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--- |
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language: "en" |
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thumbnail: |
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tags: |
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- ASR |
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- CTC |
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- Attention |
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- Tranformer |
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- pytorch |
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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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# 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 with 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 normalisation 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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## Intended uses & limitations |
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This model has been primilarly developed to be run within SpeechBrain as a pretrained ASR model |
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for the english language. Thanks to the flexibility of SpeechBrain, any of the 3 blocks |
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detailed above can be extracted and connected to you custom pipeline as long as SpeechBrain is |
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installed. |
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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 \\we hide ! SpeechBrain is still private :p |
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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 |
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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") |
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asr_model.transcribe_file("path_to_your_file.wav") |
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``` |
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#### Referencing SpeechBrain |
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|
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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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