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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.
speechbrain/speechbrain.github.io
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<li class="nav-item active"><a class="nav-link" href="index.html">Home</a></li>
<li class="nav-item"><a class="nav-link" href="about.html">About SpeechBrain</a></li>
<li class="nav-item"><a class="nav-link" href="contributing.html">Contributing</a></li>
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<h2>Contributing</h2>
<h3>Everyone is welcome!</h3>
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🎉 SpeechBrain 1.0 is out. <a href="https://colab.research.google.com/drive/1IEPfKRuvJRSjoxu22GZhb3czfVHsAy0s?usp=sharing"><strong> Check out what's new!</strong></a>
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<h2> 🚀 Contributing to SpeechBrain</h2>
<p class="justified large"> The goal is to collectively write a set of open-source libraries for Conversational AI.
It is crucial to write a set of homogeneous libraries that are all compliant with a set of guidelines described
in our <a href="https://speechbrain.readthedocs.io/en/latest/contributing.html"> documentation </a>.
</p>
<br>
<h3> 🌟 Zen of SpeechBrain </h3>
<p class="justified large"> SpeechBrain could be used for research, academic, commercial, non-commercial purposes.If you want to contribute, keep in mind the following features:
<br/><br/>
<b>Simplicity:</b> the code must be easy to understand even by students or users that are not professional programmers or speech researchers.
Design your code such that it can be easily read. Given alternatives with the same level of performance, code the simplest one. <br/><br/>
<b>Modularity:</b> Write your code to be modular and well-fitting with the other functionalities of the toolkit. The idea is to develop a bunch of models that can be naturally interconnected with each other. <br/><br/>
<b>Efficiency:</b> The code should be as efficient as possible. Contributors should maximize the use of pytorch native operations
<br/><br/>
<b>Documentation:</b> Given the goals of SpeechBrain, writing rich and good documentation is a crucial step. Write docstrings with runnable examples (as done in PyTorch code).
<br/><br/>
<h3> 🔧 How to get my code into SpeechBrain? </h3>
<p class="justified large"> SpeechBrain is hosted via <a href="https://github.com/speechbrain/speechbrain"> GitHub </a>. Contributing requires three steps: <br/><br/>
<b>1.</b> Fork, clone the <a href="https://github.com/speechbrain/speechbrain">repository</a> and install our test suite as detailed in <a href="https://speechbrain.readthedocs.io/en/latest/contributing.html"> the documentation </a>. <br/>
<b>2.</b> Write your code and test it properly. Commit your changes to your fork with our pre-commit tests to ensure tests are passing.
Then open a pull request on the official repository.<br/>
<b>3.</b> Participate in the review process. Each pull request is reviewed by one or two reviewers.
Please integrate their feedback into your code. Once reviewers are happy with your pull request, they will merge it into the official code. <br/><br/>
<b>Details about this process (i.e including steps for installing the tests) are given in <a href="https://speechbrain.readthedocs.io/en/latest/contributing.html"> the documentation </a>.</b>
</p>
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<h3> 🙌 How can I help? </h3>
<p class="justified large"> Examples of contributions include new recipes, new models, new external functionalities, solving issues/bugs.
</p>
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<h2>🌟 Contributors</h2>
<p class="justified large">We would like to thank the following contributors:</p>
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<ul class="contributors-list">
<li>Mirco Ravanelli, Concordia University, Mila (CA)</li>
<li>Titouan Parcollet, Samsung Research, University of Cambridge (UK)</li>
<li>Peter Plantinga, Ohio State University (USA)</li>
<li>Aku Rouhe, Aalto University (FI)</li>
<li>Adel Moumen, University of Cambridge (UK) </li>
<li>Sylvain de Langen, Avignon Université (LIA, FR) </li>
<li>Cem Subakan, Mila, Laval University (CA)</li>
<li>Luca Della Libera, Concordia University, Mila (CA)</li>
<li>Pooneh Mousavi, Concordia University, Mila (CA)</li>
<li>Artem Ploujnikov, Université de Montréal, Mila (CA)</li>
<li>Davide Borra, University of Bologna (IT)</li>
<li>Francesco Paissan, Fondazione Bruno Kessler (IT)</li>
<li>Mahed Mousavi, University of Trento (IT)</li>
<li>Salah Zaiem, Telecom Paris (FR)</li>
<li>Zeyu Zhao, University of Edinburgh (UK)</li>
<li> Pierre Champion, INRIA (FR)</li>
<li> Georgios Karakasidis, University of Edinburgh (UK)</li>
<li> Sung-Lin Yeh, University of Edinburgh (UK)</li>
<li> Yingzhi Wang, Zaion (FR)</li>
<li> Dongwon Kim, Krafton AI (KR)</li>
<li> Xuechen Liu, Aalto University (FI)</li>
<li> Andreas Nautsch, Avignon Université (LIA, FR)</li>
<li> Pradnya Kandarkar, Concordia University</li>
<li> Jarod Duret, Avignon Université (LIA, FR)</li>
<li> Sangeet Sagar, Saarland University (GE) </li>
<li> Gaëlle Laperrière, Avignon Université (LIA, FR) </li>
<li> Ha Nguyen, Oracle (FR) </li>
<li> Pablo Zuluaga, École Polytechnique Fédérale de Lausanne (EPFL, CH)</li>
<li> Florian Mai, École Polytechnique Fédérale de Lausanne (EPFL, CH)</li>
<li> Loren Lugosch, Mila, McGill University (CA)</li>
<li> Nauman Dawalatabad, Indian Institute of Technology Madras (IN)</li>
<li> Ju-Chieh Chou, National Taiwan University (TW)</li>
<li> Abdel Heba, Linagora / University of Toulouse (IRIT, FR)</li>
<li> Francois Grondin, University of Sherbrooke (CA)</li>
<li> William Aris, University of Sherbrooke (CA)</li>
<li> Chien-Feng Liao, National Taiwan University (TW)</li>
<li> Samuele Cornell, Università Politecnica delle Marche (IT)</li>
<li> Sung-Lin Yeh, National Tsing Hua University (TW)</li>
<li> Hwidong Na, Visiting Researcher Samsung SAIL (CA)</li>
<li> Yan Gao, University of Cambridge (UK)</li>
<li> Szu-Wei Fu, Academia Sinica (TW)</li>
<li> Elena Rastorgueva, University of Cambridge (UK)</li>
<li> Jianyuan Zhong, University of Rochester (USA)<li>
<li> Brecht Desplanques, Ghent University (BE)<li>
<li> Jenthe Thienpondt, Ghent University (BE)<li>
<li> Salima Mdhaffar, Avignon Université (LIA, FR)<li>
<li> Mickael Rouvier, Avignon University (LIA, FR)</li>
<li> Yannick Estève, Avignon University (LIA, FR)</li>
<li> Renato De Mori, McGill University (CA), Avignon University (LIA, FR)</li>
<li> Yoshua Bengio, Mila, University of Montréal (CA)</li>
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About
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.
Topics
deep-learning
neural-network
speech
speech-recognition
neural-networks
deeplearning
speech-to-text
speaker-recognition
speaker-verification
speech-processing
speech-recognizer
beamforming
speech-analysis
timit
speechrecognition
speech-api
speech-separation
librispeech
speech-emotion-recognition
speaker-identification
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