--- license: cc-by-4.0 task_categories: - automatic-speech-recognition - voice-activity-detection tags: - diarization - speaker-diarization - multi-speaker - synthetic - parquet - streaming - fastmss pretty_name: FastMSS synthetic multi-speaker meetings (parquet) configs: - config_name: debug_v0.5 data_files: - split: train path: - debug_v0.5/data/debug_v0.5-00000-of-00001.parquet --- # FastMSS synthetic multi-speaker meetings - parquet edition Streaming-friendly parquet shards of the FastMSS synthetic multi-speaker conversational corpus. Each row is one mixture with the audio bytes embedded inline (16 kHz mono WAV) plus per-segment diarization timestamps, per-word transcript and the full lhotse cut as a JSON blob. See ``fastmss/hf_dataset.py`` for the schema docstring. ## Subsets and splits - **`debug_v0.5`** — splits: `train` — 10 mixtures, 20.5 min total, 39 unique speakers, 1 shard(s) (40.0 MB). ## Layout ``` / data/ train-XXXXX-of-YYYYY.parquet val-XXXXX-of-YYYYY.parquet # if subsplit split_assignment.json # if subsplit provenance/ all_cuts.jsonl.gz # source utterance pool all_rooms.json # RIR pool metadata noise_files.txt # background noise pool sim.log # generator log ``` ## Per-row schema | Field | Type | Source | Description | |---|---|---|---| | `audio` | `datasets.Audio` (16 kHz) | `audio/.wav` | Mixture WAV, bytes embedded inline. | | `timestamps_start` | `list[float]` | parsed from `rttm_word/` | Per-segment start times (s). | | `timestamps_end` | `list[float]` | parsed from `rttm_word/` | Per-segment end times (s). | | `speakers` | `list[str]` | parsed from `rttm_word/` | Per-segment speaker label. | | `transcript` | `list[str]` | cut supervisions | Per-word tokens. | | `word_speakers` | `list[str]` | cut supervisions | Per-word speakers (parallel to `transcript`). | | `rttm_word` | `str` | `rttm_word/.rttm` | Full word-level RTTM file text. | | `rttm_segment` | `str` | `rttm_segment/.rttm` | Full segment-level RTTM file text. | | `recording_id` | `str` | cut/recording | Lhotse recording id (also the wav stem). | | `duration` | `float` | cut/recording | Mixture length in seconds. | | `sampling_rate` | `int` | cut/recording | Source rate of the WAV. | | `num_samples` | `int` | cut/recording | Sample count of the WAV. | | `num_speakers` | `int` | cut/supervisions | Distinct speakers active in the mixture. | | `transition_type` | `list[str]` | supervision `custom` | FIRST / TURN_SWITCH / BACKCHANNEL / ... per word. | | `original_cut_id` | `list[str]` | supervision `custom` | Source utterance id per word. | | `speech_level_db` | `list[float]` | supervision `custom` | Per-word loudness target. | | `word_index` | `list[int]` | supervision `custom` | Per-utterance word position. | | `manifest_json` | `str` | cuts manifest | Full lhotse `Cut` (recording + supervisions) as JSON. | ## Loading With the YAML `configs` block above, HF datasets exposes each subset as a config and the train/val shards as proper splits: ```python from datasets import load_dataset # whole subset (default = train split): ds = load_dataset("/", "v0.1") # explicit split: train = load_dataset("/", "v0.1", split="train") val = load_dataset("/", "v0.1", split="val") # streaming: stream = load_dataset( "/", "v0.1", split="train", streaming=True ) for sample in stream: sample["audio"]["array"] # decoded float32 waveform sample["timestamps_start"] # diarization segment starts sample["timestamps_end"] # diarization segment ends sample["speakers"] # one label per segment sample["transcript"] # word tokens sample["word_speakers"] # per-word speakers ``` Drop the lhotse JSON blob if you don't need it: ```python ds = ds.remove_columns(["manifest_json"]) ``` Rebuild a lhotse `CutSet` from any subset: ```python import json from lhotse import CutSet, MonoCut cuts = CutSet.from_cuts( MonoCut.from_dict(json.loads(s["manifest_json"])) for s in ds ) ``` ## Generating an HF-compatible dataset from scratch The generation pipeline lives in the [FastMSS repo](https://github.com/argmaxinc/FastMSS-internal). It produces lhotse manifests + audio first, then converts them into the parquet layout shipped here. Reproduce a subset with: **1. Synthesize the lhotse split** — mixes utterances + RIRs + noise into ``//`` with ``audio/``, ``manifests/``, ``rttm_word/`` and ``rttm_segment/`` subfolders. ```bash # Adjust config_name / dataset_root for the subset you want python sim.py \ --config-path config/table1 --config-name datagen_v0.1 \ output_dir=generated_dataset/v0.1 ``` **2. Convert to streamable parquet** — writes one parquet shard per ``--shard-size`` mixtures, embedding WAV bytes inline and computing every column above. The default ``--parquet-batch-size 64`` keeps row groups small enough for the Hugging Face dataset viewer on long-audio subsets. ``--subsplits`` performs a deterministic train/val split with a reproducible seed. ```bash python scripts/convert_to_parquet.py \ --dataset-root generated_dataset \ --output-root generated_dataset_parquet \ --splits v0.1 \ --subsplits train:800,val:200 \ --subsplit-seed 42 \ --shard-size 256 \ --parquet-batch-size 64 # Smaller subset that doesn't need a train/val split (e.g. debug): python scripts/convert_to_parquet.py \ --dataset-root generated_dataset \ --output-root generated_dataset_parquet \ --splits debug ``` **3. Upload to the Hub** — stages a ``/data/`` + ``/provenance/`` layout, generates this README's YAML ``configs:`` block automatically, and pushes via ``HfApi.upload_large_folder`` (resumable / parallel). ```bash hf auth login # or set HF_TOKEN python scripts/upload_parquet_to_hf.py \ --repo-id / \ --parquet-root generated_dataset_parquet \ --dataset-root generated_dataset ``` Useful flags: - ``--splits debug v0.1`` — push only some subsets - ``--private`` — only honored on first repo create - ``--dry-run`` — stage the layout to a temp dir and print it without contacting the Hub - ``--no-provenance`` — skip the ``provenance/`` sidecars **4. Verify the round-trip** locally: ```bash pytest tests/test_hf_parquet_conversion.py ``` These tests build a synthetic FastMSS split in a tmp dir, run the converter, and assert byte-for-byte equivalence between the lhotse manifests/RTTM/audio and the parquet rows (including a ``json.loads(row['manifest_json']) == cut`` round-trip and a deterministic-shuffle subsplit check). See ``fastmss/hf_dataset.py`` for the per-row schema and helper API; both scripts above are thin CLI wrappers over it.