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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.
Table of Contents
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" 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 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
API
"""
This is a sample API code to send a text to the server and recieve speech
for the given text.
Supported languages:
Assamese, Bengali, Bodo, Gujarati, Hindi, Kannada, Malayalam, Manipuri
Marathi, Odia, Punjabi, Rajasthani, Tamil, Telugu, Urdu
"""
import requests
import json
import base64
# endpoint
url = "http://localhost:5000/tts"
lang = 'hindi'
gender = 'female'
text = "सुप्रभात, आप कैसे हैं?" # hindi
# text = "സുപ്രഭാതം, സുഖമാ?" # malayalam
# text = "সুপ্ৰভাত, তুমি কেনে?" # manipuri
# text = "सुप्रभात, तुम्ही कसे आहात?" # marathi
# text = "ಶುಭೋದಯ, ನೀವು ಹೇಗಿದ್ದೀರಿ?" # kannada
# text = "बसु म्विथ्बो, बरि दिबाबो?" # bodo male yet to be added <---
# text = "Good morning, how are you?" # english
# text = "সুপ্ৰভাত, আপুনি কেমন আছে?" # assamese
# text = "காலை வணக்கம், நீங்கள் எப்படி இருக்கின்றீர்கள்?" # tamil
# text = "ସୁପ୍ରଭାତ, ଆପଣ କେମିତି ଅଛନ୍ତି?"
# text = "सुप्रभात, आप कैसे छो?" # rajasthani
# text = "శుభోదయం, మీరు ఎలా ఉన్నారు?" # telugu
# text = "সুপ্রভাত, আপনি কেমন আছেন?" # bengali
# text = "સુપ્રભાત, તમે કેમ છો?" # gujarati
payload = json.dumps(
{
"input": text,
"gender": gender,
"lang": lang,
"alpha": 1 # to control speed
})
headers = {'Content-Type': 'application/json'}
response = requests.request("POST", url, headers=headers, data=payload).json()
# save the received encoded audio
audio = response['audio']
file_name = "tts.wav"
wav_file = open(file_name,'wb')
decode_string = base64.b64decode(audio)
wav_file.write(decode_string)
wav_file.close()
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: 
This work is licensed under a Creative Commons Attribution 4.0 International License.
