Open-source Text-to-Speech Model: kan-bayashi_vctk_xvector_conformer_fastspeech2

Kan Bayashi Vctk Xvector Conformer Fastspeech2

Developed by espnet

A text-to-speech model trained using the ESPnet framework, utilizing the VCTK dataset, supporting multi-speaker speech synthesis

Speech Synthesis English#Multi-speaker speech synthesis #xvector speaker embedding #Conformer architecture

Downloads 15

Release Time : 3/2/2022

Model Overview

This model is a text-to-speech (TTS) model based on the FastSpeech2 architecture, incorporating a Conformer encoder and xvector speaker embeddings, capable of generating high-quality speech output and supporting multi-speaker speech synthesis.

Model Features

Multi-speaker support

Through xvector speaker embedding technology, the model can synthesize speech from different speakers

High-quality speech synthesis

Utilizes the FastSpeech2 architecture combined with a Conformer encoder to generate natural and fluent speech

Based on ESPnet framework

Trained using the open-source ESPnet toolkit, ensuring good reproducibility and scalability

Model Capabilities

Text-to-speech

Multi-speaker speech synthesis

English speech generation

Use Cases

Speech synthesis applications

Audiobook generation

Convert text content into natural speech for creating audiobooks

Can generate audiobook content in different speaker styles

Voice assistants

Provide speech synthesis capabilities for voice assistant systems

Supports multiple voice style options

🚀 Example ESPnet2 TTS model

This is an ESPnet2 TTS model, which provides a solution for text - to - speech conversion. It is trained on specific datasets and can be used in relevant speech processing scenarios.

🚀 Quick Start

`kan-bayashi/vctk_xvector_conformer_fastspeech2`

♻️ Imported from https://zenodo.org/record/4394602/

This model was trained by kan-bayashi using vctk/tts1 recipe in espnet.

💻 Usage Examples

Basic Usage

# coming soon

📄 License

This project is licensed under the cc - by - 4.0 license.

📚 Documentation

Citing ESPnet

@inproceedings{watanabe2018espnet,
  author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
  title={{ESPnet}: End-to-End Speech Processing Toolkit},
  year={2018},
  booktitle={Proceedings of Interspeech},
  pages={2207--2211},
  doi={10.21437/Interspeech.2018-1456},
  url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
@inproceedings{hayashi2020espnet,
  title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
  author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu},
  booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  pages={7654--7658},
  year={2020},
  organization={IEEE}
}

or arXiv:

@misc{watanabe2018espnet,
      title={ESPnet: End-to-End Speech Processing Toolkit}, 
      author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
      year={2018},
      eprint={1804.00015},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

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