Datasets:

DMU-ITREC
/

CSRTE

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)
Code: 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.

Example:

{
  "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

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 (zh-CN)	9,908	CC0 1.0
AISHELL-1	9,947	Apache 2.0

The CSRTE annotations are released under CC BY 4.0. The audio stays under the license of its source corpus. Do not try to identify the speakers.

Citation

@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.