---
base_model:
- LiquidAI/LFM2-350M
tags:
- text-generation-inference
- transformers
- unsloth
- lfm2
- trl
license: apache-2.0
language:
- en
- ml
pipeline_tag: text-to-speech
---

# Malayalam TTS Model (LFM2-350M Fine-tuned)

This repository contains a fine-tuned **Malayalam Text-to-Speech (TTS)** model based on **LFM2-350M**, trained using [VyvoTTS](https://github.com/Vyvo-Labs/VyvoTTS) (LLM-based TTS framework) and [Unsloth](https://github.com/unslothai/unsloth).

---
Malayalam TTS — 24 kHz (LLM + SNAC Codec)

High-quality Malayalam text-to-speech model targeting natural pronunciation and clean prosody at 24 kHz, using a discrete audio codec (SNAC 24 kHz) for waveform reconstruction. Designed for lightweight deployment (~350M parameters) with GPU/CPU support.

Status: v0.1 — stable inference, strong pronunciation, limited emotional expressiveness. Roadmap includes expressive styles and non‑verbal cues (laughter, giggles, breaths).

✨ Highlights

Language: Malayalam (with support for basic English loanwords).

Sample Rate: 24 kHz, mono.

Codec: [SNAC 24 kHz] for fast decoding.

Model Size: ~350M parameters (small/efficient).

Strengths: Clear, non‑robotic pronunciation; punctuation‑aware phrasing.

Known Limits: Emotion range is narrow; limited style transfer; no speaker cloning in v0.1.

## 📖 Model Details
- **Base Model:** LFM2-350M  
- **Language:** Malayalam  
- **Dataset:** [ai4bharat/rasa](https://huggingface.co/datasets/ai4bharat/rasa) (Malayalam subset)  
- **Training:** 10 epochs, ~77k steps  
- **Frameworks Used:** VyvoTTS, Unsloth  

---
## 🔮 Future Work
- Emotion and expressive style support  
- Non-verbal cues (laughter, giggles, breaths)  
- Multi-speaker extension