---
pretty_name: CSRTE
license: cc-by-4.0
language:
- zh
size_categories:
- 10K<n<100K
task_categories:
- automatic-speech-recognition
- token-classification
task_ids:
- named-entity-recognition
tags:
- audio
- speech-relation-extraction
- relation-extraction
- information-extraction
- chinese
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*.parquet
  - split: validation
    path: data/validation-*.parquet
  - split: test
    path: data/test-*.parquet
dataset_info:
- config_name: default
  features:
  - name: audio
    dtype:
      audio:
        sampling_rate: 16000
  - name: sentence
    dtype: string
  - name: triplets
    list:
    - name: head
      dtype: string
    - name: relation
      dtype: string
    - name: tail
      dtype: string
  - name: entities
    list:
    - name: id
      dtype: string
    - name: start
      dtype: int64
    - name: end
      dtype: int64
    - name: text
      dtype: string
    - name: label
      dtype: string
  - name: relations
    list:
    - name: from_id
      dtype: string
    - name: to_id
      dtype: string
    - name: labels
      list: string
    - name: direction
      dtype: string
  - name: audio_id
    dtype: string
  - name: source
    dtype: string
  - name: speaker_id
    dtype: string
  - name: duration_seconds
    dtype: float64
  splits:
  - name: train
    num_examples: 15000
  - name: validation
    num_examples: 2000
  - name: test
    num_examples: 2855
---

# CSRTE

CSRTE is a dataset for Chinese speech relation extraction. Each sample is a Mandarin speech clip with a transcript, entity spans, and relation triplets. The task is to extract (head, relation, tail) triplets directly from the audio.

- 19,855 annotated clips, 33.78 hours, 16 kHz mono
- 10 entity types and 49 relation types
- Paper: [ACRE (Findings of EMNLP 2026)](https://aclanthology.org/2026.findings-emnlp.1244/)
- Code: [MWXGOD/CSRTE](https://github.com/MWXGOD/CSRTE)

## Splits

| Split | Clips | Hours | Entities | Relations |
| --- | ---: | ---: | ---: | ---: |
| train | 15,000 | 25.52 | 37,743 | 18,439 |
| validation | 2,000 | 3.42 | 5,000 | 2,465 |
| test | 2,855 | 4.84 | 6,921 | 3,345 |
| total | 19,855 | 33.78 | 49,664 | 24,249 |

`validation` corresponds to `dev.json` in the original release.

## Fields

| Field | Description |
| --- | --- |
| `audio` | 16 kHz mono audio (FLAC, 16-bit) |
| `sentence` | Chinese transcript |
| `triplets` | Relation triplets as `{head, relation, tail}`, e.g. `{庹庆明, 居住于, 荥经}` |
| `entities` | Entity spans: `id`, `start`, `end` (character offsets, end exclusive), `text`, `label` |
| `relations` | Raw relation annotations: `from_id`, `to_id`, `labels`, `direction` |
| `audio_id` | Clip ID, e.g. `common_voice_zh-CN_18776086` or `BAC009S0185W0305` |
| `source` | `Common Voice 17.0` or `AISHELL-1` |
| `speaker_id` | AISHELL-1 speaker ID (e.g. `S0185`). Empty for Common Voice clips, which have no speaker ID |
| `duration_seconds` | Clip length in seconds |

`triplets` is built from `relations` and `entities`. `head` is the text of the `from_id` entity, `tail` is the text of the `to_id` entity, and `relation` is the middle part of the label (`人物:居住于:地点` → `居住于`). The full label with entity types is still in `relations`.

The entity types are `<人物>`, `<地点>`, `<组织>`, `<称号>`, `<时间>`, `<金钱>`, `<事件>`, `<著作>`, `<产品>` and `<规则条例>`. The full list of relation types is in [`annotations/dataset_info.json`](annotations/dataset_info.json).

Example:

```json
{
  "sentence": "庹庆明，四川荥经人，汉族，中国民主同盟盟员。",
  "triplets": [
    {"head": "庹庆明", "relation": "居住于", "tail": "荥经"},
    {"head": "荥经", "relation": "位于", "tail": "四川"}
  ],
  "entities": [
    {"id": "Uh6LiuB7Am", "start": 0, "end": 3, "text": "庹庆明", "label": "<人物>"},
    {"id": "P9qwmYXbpx", "start": 6, "end": 8, "text": "荥经", "label": "<地点>"}
  ]
}
```

## Usage

```python
from datasets import load_dataset

ds = load_dataset("DMU-ITREC/CSRTE")            # train / validation / test
sample = ds["train"][0]
print(sample["sentence"], sample["triplets"])
waveform = sample["audio"]["array"]              # 16 kHz
```

To browse without downloading everything, pass `streaming=True`.

## Other files

- `annotations/train.json`, `dev.json`, `test.json`: the original annotation files, including the `data_info` speaker metadata (gender, age, accent, etc.)
- `release_manifest.json`: file checksums and row counts

## Audio processing

- Common Voice clips: we converted the 16 kHz float64 NumPy arrays to 16-bit PCM. 125 clips peaked above 1.0. We scaled each of them down by its peak to avoid clipping.
- AISHELL-1 clips: we re-encoded the 16-bit WAV files to FLAC without changing any samples.
- We did no resampling, trimming, or denoising.

## Known issues

- In five training samples, an entity's `text` does not match the corresponding slice of `sentence`. These are `BAC009S0230W0457`, `BAC009S0346W0368`, `BAC009S0710W0321`, `BAC009S0718W0319` and `BAC009S0907W0481`. We kept them as they are.
- 715 clips have no entities and 9,321 have no relations. Their lists are empty.
- Speaker metadata is incomplete, and the splits are not speaker-disjoint.

## Source data and license

| Source | Clips | License |
| --- | ---: | --- |
| [Common Voice 17.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_17_0) (zh-CN) | 9,908 | CC0 1.0 |
| [AISHELL-1](https://www.openslr.org/33/) | 9,947 | Apache 2.0 |

The CSRTE annotations are released under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). The audio stays under the license of its source corpus. Do not try to identify the speakers.

## Citation

```bibtex
@inproceedings{mu-etal-2026-acre,
  title = "{ACRE}: An Acoustic-Cue and Contextual-Knowledge Reasoning Enhancement Framework for Speech Relation Extraction",
  author = "Mu, Wenxuan and Ning, Jinzhong and Zhao, Di and Zhang, Yijia",
  booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2026",
  year = "2026",
  publisher = "Association for Computational Linguistics",
  pages = "26343--26352",
  url = "https://aclanthology.org/2026.findings-emnlp.1244/",
  doi = "10.18653/2026.findings-emnlp.1244"
}
```

Questions or corrections: please open a [discussion](https://huggingface.co/datasets/DMU-ITREC/CSRTE/discussions).