A PyTorch-based Speech Toolkit
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Updated
Jul 1, 2024 - Python
A PyTorch-based Speech Toolkit
End-to-End Speech Processing Toolkit
This speech separation based framework is for multi-talker keyword spotting tasks and is implemented in the ESPnet2 toolkit.
Make the sound you hear pure and clean by deep learning.
Scripts for data generation, scoring and data manifest preparation for CHiME-8 DASR task.
The dataset of Speech Recognition
💎 A list of accessible speech corpora for ASR, TTS, and other Speech Technologies
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
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.
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