README.md

---
language: "en"
thumbnail:
tags:
- automatic-speech-recognition
- CTC
- Attention
- Tranformer
- pytorch
- speechbrain
license: "apache-2.0"
datasets:
- librispeech
metrics:
- wer
- cer
---

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# CRDNN with CTC/Attention and RNNLM trained on LibriSpeech

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 performance of the model is the following:

| Release | Test clean WER | Test other WER | GPUs |
|:-------------:|:--------------:|:--------------:|:--------:|
| 05-03-21 | 2.90 | 8.51 | 1xV100 16GB |

## 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 (CRDNN + CTC/Attention). The CRDNN architecture is made of
N blocks of convolutional neural networks with normalization and pooling on the
frequency domain. Then, a bidirectional LSTM with projection layers is connected
to a final DNN to obtain the final acoustic representation that is given to
the CTC and attention decoders.

## 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-crdnn-transformerlm-librispeech", savedir="pretrained_models/asr-crdnn-transformerlm-librispeech")
asr_model.transcribe_file("speechbrain/asr-crdnn-transformerlm-librispeech/example.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 (Commit hash: 'eca313cc').
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/ASR/seq2seq
python train.py hparams/train_BPE_5000.yaml --data_folder=your_data_folder
```

You can find our training results (models, logs, etc) [here](https://drive.google.com/drive/folders/1SAndjcThdkO-YQF8kvwPOXlQ6LMT71vt?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