---
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

[![Paper](https://img.shields.io/badge/Paper-Japanese-red)](https://drive.google.com/file/d/1CZ74Nvmca7nneK2zCfRyogFAMVLUx0Ik/view?usp=drive_link)
[![GitHub](https://img.shields.io/badge/GitHub-Repository-black?logo=github)](https://github.com/Parakeet-Inc/J-HARD-TTS-Eval)

> [!NOTE]
> For full documentation, detailed benchmark results, and methodology, please refer to the [**GitHub Repository**](https://github.com/Parakeet-Inc/J-HARD-TTS-Eval).

## 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.

```python
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:

```bibtex
@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}
}
```