

• Speech Recognition
• Audio Classification
• Text-to-Speech Generation
• Real-time Speech Transcription

Course Features

🎯 Highly Practical

• Provides real-time demo functionality, allowing learners to directly experience the model's speech transcription capabilities
• Includes numerous hands-on exercises and projects
• Developed based on powerful pre-trained models

📚 Systematic Learning

• Gain a deep understanding of the unique aspects of audio data processing
• Learn about different Transformer architectures
• Train your own audio Transformers models

🆓 Completely Free

• 100% free, public, and open-source
• All learning materials are freely accessible

Course Team

Sanchit Gandhi

• Machine Learning Research Engineer at Hugging Face
• Specializes in Automatic Speech Recognition and Translation
• Dedicated to making speech models faster, lighter, and easier to use

Matthijs Hollemans

• Machine Learning Engineer at Hugging Face
• Author of books related to audio synthesizers
• Audio plugin developer

Maria Khalusova

• Documentation and Course Lead at Hugging Face
• Specializes in creating educational content and documentation
• Skilled at simplifying complex technical concepts

Vaibhav Srivastav

• ML Developer Advocate Engineer at Hugging Face
• Researches low-resource Text-to-Speech technologies
• Committed to democratizing state-of-the-art speech research

Course Structure

Unit 1: Audio Data Fundamentals

• Learn the unique characteristics of audio data processing
• Audio processing techniques and data preparation

Unit 2: Introduction to Audio Applications

• Understand audio application scenarios
• Learn to use 🤗 Transformers pipelines
• Practice audio classification and speech recognition tasks

Unit 3: Exploring Transformer Architectures

• Deep dive into audio Transformer architectures
• Learn the differences and suitable scenarios for various architectures

Unit 4: Music Genre Classifier

• Build your own music genre classifier
• Practice project development

Unit 5: Deep Learning for Speech Recognition

• In-depth study of speech recognition technology
• Build a meeting recording transcription model

Unit 6: Text-to-Speech

• Learn techniques for generating speech from text
• Implement a TTS system

Unit 7: Real-world Application Development

• Learn to build real-world audio applications
• Develop complete solutions using Transformers

Learning Path and Certification

Course Flexibility

• Learn at your own pace
• Recommended to follow the unit order
• Quizzes provided to test learning effectiveness

Certification Options

Certificate of completion

• Requirement: Complete 80% of the hands-on exercises

Certificate of honors

• Requirement: Complete 100% of the hands-on exercises

Prerequisites

Required Background

• Foundational knowledge of deep learning
• Basic understanding of Transformers

Not Required Background

• No prior audio data processing expertise is required
• For supplementary Transformers knowledge, refer to the NLP Course

Release Schedule

Unit	Release Date
Unit 0, Unit 1, Unit 2	June 14, 2023
Unit 3, Unit 4	June 21, 2023
Unit 5	June 28, 2023
Unit 6	July 5, 2023
Unit 7, Unit 8	July 12, 2023

Tech Stack

Key Tools

• 🤗 Transformers library
• 🤗 Datasets
• 🤗 Tokenizers
• 🤗 Accelerate
• Hugging Face Hub

Covered Technologies

• Usage of pre-trained models
• Audio data preprocessing
• Model fine-tuning and training
• Real-time audio processing
• Audio feature extraction

Learning Outcomes

Upon completing this course, learners will possess:

1. Solid Theoretical Foundation: A deep understanding of the principles behind Transformers' application in audio.
2. Practical Skills: Ability to handle various audio-related tasks.
3. Project Experience: Completion of multiple practical projects, including classifiers, recognition systems, etc.
4. Engineering Capability: Ability to build and deploy audio processing applications.

Open Source Contribution

This course is fully open-source, hosted on GitHub, and welcomes community contributions and translations. Course materials can be found in the GitHub repository.

Target Audience

• Deep learning practitioners interested in audio processing
• Researchers looking to apply Transformers in the audio domain
• Developers who need to build audio-related applications
• Learners interested in technologies such as speech recognition and audio classification