PaddleSpeech 是基于飞桨 PaddlePaddle 的语音方向的开源模型库，用于语音和音频中的各种关键任务的开发，典型的应用包括：语音识别、语音翻译、语音合成等
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
翻译 - 生产优先和生产就绪的端到端语音识别工具包
[Unofficial] PyTorch implementation of "Conformer: Convolution-augmented Transformer for Speech Recognition" (INTERSPEECH 2020)
⚡ TensorFlowASR: Almost State-of-the-art Automatic Speech Recognition in Tensorflow 2. Supported languages that can use characters or subwords
#计算机科学#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
#大语言模型#Open-source industrial-grade ASR models supporting Mandarin, Chinese dialects and English, achieving a new SOTA on public Mandarin ASR benchmarks, while also offering outstanding singing lyrics recogn...
#计算机科学#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。
Open-Source Toolkit for End-to-End Korean Automatic Speech Recognition leveraging PyTorch and Hydra.
EEG Transformer 2.0. i. Convolutional Transformer for EEG Decoding. ii. Novel visualization - Class Activation Topography.
#计算机科学#Pytorch implementation of Noisy Student Training for Automatic Speech Recognition and Automatic Pronunciation Error Detection problem
Python toolkit for speech processing
🧪 Learning Neural Generative Dynamics for Molecular Conformation Generation (ICLR 2021)
Modular and extensible speech recognition library leveraging pytorch-lightning and hydra.
Dynamic Chunk Streaming and Offline Conformer based on athena-team/Athena.
#计算机科学#An implementation of Conformer: Convolution-augmented Transformer for Speech Recognition, a Transformer Variant in TensorFlow/Keras
A python implementation of “Self-Supervised Learning of Spatial Acoustic Representation with Cross-Channel Signal Reconstruction and Multi-Channel Conformer” [TASLP 2024]
[ICLR 2022] "Audio Lottery: Speech Recognition Made Ultra-Lightweight, Noise-Robust, and Transferable", by Shaojin Ding, Tianlong Chen, Zhangyang Wang
#计算机科学#Pytorch implementation of conformer with with training script for end-to-end speech recognition on the LibriSpeech dataset.