metadata

language:
  - en
license: mit
size_categories:
  - n<1K
task_categories:
  - text-to-speech
pretty_name: AniSpeech
tags:
  - anime
  - speech
  - text-to-speech
  - voice
dataset_info:
  features:
    - name: audio
      dtype: audio
    - name: caption
      dtype: string
    - name: phonetic captions
      dtype: string
    - name: voice
      dtype: string
  splits:
    - name: ENGLISH
      num_bytes: 18875728249.368
      num_examples: 23656
  download_size: 20449215803
  dataset_size: 18875728249.368
configs:
  - config_name: default
    data_files:
      - split: ENGLISH
        path: data/ENGLISH-*

AniSpeech Dataset

Welcome to the AniSpeech dataset, a continually expanding collection of captioned anime voices brought to you by ShoukanLabs.

As we label more and more audio, they'll automagically be uploaded here for use, seperated by language

ANNOUNCMENTS:

An upcoming update will add an immense ammount of data to the dataset... however... because we cannot manually go through this dataset we have had to rely on manual quality estimation, as such, speaker splits may be innacurate, this shouldnt impact finetuning multispeaker models, but when training single speaker models you may have to listen to multiple speakers to find missing data, we plan on eventually completely overhauling this dataset eventually

Key Features

LJSpeech Format Compatibility: The captions in this dataset can be converted to (recent changes have sacrificed native LJSpeech support for better captions) comply with the LJSpeech format, and we plan to offer conversion scripts to said format eventually.
Diverse Anime Voices: Train your TTS models on high-quality vocal performances with variations in intonation, timbre, and pitch. The dataset offers a rich assortment of anime voices for creating generalised models.
Ideal for Generalized Models: AniSpeech is a perfect choice for fine-tuning generalized models. With a diverse range of voices, it provides a solid foundation for training models that can handle a wide variety of speaking styles (all speakers are labeled with a seperate speaker id).

Limitations

Single-Voice Fine-Tuning: While AniSpeech excels in training foundation models (due to it's diversity), it's not recommended for fine-tuning on a single voice. Its strength lies in contributing to the development of versatile TTS models.
Dataset Curation: Due to its size, manually curating the entire dataset can be impractical. If you encounter low-quality files or incorrect captions, we encourage you to contribute by creating a pull request to help maintain and improve the dataset.

License

This dataset is released under the MIT License.

Your contributions to the AniSpeech dataset are invaluable, and we appreciate your efforts in advancing the field of Text-to-Speech technology.

Happy coding and synthesizing!