# MultiEmo-Test

MultiEmo-Test is an English evaluation set for instruction-following multi-emotion text-to-speech synthesis. It accompanies [HybridEmo](https://github.com/ictnlp/HybridEmo), a system for modeling sequential emotion trajectories and simultaneous emotion blending within an utterance.

The dataset is intended for evaluation only. It contains synthesis text, natural-language emotion instructions, emotion annotations, and prompt audio for speaker-timbre conditioning. It does not contain target synthesized speech.

## Dataset Composition

MultiEmo-Test contains 720 examples in a single test split.

| Task | Subset | Number of examples | Description |
|---|---:|---:|---|
| Emotion trajectory | 1E | 200 | A single emotion is expressed throughout the utterance. |
| Emotion trajectory | 2E | 200 | Two emotions are expressed sequentially. |
| Emotion trajectory | 3E | 200 | Three emotions are expressed sequentially. |
| Emotion blending | 2E blending | 120 | Two emotions are expressed simultaneously. |
| **Total** |  | **720** |  |

The trajectory subset uses seven emotion labels:

`angry`, `disgusted`, `fearful`, `happy`, `neutral`, `sad`, and `surprised`.

The blending subset uses six emotion labels:

`angry`, `disgusted`, `fearful`, `happy`, `sad`, and `surprised`.

## Data Format

The dataset is distributed as a JSON Lines manifest and a directory of prompt audio files:

```text
MultiEmo-Test/
├── README.md
├── test.jsonl
└── prompt-audio/
    └── *.wav
```

Each line in `test.jsonl` contains the following fields:

| Field | Type | Description |
|---|---|---|
| `id` | integer | Unique example identifier. |
| `trajectory_type` | integer or null | Number of stages for a trajectory example (`1`, `2`, or `3`); null for blending examples. |
| `emotion_trajectory` | list of strings or null | Ordered emotion labels for a trajectory example. |
| `blended_type` | string or null | Set to `blended` for blending examples; null for trajectory examples. |
| `blended_emotion` | list of strings or null | Two emotion labels to be expressed simultaneously. |
| `text` | string | Text to synthesize. |
| `instruction` | string | Natural-language instruction describing the intended emotional expression. |
| `prompt_audio` | string | Relative path to the speaker-timbre reference audio. |
| `prompt_text` | string | Transcript of the prompt audio. |
| `prompt_key` | string | Source key associated with the prompt audio. |

Example trajectory record:

```json
{
  "id": 1,
  "trajectory_type": 1,
  "emotion_trajectory": ["angry"],
  "blended_type": null,
  "blended_emotion": null,
  "text": "Can you believe the audacity of that person?",
  "instruction": "Read this passage with a consistently angry tone.",
  "prompt_audio": "prompt-audio/common_voice_en_509177.wav",
  "prompt_text": "The autonomous ship floated closer to receiving its flying cargo.",
  "prompt_key": "yuekai/seed_tts_cosy2::path::common_voice_en_509177.wav"
}
```

## Loading the Dataset

After downloading the repository, the manifest can be loaded with the Hugging Face `datasets` library:

```python
from datasets import load_dataset

dataset = load_dataset(
    "json",
    data_files={"test": "test.jsonl"},
)["test"]

example = dataset[0]
print(example["instruction"])
print(example["prompt_audio"])
```

The value of `prompt_audio` is relative to the dataset root. For example, `prompt-audio/example.wav` should be resolved from the directory containing `test.jsonl`.

## Intended Use

MultiEmo-Test is designed to evaluate whether instruction-following TTS systems can:

- maintain a specified emotion throughout an utterance;
- follow two- or three-stage emotion trajectories in the requested order;
- express two target emotions simultaneously;
- preserve the speaker timbre provided by the prompt audio.

The dataset is not intended as a training corpus or as a comprehensive representation of all emotions, emotion transitions, languages, speakers, or real-world speaking conditions.

## License

MultiEmo-Test is released under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0).

## Citation

If you use this dataset, please cite the HybridEmo paper:

```bibtex
@misc{zhou2026sequentialtrajectoriessimultaneousblending,
  title={Sequential Trajectories and Simultaneous Blending: Multi-Emotion Modeling for Instruction-Following TTS},
  author={Yan Zhou and Yun Hong and Yang Feng},
  year={2026},
  eprint={2608.30325},
  archivePrefix={arXiv},
  primaryClass={cs.CL},
  url={https://arxiv.org/abs/2608.30325},
}
```

Project repository: [https://github.com/ictnlp/HybridEmo](https://github.com/ictnlp/HybridEmo)