MahtaFetrat/Mana-Persian-Piper

Mana Persian Piper (fa-IR)

This repository hosts a Persian (fa-IR) Piper TTS model trained for low-latency, high-quality speech synthesis.

The model is a medium-sized Piper checkpoint, fine-tuned on the Mana-TTS dataset to produce natural and intelligible Persian speech while remaining suitable for real-time and on-device inference.

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Model Description

• Architecture: Piper (medium)
• Language: Persian (fa-IR)
• Base Checkpoint: https://huggingface.co/SadeghK/persian-text-to-speech/tree/main/farsi/amir
• Fine-tuning: ~1000 epochs on Mana-TTS
• Training Dataset: https://huggingface.co/datasets/MahtaFetrat/Mana-TTS

This model was trained as part of a broader effort to build efficient Persian TTS systems that integrate well with lightweight and context-aware phonemization pipelines.

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Inference

Install Piper

pip install piper-tts

Download the Model

git clone https://huggingface.co/MahtaFetrat/Mana-Persian-Piper

Run Inference (Python)

import wave
from piper import PiperVoice

voice = PiperVoice.load("/content/Mana-Persian-Piper/fa_IR-mana-medium.onnx")

with wave.open("test.wav", "wb") as wav_file:
    voice.synthesize_wav("سلام به همگی!", wav_file)

This will generate a test.wav file containing synthesized Persian speech.

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Model Files

• fa_IR-mana-medium.onnx – Piper acoustic model
• fa_IR-mana-medium.onnx.json – Model configuration and metadata

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Recommended Usage

This model is best used in conjunction with context-aware phonemization, as proposed in the paper:

Beyond Unified Models: A Service-Oriented Approach to Low-Latency, Context-Aware Phonemization for Real-Time TTS

In particular, combining this Piper model with:

• Lightweight G2P
• Ezafe-aware context disambiguation

results in improved pronunciation accuracy while preserving real-time performance.

The full system implementation is available in the companion repository associated with the paper.

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Citation

If you use this model in your research or applications, please cite the following paper:

@misc{fetrat2025servicetts,
      title={Beyond Unified Models: A Service-Oriented Approach to Low Latency, Context Aware Phonemization for Real Time TTS}, 
      author={Mahta Fetrat and Donya Navabi and Zahra Dehghanian and Morteza Abolghasemi and Hamid R. Rabiee},
      year={2025},
      eprint={2512.08006},
      archivePrefix={arXiv},
      primaryClass={cs.SD},
      url={https://arxiv.org/abs/2512.08006}, 
}