Blame for README.md

---
license: other
language:
- km
- en
- zh
tags:
- speaker-diarization
- khmer
- audio
- simulated
pretty_name: Smean diarization training data
---

# Smean diarization training data

The training data of [smean-diarizer-1](https://huggingface.co/SmeanAI/smean-diarizer-1) and
[smean-diarizer-large](https://huggingface.co/SmeanAI/smean-diarizer-large): about 1,190 h of 16 kHz mono audio
with speaker-diarization labels (RTTM). Half is real meetings and broadcasts, half is simulated conversation,
mostly in Khmer. Each training batch drew from the sets with the weights below.

| Set | Train | Dev | Batch weight | What it is |
|---|---|---|---|---|
| `real` | 1,155 recordings, 351.6 h | 144, 35.1 h | 0.50 | AMI, AISHELL-4, AliMeeting, NOTSOFAR-1, VoxConverse (single channel) |
| `sim_khmer_meetings` | 12,000, 417.3 h | 150, 5.2 h | 0.20 | simulated Khmer meetings |
| `sim_khmer_broadcast` | 6,000, 209.7 h | 150, 5.3 h | 0.15 | simulated Khmer broadcast-style conversations |
| `sim_multi_meetings` | 6,000, 206.7 h | 150, 5.2 h | 0.15 | simulated multilingual meetings (English, Mandarin) |

The real training recordings are AMI (134), AISHELL-4 (173), AliMeeting (209), NOTSOFAR-1 (465) and VoxConverse (174).

## Layout

```
<set>/{train,dev}/wav.scp        recording id -> audio path (relative to the repo root)
<set>/{train,dev}/rttm           labels to use (see below)
<set>/{train,dev}/rttm_original  labels as first built
<set>/{train,dev}/all.uem        scored region of each recording (the whole file)
<set>/audio/...                  16 kHz mono WAV
```

## Labels

Two label versions are included for every set:

- **`rttm`** — the current labels.
  - `real`: the corpora's reference labels with silence removed. These labels marked many pauses as speech
    (about 31% of AMI and 25% of AISHELL-4 label time was silence). A frame is kept only if Silero VAD hears speech
    or its energy is at least 6 dB above the recording's noise floor; gaps under 0.1 s are bridged. Overlap and
    speaker identities are unchanged.
  - `sim_*`: rebuilt from each source utterance with the same rule, so more pauses inside a turn are marked as
    silence: short pauses (< 0.5 s) per speech-minute go from about 1 to 3.6 (Khmer) and 5.8 (multilingual). The
    source speech is read sentences, which pause rarely, so the change is modest. The energy test also widens
    segment edges slightly: about 2.5% of the rebuilt label time lies outside `rttm_original` and 0.3% of the
    original label time is removed. Neither version has been compared in training yet.
- **`rttm_original`** — the labels smean-diarizer-1 and smean-diarizer-large were trained on: the corpora's
  reference labels for `real`; FastMSS output for `sim_*`, which merges same-speaker gaps up to 0.2–0.3 s.

## How the simulated sets were made

Built with [FastMSS](https://github.com/popcornell/FastMSS) from single-speaker speech:

- **Khmer:** Khmer ASR Cultural Dataset and Khmer ASR dataset by Digital Divide Data, and OpenSLR 42.
  The train sets use 27 speakers. The dev sets use 4 different speakers, held out of all training simulation.
- **Multilingual:** LibriSpeech and AISHELL-1, 1,336 speakers. Dev speakers are held out.
- **Turn-taking:** an HMM fitted on real meetings (meetings sets) and on VoxConverse broadcasts (broadcast set).
- **Augmentation:** room acoustics from 500 simulated rooms (pyroomacoustics) and MUSAN noise and music.

Augmentation at training time (extra noise, reverberation, telephone band, gain) is not baked into the audio.

## Not included

The Khmer evaluation set ([khmer-diarization-eval](https://huggingface.co/datasets/Darayut/khmer-diarization-eval))
was never used for training and is not part of this dataset.

## Licences

This dataset combines material under these licences; use must respect each of them:
- AMI: CC BY 4.0
- AISHELL-4: CC BY-SA 4.0
- AliMeeting: CC BY-SA 4.0
- NOTSOFAR-1: as published by its authors
- VoxConverse: CC BY 4.0
- Khmer ASR Cultural Dataset and Khmer ASR dataset by Digital Divide Data: CC BY-SA 4.0
- OpenSLR 42: CC BY-SA 4.0
- LibriSpeech: CC BY 4.0
- AISHELL-1: Apache 2.0
- MUSAN: CC BY 4.0 and public-domain parts