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--- |
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language: "en" |
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thumbnail: |
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tags: |
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- automatic-speech-recognition |
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- CTC |
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- Attention |
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- Tranformer |
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- pytorch |
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- speechbrain |
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license: "apache-2.0" |
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datasets: |
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- librispeech |
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metrics: |
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- wer |
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- cer |
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--- |
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<iframe src="https://ghbtns.com/github-btn.html?user=speechbrain&repo=speechbrain&type=star&count=true&size=large&v=2" frameborder="0" scrolling="0" width="170" height="30" title="GitHub"></iframe> |
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<br/><br/> |
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# CRDNN with CTC/Attention and RNNLM trained on LibriSpeech |
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This repository provides all the necessary tools to perform automatic speech |
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recognition from an end-to-end system pretrained on LibriSpeech (EN) within |
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SpeechBrain. For a better experience, we encourage you to learn more about |
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[SpeechBrain](https://speechbrain.github.io). The given ASR model performance are: |
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| Release | Test clean WER | Test other WER | GPUs | |
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|:-------------:|:--------------:|:--------------:|:--------:| |
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| 05-03-21 | 2.90 | 8.51 | 1xV100 16GB | |
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## Pipeline description |
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This ASR system is composed of 3 different but linked blocks: |
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1. Tokenizer (unigram) that transforms words into subword units and trained with |
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the train transcriptions of LibriSpeech. |
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2. Neural language model (Transformer LM) trained on the full 10M words dataset. |
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3. Acoustic model (CRDNN + CTC/Attention). The CRDNN architecture is made of |
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N blocks of convolutional neural networks with normalization and pooling on the |
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frequency domain. Then, a bidirectional LSTM with projection layers is connected |
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to a final DNN to obtain the final acoustic representation that is given to |
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the CTC and attention decoders. |
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## Install SpeechBrain |
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First of all, please install SpeechBrain with the following command: |
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``` |
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pip install speechbrain |
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``` |
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Please notice that we encourage you to read our tutorials and learn more about |
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[SpeechBrain](https://speechbrain.github.io). |
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### Transcribing your own audio files (in English) |
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```python |
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from speechbrain.pretrained import EncoderDecoderASR |
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asr_model = EncoderDecoderASR.from_hparams(source="speechbrain/asr-crdnn-transformerlm-librispeech", savedir="pretrained_models/asr-crdnn-transformerlm-librispeech") |
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asr_model.transcribe_file("speechbrain/asr-crdnn-transformerlm-librispeech/example.wav") |
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``` |
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### Inference on GPU |
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To perform inference on the GPU, add `run_opts={"device":"cuda"}` when calling the `from_hparams` method. |
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### Limitations |
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The SpeechBrain team does not provide any warranty on the performance achieved by this model when used on other datasets. |
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#### Referencing SpeechBrain |
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``` |
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@misc{SB2021, |
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author = {Ravanelli, Mirco and Parcollet, Titouan and Rouhe, Aku and Plantinga, Peter and Rastorgueva, Elena and Lugosch, Loren and Dawalatabad, Nauman and Ju-Chieh, Chou and Heba, Abdel and Grondin, Francois and Aris, William and Liao, Chien-Feng and Cornell, Samuele and Yeh, Sung-Lin and Na, Hwidong and Gao, Yan and Fu, Szu-Wei and Subakan, Cem and De Mori, Renato and Bengio, Yoshua }, |
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title = {SpeechBrain}, |
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year = {2021}, |
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publisher = {GitHub}, |
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journal = {GitHub repository}, |
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howpublished = {\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\url{https://github.com/speechbrain/speechbrain}}, |
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} |
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``` |
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#### About SpeechBrain |
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SpeechBrain is an open-source and all-in-one speech toolkit. It is designed to be simple, extremely flexible, and user-friendly. Competitive or state-of-the-art performance is obtained in various domains. |
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Website: https://speechbrain.github.io/ |
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GitHub: https://github.com/speechbrain/speechbrain |
