Datasets:

Parakeet-Inc
/

J-HARD-TTS-Eval

license: apache-2.0
dataset_info:
  - config_name: continuation
    features:
      - name: id
        dtype: string
      - name: prompt_audio
        dtype: audio
      - name: prompt_text
        dtype: string
      - name: text
        dtype: string
    splits:
      - name: test
        num_bytes: 2788053
        num_examples: 50
    download_size: 2787410
    dataset_size: 2788053
  - config_name: repetition
    features:
      - name: id
        dtype: string
      - name: prompt_audio
        dtype: audio
      - name: prompt_text
        dtype: string
      - name: text
        dtype: string
    splits:
      - name: test
        num_bytes: 2942842
        num_examples: 50
    download_size: 2940024
    dataset_size: 2942842
  - config_name: rhyme
    features:
      - name: id
        dtype: string
      - name: prompt_audio
        dtype: audio
      - name: prompt_text
        dtype: string
      - name: text
        dtype: string
    splits:
      - name: test
        num_bytes: 2937070
        num_examples: 50
    download_size: 2936441
    dataset_size: 2937070
  - config_name: short
    features:
      - name: id
        dtype: string
      - name: prompt_audio
        dtype: audio
      - name: prompt_text
        dtype: string
      - name: text
        dtype: string
    splits:
      - name: test
        num_bytes: 3013215
        num_examples: 50
    download_size: 3012856
    dataset_size: 3013215
configs:
  - config_name: continuation
    data_files:
      - split: test
        path: continuation/test-*
  - config_name: repetition
    data_files:
      - split: test
        path: repetition/test-*
  - config_name: rhyme
    data_files:
      - split: test
        path: rhyme/test-*
  - config_name: short
    data_files:
      - split: test
        path: short/test-*
task_categories:
  - text-to-speech
language:
  - ja
size_categories:
  - 1K<n<10K

J-HARD-TTS-Eval

For full documentation, detailed benchmark results, and methodology, please refer to the GitHub Repository.

Overview

J-HARD-TTS-Eval is a benchmark designed to evaluate the robustness of autoregressive Japanese Text-To-Speech (TTS) models. It focuses on specific failure modes such as stability in short sequences, repetition handling, and context completion.

Usage

You can easily load the dataset using the Hugging Face datasets library.

from datasets import load_dataset

# Load a specific subset: "short", "repetition", "rhyme", or "continuation"
dataset = load_dataset("Parakeet-Inc/J-HARD-TTS-Eval", "rhyme", split="test")

sample = dataset[0]

# Accessing data
print(f"ID: {sample['id']}")
print(f"Target Text: {sample['text']}")
print(f"Prompt Text: {sample['prompt_text']}")
print(f"Prompt Audio: {sample['prompt_audio']['array'].shape}, Sampling Rate: {sample['prompt_audio']['sampling_rate']}")

# Play audio in a notebook
# import IPython.display as ipd
# ipd.Audio(sample["prompt_audio"]["array"], rate=sample["prompt_audio"]["sampling_rate"])

Dataset Structure

id (string): Unique identifier for the sample (e.g., rhyme-01).
text (string): The target text to be synthesized (Ground Truth Text).
prompt_text (string): The transcript of the prompt audio.
prompt_audio (Audio): The reference audio used for zero-shot prompting (sourced from Common Voice).

Citation

If you use this benchmark in your research, please cite our paper:

@inproceedings{imai2025jhard,
  author    = {Imai, Shuhei and Enomoto, Haruhisa and Kaneko, Takeshi and Nakamura, Taiki},
  title     = {A Study on a High-Difficulty Japanese Text-to-Speech Corpus Specialized for Sequence Consistency Evaluation},
  booktitle = {Proc. ASJ},
  year      = {2025},
  month     = {9}
}