README.md

---
license: apache-2.0
language:
- ro
pipeline_tag: text-to-speech
tags:
- tts
- romanian
- matcha-tts
- conditional-flow-matching
- swara
library_name: pytorch
datasets:
- SWARA-1.0
---

# Matcha-TTS Romanian Models

Pre-trained Romanian text-to-speech models based on [Matcha-TTS](https://github.com/shivammehta25/Matcha-TTS) trained on the SWARA 1.0 dataset.

## Quick Start

### Clone Repository

Since this repository contains custom inference code and model loading utilities, you need to clone it:

```bash
# Clone from HuggingFace Hub
git clone https://huggingface.co/adrianstanea/Ro-Matcha-TTS
cd Ro-Matcha-TTS

# Install Git LFS (if not already installed) to download large model files
git lfs install
git lfs pull
```

### Installation

```bash
# Install system dependencies (required for phonemization)
sudo apt-get install espeak-ng

# Install the main Matcha-TTS repository
pip install git+https://github.com/adrianstanea/Matcha-TTS.git

# Install required dependencies
pip install -r requirements.txt
```

### Usage

```python
import sys
sys.path.append("src")
from model_loader import ModelLoader

# Load from local cloned repository
loader = ModelLoader.from_pretrained("./")

# List available models
print(loader.list_models())
# {'swara': {...}, 'bas_10': {...}, 'bas_950': {...}, ...}

# Load production-ready BAS speaker
model_info = loader.load_models(model="bas_950")
print(f"Model: {model_info['model_name']}")
print(f"Path: {model_info['model_path']}")

# Load few-shot SGS speaker
model_info = loader.load_models(model="sgs_10")
print(f"Training data: {model_info['model_info']['training_data']}")

# Use with original Matcha-TTS inference code
# See examples/inference_example.py for complete usage
```

### Run Example

```bash
cd examples
python inference_example.py
```

## Available Models

### Baseline Model

| Model     | Type     | Description                                          |
| --------- | -------- | ---------------------------------------------------- |
| **swara** | Baseline | Speaker-agnostic model trained on full SWARA dataset |

### Fine-tuned Speaker Models

| Model       | Speaker    | Training Samples | Fine-tune Epochs | Use Case                         |
| ----------- | ---------- | ---------------- | ---------------- | -------------------------------- |
| **bas_10**  | BAS (Male) | 10 samples       | 100              | Few-shot learning / Low-resource |
| **bas_950** | BAS (Male) | 950 samples      | 100              | Production-ready speaker         |
| **sgs_10**  | SGS (Male) | 10 samples       | 100              | Few-shot learning / Low-resource |
| **sgs_950** | SGS (Male) | 950 samples      | 100              | Production-ready speaker         |

**Vocoder**: Universal HiFi-GAN vocoder

### Research Methodology

- **Training Strategy**: Baseline → Speaker Fine-tuning (100 epochs)
- **Data Efficiency Study**: 10 vs 950 samples comparison
- **Low-Resource Learning**: Demonstrates few-shot TTS adaptation

## Model Details

- **Architecture**: Matcha-TTS (Conditional Flow Matching)
- **Dataset**: SWARA 1.0 Romanian Speech Corpus
- **Sample Rate**: 22,050 Hz
- **Language**: Romanian (ro)
- **Text Processing**: eSpeak Romanian phonemizer
- **Model Size**: ~100M parameters per model

## Repository Structure

```
├── models/                          # Model checkpoints (Git LFS)
│   ├── swara/
│   │   └── matcha-base-1000.ckpt   # Baseline model (1000 epochs)
│   ├── bas/
│   │   ├── matcha-bas-10_100.ckpt  # BAS speaker (10 samples, 100 epochs)
│   │   └── matcha-bas-950_100.ckpt # BAS speaker (950 samples, 100 epochs)
│   ├── sgs/
│   │   ├── matcha-sgs-10_100.ckpt  # SGS speaker (10 samples, 100 epochs)
│   │   └── matcha-sgs-950_100.ckpt # SGS speaker (950 samples, 100 epochs)
│   └── vocoder/
│       └── hifigan_univ_v1         # Universal HiFi-GAN vocoder
├── configs/
│   └── config.json                  # Model configuration
├── src/
│   └── model_loader.py              # HuggingFace-compatible loader
└── examples/
    ├── sample_texts_ro.txt          # Sample Romanian texts
    └── inference_example.py         # Complete usage example
```

## Usage with Original Repository

This repository provides model weights and HuggingFace integration. For training, evaluation, and advanced features, use the [main repository](https://github.com/adrianstanea/Matcha-TTS).

```python
# After loading models with ModelLoader
from matcha.models.matcha_tts import MatchaTTS
import torch

# Load using paths from ModelLoader
model = MatchaTTS.load_from_checkpoint(model_info['model_path'])
# ... continue with original inference code
```

## Requirements

- Python 3.10
- Main Matcha-TTS repository for inference
- HuggingFace Hub for model downloading

## License

Same as the original [Matcha-TTS repository](https://github.com/adrianstanea/Matcha-TTS).

## Citation

If you use this Romanian adaptation in your research, please cite:

```bibtex
@ARTICLE{11269795,
  author={Răgman, Teodora and Bogdan Stânea, Adrian and Cucu, Horia and Stan, Adriana},
  journal={IEEE Access},
  title={How Open Is Open TTS? A Practical Evaluation of Open Source TTS Tools},
  year={2025},
  volume={13},
  number={},
  pages={203415-203428},
  keywords={Computer architecture;Training;Text to speech;Spectrogram;Decoding;Computational modeling;Codecs;Predictive models;Acoustics;Low latency communication;Speech synthesis;open tools;evaluation;computational requirements;TTS adaptation;text-to-speech;objective measures;listening test;Romanian},
  doi={10.1109/ACCESS.2025.3637322}
}
```

**Original Matcha-TTS Citation:**

```bibtex
@inproceedings{mehta2024matcha,
  title={Matcha-{TTS}: A fast {TTS} architecture with conditional flow matching},
  author={Mehta, Shivam and Tu, Ruibo and Beskow, Jonas and Sz{\'e}kely, {\'E}va and Henter, Gustav Eje},
  booktitle={Proc. ICASSP},
  year={2024}
}
```

## Links

- [Main Repository](https://github.com/adrianstanea/Matcha-TTS) - Training, documentation, and research details
- [Original Matcha-TTS](https://github.com/shivammehta25/Matcha-TTS) - Base architecture and paper