| --- |
| 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). |
|
|
