readme: ---
  language: "fr"
  thumbnail:
  tags:
  - automatic-speech-recognition
  - CTC
  - Attention
  - pytorch
  - speechbrain
  license: "apache-2.0"
  datasets:
  - common_voice
  metrics:
  - wer
  - cer
  ---
  
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  <br/><br/>
  
  # CRDNN with CTC/Attention trained on CommonVoice French (No LM)
  
  This repository provides all the necessary tools to perform automatic speech
  recognition from an end-to-end system pretrained on CommonVoice (French Language) within
  SpeechBrain. For a better experience, we encourage you to learn more about
  [SpeechBrain](https://speechbrain.github.io). 
  
  The performance of the model is the following:
  
  | Release | Test CER | Test WER | GPUs |
  |:-------------:|:--------------:|:--------------:| :--------:|
  | 07-03-21 | 6.54 | 17.70 | 2xV100 16GB |
  
  ## Pipeline description
  
  This ASR system is composed of 2 different but linked blocks:
  - Tokenizer (unigram) that transforms words into subword units and trained with
  the train transcriptions (train.tsv) of CommonVoice (FR).
  - Acoustic model (CRDNN + CTC/Attention). The CRDNN architecture is made of
  N blocks of convolutional neural networks with normalization 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.
  
  The system is trained with recordings sampled at 16kHz (single channel).
  The code will automatically normalize your audio (i.e., resampling + mono channel selection) when calling *transcribe_file* if needed.
  ## Install SpeechBrain
  
  First of all, please install SpeechBrain with the following command:
  
  ```
  pip install speechbrain
  ```
  
  Please notice that we encourage you to read our tutorials and learn more about
  [SpeechBrain](https://speechbrain.github.io).
  
  ### Transcribing your own audio files (in French)
  
  ```python
  from speechbrain.pretrained import EncoderDecoderASR
  
  asr_model = EncoderDecoderASR.from_hparams(source="speechbrain/asr-crdnn-commonvoice-fr", savedir="pretrained_models/asr-crdnn-commonvoice-fr")
  asr_model.transcribe_file("speechbrain/asr-crdnn-commonvoice-fr/example-fr.wav")
  
  ```
  
  ### Inference on GPU
  To perform inference on the GPU, add  `run_opts={"device":"cuda"}`  when calling the `from_hparams` method.
  
  ## Parallel Inference on a Batch
  Please, [see this Colab notebook](https://colab.research.google.com/drive/1hX5ZI9S4jHIjahFCZnhwwQmFoGAi3tmu?usp=sharing) to figure out how to transcribe in parallel a batch of input sentences using a pre-trained model.
  
  ### Training
  The model was trained with SpeechBrain (986a2175).
  To train it from scratch follows these steps:
  1. Clone SpeechBrain:
  ```bash
  git clone https://github.com/speechbrain/speechbrain/
  ```
  2. Install it:
  ```
  cd speechbrain
  pip install -r requirements.txt
  pip install -e .
  ```
  
  3. Run Training:
  ```
  cd recipes/CommonVoice/ASR/seq2seq
  python train.py hparams/train_fr.yaml --data_folder=your_data_folder
  ```
  
  You can find our training results (models, logs, etc) [here](https://drive.google.com/drive/folders/13i7rdgVX7-qZ94Rtj6OdUgU-S6BbKKvw?usp=sharing)
  
  ### Limitations
  The SpeechBrain team does not provide any warranty on the performance achieved by this model when used on other datasets.
  
  
  # **About SpeechBrain**
  - Website: https://speechbrain.github.io/
  - Code: https://github.com/speechbrain/speechbrain/
  - HuggingFace: https://huggingface.co/speechbrain/
  
  
  # **Citing SpeechBrain**
  Please, cite SpeechBrain if you use it for your research or business.
  
  ```bibtex
  @misc{speechbrain,
    title={{SpeechBrain}: A General-Purpose Speech Toolkit},
    author={Mirco Ravanelli and Titouan Parcollet and Peter Plantinga and Aku Rouhe and Samuele Cornell and Loren Lugosch and Cem Subakan and Nauman Dawalatabad and Abdelwahab Heba and Jianyuan Zhong and Ju-Chieh Chou and Sung-Lin Yeh and Szu-Wei Fu and Chien-Feng Liao and Elena Rastorgueva and François Grondin and William Aris and Hwidong Na and Yan Gao and Renato De Mori and Yoshua Bengio},
    year={2021},
    eprint={2106.04624},
    archivePrefix={arXiv},
    primaryClass={eess.AS},
    note={arXiv:2106.04624}
  }
  ```
  