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pytorch-kaldi is a project for developing state-of-the-art DNN/RNN hybrid speech recognition systems. The DNN part is managed by pytorch, while feature extraction, label computation, and decoding are performed with the kaldi toolkit.
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# The PyTorch-Kaldi Speech Recognition Toolkit
<img src="pytorch-kaldi_logo.png" width="220" img align="left">
PyTorch-Kaldi is an open-source repository for developing state-of-the-art DNN/HMM speech recognition systems. The DNN part is managed by PyTorch, while feature extraction, label computation, and decoding are performed with the Kaldi toolkit.
This repository contains the last version of the PyTorch-Kaldi toolkit (PyTorch-Kaldi-v1.0). To take a look into the previous version (PyTorch-Kaldi-v0.1), [click here](https://bitbucket.org/mravanelli/pytorch-kaldi-v0.0/src/master/).
If you use this code or part of it, please cite the following paper:
*M. Ravanelli, T. Parcollet, Y. Bengio, "The PyTorch-Kaldi Speech Recognition Toolkit", [arXiv](https://arxiv.org/abs/1811.07453)*
```
@inproceedings{pytorch-kaldi,
title = {The PyTorch-Kaldi Speech Recognition Toolkit},
author = {M. Ravanelli and T. Parcollet and Y. Bengio},
booktitle = {In Proc. of ICASSP},
year = {2019}
}
```
The toolkit is released under a **Creative Commons Attribution 4.0 International license**. You can copy, distribute, modify the code for research, commercial and non-commercial purposes. We only ask to cite our paper referenced above.
To improve transparency and replicability of speech recognition results, we give users the possibility to release their PyTorch-Kaldi model within this repository. Feel free to contact us (or doing a pull request) for that. Moreover, if your paper uses PyTorch-Kaldi, it is also possible to advertise it in this repository.
[See a short introductory video on the PyTorch-Kaldi Toolkit](https://www.youtube.com/watch?v=VDQaf0SS4K0&t=2s)
## SpeechBrain
We are happy to announce that the SpeechBrain project (https://speechbrain.github.io/) is now public! We strongly encourage users to migrate to [Speechbrain](https://speechbrain.github.io/). It is a much better project which already supports several speech processing tasks, such as speech recognition, speaker recognition, SLU, speech enhancement, speech separation, multi-microphone signal processing and many others.
The goal is to develop a *single*, *flexible*, and *user-friendly* toolkit that can be used to easily develop state-of-the-art speech systems for speech recognition (both end-to-end and HMM-DNN), speaker recognition, speech separation, multi-microphone signal processing (e.g, beamforming), self-supervised learning, and many others.
The project will be lead by Mila and is sponsored by Samsung, Nvidia, Dolby.
SpeechBrain will also benefit from the collaboration and expertise of other companies such as Facebook/PyTorch, IBMResearch, FluentAI.
We are actively looking for collaborators. Feel free to contact us at [email protected] if you are interested to collaborate.
Thanks to our sponsors we are also able to hire interns working at Mila on the SpeechBrain project. The ideal candidate is a PhD student with experience on pytorch and speech technologies (send your CV to [email protected])
The development of SpeechBrain will require some months before having a working repository. Meanwhile, we will continue providing support for the pytorch-kaldi project.
Stay Tuned!
## Table of Contents
* [Introduction](#introduction)
* [Prerequisites](#prerequisites)
* [How to install](#how-to-install)
* [Recent Updates](#recent-updates)
* [Tutorials:](#timit-tutorial)
* [TIMIT tutorial](#timit-tutorial)
* [Librispeech tutorial](#librispeech-tutorial)
* [Toolkit Overview:](#overview-of-the-toolkit-architecture)
* [Toolkit architecture](#overview-of-the-toolkit-architecture)
* [Configuration files](#description-of-the-configuration-files)
* [FAQs:](#how-can-i-plug-in-my-model)
* [How can I plug-in my model?](#how-can-i-plug-in-my-model)
* [How can I tune the hyperparameters?](#how-can-i-tune-the-hyperparameters)
* [How can I use my own dataset?](#how-can-i-use-my-own-dataset)
* [How can I plug-in my own features?](#how-can-iExcerpt of 76,777 characters
Read on GitHub
Mirco Ravanelli · Concordia University/Mila · Canada
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Dmitriy Serdyuk · @mila-iqia · Canada
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:669cbf33683890dd, topic:speech-recognition, topic:asr, desc:speech recognition
matched fp:669cbf33683890dd, topic:deep-learning, topic:pytorch