Tts Transformer Ar Cv7

🚀 tts_transformer-ar-cv7

A Transformer-based text-to-speech model from fairseq S^2, capable of converting text into high - quality Arabic speech.

🚀 Quick Start

This is a Transformer text - to - speech model from fairseq S^2. It supports Arabic text - to - speech conversion, uses a single - speaker male voice, and is trained on Common Voice v7.

✨ Features

• Language Support: Supports Arabic text input.
• Voice Type: Single - speaker male voice.
• Training Data: Trained on Common Voice v7.

📦 Installation

No specific installation steps are provided in the original document.

💻 Usage Examples

Basic Usage

from fairseq.checkpoint_utils import load_model_ensemble_and_task_from_hf_hub
from fairseq.models.text_to_speech.hub_interface import TTSHubInterface
import IPython.display as ipd


models, cfg, task = load_model_ensemble_and_task_from_hf_hub(
    "facebook/tts_transformer-ar-cv7",
    arg_overrides={"vocoder": "hifigan", "fp16": False}
)
model = models[0]
TTSHubInterface.update_cfg_with_data_cfg(cfg, task.data_cfg)
generator = task.build_generator(model, cfg)

text = "مرحبًا ، هذا اختبار تشغيل."

sample = TTSHubInterface.get_model_input(task, text)
wav, rate = TTSHubInterface.get_prediction(task, model, generator, sample)

ipd.Audio(wav, rate=rate)

See also fairseq S^2 example.

📚 Documentation

Model Information

📄 License

No license information is provided in the original document.

📚 Citation

@inproceedings{wang-etal-2021-fairseq,
    title = "fairseq S{\^{}}2: A Scalable and Integrable Speech Synthesis Toolkit",
    author = "Wang, Changhan  and
      Hsu, Wei-Ning  and
      Adi, Yossi  and
      Polyak, Adam  and
      Lee, Ann  and
      Chen, Peng-Jen  and
      Gu, Jiatao  and
      Pino, Juan",
    booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    month = nov,
    year = "2021",
    address = "Online and Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.emnlp-demo.17",
    doi = "10.18653/v1/2021.emnlp-demo.17",
    pages = "143--152",
}