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---
Model Type: Text to Speech
Supported Languages: Assamese, Bengali, Bodo, Gujarati, Hindi, Kannada, Malayalam, Manipuri, Marathi, Odia, Punjabi, Rajasthani, Tamil, Telugu, Urdu
---
***Demo: [IITM-TTS Demo](https://iitm-tts.onrender.com) | This may take approximately 30 seconds to load the first time and will go idle after 15 minutes of inactivity.***
# Fastspeech2_HS_Flask_API
This repository contains the Flask API implementation of the Text to Speech Model developed by the Speech Lab at IIT Madras.
For a comprehensive understanding of the models and inference details, please consult the original repository
[Fastspeech2_HS](https://github.com/smtiitm/Fastspeech2_HS).
### Table of Contents
- [Setup](#setup)
- [Installation](#installation)
- [Run Flask server](#run-flask-server)
- [Citation for the original repo](#citation-for-the-original-repo)
### Setup
Some of the large files in this repo are uploaded using git lfs. Install latest git LFS by following the given commands:
Some of the large files in this repository have been uploaded using Git-LFS.
To ensure seamless handling of these files, please install Git-LFS by executing the provided commands:
```
curl -s https://packagecloud.io/install/repositories/github/git-lfs/script.python.sh | bash
sudo apt-get install git-lfs
git lfs install
```
The entire repository, including the models, has been uploaded to Hugging Face
"[Fastspeech2_HS_Flask_API](https://huggingface.co/k-m-irfan/Fastspeech2_HS_Flask_API)" due to size restrictions on GitHub for Git LFS.
To clone the repository from Hugging Face, please use the following command:
```
git clone https://huggingface.co/k-m-irfan/Fastspeech2_HS_Flask_API
```
Alternatively, you can download the models from the original repository [Fastspeech2_HS](https://github.com/smtiitm/Fastspeech2_HS)
and organize the folder structure as specified below. Skip this step if already cloned the repository from Hugging Face.
```
models
├── hindi
│ ├── female
│ └── male
├── tamil
│ ├── female
│ └── male
.
.
.
└── marathi
├── female
└── male
```
### Installation:
Create a virtual environment and activate it:
```
python3 -m venv tts-hs-hifigan
source tts-hs-hifigan/bin/activate
```
Install the required dependencies by running:
```
pip install -r requirements.txt
```
### Run Flask server:
Ensure the server application is running correctly before proceeding. Use the following commands and check for any errors:
```
python3 flask_app.py
# OR
gunicorn -w 2 -b 0.0.0.0:5000 flask_app:app --timeout 600
```
If the application is running without any issues, proceed to start the server using the following command:
```
bash start.sh
```
### Citation for the original repo
If you use this Fastspeech2 Model in your research or work, please consider citing:
“
COPYRIGHT
2023, Speech Technology Consortium,
Bhashini, MeiTY and by Hema A Murthy & S Umesh,
DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING
and
ELECTRICAL ENGINEERING,
IIT MADRAS. ALL RIGHTS RESERVED "
Shield: [![CC BY 4.0][cc-by-shield]][cc-by]
This work is licensed under a
[Creative Commons Attribution 4.0 International License][cc-by].
[![CC BY 4.0][cc-by-image]][cc-by]
[cc-by]: http://creativecommons.org/licenses/by/4.0/
[cc-by-image]: https://i.creativecommons.org/l/by/4.0/88x31.png
[cc-by-shield]: https://img.shields.io/badge/License-CC%20BY%204.0-lightgrey.svg
|