| | ---
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| | license: apache-2.0
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| | language:
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| | - ro
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| | tags:
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| | - text-to-speech
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| | - Grad-TTS
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| | - Diffusion
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| | library_name: pytorch
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| | datasets:
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| | - SWARA-1.0
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| | ---
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| |
|
| | # Ro-Grad-TTS: Romanian Text-to-Speech
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| |
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| | Romanian adaptation of [Grad-TTS](https://arxiv.org/abs/2105.06337), trained on the [SWARA 1.0 dataset](https://speech.utcluj.ro/swarasc/).
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| |
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| | ## Quick Start
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| |
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| | This repository only contains the pretrained model weights for Romanian Grad-TTS. The actual package for Romanian TTS inference, including installation and usage instructions, is hosted on GitHub at [adrianstanea/Ro-Grad-TTS](https://github.com/adrianstanea/Ro-Grad-TTS.git).
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| |
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| | When using the Romanian Grad-TTS package, the weights from this repository will be automatically downloaded as needed. To install and run Romanian TTS inference, please follow the instructions in the main repository linked above.
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| |
|
| | ## Details
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| |
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| | - **Architecture**: Grad-TTS (diffusion-based TTS)
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| | - **Language**: Romanian
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| | - **Phonemization**: Espeak-ng
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| | - **Vocoder**: HiFi-GAN (universal v1)
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| | - **Sample rate**: 22050 Hz
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| | - **Training data**: SWARA 1.0 Romanian speech corpus
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| |
|
| | ## Available Models
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| |
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| | ### Baseline Model
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| |
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| | | Model | Type | Description |
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| | | --------- | -------- | ---------------------------------------------------- |
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| | | **swara** | Baseline | Speaker-agnostic model trained on full SWARA dataset |
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| |
|
| | ### Fine-tuned Speaker Models
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| |
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| | | Model | Speaker | Training Samples | Fine-tune Epochs | Use Case |
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| | | ----------- | ------------ | ---------------- | ---------------- | -------------------------------- |
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| | | **bas_10** | BAS (Female) | 10 samples | 100 | Few-shot learning / Low-resource |
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| | | **bas_950** | BAS (Female) | 950 samples | 100 | Production-ready speaker |
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| | | **sgs_10** | SGS (Male) | 10 samples | 100 | Few-shot learning / Low-resource |
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| | | **sgs_950** | SGS (Male) | 950 samples | 100 | Production-ready speaker |
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| |
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| | **Vocoder**: Universal HiFi-GAN vocoder
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| |
|
| | ## Repository Structure
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| |
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| | ```sh
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| | adrianstanea/Ro-Grad-TTS/
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| | βββ config.json # Model hyperparameters
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| | βββ hifigan_config.json # Vocoder configuration
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| | βββββ models/
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| | βββ swara/
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| | β βββ grad-tts-base-1000.pt # Baseline model
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| | βββ bas/
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| | β βββ grad-tts-bas-{10,950}_{15,50,100}.pt
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| | βββ sgs/
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| | β βββ grad-tts-sgs-{10,950}_{15,50,100}.pt
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| | βββ vocoder/
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| | βββ hifigan_univ_v1 # Universal HiFi-GAN
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| | ```
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| |
|
| | ## Citation
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| |
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| | If you use this Romanian adaptation in your research, please cite:
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| |
|
| | ```bibtex
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| | @ARTICLE{11269795,
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| | author={RΔgman, Teodora and Bogdan StΓ’nea, Adrian and Cucu, Horia and Stan, Adriana},
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| | journal={IEEE Access},
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| | title={How Open Is Open TTS? A Practical Evaluation of Open Source TTS Tools},
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| | year={2025},
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| | volume={13},
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| | number={},
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| | pages={203415-203428},
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| | 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},
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| | doi={10.1109/ACCESS.2025.3637322}
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| | }
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| | ```
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| |
|
| | ### Origianl Grad-TTS Citation
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| |
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| | ```bibtex
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| | @article{popov2021grad,
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| | title={Grad-TTS: A Diffusion Probabilistic Model for Text-to-Speech},
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| | author={Popov, Vadim and Vovk, Ivan and Gogoryan, Vladimir and Sadekova, Tasnima and Kudinov, Mikhail},
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| | journal={International Conference on Machine Learning},
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| | year={2021}
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| | }
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| | ```
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| |
|
| | ## References
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| |
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| | - [adrianstanea/Ro-Grad-TTS](https://github.com/adrianstanea/Ro-Grad-TTS.git) - Training, documentation, and research details
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| | - [huawei-noah/Speech-Backbones](https://github.com/huawei-noah/Speech-Backbones) - Base architecture and paper
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| |
|
