Asr Wav2vec2 Commonvoice En

🚀 wav2vec 2.0 with CTC trained on CommonVoice English (No LM)

This repository offers all essential tools for automatic speech recognition using an end - to - end system pretrained on CommonVoice (English) within SpeechBrain. For a better experience, explore SpeechBrain.

The performance of the model is as follows:

🚀 Quick Start

📦 Installation

First, install transformers and SpeechBrain with the following command:

pip install speechbrain transformers

💡 Usage Tip

Read our tutorials and learn more about SpeechBrain.

💻 Usage Examples

Basic Usage

from speechbrain.inference.ASR 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")

Advanced Usage

To perform inference on the GPU, add run_opts={"device":"cuda"} when calling the from_hparams method.

Parallel Inference on a Batch

See this Colab notebook to learn how to transcribe a batch of input sentences in parallel using a pre - trained model.

Training

The model was trained with SpeechBrain. To train it from scratch, follow these steps:

1. Clone SpeechBrain:

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_en_with_wav2vec.yaml --data_folder=your_data_folder

You can find our training results (models, logs, etc) here.

✨ Features

This ASR system consists of 2 different but linked blocks:

• Tokenizer (unigram): Transforms words into subword units and is trained with the train transcriptions (train.tsv) of CommonVoice (EN).
• Acoustic model (wav2vec2.0 + CTC): 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 decoder.

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.

📚 Documentation

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). 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 decoder.

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.

📄 License

This project is licensed under the "apache - 2.0" license.

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}
}

⚠️ Important Note

The SpeechBrain team does not provide any warranty on the performance achieved by this model when used on other datasets.