- Lecture and seminar materials for each week are in ./week* folders, see README.md for materials and instructions
- Any technical issues, ideas, bugs in course materials, contribution ideas - add an issue
- The current version of the course is conducted in autumn 2021 at the CS Faculty of HSE
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week01 Introduction to Digital Signal Processing
- Lecture: Introduction to Course
- Seminar: Intro in
pytorch
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week02 Introduction to Digital Signal Processing
- Lecture: Signals, Fourier transform, Spectrograms, MelScale, MFCC and etc
- Seminar:
torchaudio, spoken digit classification
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week03 Automatic Speech Recognition (ASR) I
- Lecture: Metrics, Attention, CTC, LAS, BeamSearch
- Seminar: Audio augmentations, CTC decoding, CTC BeamSearch
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week04 Automatic Speech Recognition (ASR) II
- Lecture: RNN-T, LM-fusion, BPE
- Seminar: W&B tutorial, homework barebones overview
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week05 Speaker verification and identification
- Lecture: Metric Learning: Cosine, Contrastive, Triplet Losses. Angular Softmax. ArcFace
- Seminar: Q&A about homework
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week06 Key-word spottind (KWS)
- Lecture: (DNN, CNN, RNN+Attention) based KWS, SVDF, Orthogonality Regularization and other Tricks
- Seminar: Implementation of CNN+Attention+RNN KWS model
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week07 Text to Speech (TTS)
- Lecture: Tacotron, DeepVoice, GST, FastSpeech, AdaSpeech, Attention Tricks
- Seminar: TTS in
torchaudio
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week08 Neural Vocoders
- Lecture: WaveNet, Parallel WaveGAN
- Seminar: Implementation of WaveNet
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week09 Advanced TTS and Vocoders
- Lecture: Introduction into generative models. ParallelWaveNet, WaveGlow, WaveFlow, MelGAN, HiFiGAN, VITS
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week10 Voice Conversion
- Lecture: AutoVC, ConVoice, CycleGAN-VC, StarGAN-VC, Blow, NVC, MOSNet
- Seminar: Q&A about homework
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week11 Self-supervision in Audio and Speech
- Lecture:
- Seminar: Reading Group
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ASR Implementation of ASR model
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KWS Implementation of KWS model
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TTS Implementation of TTS model
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NV Implementation of Neural Vocoder Model
Course materials and teaching performed by