---
license: cc-by-4.0
language:
- as
- bn
- brx
- doi
- kn
- mai
- ml
- mr
- ne
- pa
- sa
- ta
- te
library_name: transformers
pipeline_tag: text-to-speech
tags:
- text-to-speech
---
# VITS TTS for Indian Languages

This repository contains a VITS-based Text-to-Speech (TTS) model fine-tuned for Indian languages. The model supports multiple Indian languages and a wide range of speaking styles and emotions, making it suitable for diverse use cases such as conversational AI, audiobooks, and more.

---

## Model Overview

The model `ai4bharat/vits_rasa_13` is based on the VITS architecture and supports the following features:
- **Languages**: Multiple Indian languages.
- **Styles**: Various speaking styles and emotions.
- **Speaker IDs**: Predefined speaker profiles for male and female voices.

---

## Installation

```bash
pip install transformers torch
```

---

## Usage

Here's a quick example to get started:

```python
import soundfile as sf
from transformers import AutoModel, AutoTokenizer

model = AutoModel.from_pretrained("ai4bharat/vits_rasa_13", trust_remote_code=True).to("cuda")
tokenizer = AutoTokenizer.from_pretrained("ai4bharat/vits_rasa_13", trust_remote_code=True)

text = "ਕੀ ਮੈਂ ਇਸ ਹਫਤੇ ਦੇ ਅੰਤ ਵਿੱਚ ਰੁੱਝਿਆ ਹੋਇਆ ਹਾਂ?"  # Example text in Punjabi
speaker_id = 16  # PAN_M
style_id = 0  # ALEXA

inputs = tokenizer(text=text, return_tensors="pt").to("cuda")
outputs = model(inputs['input_ids'], speaker_id=speaker_id, emotion_id=style_id)
sf.write("audio.wav", outputs.waveform.squeeze(), model.config.sampling_rate)
print(outputs.waveform.shape)
```

---

## Supported Languages

- `Assamese` 
- `Bengali`   
- `Bodo`     
- `Dogri`      
- `Kannada`    
- `Maithili`   
- `Malayalam`  
- `Marathi`    
- `Nepali`     
- `Punjabi`    
- `Sanskrit`   
- `Tamil`      
- `Telugu`

---

## Speaker-Style Identifier Overview

<div style="display: flex; align-items: flex-start; gap: 20px; margin: 0; padding: 0;">

<table style="margin: 0; padding: 0; border-spacing: 0;">
  <tr>
    <th>Speaker Name</th>
    <th>Speaker ID</th>
  </tr>
  <tr>
    <td>ASM_F</td>
    <td>0</td>
  </tr>
  <tr>
    <td>ASM_M</td>
    <td>1</td>
  </tr>
  <tr>
    <td>BEN_F</td>
    <td>2</td>
  </tr>
  <tr>
    <td>BEN_M</td>
    <td>3</td>
  </tr>
  <tr>
    <td>BRX_F</td>
    <td>4</td>
  </tr>
  <tr>
    <td>BRX_M</td>
    <td>5</td>
  </tr>
  <tr>
    <td>DOI_F</td>
    <td>6</td>
  </tr>
  <tr>
    <td>DOI_M</td>
    <td>7</td>
  </tr>
  <tr>
    <td>KAN_F</td>
    <td>8</td>
  </tr>
  <tr>
    <td>KAN_M</td>
    <td>9</td>
  </tr>
  <tr>
    <td>MAI_M</td>
    <td>10</td>
  </tr>
  <tr>
    <td>MAL_F</td>
    <td>11</td>
  </tr>
  <tr>
    <td>MAR_F</td>
    <td>12</td>
  </tr>
  <tr>
    <td>MAR_M</td>
    <td>13</td>
  </tr>
  <tr>
    <td>NEP_F</td>
    <td>14</td>
  </tr>
  <tr>
    <td>PAN_F</td>
    <td>15</td>
  </tr>
  <tr>
    <td>PAN_M</td>
    <td>16</td>
  </tr>
  <tr>
    <td>SAN_M</td>
    <td>17</td>
  </tr>
  <tr>
    <td>TAM_F</td>
    <td>18</td>
  </tr>
  <tr>
    <td>TEL_F</td>
    <td>19</td>
  </tr>
</table>

<table>
  <tr>
    <th>Style Name</th>
    <th>Style ID</th>
  </tr>
  <tr>
    <td>ALEXA</td>
    <td>0</td>
  </tr>
  <tr>
    <td>ANGER</td>
    <td>1</td>
  </tr>
  <tr>
    <td>BB</td>
    <td>2</td>
  </tr>
  <tr>
    <td>BOOK</td>
    <td>3</td>
  </tr>
  <tr>
    <td>CONV</td>
    <td>4</td>
  </tr>
  <tr>
    <td>DIGI</td>
    <td>5</td>
  </tr>
  <tr>
    <td>DISGUST</td>
    <td>6</td>
  </tr>
  <tr>
    <td>FEAR</td>
    <td>7</td>
  </tr>
  <tr>
    <td>HAPPY</td>
    <td>8</td>
  </tr>
  <tr>
    <td>NEWS</td>
    <td>10</td>
  </tr>
  <tr>
    <td>SAD</td>
    <td>12</td>
  </tr>
  <tr>
    <td>SURPRISE</td>
    <td>14</td>
  </tr>
  <tr>
    <td>UMANG</td>
    <td>15</td>
  </tr>
  <tr>
    <td>WIKI</td>
    <td>16</td>
  </tr>
</table>

</div>

---

## Citation

If you use this model in your research, please cite:

```bibtex
@article{ai4bharat_vits_rasa_13,
  title={VITS TTS for Indian Languages},
  author={Ashwin Sankar},
  year={2024},
  publisher={Hugging Face}
}
```