Text To Speech

🚀 fastspeech2-en-ljspeech

This is a text-to-speech model based on FastSpeech 2 from fairseq S^2. It can convert English text into a single - speaker female voice and is trained on the LJSpeech dataset.

🚀 Quick Start

This fastspeech2-en-ljspeech is a text - to - speech model from fairseq S^2 (paper/code) based on FastSpeech 2:

• Language: English
• Voice Type: Single - speaker female voice
• Training Dataset: LJSpeech

✨ Features

• Library and Task: It belongs to the fairseq library and is designed for the text - to - speech task.
• Tags: Associated with tags like fairseq, audio, and text - to - speech.
• Datasets: Trained on the LJSpeech dataset.
• Widget Example: You can test it with the text "Hello, this is a test run."

📦 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/fastspeech2-en-ljspeech",
    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 = "Hello, this is a test run."

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

No detailed documentation other than the usage example is provided in the original document.

🔧 Technical Details

No specific technical details are provided in the original document.

📄 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",
}