--- language: "en" thumbnail: tags: - automatic-speech-recognition - CTC - Attention - pytorch - speechbrain - Transformer license: "apache-2.0" datasets: - commonvoice metrics: - wer - cer ---

# wav2vec 2.0 with CTC/Attention trained on CommonVoice English (No LM) This repository provides all the necessary tools to perform automatic speech recognition from an end-to-end system pretrained on CommonVoice (English 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 WER | GPUs | |:--------------:|:--------------:| :--------:| | 03-06-21 | 15.69 | 2xV100 32GB | ## 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 (EN). - Acoustic model (wav2vec2.0 + CTC/Attention). A pretrained wav2vec 2.0 model ([wav2vec2-lv60-large](https://huggingface.co/facebook/wav2vec2-large-lv60)) is combined with two DNN layers and finetuned on CommonVoice En. The obtained final acoustic representation is given to the CTC and attention decoders. ## Install SpeechBrain First of all, please install tranformers and SpeechBrain with the following command: ``` pip install speechbrain transformers ``` 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 English) ```python from speechbrain.pretrained import EncoderDecoderASR asr_model = EncoderDecoderASR.from_hparams(source="speechbrain/asr-wav2vec2-commonvoice-en", savedir="pretrained_models/asr-wav2vec2-commonvoice-en") asr_model.transcribe_file("example.wav") ``` ### Inference on GPU To perform inference on the GPU, add `run_opts={"device":"cuda"}` when calling the `from_hparams` method. ### Training The model was trained with SpeechBrain. To train it from scratch follow these steps: 1. Clone SpeechBrain: ```bash git clone https://github.com/speechbrain/speechbrain/ ``` 2. Install it: ```bash cd speechbrain pip install -r requirements.txt pip install -e . ``` 3. Run Training: ```bash cd recipes/CommonVoice/ASR/seq2seq python train.py hparams/train_en_with_wav2vec.yaml --data_folder=your_data_folder ``` You can find our training results (models, logs, etc) [here](https://drive.google.com/drive/folders/1tjz6IZmVRkuRE97E7h1cXFoGTer7pT73?usp=sharing). ### Limitations The SpeechBrain team does not provide any warranty on the performance achieved by this model when used on other datasets. #### 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}}, } ``` #### About SpeechBrain 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. Website: https://speechbrain.github.io/ GitHub: https://github.com/speechbrain/speechbrain