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dataset_info:
- config_name: en
features:
- name: audio_output_path
dtype: string
- name: prompt_text
dtype: string
- name: prompt_audio
dtype: string
- name: text_input
dtype: string
- name: audio_ground_truth
dtype: string
splits:
- name: test_wer
num_examples: 1088 # Update with actual numbers
- name: test_sim
num_examples: 1086 # Update with actual numbers
- config_name: zh
features:
- name: audio_output_path
dtype: string
- name: prompt_text
dtype: string
- name: prompt_audio
dtype: string
- name: text_input
dtype: string
- name: audio_ground_truth
dtype: string
splits:
- name: test_wer
num_examples: 2020 # Update with actual numbers
- name: test_sim
num_examples: 2018 # Update with actual numbers
- name: test_wer_hardcase
num_examples: 400 # Update with actual numbers
configs:
- config_name: en
data_files:
- split: test_wer
path: en/meta.jsonl
- split: test_sim
path: en/non_para_reconstruct_meta.jsonl
- config_name: zh
data_files:
- split: test_wer
path: zh/meta.jsonl
- split: test_sim
path: zh/non_para_reconstruct_meta.jsonl
- split: test_wer_hardcase
path: zh/hardcase.jsonl
---
# SeedTTS Evaluation Dataset
This dataset contains evaluation data for SeedTTS text-to-speech model testing in multiple languages.
Original repo from: [https://github.com/BytedanceSpeech/seed-tts-eval](https://github.com/BytedanceSpeech/seed-tts-eval)
---
## Languages
* **English (en)**: Contains `test_wer` and `test_sim` splits
* **Chinese (zh)**: Contains `test_wer`, `test_sim`, and `test_wer_hardcase` splits
---
## Usage
```python
# makesure: pip install datasets==3.5.1
import os
from datasets import load_dataset
repo_dir = "hhqx/seedtts_testset"
ds_en = load_dataset(repo_dir, 'en', trust_remote_code=True)
print(ds_en['test_wer'][0])
ds_zh = load_dataset(repo_dir, 'zh', trust_remote_code=True)
print(ds_zh['test_sim'][0])
# Access specific splits
en_wer = ds_en['test_wer']
en_sim = ds_en['test_sim']
zh_wer = ds_zh['test_wer']
zh_sim = ds_zh['test_sim']
zh_hardcase = ds_zh['test_wer_hardcase']
for config, split in [
['en', 'test_wer'],
['en', 'test_sim'],
['zh', 'test_wer'],
['zh', 'test_sim'],
['zh', 'test_wer_hardcase'],
]:
data = load_dataset(repo_dir, config, trust_remote_code=True, split=split)
for item in data:
for key, value in item.items():
if key in ['audio_ground_truth', 'prompt_audio', ] and value:
assert os.path.exists(value), f'path not exist: {value}'
print("len of {} {}: {}".format(config, split, len(data)))
```
---
## Data Structure
### Dataset Info (example)
```yaml
dataset_info:
- config_name: en
features:
- audio_output_path: string
- prompt_text: string
- prompt_audio: string
- text_input: string
- audio_ground_truth: string
splits:
- name: test_wer
num_examples: 1088 # Update with actual numbers
- name: test_sim
num_examples: 1086 # Update with actual numbers
- config_name: zh
features:
- audio_output_path: string
- prompt_text: string
- prompt_audio: string
- text_input: string
- audio_ground_truth: string
splits:
- name: test_wer
num_examples: 2020 # Update with actual numbers
- name: test_sim
num_examples: 2018 # Update with actual numbers
- name: test_wer_hardcase
num_examples: 400 # Update with actual numbers
```
---
## Configs & Data Files Mapping
```yaml
configs:
- config_name: en
data_files:
- split: test_wer
path: data/en_meta.jsonl
- split: test_sim
path: data/en_non_para_reconstruct_meta.jsonl
- config_name: zh
data_files:
- split: test_wer
path: data/zh_meta.jsonl
- split: test_sim
path: data/zh_non_para_reconstruct_meta.jsonl
- split: test_wer_hardcase
path: data/zh_hardcase.jsonl
```
---
## File Structure
```
.
├── seedtts_dataset.py # Your dataset loading script
├── README.md # This file
├── data/
│ ├── en_meta.jsonl
│ ├── en_non_para_reconstruct_meta.jsonl
│ ├── en.tgz # Compressed wav/audio files for English
│ ├── zh_meta.jsonl
│ ├── zh_non_para_reconstruct_meta.jsonl
│ ├── zh_hardcase.jsonl
│ └── zh.tgz # Compressed wav/audio files for Chinese
├── convert_seedtts_to_dataset.py
├── test_demo.py
```
---
## Notes
* The `.tgz` files contain the audio `.wav` files and will be automatically extracted to the local Hugging Face cache directory during dataset loading.
* To control where the data archive is extracted and cached, use the `cache_dir` argument in `load_dataset`, e.g.:
```python
ds = load_dataset("path/to/seedtts-dataset-repo", "en", cache_dir="/your/fast/storage/path")
```
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