Transformer trained on CommonVoice 14.0 French (No LM)
This repository provides all the necessary tools to perform automatic speech recognition from an end-to-end system within SpeechBrain. For a better experience, we encourage you to learn more about SpeechBrain. The performance of the model is the following:
| Release | Test CER | Test WER | GPUs |
|---|---|---|---|
| 15.08.23 | 4.20 | 16.00 | 1xV100 32GB |
Credits
The model is provided by vitas.ai.
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).
- transformer augment CTC by adding an autoregressive predictor and a join network.
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.
Transcribing your own audio files (in French)
from speechbrain.inference.ASR import EncoderDecoderASR
asr_model = EncoderDecoderASR.from_hparams(source="speechbrain/asr-transformer-commonvoice-14-fr", savedir="pretrained_models/asr-transformer-commonvoice-14-fr")
asr_model.transcribe_file("speechbrain/asr-transformer-commonvoice-14-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 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:
- 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/transformer
python train.py hparams/train_fr.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.
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.
@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}
}
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