CRDNN with CTC/Attention and RNNLM trained on LibriSpeech

This repository provides all the necessary tools to perform automatic speech recognition from an end-to-end system pretrained on LibriSpeech (EN) within SpeechBrain. For a better experience we encourage you to learn more about SpeechBrain. The given ASR model performance are:

Release hyperparams file Test WER Model link GPUs
20-05-22 BPE_1000.yaml 3.08 Not Available 1xV100 32GB
20-05-22 BPE_5000.yaml 2.89 Not Available 1xV100 32GB

Pipeline description

This ASR system is composed with 3 different but linked blocks:

  1. Tokenizer (unigram) that transforms words into subword units and trained with the train transcriptions of LibriSpeech.
  2. Neural language model (RNNLM) trained on the full 10M words dataset.
  3. Acoustic model (CRDNN + CTC/Attention). The CRDNN architecture is made of N blocks of convolutional neural networks with normalisation and pooling on the frequency domain. Then, a bidirectional LSTM is connected to a final DNN to obtain the final acoustic representation that is given to the CTC and attention decoders.

Intended uses & limitations

This model has been primilarly developed to be run within SpeechBrain as a pretrained ASR model for the english language. Thanks to the flexibility of SpeechBrain, any of the 3 blocks detailed above can be extracted and connected to you custom pipeline as long as SpeechBrain is installed.

Install SpeechBrain

First of all, please install SpeechBrain with the following command:

pip install \\we hide ! SpeechBrain is still private :p

Also, for this model, you need SentencePiece. Install with

pip install sentencepiece

Please notice that we encourage you to read our tutorials and learn more about SpeechBrain.

Transcribing your own audio files

from speechbrain.pretrained import EncoderDecoderASR

asr_model = EncoderDecoderASR.from_hparams(source="Gastron/asr-crdnn-librispeech")
asr_model.transcribe_file("path_to_your_file.wav")

Obtaining encoded features

The SpeechBrain EncoderDecoderASR() class also provides an easy way to encode the speech signal without running the decoding phase by calling EncoderDecoderASR.encode_batch()

Referencing SpeechBrain

@misc{SB2021,
    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 },
    title = {SpeechBrain},
    year = {2021},
    publisher = {GitHub},
    journal = {GitHub repository},
    howpublished = {\url{https://github.com/speechbrain/speechbrain}},
  }
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