A PyTorch-based Speech Toolkit
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Updated
Jun 7, 2024 - Python
A PyTorch-based Speech Toolkit
End-to-End Speech Processing Toolkit
Scripts for data generation, scoring and data manifest preparation for CHiME-8 DASR task.
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.
The PyTorch-based audio source separation toolkit for researchers
UniSpeech - Large Scale Self-Supervised Learning for Speech
Make the sound you hear pure and clean by deep learning.
This is the official implementation of our multi-channel multi-speaker multi-spatial neural audio codec architecture.
Unofficial PyTorch implementation of Google AI's VoiceFilter system
Thesis project for Speech Separation using Deep Learning
Typing to Listen at the Cocktail Party: Text-Guided Target Speaker Extraction (LLM-TSE)
PyAnnote Voice Activity Detection (ONNX version)
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.
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.
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)
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