README.md

metadata

language:
  - en
  - zh
  - ml
  - ja
  - hi
  - ko
  - te
  - ta
  - pa
  - fr
  - bn
  - kn
  - ar
  - th
  - de
  - es
pretty_name: MISP-M3SD
license: apache-2.0
task_categories:
  - audio-classification
tags:
  - speaker-diarization
  - audio-visual
  - multimodal
  - multilingual
  - dataset
  - robust-speech-processing
  - in-the-wild

MISP-M3SD

Dataset Summary

MISP-M3SD is a large-scale multimodal, multi-scenario, and multilingual dataset for robust speaker diarization, constructed from in-the-wild online videos. It contains more than 770 hours of synchronised audio-visual recordings, covering 14 scenarios and 16 languages.

The dataset is designed to support the development of speaker diarization systems with stronger cross-domain generalisation under realistic conditions, including background noise, reverberation, overlapping speech, off-screen speech, motion blur, unstable speaker visibility, and camera switching.

To enable scalable construction, MISP-M3SD is built through a largely automated pipeline, including:

1. multilingual multi-scenario media acquisition,
2. data cleaning and preprocessing, and
3. cross-modal consistency-guided annotation with selective manual verification.

Access

To use MISP-M3SD:

1. Download audio.zip together with all split files (audio.z01, audio.z02, audio.z03, audio.z04, audio.z05, audio.z06);
2. Extract the audio archive to obtain the released WAV files;
3. Use oracle.rttm as the final diarization annotation file;
4. Because of storage limitations, the videos are not distributed as a complete packaged archive. Instead, we provide metadata for each source video, including the corresponding video ID, in video_information.xlsx, together with video download scripts in the GitHub repository. This allows users to retrieve the videos directly from the original platforms.

Key Features

• Large scale: 770.55 hours of synchronised audio-visual recordings
• Multilingual: 16 languages
• Multi-scenario: 14 scenarios
• Rich interaction complexity: 7,276 speakers in total, with 5.30 speakers per sample on average
• Rich metadata: includes source video identifiers, duration, title, description, language, and scenario
• Realistic conditions: collected from in-the-wild online videos
• Reliable annotation: cross-modal consistency-guided annotation with selective manual verification
• Practical release format: audio and annotations are directly provided, while source videos can be retrieved through released scripts and video IDs

Scenarios

MISP-M3SD covers 14 scenarios:

• Lesson
• Interview
• News
• Debate
• Discussion
• Conversation
• Job Interview
• Meeting
• Lecture
• Tutorial
• Entertainment Vlog
• Home Interaction
• Dinner Party
• Other

The scenario distribution is diverse but naturally uneven, reflecting the characteristics of publicly accessible online videos rather than an artificially balanced design.

Data Splits

The dataset is divided into train / dev / eval splits at the sample level, with the split assignment for each sample provided in split.txt.

Split	#Samples	Duration (h)	Avg. Duration (min)	Median Duration (min)	Speech Activity (h)	#Speakers	Avg. Speakers	#Languages	#Scenarios
Train	1272	716.54	33.80	18.38	656.17	6756	5.31	14	14
Dev	50	27.10	32.52	28.42	25.71	275	5.50	11	13
Eval	50	26.91	32.30	29.62	25.04	245	4.90	11	13
Total	1372	770.55	33.70	20.98	706.91	7276	5.30	16	14

The splits are constructed to preserve the diversity of the full dataset in terms of scenario, language, duration, speaker-number distribution, and overlap characteristics.

A comparison with representative audio-visual speaker diarization datasets is shown below.

Dataset	Scenarios	#Samples	Speakers	Duration (h)	Speech (%)	Noise	Languages
AMI	Meetings	170	3-5	100	80.91	No	EN
AVDIAR	Chat	27	1-4	0.35	82.6	No	EN, FR
AVA-AVD	Daily Activities	351	2-24	29.25	45.95	Yes	EN, FR, ZH, DE, KO, ES
MSDWild	Vlogs	3143	2-10	80	91.29	Yes	EN, ZH, TH, KO, JA, DE, PT, AR
MISP2021&2022	Conversations	373	2-6	121	92.30	Yes	CN
MISP-M3SD	Lesson, Interview, News, Debate, Meeting, Home Interaction, etc.	1372	1-19	770.55	91.74	Yes	EN, ZH, ML, JA, HI, KO, TE, TA, PA, etc.

Compared with previous datasets, MISP-M3SD provides:

• substantially larger scale
• broader scenario coverage
• richer multilingual content
• more realistic in-the-wild conditions
• a scalable construction pipeline for robust multimodal diarization research