This repository provides comprehensive course materials for speech processing, covering topics from digital signal processing fundamentals to advanced text-to-speech and noise reduction techniques. It is designed for students and researchers interested in building practical speech processing systems, offering lectures, seminars, and homework assignments with a focus on modern neural network architectures.

How It Works

The course material is structured weekly, with each week focusing on a specific area of speech processing. It progresses from foundational concepts like DSP and mel-spectrograms to discriminative models for tasks like Voice Activity Detection (VAD) and Sound Event Detection (SED). Later weeks delve into Automatic Speech Recognition (ASR) using CTC and Wav2Vec2, Text-to-Speech (TTS) with models like FastPitch and transformers, and audio enhancement techniques such as noise reduction and acoustic echo cancellation.

Quick Start & Requirements

The repository primarily serves as a collection of lecture notes, seminar materials, and homework assignments. Specific code implementations for homework are not directly provided as installable packages but are expected to be developed by students using common Python libraries for machine learning and signal processing.

Highlighted Details

• Covers a broad spectrum of speech processing tasks: VAD, SED, Keyword Spotting, Speech Biometrics, ASR, TTS, and noise reduction.
• Includes practical assignments involving implementing DSP pipelines, training neural network models (e.g., ECAPA-TDNN, Wav2Vec2, FastPitch), and working with advanced TTS transformers.
• Features lectures and seminars on both traditional signal processing methods and state-of-the-art deep learning approaches.
• Materials are updated for Spring 2024, indicating active development and relevance.

Maintenance & Community

The course materials are associated with Yandex Data School (YSDA) and feature contributions from multiple instructors and teaching assistants, indicating a structured educational environment. Links to lecture slides and materials are provided via Google Slides and Yandex Disk.

Licensing & Compatibility

The repository's license is not explicitly stated in the provided README. Users should verify licensing for any code or materials they intend to use, especially for commercial purposes.

Limitations & Caveats

This repository contains course materials and assignments, not a ready-to-use software library. Users will need to implement the described algorithms and models themselves, requiring significant effort and expertise in speech processing and deep learning frameworks. Specific dependencies and setup instructions for homework solutions are not consolidated.