SpeechBrain is an all-in-one conversational AI toolkit based on PyTorch.
The goal is to create a single, flexible, and user-friendly toolkit that can be used to easily develop state-of-the-art speech technologies, including systems for speech recognition, speaker recognition, speech enhancement, speech separation, language identification, multi-microphone signal processing, and many others.
SpeechBrain supports both CPU and GPU computations. For most recipes, however, a GPU is necessary during training. Please note that CUDA must be properly installed to use GPUs.
This is free and open source software.
SpeechBrain supports state-of-the-art methods for end-to-end speech recognition:
- Support of wav2vec 2.0 pretrained model with finetuning.
- State-of-the-art performance or comparable with other existing toolkits in several ASR benchmarks.
- Easily customizable neural language models, including RNNLM and TransformerLM. The project shares several pre-trained models that you can easily use. The Hugging Face dataset is supported to facilitate the training over a large text dataset.
- Hybrid CTC/Attention end-to-end ASR:
- Many available encoders: CRDNN (VGG + {LSTM,GRU,LiGRU} + DNN), ResNet, SincNet, vanilla transformers, context net-based transformers or conformers. Thanks to the flexibility of SpeechBrain, any fully customized encoder could be connected to the CTC/attention decoder and trained in a few hours of work. The decoder is fully customizable: LSTM, GRU, LiGRU, transformer, or your neural network!
- Optimised and fast beam search on both CPUs and GPUs.
- Transducer end-to-end ASR with both a custom Numba loss and the torchaudio one. Any encoder or decoder can be plugged into the transducer ranging from VGG+RNN+DNN to conformers.
- Pre-trained ASR models for transcribing an audio file or extracting features for a downstream task.
Website: speechbrain.github.io
Support: github.com/speechbrain/speechbrain
Developer: Mirco Ravanelli, Parcollet Titouan and contributor
License: Apache License 2.0
SpeechBrain is written in Python. Learn Python with our recommended free books and free tutorials.
Related Software
| Speech Recognition Tools | |
|---|---|
| Whisper | Automatic speech recognition (system trained on 680,000 hours of data |
| Flashlight | Fast, flexible machine learning library written entirely in C++. |
| Coqui STT | Deep-learning toolkit for training and deploying speech-to-text models |
| Kaldi | C++ toolkit designed for speech recognition researchers. |
| SpeechBrain | All-in-one conversational AI toolkit based on PyTorch |
| Handy | Offline speech-to-text application |
| ESPnet | End-to-End speech processing toolkit |
| deepspeech.pytorch | Implementation of DeepSpeech2 using Baidu Warp-CTC. |
| Whispering | Transcription application with global speech-to-text functionality |
| Julius | Two-pass large vocabulary continuous speech recognition engine |
| CMUSphinx | Speech recognition system for mobile and server applications |
| Simon | Flexible speech recognition software |
| hyprwhspr | Native speech-to-text designed for Arch / Omarchy |
| ostt | Open Speech-to-Text |
| DeepSpeech | TensorFlow implementation of Baidu's DeepSpeech architecture. |
| OpenSeq2Seq | TensorFlow-based toolkit for sequence-to-sequence models |
| Eesen | End-to-End Speech Recognition |
Read our verdict in the software roundup.
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