---
language: "en"
thumbnail:
tags:
- ASR
- CTC
- Attention
- Transformer
- pytorch
- speechbrain
license: "apache-2.0"
datasets:
- librispeech
metrics:
- wer
- cer
---

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<br/><br/>

# Transformer for LibriSpeech (with Transformer LM)

This repository provides all the necessary tools to perform automatic speech
recognition from an end-to-end system pretrained on LibriSpeech (EN) within
SpeechBrain. For a better experience, we encourage you to learn more about
[SpeechBrain](https://speechbrain.github.io). The given ASR model performance are:

| Release | Test clean WER | Test other WER | GPUs |
|:-------------:|:--------------:|:--------------:|:--------:|
| 05-03-21 | 2.46 | 5.86 | 2xV100 32GB |

## Pipeline description

This ASR system is composed of 3 different but linked blocks:
1. Tokenizer (unigram) that transforms words into subword units and trained with
the train transcriptions of LibriSpeech.
2. Neural language model (Transformer LM) trained on the full 10M words dataset.
3. Acoustic model made of a transformer encoder and a joint decoder with CTC +
transformer. Hence, the decoding also incorporates the CTC probabilities.

## Intended uses & limitations

This model has been primarily developed to be run within SpeechBrain as a pretrained ASR model
for the English language. Thanks to the flexibility of SpeechBrain, any of the 3 blocks
detailed above can be extracted and connected to your custom pipeline as long as SpeechBrain is
installed.

## 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).

### Transcribing your own audio files (in English)

```python
from speechbrain.pretrained import EncoderDecoderASR

asr_model = EncoderDecoderASR.from_hparams(source="speechbrain/asr-transformer-transformerlm-librispeech", savedir="pretrained_models/asr-transformer-transformerlm-librispeech")
asr_model.transcribe_file("speechbrain/asr-transformer-transformerlm-librispeech/example.wav")

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

#### 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