SpeechBrain: A PyTorch Speech Toolkit

SpeechBrain: A PyTorch Speech Toolkit

SpeechBrain: A PyTorch Speech Toolkit

“SpeechBrain supports state-of-the-art methods for end-to-end speech recognition, including models based on CTC, CTC+attention, transducers, transformers, and neural language models relying on recurrent neural networks and transformers.

Speaker recognition is already deployed in a wide variety of realistic applications. SpeechBrain provides different models for speaker recognition, including X-vector, ECAPA-TDNN, PLDA, contrastive learning

Spectral masking, spectral mapping, and time-domain enhancement are different methods already available within SpeechBrain. Separation methods such as Conv-TasNet, DualPath RNN, and SepFormer are implemented as well.

SpeechBrain provides efficient and GPU-friendly speech augmentation pipelines and acoustic features extraction, normalisation that can be used on-the-fly during your experiment.

Combining multiple microphones is a powerful approach to achieve robustness in adverse acoustic environments. SpeechBrain provides various techniques for beamforming (e.g, delay-and-sum, MVDR, and GeV) and speaker localization.

SpeechBrain is designed to speed-up research and development of speech technologies. It is modular, flexible, easy-to-customize, and contains several recipes for popular datasets. Documentation and tutorials are here to help newcomers using SpeechBrain…”

Source: speechbrain.github.io/

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