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license: cc-by-4.0
task_categories:
- automatic-speech-recognition
- voice-activity-detection
tags:
- diarization
- speaker-diarization
- multi-speaker
- synthetic
- lhotse
- fastmss
pretty_name: FastMSS synthetic multi-speaker meetings
---
# FastMSS synthetic multi-speaker meetings
Synthetic multi-speaker conversational audio generated with [FastMSS](https://github.com/argmaxinc/FastMSS-internal). Each split contains mixture WAVs (16 kHz, mono), Lhotse manifests (recordings / supervisions / cuts), and per-mixture RTTM files with word-level speaker labels.
## Splits / subfolders
- **`debug/`** — 1 mixtures, 1.6 min total, 6 unique speakers
- **`v0.1/`** — 1000 mixtures, 1546.0 min total, 40 unique speakers
## Per-split layout
```
<split>/
audio/<recording_id>.wav # 16 kHz mono mixture
manifests/
synth-*-train-recordings.jsonl.gz
synth-*-train-supervisions.jsonl.gz
synth-*-train-cuts.jsonl.gz # the file you want for training
all_cuts.jsonl.gz # source utterances used by the sim
all_rooms.json # RIR room metadata
noise_files.txt # noise files used
rttm_word/<recording_id>.rttm # word-level SPEAKER lines
sim.log # generator log
```
## Loading
```python
from huggingface_hub import snapshot_download
from lhotse import CutSet
import os
local = snapshot_download("<user-or-org>/<repo-name>", repo_type="dataset")
os.chdir(os.path.join(local, 'v0.1')) # or 'debug'
cuts = CutSet.from_file('manifests/synth-librispeech-train-cuts.jsonl.gz')
for cut in cuts:
audio = cut.load_audio() # path is resolved relative to cwd
...
```
## Generation
See the [FastMSS repo](https://github.com/argmaxinc/FastMSS-internal) for the exact simulator config. Each split contains its own `sim.log` for the full generator output.
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