--- 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") ```