---
size_categories:
- 10K<n<100K
dataset_info:
  features:
  - name: audio_url
    dtype: string
  - name: video_url
    dtype: string
  - name: duration
    dtype: float32
  - name: language
    dtype: string
  - name: transcript
    dtype: string
  - name: split
    dtype: string
  - name: license
    dtype: string
  - name: attribution
    dtype: string
  splits:
  - name: youtube_meeting
    num_bytes: 7977001353
    num_examples: 122407
  - name: eu_council
    num_bytes: 426394884
    num_examples: 45411
  download_size: 3054748725
  dataset_size: 8403396237
configs:
- config_name: default
  data_files:
  - split: youtube_meeting
    path: data/youtube_meeting-*
  - split: eu_council
    path: data/eu_council-*
---

# 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 referenced via source URLs; no audio or video files are redistributed. Transcripts and metadata are included.


## 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 (JSON) | Full transcript as JSON-encoded array of segments. Each segment contains `language`, `start_seconds`, `end_seconds`, and `text` fields with time-aligned transcription data. |
| `split` | string | Source tag (`EU_Council` or `YouTube_Meeting`) |
| `license` | string | License information for the content |
| `attribution` | string (JSON) | Attribution as required by the license |
---

## Statistic Summary

The dataset is provided as two subsets: eu_council and youtube_meeting.

| 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 |
---
## Language Distribution

The table below reports the primary spoken language per recording.

| 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                               |
| Vietnamese      | 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                               |
---

## Notes on Transcripts

- **EU Council (audio & video):**
  Transcripts are generated using the **Whisper ASR package** and are not official EU transcripts. 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** or **user-uploaded**, marked in the metadata. Transcript quality may therefore vary.

---

## Usage

```python
from datasets import load_dataset

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


# Access first row of from eu_council set
first_row = next(iter(dataset["eu_council"]))
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["eu_council"]):
    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["eu_council"]))
print("Listen to audio:", first_example["audio_url"])
print("Watch video:", first_example["video_url"])

```
# Licensing Information

**YouTube CC-BY:**  
All YouTube content in Mumospee v2 is selected from videos marked with the **Creative Commons Attribution (CC BY)** license. CC BY permits reuse, transformation, and training uses as long as proper attribution is maintained (e.g., title, uploader, source URL). Users must comply with the CC BY terms when accessing media via URLs.  [Creative Commons](https://creativecommons.org/using-cc-licensed-works-for-ai-training-2/)

**European Council reuse notice:**  
Council of the EU content is published with a copyright notice stating that **reproduction is authorised** provided the **original source is acknowledged** and the meaning is not distorted. Mumospee distributes derived metadata and signal information only; raw media remains at the source and is accessed by URL.  [European Council](https://www.consilium.europa.eu/en/about-site/copyright/)

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


# Intended Use and Data Quality

Mumospee v2 is intended for speech and multimodal research, including speech recognition, meeting analysis, and language understanding. The current dataset includes metadata and filtered based on LLM prompting. As such, some videos may be incorrectly labeled for meetings or have misidentified languages. In future releases we will incorporate audio and video analysis pipelines to refine classification and release timestamped segments for downstream tasks.