End-to-End speech recognition implementation base on TensorFlow (CTC, Attention, and MTL training)
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Updated
Jan 23, 2018 - Python
End-to-End speech recognition implementation base on TensorFlow (CTC, Attention, and MTL training)
Voice Activity Detection based on Deep Learning & TensorFlow
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.
Python implementation of pre-processing for End-to-End speech recognition
End-to-End Speech Recognition using Neural Networks.
SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition
Final project for the Speaker Recognition course on Udemy, 机器之心, 深蓝学院 and 语音之家
Pytorch and TensorFlow data loaders for several audio datasets
End-to-End Speech Recognition Using Tensorflow
Quartznet implementation on pytorch [https://arxiv.org/abs/1910.10261]
Pytorch implementation of conformer with with training script for end-to-end speech recognition on the LibriSpeech dataset.
Some approaches based on deep learning to build the acoustic model for an end-to-end automatic speech recognition (ASR) pipeline.
Speech Recognition Using Tensorflow
Gender Classification with different Machine Learning models, using the LibriSpeech ASR dataset.
Baselines for the Zero-Resources Speech Challenge using VisuallyGrounded Models of Spoken Language, 2021 edition
Gender Classification of the speaker from LibriSpeech Dataset
Implement a deep neural network that functions as part of an end-to-end automatic speech recognition (ASR) pipeline
Automatic speech recognition using neural networks
This repository contains Kaldi recipes on the LibriSpeech corpora to extract fMLLR features
A web-app/library for transcribing speech
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