manhph2211 / ViSTT

I'm building an end-to-end Vietnamese Speech Recognition System. I'll deploy it into production with the help of Flask, Uwsgi, Nginx, and AWS ...

Vietnamese Speech-to-Text 😄

Introduction

In this project, I aimed to develop an end-to-end automatic speech recognition system in which I used diffenent frameworks such as Pytorch-lightning. Flask, Docker, Uwsgi, Nginx, and AWS service to deploy and extend the project into a simple production scenario. Also, I focused on Vietnamese language but other ones are easily modified and used.

About the model, here I used several well-known ones(Deepspeech, Conformer CTC and RNN-Transducer). As usual, I'd love to write some notes about the models I used 🙋. But now I don't have much time, so I update serveral days later...

Setup

Data preparation

The project used VIVOs, which is a widely-used Vietnamese public dataset. Download it and put it into ./local/data:

└───VASR
    ├───local
    │   ├───data
    │   ├───ckpts
    │   ├───configs
    │   ├───outputs
    │   │   └───version_3        
    │   │       └───checkpoints  
    │   ├───src
    │   │   ├───augs
    │   │   ├───datasets
    │   │   ├───engine
    │   │   ├───losses
    │   │   ├───metrics
    │   │   ├───models
    │   │   │   ├───conformer
    │   │   │   │   └───base
    │   │   │   └───deepspeech
    │   │   ├───optimizers
    │   │   └───utils
    │   └───tools
    └───server
        ├───flask
        │   └───templates
        └───nginx

Checkpoints

All the needed checkpoints can be found [here](Not now bro). You can download and put them into the project as the folder architecture above.

EC2 service

First, if you are new to AWS, please create an account and access to the EC2 service, then you launch a new instance and choose the instance type and suitable resource at the same time. Then create a new key pair for logging into the server you've just chosen. After all, you will see an new dashboard showing up and you shoud copy the public DNS then run on terminal:

chmod 400 speech_recognition.pem
scp -i speech_recognition.pem -r server/ ubuntu@<paste here>:~

Now you run: ssh -i speech_recognition.pem ubuntu@<paste here> to log into the server!

Tools

Several tools are necessary to run the final web app, just follow the instruction:

Denoise

Due to the cleanliness of the VIVOs dataset, it's hard to apply the trained model for noisy datasets and even real-life cases and therefore it needs to be added a denoising tool. I'm developing it which is seperately implemented with this project and then utilizing it through a public api ... Should be done soon.

Train and Evaluate

Modify the config file and simply just run:

export PYTHONPATH=/home/path_to_project/VASR/local
python3 tools/train.py

Manage antifacts

Tensorboard was used here:

tensorboard --logdir <path_to_log_folder> --load_fast true

Web Demo

Finally, after you log into the server, you just need to run:

chomd +x init.sh
./init.sh

Future works

Lots of works should be done later, it will take time haha:

• Develop denoising tool
• Enjoy the benefits of semi-supervised learning
• Build speech augmentation modules
• Experience more approaches such as fine-tuning wav2vec based and
• Develop better web user interface, new branch with streamlit

References

Many thanks to the authors of the related papers and great implementations that I mainly based on:

• Conformer Paper
• Deepspeech 2 Paper
• Transducer Paper
• Tuanio's implementation
• Openspeech's implementation
• Valerio Velardo