readme: ---
  language: "en"
  thumbnail:
  tags:
  - ASR
  - CTC
  - Attention
  - pytorch
  license: "apache-2.0"
  datasets:
  - librispeech
  metrics:
  - wer
  - cer
  ---
  
  # 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](https://speechbrain.github.io). 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](https://speechbrain.github.io).
  
  ### Transcribing your own audio files
  
  ```python
  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}},
    }
  ```
  