Error code:   ConfigNamesError
Exception:    BadZipFile
Message:      zipfiles that span multiple disks are not supported
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/dataset/config_names.py", line 66, in compute_config_names_response
                  config_names = get_dataset_config_names(
                                 ^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 161, in get_dataset_config_names
                  dataset_module = dataset_module_factory(
                                   ^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/load.py", line 1207, in dataset_module_factory
                  raise e1 from None
                File "/usr/local/lib/python3.12/site-packages/datasets/load.py", line 1182, in dataset_module_factory
                  ).get_module()
                    ^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/load.py", line 645, in get_module
                  module_name, default_builder_kwargs = infer_module_for_data_files(
                                                        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/load.py", line 299, in infer_module_for_data_files
                  split: infer_module_for_data_files_list(data_files_list, download_config=download_config)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/load.py", line 243, in infer_module_for_data_files_list
                  return infer_module_for_data_files_list_in_archives(data_files_list, download_config=download_config)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/load.py", line 270, in infer_module_for_data_files_list_in_archives
                  f.split("::")[0] for f in xglob(extracted, recursive=True, download_config=download_config)
                                            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/utils/file_utils.py", line 1050, in xglob
                  fs, *_ = url_to_fs(urlpath, **storage_options)
                           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/fsspec/core.py", line 395, in url_to_fs
                  fs = filesystem(protocol, **inkwargs)
                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/fsspec/registry.py", line 293, in filesystem
                  return cls(**storage_options)
                         ^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/fsspec/spec.py", line 80, in __call__
                  obj = super().__call__(*args, **kwargs)
                        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/fsspec/implementations/zip.py", line 62, in __init__
                  self.zip = zipfile.ZipFile(
                             ^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/zipfile/__init__.py", line 1354, in __init__
                  self._RealGetContents()
                File "/usr/local/lib/python3.12/zipfile/__init__.py", line 1417, in _RealGetContents
                  endrec = _EndRecData(fp)
                           ^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/zipfile/__init__.py", line 311, in _EndRecData
                  return _EndRecData64(fpin, -sizeEndCentDir, endrec)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/zipfile/__init__.py", line 257, in _EndRecData64
                  raise BadZipFile("zipfiles that span multiple disks are not supported")
              zipfile.BadZipFile: zipfiles that span multiple disks are not supported

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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:

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

Access

To use MISP-M3SD:

Download audio.zip together with all split files (audio.z01, audio.z02, audio.z03, audio.z04, audio.z05, audio.z06);
Extract the audio archive to obtain the released WAV files;
Use oracle.rttm as the final diarization annotation file;
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

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