---
license: cc-by-4.0
task_categories:
  - text-to-speech
language:
  - en
  - zh
tags:
  - tts
  - speech-synthesis
  - voice-cloning
  - seed-tts-eval
  - sglang
pretty_name: seed-tts-eval
size_categories:
  - 1K<n<10K
---

# seed-tts-eval

A preprocessed copy of the [seed-tts-eval](https://github.com/BytedanceSpeech/seed-tts-eval) test set, used by [SGLang Omni](https://github.com/sgl-project/sglang-omni) for TTS benchmarking (WER and speed evaluation).

We thank the researchers of ByteDance for releasing the original evaluation data and methodology. This dataset simply reorganizes their test sets into a single Hugging Face repository for convenience.

## Evaluation Sets

This dataset contains 5 evaluation sets across English and Chinese:

| # | File | Language | Samples | Columns | Difficulty | Description |
|---|---|---|---|---|---|---|
| 1 | `en/meta.lst` | English | 1,088 | 4 | Standard | Same-speaker voice cloning (CommonVoice) |
| 2 | `zh/meta.lst` | Chinese | 2,020 | 4 | Standard | Same-speaker voice cloning (DiDiSpeech-2) |
| 3 | `en/non_para_reconstruct_meta.lst` | English | 1,086 | 5 | Hard | Cross-speaker voice cloning |
| 4 | `zh/non_para_reconstruct_meta.lst` | Chinese | 2,018 | 5 | Hard | Cross-speaker voice cloning |
| 5 | `zh/hardcase.lst` | Chinese | 400 | 4 | Hard | Tongue twisters and repetition patterns |

Sets 1 and 2 (`en/meta.lst` and `zh/meta.lst`) are the standard evaluation sets used by SGLang Omni benchmarks.

Note: Hugging Face may display ~5K samples on this page. That number comes from the auto-detected `audiofolder` format counting every `.wav` file (both prompt wavs and target wavs) individually. The actual evaluation sample counts are listed in the table above.

## File Format

### Standard sets (4 columns)

In `en/meta.lst`, `zh/meta.lst`, and `zh/hardcase.lst`, each line contains the following columns:

```
utterance_id | prompt_text | prompt_wav_path | target_text
```

| Column | Description |
|---|---|
| `utterance_id` | Unique sample identifier |
| `prompt_text` | Transcript of the prompt (reference) audio |
| `prompt_wav_path` | Relative path to the prompt audio file (e.g., `prompt-wavs/xxx.wav`) |
| `target_text` | Text to be synthesized by the TTS model |

```
common_voice_en_10119832-common_voice_en_10119840|We asked over twenty different people, and they all said it was his.|prompt-wavs/common_voice_en_10119832.wav|Get the trust fund to the bank early.
```

### Cross-speaker sets (5 columns)

In `en/non_para_reconstruct_meta.lst` and `zh/non_para_reconstruct_meta.lst`, each line contains the following columns:

```
utterance_id | prompt_text | prompt_wav_path | target_text | target_wav_path
```

In addition to the 4 columns, these files have an additional 5th column:

| Column | Description |
|---|---|
| `target_wav_path` | Relative path to the ground-truth target audio (for reconstruction-based evaluation) |

In cross-speaker sets, the prompt speaker and the target speaker are different people, making voice cloning significantly harder.

## Set Details

### English Standard (`en/meta.lst`)

1,088 samples from [CommonVoice](https://commonvoice.mozilla.org/). The prompt audio and the target text come from the same speaker, testing parallel (same-speaker) voice cloning.

### Chinese Standard (`zh/meta.lst`)

2,020 samples from [DiDiSpeech-2](https://arxiv.org/abs/2010.14956). Same-speaker voice cloning, analogous to the English set.

### English Cross-Speaker (`en/non_para_reconstruct_meta.lst`)

1,086 samples. The prompt and target are from different speakers -- the model must synthesize the target text in the prompt speaker's voice, without having heard that speaker say anything similar. Shares the same target texts as set 1.

### Chinese Cross-Speaker (`zh/non_para_reconstruct_meta.lst`)

2,018 samples. Cross-speaker Chinese evaluation, analogous to set 3. Shares the same target texts as set 2.

### Chinese Hard Cases (`zh/hardcase.lst`)

400 samples split into two categories:

- Tongue twisters (绕口令, `raokouling-*`): 200 samples with phonetically challenging sentences designed to stress-test pronunciation accuracy.
- Repetition patterns: 200 samples with repetitive or stutter-prone text patterns.

## Usage

```bash
# Download the full dataset
huggingface-cli download zhaochenyang20/seed-tts-eval \
    --repo-type dataset --local-dir seedtts_testset
```

For CI testing, a minimal subset is available at [`zhaochenyang20/seed-tts-eval-mini`](https://huggingface.co/datasets/zhaochenyang20/seed-tts-eval-mini).

## Directory Structure

```
seed-tts-eval/
├── en/
│   ├── meta.lst                         # Standard English eval (1,088 samples)
│   ├── non_para_reconstruct_meta.lst    # Cross-speaker English eval (1,086 samples)
│   ├── prompt-wavs/                     # Reference audio clips (1,007 files)
│   └── wavs/                            # Ground-truth target audio (1,092 files)
└── zh/
    ├── meta.lst                         # Standard Chinese eval (2,020 samples)
    ├── non_para_reconstruct_meta.lst    # Cross-speaker Chinese eval (2,018 samples)
    ├── hardcase.lst                     # Tongue twisters + repetition (400 samples)
    ├── prompt-wavs/                     # Reference audio clips (1,010 files)
    └── wavs/                            # Ground-truth target audio (2,020 files)
```

## Citation

If you use this dataset, please cite the original seed-tts-eval work:

```bibtex
@article{anastassiou2024seed,
  title={Seed-TTS: A Family of High-Quality Versatile Speech Generation Models},
  author={Anastassiou, Philip and others},
  journal={arXiv preprint arXiv:2406.02430},
  year={2024}
}
```