---
license: cc-by-nc-sa-4.0
task_categories:
  - automatic-speech-recognition
  - translation
language:
  - zh
pretty_name: Chinese-LiPS Long-Form (zh long streaming speech for slide-aware SST)
size_categories:
  - n<1K
configs:
  - config_name: orig_timeline
    data_files:
      - split: test
        path: "orig_timeline/*.longform.jsonl"
  - config_name: silence_removed
    data_files:
      - split: test
        path: "silence_removed/*.longform.jsonl"
---

# Chinese-LiPS Long-Form (zh long streaming speech)

Reconstructed continuous long-speech streams from
[BAAI/Chinese-LiPS](https://huggingface.co/datasets/BAAI/Chinese-LiPS), for
**slide-aware / streaming speech-translation** development and evaluation. Each
source video (one speaker, one scripted lecture with slides) was released as
pre-segmented clips; here they are re-joined into the full talk.

Two variants of the same 3 talks (~97 min speech total):

| config | how segments are placed | use |
|---|---|---|
| `orig_timeline` | at their **original session timestamps**, real inter-segment silence restored (from the raw release's per-segment `startTime`/`endTime`) | realistic streaming: pauses, READ/WRITE timing, latency |
| `silence_removed` | back-to-back, no gaps | compact debugging, dense transcript coverage |

| video_id | topic | speaker | segments | orig span | speech |
|---|---|---|---|---|---|
| 130_42_M_TY | 体育 sports | 42 M | 265 | 43.0 min | 37.6 min |
| 102_24_M_KJ | 科技 technology | 24 M | 206 | 32.4 min | 29.7 min |
| 041_28_F_RWLS | 人文历史 humanities | 28 F | 222 | 31.4 min | 29.4 min |

- `<video_id>.longform.wav` — 16 kHz mono 16-bit
- `<video_id>.longform.jsonl` — one line per clip: `{video_id, clip_id, start, end,
  zh_transcript, ocr_text, vl2_text, ppt_frame[, orig_start, orig_end]}`; in
  `orig_timeline`, `start`/`end` reproduce the original talk timeline (max drift
  0.001 s vs source timestamps).

## About the slide modality (why this is a strong slide-aware source)

Chinese-LiPS ships, per segment, a **1080p slide-region video** (`PPT/*.mp4`) and a
**face/lip crop** (`FACE/*.mp4`) alongside the audio. The slides are clean, dense,
high-resolution Chinese text/graphics (e.g. a titled map slide "地理位置 / 广西南部")
— substantially higher visual quality than typical web-talk frames. The per-clip
`ocr_text` / `vl2_text` fields carry slide OCR and visual labels from the source.
Fetch the PPT/FACE media from the upstream Chinese-LiPS release and align by
`clip_id`.

## Toward a zh->En benchmark

The source provides Chinese transcripts but **no English translation reference**.
To use this as a zh->En slide-aware ST benchmark, English references must be added
(and, being bilingual-verifiable, checked by a zh/en reader). Until then this is a
development/diagnostic resource for the translation direction, and a ready-made
long-form **zh ASR + slide** benchmark as-is.

## Reproduce

```bash
python repo/scripts/build_chinese_lips_longform.py \
  --meta-json meta_test.json --processed-dir processed_test \
  --video-ids 130_42_M_TY 102_24_M_KJ 041_28_F_RWLS \
  --timeline-dir raw_json --out-dir orig_timeline      # add --timeline-dir for original timeline
```

## License

Derived from Chinese-LiPS (BAAI), **CC BY-NC-SA 4.0**; shared under the same license
for non-commercial research.