language: en
thumbnail: null
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.
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) 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.
Transcribing your own audio files (in English)
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("speechbrain/asr-wav2vec2-commonvoice-en/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:
- Clone SpeechBrain:
git clone https://github.com/speechbrain/speechbrain/
- Install it:
cd speechbrain
pip install -r requirements.txt
pip install -e .
- Run Training:
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.
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/