--- license: cc-by-sa-4.0 language: - km pretty_name: FastMSS Khmer Meeting Simulation size_categories: - 1K **Important:** these are synthetic, clean, read-speech meetings, not real > conversational recordings. They contain only 20 source speakers and were > generated without reverberation, background noise, or FIR filtering. Results > on this dataset should not be treated as evidence of real-world performance. ## At a glance | Item | Value | |---|---| | Language | Khmer (`km`) | | Meetings | 6,900 | | Audio | 130.37 hours | | Speakers | 20 | | Speakers per meeting | 2, 3, or 4 | | Topics | 69 | | Sample rate | 16 kHz | | Channels | Mono | | Audio format | WAV, 16-bit PCM | | Annotation | RTTM + JSONL metadata | | Per-speaker stems | Not included | ## Source data and generation The source speech is the **Cultural Khmer ASR Dataset**: - Dataset: - License: **CC BY-SA 4.0** Meetings were generated with **FastMSS**: - Code: - Recipe: `FastMSS/recipes/khmer.yaml` - Source manifests: one Lhotse manifest per topic, then combined for simulation The simulation used these settings: - 100 meetings per topic, 69 topics - 2–4 speakers per meeting, at most 4 concurrent speakers - 60 s target duration; actual meetings are 60–318 s because turn-taking extends the target - 16 kHz mono output - `reverberate: false` - `add_noise: false` - `use_fir: false` - `save_rttm: true` - Flat turn-taking priors, with interruptions and backchannels producing overlap RTTMs were generated from the simulation's aligned speech segments. Same-speaker segments separated by **0.2 s or less** were merged. This is why the number of RTTM segments differs from the number of utterance-level manifest segments. See [`ATTRIBUTION.md`](ATTRIBUTION.md) for source and tool attribution. ## Splits The split is a **designated-speaker holdout**: - Train: 6,207 meetings / 117.18 h / 16 speakers - Dev: 611 meetings / 11.50 h / 17 speakers - Test: 82 meetings / 1.70 h / 16 speakers No designated dev or test speaker appears in a training meeting. However, the underlying speech cuts are reused across topics and splits: - 451 source cuts overlap train and dev - 39 source cuts overlap train and test This is not a fully source-cut-disjoint split. Keep that in mind when interpreting very high scores. | Split | Meetings | Hours | Speakers in meetings | Source cuts | Manifest segments | RTTM segments | 2-spk | 3-spk | 4-spk | |---|---:|---:|---:|---:|---:|---:|---:|---:|---:| | train | 6,207 | 117.176 | 16 | 49,542 | 58,556 | 50,972 | 2,105 | 2,149 | 1,953 | | dev | 611 | 11.497 | 17 | 5,511 | 6,053 | 5,318 | 127 | 228 | 256 | | test | 82 | 1.699 | 16 | 604 | 715 | 604 | 17 | 34 | 31 | | **total** | **6,900** | **130.373** | **20** | **55,151** | **65,324** | **56,894** | **2,249** | **2,411** | **2,240** | Overlap statistics are provided in `metadata/stats.json`: - `overlap_ratio` = overlap seconds / union of speech seconds - `overlap_fraction` = overlap seconds / meeting duration Dataset-wide mean `overlap_ratio` is **0.2023** (p95 **0.4038**); mean `overlap_fraction` is **0.1955** (p95 **0.3919**). ## Files ```text audio/ .wav rttm/ train/.rttm dev/.rttm test/.rttm metadata/ train.jsonl dev.jsonl test.jsonl stats.json split.json source_validation.json ``` Every WAV is a mono 16 kHz meeting mixture. Every RTTM uses the standard `SPEAKER` format with speaker labels such as `f-adt1-0001` and `m-adt2-0002`. ## Metadata schema Each JSONL line describes one meeting: ```json { "meeting_id": "00150280-3181-4e63-b7e1-6aae3e7ffe45", "split": "train", "audio": "audio/00150280-3181-4e63-b7e1-6aae3e7ffe45.wav", "rttm": "rttm/train/00150280-3181-4e63-b7e1-6aae3e7ffe45.rttm", "duration": 60.0, "sampling_rate": 16000, "channels": 1, "topic": "wildlife-condition-in-cambodia", "num_speakers": 3, "speakers": ["f-adt2-0002", "m-adt1-0004", "m-adt2-0002"], "num_segments": 9, "num_rttm_segments": 8, "speech_seconds": 58.0, "overlap_seconds": 5.0, "overlap_ratio": 0.086, "overlap_fraction": 0.083, "segments": [ { "start": 0.9115, "end": 7.9415, "speaker": "m-adt2-0002", "text": "...", "transition_type": "FIRST", "source_cut_id": "train-...", "speech_level_db": -22.97 } ] } ``` `segments` are **utterance-level** supervisions from the FastMSS manifest and include the Khmer transcript and source-cut metadata. The RTTM is the diarization reference and may merge adjacent same-speaker segments. Use the RTTM for diarization scoring; use `segments` when you need transcripts or source-cut provenance. ## Loading The JSONL stores paths relative to the dataset root. Download a snapshot and resolve them locally: ```python import json from pathlib import Path from huggingface_hub import snapshot_download root = Path( snapshot_download( "Panhapich/fastmss_meeting_simulation", repo_type="dataset", ) ) records = [ json.loads(line) for line in (root / "metadata" / "train.jsonl").open(encoding="utf-8") ] meeting = records[0] wav_path = root / meeting["audio"] rttm_path = root / meeting["rttm"] ``` With `datasets`, you can load the metadata directly: ```python from datasets import load_dataset ds = load_dataset( "Panhapich/fastmss_meeting_simulation", data_files={ "train": "metadata/train.jsonl", "dev": "metadata/dev.jsonl", "test": "metadata/test.jsonl", }, ) ``` The `audio` and `rttm` columns remain relative paths; prefix them with the local dataset root before opening the files. ## Intended use and limitations Suitable for: - Khmer speaker diarization experiments - Pipeline and scoring smoke tests - Comparing diarization systems under controlled overlap - Studying the effect of simulated conversational data Not suitable for: - Claims about real Khmer meeting performance - Commercial deployment without a separate license review - Speaker extraction or separation benchmarks requiring per-speaker references - Evaluating robustness to noise, reverberation, far-field microphones, or spontaneous conversational speech Main limitations: - Only 20 source speakers - Clean, close-talk, read speech - No noise, reverberation, or FIR filtering - Source cuts overlap across splits - No per-speaker stems - Synthetic turn-taking may not match real meeting dynamics ## License and attribution The dataset is released under **CC BY-SA 4.0**, following the source corpus license. See [`LICENSE`](LICENSE) and [`ATTRIBUTION.md`](ATTRIBUTION.md). If you use this dataset, please also cite FastMSS: ```bibtex @unpublished{polok2026mindthegap, author = {Polok, Alexander and Medennikov, Ivan and Watanabe, Shinji and {\v{C}}ernock{\'y}, Jan and Burget, Luk{\'a}{\v{s}} and Cornell, Samuele}, title = {Mind the Gap: Impact of Synthetic Conversational Data on Multi-Talker {ASR} and Speaker Diarization}, note = {Submitted to INTERSPEECH}, year = {2026} } ```