A Fundamental End-to-End Speech Recognition Toolkit and Open Source SOTA Pretrained Models, Supporting Speech Recognition, Voice Activity Detection, Text Post-processing etc.
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Updated
May 30, 2024 - Python
A Fundamental End-to-End Speech Recognition Toolkit and Open Source SOTA Pretrained Models, Supporting Speech Recognition, Voice Activity Detection, Text Post-processing etc.
Production First and Production Ready End-to-End Speech Recognition Toolkit
Easy-to-use Speech Toolkit including Self-Supervised Learning model, SOTA/Streaming ASR with punctuation, Streaming TTS with text frontend, Speaker Verification System, End-to-End Speech Translation and Keyword Spotting. Won NAACL2022 Best Demo Award.
⚡ TensorFlowASR: Almost State-of-the-art Automatic Speech Recognition in Tensorflow 2. Supported languages that can use characters or subwords
Python toolkit for speech processing
Pytorch实现的流式与非流式的自动语音识别框架,同时兼容在线和离线识别,目前支持Conformer、Squeezeformer、DeepSpeech2模型,支持多种数据增强方法。
基于PaddlePaddle实现端到端中文语音识别,从入门到实战,超简单的入门案例,超实用的企业项目。支持当前最流行的DeepSpeech2、Conformer、Squeezeformer模型
Pytorch implementation of conformer with with training script for end-to-end speech recognition on the LibriSpeech dataset.
EEG Transformer 2.0. i. Convolutional Transformer for EEG Decoding. ii. Novel visualization - Class Activation Topography.
Implementation of the paper "Conformer: Convolution-augmented Transformer for Speech Recognition" in Pytorch.
Implementing automatic speech recognition Conformer in PyTorch on Librispeech-100
JCCM: Joint conformer and CNN model for overlapping radio signals recognition .
[Unofficial] PyTorch implementation of "Conformer: Convolution-augmented Transformer for Speech Recognition" (INTERSPEECH 2020)
Target speaker automatic speech recognition (TS-ASR)
This is the official artifact for EMSAssist paper on MobiSys'23. EMSAssist: An End-to-End Mobile Voice Assistant at the Edge for Emergency Medical Services
Pytorch implementation of Noisy Student Training for Automatic Speech Recognition and Automatic Pronunciation Error Detection problem
Emotion classification from Brain EEG signals using a hybrid CNN-Transformer model and various ML algorithms.
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