End-to-End Speech Processing Toolkit
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Updated
Sep 20, 2024 - Python
End-to-End Speech Processing Toolkit
A PyTorch-based Speech Toolkit
This is the official implementation of our multi-channel multi-speaker multi-spatial neural audio codec architecture.
Scripts for data generation, scoring and data manifest preparation for CHiME-8 DASR task.
Make the sound you hear pure and clean by deep learning.
Unofficial PyTorch implementation of Google AI's VoiceFilter system
The PyTorch-based audio source separation toolkit for researchers
UniSpeech - Large Scale Self-Supervised Learning for Speech
Official source code of the INTERSPEECH 2023 paper: "Audio-Visual Speech Separation in Noisy Environments with a Lightweight Iterative Model" (AVLIT)
PyTorch implementation of "FullSubNet: A Full-Band and Sub-Band Fusion Model for Real-Time Single-Channel Speech Enhancement."
Acoustic Fence Using Multi-Microphone Speaker Separation
Tools for Speech Enhancement integrated with Kaldi
A personal toolkit for single/multi-channel speech recognition & enhancement & separation.
A unofficial Pytorch implementation of Google's VoiceFilter
A PyTorch implementation of "TasNet: Surpassing Ideal Time-Frequency Masking for Speech Separation" (see recipes in aps framework https://github.com/funcwj/aps)
Conv-TasNet: Surpassing Ideal Time-Frequency Magnitude Masking for Speech Separation Pytorch's Implement
A PyTorch implementation of Conv-TasNet described in "TasNet: Surpassing Ideal Time-Frequency Masking for Speech Separation" with Permutation Invariant Training (PIT).
Pytorch Models for Speech Enhancement
Dual-path RNN: efficient long sequence modeling for time-domain single-channel speech separation implemented by Pytorch
Executable code based on Google articles
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