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Datasets:
arda-argmax
/
simchoir-parquet

Tasks:
Automatic Speech Recognition
Voice Activity Detection
Modalities:
Audio
Text
Formats:
parquet
Size:
100K - 1M
Tags:
diarization
speaker-diarization
multi-speaker
Synthetic
parquet
streaming
Libraries:
Datasets
pandas
Polars
License:
Dataset card Data Studio Files
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  1. README.md +76 -0
  2. debug/data/debug-00000-of-00001.parquet +3 -0
  3. debug/provenance/all_cuts.jsonl.gz +3 -0
  4. debug/provenance/all_rooms.json +182 -0
  5. debug/provenance/noise_files.txt +0 -0
  6. debug/provenance/sim.log +7 -0
README.md ADDED
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+ ---
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+ license: cc-by-4.0
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+ task_categories:
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+ - automatic-speech-recognition
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+ - voice-activity-detection
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+ tags:
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+ - diarization
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+ - speaker-diarization
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+ - multi-speaker
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+ - synthetic
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+ - parquet
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+ - streaming
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+ - fastmss
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+ pretty_name: FastMSS synthetic multi-speaker meetings (parquet)
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+ ---
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+
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+ # FastMSS synthetic multi-speaker meetings - parquet edition
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+
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+ 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.
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+
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+ ## Splits
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+
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+ - **`debug/`** — 1 mixtures, 1.6 min total, 6 unique speakers, 1 shard(s) (3.1 MB).
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+
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+ ## Layout
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+
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+ ```
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+ <split>/
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+ data/<split>-XXXXX-of-YYYYY.parquet
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+ provenance/
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+ all_cuts.jsonl.gz # source utterance pool
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+ all_rooms.json # RIR pool metadata
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+ noise_files.txt # background noise pool
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+ sim.log # generator log
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+ ```
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+
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+ ## Loading
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+
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+ Streaming (recommended for large splits):
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+
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+ ```python
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+ from datasets import load_dataset
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+ ds = load_dataset(
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+ "<user-or-org>/<repo-name>",
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+ data_files="v0.1/data/*.parquet",
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+ split="train",
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+ streaming=True,
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+ )
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+ for sample in ds:
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+ sample["audio"]["array"] # decoded float32 waveform
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+ sample["timestamps_start"] # diarization segment starts
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+ sample["timestamps_end"] # diarization segment ends
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+ sample["speakers"] # one label per segment
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+ sample["transcript"] # word tokens
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+ sample["word_speakers"] # per-word speakers
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+ ```
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+
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+ Drop the lhotse JSON blob if you don't need it:
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+
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+ ```python
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+ ds = ds.remove_columns(["manifest_json"])
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+ ```
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+
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+ Rebuild a lhotse `CutSet` from any subset:
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+
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+ ```python
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+ import json
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+ from lhotse import CutSet, MonoCut
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+ cuts = CutSet.from_cuts(
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+ MonoCut.from_dict(json.loads(s["manifest_json"])) for s in ds
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+ )
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+ ```
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+
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+ ## Generation
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+
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+ See the [FastMSS repo](https://github.com/argmaxinc/FastMSS-internal) for the simulator config used for each split.
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debug/provenance/noise_files.txt ADDED
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debug/provenance/sim.log ADDED
@@ -0,0 +1,7 @@
 
 
 
 
 
 
 
 
1
+ [2026-04-23 11:41:57,017][__main__][INFO] - Saving source CutSet to disk
2
+ [2026-04-23 11:41:57,466][__main__][INFO] - Parsing background noise files.
3
+ [2026-04-23 11:42:01,037][__main__][INFO] - Discarded 0 noise files as they were shorter than 3. Now 28000, before 28000.
4
+ [2026-04-23 11:42:01,041][__main__][INFO] - Noise files paths saved in ./wham_rir_6spk_no_ir_low_eff_new/manifests/noise_files.txt
5
+ [2026-04-23 11:42:01,041][__main__][INFO] - Simulating RIRs using Pyroomacoustics
6
+ [2026-04-23 11:42:07,476][__main__][INFO] - Saving simulated manifests to /Users/arda/argmax/sortformer/FastMSS/wham_rir_6spk_no_ir_low_eff_new/manifests.
7
+ [2026-04-23 11:42:07,494][__main__][INFO] - Writing word-level RTTMs to /Users/arda/argmax/sortformer/FastMSS/wham_rir_6spk_no_ir_low_eff_new/rttm_word (skip_empty_symbols=True)