---
language: "en"
thumbnail:
tags:
- pretraining
- CTC
- pytorch
- speechbrain
- speech
license: "apache-2.0"
datasets:
- commonvoice
---

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<br/><br/>

# wav2vec 2.0 base model pretrained on librispeech 960h

This HuggingFace repository provides all the necessary tools to extract wav2vec2
embeddings from a pretrained model. For a better experience, we encourage you to learn more about
[SpeechBrain](https://speechbrain.github.io). The wav2vec2 model has entirely been
pretrained with SpeechBrain (not with fairseq or HuggingFace).

The performance of the model is the following:

| Release | Test WER | GPUs |
|:-------------:|:--------------:|:--------------:| :--------:|
| 22-09-22 | 7.X | 1xV100 32GB |

## Pipeline description

This w2v2 system is composed of 2 different but linked blocks:
- A convolutional backend to extract features from the raw waveform.
- A latent encoder made of a transformer network.
The obtained embeddings are the output of the transformer after going through each
block.

## 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).

### Extracting embeddings for your own audio files

```python
from speechbrain.pretrained import WaveformEncoder

ssl_model = WaveformEncoder.from_hparams(source="speechbrain/ssl-wav2vec2-base-librispeech", savedir="speechbrain/ssl-wav2vec2-base-librispeech")
ssl_model.transcribe_file("example-fr.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/LibriSpeech/self-supervised-learning/wav2vec2
python train_sb_wav2vec2.py hparams/wav2vec2_base.yaml --data_folder=your_data_folder
```

You can find our training results (models, logs, etc) [here](https://drive.google.com/drive/folders/1eXA6HQtiKfgrPejvvoKvRRfTEvOI3BQt?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