A PyTorch-based Speech Toolkit
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Updated
Dec 20, 2024 - Python
A PyTorch-based Speech Toolkit
End-to-End Speech Processing Toolkit
The PyTorch-based audio source separation toolkit for researchers
💎 A list of accessible speech corpora for ASR, TTS, and other Speech Technologies
Unofficial PyTorch implementation of Google AI's VoiceFilter system
A must-read paper for speech separation based on neural networks
A PyTorch implementation of Conv-TasNet described in "TasNet: Surpassing Ideal Time-Frequency Masking for Speech Separation" with Permutation Invariant Training (PIT).
PyTorch implementation of "FullSubNet: A Full-Band and Sub-Band Fusion Model for Real-Time Single-Channel Speech Enhancement."
Deep Xi: A deep learning approach to a priori SNR estimation implemented in TensorFlow 2/Keras. For speech enhancement and robust ASR.
This repo summarizes the tutorials, datasets, papers, codes and tools for speech separation and speaker extraction task. You are kindly invited to pull requests.
Conv-TasNet: Surpassing Ideal Time-Frequency Magnitude Masking for Speech Separation Pytorch's Implement
UniSpeech - Large Scale Self-Supervised Learning for Speech
Dual-path RNN: efficient long sequence modeling for time-domain single-channel speech separation implemented by Pytorch
Tools for Speech Enhancement integrated with Kaldi
The dataset of Speech Recognition
Deep Recurrent Neural Networks for Source Separation
The SpeechBrain project aims to build a novel speech toolkit fully based on PyTorch. With SpeechBrain users can easily create speech processing systems, ranging from speech recognition (both HMM/DNN and end-to-end), speaker recognition, speech enhancement, speech separation, multi-microphone speech processing, and many others.
Real-time GCC-NMF Blind Speech Separation and Enhancement
Deep learning based speech source separation using Pytorch
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 separating sources from mixtures.
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