HifiGAN From Scratch: Building a Neural Vocoder for Speech Synthesis скачать в хорошем качестве

HifiGAN From Scratch: Building a Neural Vocoder for Speech Synthesis

Code: https://github.com/priyammaz/PyTorch-... Neural Vocoders are an important part of the TTS pipeline that takes some intermediate representation such as a Mel Spectrogram and converts it to waveforms. Although the Griffin-Lim algorithm also can do this, it typically is limited and leads to poor audio quality. The HIFIGAN architecture is a popular choice today for training a Neural Network to instead learn the conversion. PREREQS: 1) I hope you saw the Tacotron2 Video    • Build your own Voice Generator w/ Tacotron...   as we will be reusing some of the data processing code from there! 2) I also hope you are comfortable with the basics of Audio Processing like Griffin-Lim and Spectrograms    • Intro to Audio Processing for Deep Learning   Timestamps: 00:00:00 - What are Vocoders? 00:03:30 - Listening to some audio samples 00:05:30 - HIFIGAN Overview 00:06:15 - Audio to Mel Conversion / Data Processing 00:14:15 - Pretraining vs Finetuning 00:15:30 - Write the MelDataset 00:39:30 - Start the Model Implementation 00:42:30 - Weight Init 00:43:30 - Implement ResidualBlock 00:55:05 - Implement Generator 01:07:40 - What is Periodicity? 01:09:30 - Implement the Multi-Period Discriminator 01:26:00 - Implement the Multi-Scale Discriminator 01:31:30 - Wrap up the Model 01:32:05 - Feature Matching Loss 01:35:30 - Discriminator Loss from LS-GAN 01:39:00 - Generator Loss 01:40:40 - Training Script 01:58:45 - Finetuning HIFIGAN on Synthetic Mel Spectrograms 02:01:20 - Results 02:03:08 - Listen to Generations 02:08:10 - What new TTS systems are doing Socials! X   / data_adventurer   Instagram   / nixielights   Linkedin   / priyammaz   Discord   / discord   🚀 Github: https://github.com/priyammaz 🌐 Website: https://www.priyammazumdar.com/