---
license: cc-by-nc-4.0
tags:
- tts
- gpt2
- vae
pipeline_tag: text-to-speech
---

# Malayalam Text-to-Speech

This repository contains the **Swaram (mal)** text-to-speech (TTS) model checkpoint.

## Model Details

**Swaram** (**S**tochastic **W**aveform **A**daptive **R**ecurrent **A**utoencoder for **M**alayalam) is an advanced speech synthesis model that generates speech waveforms conditioned on input text sequences. It is based on a **conditional variational autoencoder** (VAE) architecture.

Swaram's text encoder is built on top of the **Wav2Vec2 decoder**. A **VAE** is used as the decoder. A **flow-based module** predicts **spectrogram-based acoustic features**, which is composed of the **Transformer-based Contextualizer** and cascaded dense layers. The spectrogram is then transformed into a speech waveform using a stack of **transposed convolutional layers**. To capture the one-to-many nature of TTS, where the same text can be spoken in multiple ways, the model also includes a stochastic duration predictor, allowing for varied speech rhythms from the same text input.

## Architecture

![architecture](architecture.png)

## Usage

```
pip install --upgrade transformers accelerate
```

Then, run inference with the following code-snippet:

```python
from transformers import VitsModel, AutoTokenizer
import torch

model = VitsModel.from_pretrained("aoxo/swaram")
tokenizer = AutoTokenizer.from_pretrained("aoxo/swaram")

text = "കള്ളാ കടയാടി മോനെ"
inputs = tokenizer(text, return_tensors="pt")

with torch.no_grad():
    output = model(**inputs).waveform
```

The resulting waveform can be saved as a `.wav` file:

```python
import scipy

scipy.io.wavfile.write("kadayadi_mone.wav", rate=model.config.sampling_rate, data=output)
```

Or displayed in a Jupyter Notebook / Google Colab:

```python
from IPython.display import Audio

Audio(output, rate=model.config.sampling_rate)
```

## License

The model is licensed as **CC-BY-NC 4.0**.