README.md

---
license: mit
language:
- hi
- en
- bn
- gu
- te
- mr
---
# SYSPIN Hackathon TTS API Documentation

## Overview

This API provides a Text-to-Speech (TTS) service that converts input text into speech audio. It supports multiple Indian languages and offers voice customization through speaker references provided by the user .

---

## Endpoint: `/Get_Inference`

* **Method**: `GET`
* **Description**: Generates speech audio from the provided text using the specified language and speaker reference file.

### Query Parameters

| Parameter | Type   | Required | Description |
| --------- | ------ | -------- | ----------- |
| `text`    | string | Yes      | The input text to be converted into speech.|
| `lang`    | string | Yes      | The language of the input text. Acceptable values include: `bhojpuri`, `bengali`, `english`, `gujarati`, `hindi`, `chhattisgarhi`, `kannada`, `magahi`, `maithili`, `marathi`, `telugu`. |
| `speaker_wav` | WAV file (Bytes) | Yes      | Must be a WAV file  |

### Available Languages

| Language  |  Language codes  |
| --------- | ---------------- |
| chhattisgarhi | hne |
| kannada    |  kn |
| maithili   | mai  |
| telugu      | te  |
| bengali    |  bn  | 
| bhojpuri   |  bho | 
| marathi    |  mr | 
| gujarati  |  gu  | 
| hindi     |  hi  | 
| magahi    |  mag | 
| english   |  en  | 

### Responses

* **200 OK**: Returns a WAV audio file as a streaming response containing the synthesized speech.

* **422 Unprocessable Entity**: Returned when:

  * Any of the required query parameters (`text`, `lang`, `speaker_wav`) are missing.
  * The specified `lang` is not supported.
  * The specified `speaker_wav` is not available.



## Running the Server

To start the FastAPI server:

```bash
docker build -t your_image_name ./
docker run -d -v /path/to/this/code/dir/:/app/ -p 8080:8080 your_image_name API_main.py
```

## Hosting on a GPU

To run your FastAPI-based Text-to-Speech (TTS) server inside a Docker container with GPU support, follow these steps:

---

## Prerequisites

1. **NVIDIA GPU**: Ensure your system has an NVIDIA GPU installed.

2. **NVIDIA Drivers**: Install the appropriate NVIDIA drivers for your GPU.

3. **Docker**: Install Docker on your system.

4. **NVIDIA Container Toolkit**: Install the NVIDIA Container Toolkit to enable GPU support in Docker containers.

---

## Installation Steps

### 1. Install NVIDIA Drivers

Ensure that the NVIDIA drivers compatible with your GPU are installed on your system.

### 2. Install Docker

If Docker is not already installed, you can install it by following the official Docker installation guide for your operating system.

### 3. Install NVIDIA Container Toolkit

The NVIDIA Container Toolkit allows Docker containers to utilize the GPU.

**For Ubuntu:**

```bash
# Add the package repositories
distribution=$(. /etc/os-release;echo $ID$VERSION_ID)
curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add -
curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | \
  sudo tee /etc/apt/sources.list.d/nvidia-docker.list

# Update the package lists
sudo apt-get update

# Install the NVIDIA Container Toolkit
sudo apt-get install -y nvidia-container-toolkit

# Restart the Docker daemon to apply changes
sudo systemctl restart docker
```

**For other operating systems:** Refer to the [NVIDIA Container Toolkit installation guide](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html) for detailed instructions.

### 4. Verify GPU Access in Docker

To confirm that Docker can access your GPU, run the following command:

```bash
docker run --rm --gpus all nvidia/cuda:12.4.0-base-ubuntu22.04 nvidia-smi
```


## Running Your FastAPI TTS Server with GPU Support

Assuming your FastAPI TTS application is containerized and ready to run:

1. **Build Your Docker Image**

Navigate to the directory containing your `Dockerfile` and build the Docker image:

```bash
docker build -t your_image_name .
```


2. **Run the Docker Container with GPU Support**

Start the container with GPU access enabled:

```bash
docker run --gpus all -p 8080:8080 -v /path/to/this/code/dir/:/app/ your_image_name API_main.py
```

## Example API Call

```python
import requests

# Define the base URL of your API
base_url = 'http://localhost:8080/Get_Inference'

# Set up the query parameters
WavPath = 'path/to/wavfile.wav'

params = {
    'text': 'ಮಾದರಿಯು ಸರಿಯಾಗಿ ಕಾರ್ಯನಿರ್ವಹಿಸುತ್ತಿದೆಯೇ ಎಂದು ಖಚಿತಪಡಿಸಿಕೊಳ್ಳಲು ಬಳಸಲಾಗುವ ಪರೀಕ್ಷಾ ವಾಕ್ಯ ಇದು.',
    'lang': 'kannada',
}

# Send the GET request
with open(WavPath, "rb") as AudioFile:
  response = requests.get(base_url, params = params, files = { 'speaker_wav': AudioFile.read() })

# Check if the request was successful
if response.status_code == 200:
    # Save the audio content to a file
    with open('output.wav', 'wb') as f:
        f.write(response.content)
    print("Audio saved as 'output.wav'")
else:
    # Print the error message
    print(f"Request failed with status code {response.status_code}")
    print("Response:", response.text)
```