---
language: "en"
thumbnail:
tags:
- ASR
- CTC
- Attention
- Transformers
- pytorch
license: "apache-2.0"
datasets:
- aishell
metrics:
- wer
- cer
---

# Transformer for AISHELL (Mandarin Chinese)

This repository provides all the necessary tools to perform automatic speech
recognition from an end-to-end system pretrained on AISHELL (Mandarin Chinese)
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 | Dev CER | Test CER | GPUs | Full Results |
|:-------------:|:--------------:|:--------------:|:--------:|:--------:|
| 05-03-21 | 5.60 | 6.04 | 2xV100 32GB | [Google Drive](https://drive.google.com/drive/folders/1zlTBib0XEwWeyhaXDXnkqtPsIBI18Uzs?usp=sharing)|



## Pipeline description

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

To Train this system from scratch, [see our SpeechBrain recipe](https://github.com/speechbrain/speechbrain/tree/develop/recipes/AISHELL-1).

## Intended uses & limitations

This model has been primarily developed to be run within SpeechBrain as a pretrained ASR model
for the Mandarin Chinese 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-aishell", savedir="pretrained_models/asr-transformer-aishell")
asr_model.transcribe_file("speechbrain/asr-transformer-aishell/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