meetween/mumospee_V2

Mumospee V2: A MUltiMOdal SPEEch Corpus

Dataset Description

MuMoSPEE v2 is a large-scale multimodal speech dataset containing audio and video recordings with transcripts, aggregated from:

1. European Council events — official speeches, interviews, and doorstep appearances.
2. Public YouTube meetings — webinars, conferences, panel discussions, and institutional videos.

This release is built upon the first version of the MuMoSPEE dataset, significantly expanding both the scale and diversity of content, and unifying audio and video data into a consistent metadata format suitable for large-scale speech and multimodal research.

The original MuMoSPEE v1 dataset is available at:
👉 https://huggingface.co/datasets/meetween/mumospee

The dataset is designed for research in speech recognition, multimodal modeling, meeting analysis, and AI-driven content understanding.

All media are linked via URLs, with transcripts and metadata included. Audio and video recordings are stored as separate entries.

Sources & HuggingFace pages:

• EU Council: https://huggingface.co/datasets/meetween/eu_council. Original source at European Council Newsroom.
• YouTube Meetings: https://huggingface.co/datasets/meetween/youtube_meeting

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Dataset Structure

Column	Type	Description
audio_url	string	URL to audio
video_url	string	URL to video
duration	float	Duration in seconds
language	string	Primary spoken language
transcript	string	Full transcript text in segments
split	string	Source tag (EU_Council or YouTube_Meeting)
license	string	License information for the content

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Statistic Summary

Source / Tag	Number of Records	Total Duration (hours)	Average Duration (hours)
EU_Council	45,411	3,665.68	0.0807
YouTube_Meeting	122,407	80,127.65	0.6546

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Language Distribution

Language	EU_Council Count	EU_Council Duration (hours)	YouTube_Meeting Count	YouTube_Meeting Duration (hours)
English	23635	1754.21	95896	59568.38
German	3582	294.16	1467	831.99
French	2953	240.68	8222	5910.38
Spanish	2427	230.31	11382	9497.5
Italian	1374	122.0	1471	1092.87
Portuguese	1230	113.16	3030	2706.79
Swedish	1092	96.51	82	49.71
Dutch	1064	97.09	782	418.2
Polish	970	91.22	29	24.79
Czech	873	77.6	13	8.19
Croatian	826	71.11	2	2.96
Danish	787	71.06	5	0.95
Slovak	684	62.17	1	1.15
Finnish	647	62.9	5	5.36
Greek	604	37.16	13	4.87
Slovenian	519	40.49	0	0.0
Bulgarian	401	66.11	2	0.62
Hungarian	384	34.39	2	0.48
Luxembourgish	297	28.92	0	0.0
Romanian	295	25.66	1	0.07
Maltese	156	5.63	0	0.0
Lithuanian	92	5.32	0	0.0
Arabic	79	4.53	0	0.0
Latvian	73	5.18	0	0.0
Ukrainian	56	4.48	0	0.0
Russian	38	2.67	0	0.0
Estonian	37	2.36	0	0.0
Serbian	36	2.84	0	0.0
Georgian	33	1.99	0	0.0
Norwegian	29	2.98	0	0.0
Albanian	25	1.89	0	0.0
Macedonian	23	1.78	0	0.0
Bosnian	17	1.36	0	0.0
Montenegrin	16	1.09	0	0.0
Turkish	14	1.37	0	0.0
Belarusian	6	0.31	0	0.0
Moldavian	5	0.34	0	0.0
Catalan	4	0.41	0	0.0
Persian	4	0.36	0	0.0
Japanese	4	0.25	0	0.0
Indonesian	3	0.25	0	0.0
Viêt Namese	3	0.33	0	0.0
Chinese	3	0.28	0	0.0
Armenian	3	0.07	0	0.0
Korean	3	0.33	0	0.0
Hindi	1	0.1	0	0.0
Kazakh	1	0.1	0	0.0
Khmer	1	0.06	0	0.0
Tajik	1	0.09	0	0.0
Swahili	1	0.05	0	0.0
Español	0	0.0	1	2.2
Français	0	0.0	1	0.19

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Notes:

1. For recordings labeled as a single specific language but containing multiple spoken languages, the total duration is split equally among the detected languages, as precise language-level timestamps are not available. This avoids inflating the total duration across languages.
2. EU_counts represent language occurrences rather than unique recordings; multilingual recordings contribute to multiple language counts, causing the summed total to exceed the number of distinct recordings.

Notes on Transcripts

• EU Council (audio & video):
  Transcripts are generated using the Whisper ASR package. Each entry contains one or more languages per recording, corresponding to the full speech.

• YouTube Meeting (video):
  Transcripts are extracted from YouTube subtitle tracks. They may be auto-generated, and some videos may lack transcripts entirely.

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Usage

from datasets import load_dataset

# Load the dataset in streaming mode (recommended for large datasets)
dataset = load_dataset("meetween/mumospee_v2", streaming=True)

# Show dataset info
print(dataset)
# Example output:
# DatasetDict({
#     'train': Dataset(...)
# })


# Access first row
first_row = next(iter(dataset["train"]))
print("Transcript:", first_row["transcript"])
print("Audio URL:", first_row["audio_url"])
print("Video URL:", first_row["video_url"])
print("Language:", first_row["language"])
print("Duration (s):", first_row["duration"])


# Iterate over the first 5 examples
for i, example in enumerate(dataset["train"]):
    print(f"{i+1}. {example['language']} - {example['duration']}s")
    print(f"Transcript snippet: {example['transcript'][:100]}...\n")
    if i >= 4:
        break

# Access audio or video URLs
first_example = next(iter(dataset["train"]))
print("Listen to audio:", first_example["audio_url"])
print("Watch video:", first_example["video_url"])

Licensing Information

• EU Council: Attribution only, non-commercial (© EU 2025).
• YouTube: Metadata and transcripts are CC0 / Public Domain; videos follow original uploader’s license (CC-BY).

Users must comply with the source license when accessing media via URLs.