https://github.com/msalhab96/Listen-Attend-and-Spell

PyTorch implementation of Listen, Attend and Spell (LAS) speech recognition paper
https://github.com/msalhab96/Listen-Attend-and-Spell

asr las listen-attend-and-spell speech-recognition speech-to-text

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PyTorch implementation of Listen, Attend and Spell (LAS) speech recognition paper

• Host: GitHub
• URL: https://github.com/msalhab96/Listen-Attend-and-Spell
• Owner: msalhab96
• Created: 2022-02-02T11:12:01.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2022-03-04T16:22:35.000Z (over 2 years ago)
• Last Synced: 2024-06-28T13:34:56.540Z (3 months ago)
• Topics: asr, las, listen-attend-and-spell, speech-recognition, speech-to-text
• Language: Python
• Homepage:
• Size: 43.9 KB
• Stars: 10
• Watchers: 2
• Forks: 2
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Listen, Attend and Spell (LAS)

This is a PyTorch implementation of [Listen, Attend and Spell (LAS)](https://arxiv.org/pdf/1508.01211v2.pdf) paper 

```

@article{DBLP:journals/corr/ChanJLV15,

  author    = {William Chan and

               Navdeep Jaitly and

               Quoc V. Le and

               Oriol Vinyals},

  title     = {Listen, Attend and Spell},

  journal   = {CoRR},

  volume    = {abs/1508.01211},

  year      = {2015},

  url       = {http://arxiv.org/abs/1508.01211},

  eprinttype = {arXiv},

  eprint    = {1508.01211},

  timestamp = {Mon, 13 Aug 2018 16:46:45 +0200},

  biburl    = {https://dblp.org/rec/journals/corr/ChanJLV15.bib},

  bibsource = {dblp computer science bibliography, https://dblp.org}

}

```

# Train on your data

In order to train the model on your data follow the steps below 

### 1. data preprocessing 

* prepare your data and make sure the data is formatted in an CSV format as below 

```

audio_path,text,duration

file/to/file.wav,the text in that file,3.2 

```

* make sure the audios are MONO if not make the proper conversion to meet this condition

### 2. Setup development environment

* create enviroment 

```bash

python -m venv env

```

* activate the enviroment

```bash

source env/bin/activate

```

* install the required dependencies

```bash

pip install -r requirements.txt

```

### 3. Training 

* update the config file if needed

* train the model 

  * from scratch 

  ```bash

  python train.py

  ```

  * from checkpoint 

  ```

  python train.py checkpoint=path/to/checkpoint tokenizer.tokenizer_file=path/to/tokenizer.json

  ```

# TODO

- [ ] Compeleting the inference module 

- [ ] Adding Demo