Deep Speaker: an End-to-End Neural Speaker Embedding System.

A must-read paper for speech separation based on neural networks

Deep learning for audio denoising

Code for SuDoRm-Rf networks for efficient audio source separation. SuDoRm-Rf stands for SUccessive DOwnsampling and Resampling of Multi-Resolution Features which enables a more efficient way of sep…

A timeline of the latest AI models for audio generation, starting in 2023!

End-to-End Speech Processing Toolkit

Speech Enhancement Generative Adversarial Network in TensorFlow

Clone a voice in 5 seconds to generate arbitrary speech in real-time

speech enhancement\speech seperation\sound source localization

Implement Wave-U-Net by PyTorch, and migrate it to the speech enhancement.

Conformer-based Metric GAN for speech enhancement

Speech Enhancement Generative Adversarial Network in PyTorch

The Hugging Face Course on Transformers for Audio

Improved speech enhancement with the Wave-U-Net, a deep convolutional neural network architecture for audio source separation, implemented for the task of speech enhancement in the time-domain.

A neural network for end-to-end speech denoising

🐸 collection of TTS papers

A wrapper around speech quality metrics MOSNet, BSSEval, STOI, PESQ, SRMR, SISDR

Audio super resolution using neural networks

Tacotron 2 - PyTorch implementation with faster-than-realtime inference modified to enable cross lingual voice cloning.

AEC Challenge

A PyTorch implementation of SEGAN based on INTERSPEECH 2017 paper "SEGAN: Speech Enhancement Generative Adversarial Network"