metadata

configs:
  - config_name: mcorec-processed
    data_files:
      - split: train
        path: processed/mcorec-train-*.tar
      - split: valid
        path: processed/mcorec-valid-*.tar
language:
  - en
extra_gated_prompt: >
  # MCoRec Data Use Agreement


  **Dataset Version**: July 2025  

  **Maintained by**: MCoRec Challenge Organizers  

  **Contact**: [[email protected]](mailto:[email protected])



  ## Overview


  The MCoRec Dataset contains multi-speaker, multi-modal recordings (audio,
  video, transcripts, and face landmarks) collected under natural conversational
  settings. This dataset is owned by **Datoid LLC** and provided for the MCoRec
  Challenge under the administration of the MCoRec Challenge Organizers. The
  data includes audio and video recordings of real individuals. Due to the
  sensitive and identifiable nature of the data, its use is subject to the
  following terms and conditions, as well as strict privacy-preserving
  regulations.  


  By accessing or using the MCoRec Dataset, you acknowledge that you have read,
  understood, and agreed to comply with the terms below. Each user accessing or
  using the data must also sign and comply with this same agreement.



  ## 1. License Rights and Permitted Use


  ### a. Grant of Rights


  You are granted a non-exclusive, non-transferable, worldwide, royalty-free
  license to use the MCoRec Dataset solely for:


  - Non-commercial  

  - Academic and educational purposes  

  - Algorithm development and benchmarking  

  - Publication of models, visualizations, or results, provided proper
  attribution is given (see Section 3)  


  For commercial licensing, please contact Datoid LLC via the MCoRec Challenge
  Organizers at [[email protected]](mailto:[email protected]).


  ### b. Prohibited Use


  You may not, without prior written permission from Datoid LLC:


  - Use the dataset for commercial purposes (e.g., product development,
  consulting, monetization)  

  - Redistribute or publicly repost any part of the raw dataset (video, audio,
  transcripts, face landmarks)  

  - Upload the dataset to public repositories, cloud platforms, or third-party
  hosting services  

  - Create applications or services that provide access to the dataset for
  commercial users  



  ## 2. Access, Sharing, and Distribution


  ### a. Access Requirements


  Access is granted only upon:


  - Submission of a signed Data Use Agreement  

  - Verification and approval by the MCoRec Challenge Organizers  


  ### b. Redistribution Policy


  Redistribution of the dataset or identifiable components to third parties is
  strictly prohibited. This includes:


  - Raw data (e.g., video, audio, transcripts, face landmarks)  

  - Reversible or personally identifiable processed data (e.g., biometric
  embeddings)  


  ### c. Sharing of Derived Works


  You may share trained models, features, or results only if:


  - They do not expose raw or identifiable data  

  - They are not reversible to reconstruct original data  

  - You provide proper attribution as described in Section 3  



  ## 3. Attribution and Citation


  If you use the MCoRec Dataset in any publication, presentation, or shared
  work, you must include the following citation:


  > Thai-Binh Nguyen, Ngoc-Quan Pham, Alexander Waibel.  

  > *Cocktail-Party Audio-Visual Speech Recognition*, Interspeech 2025.  

  > [https://arxiv.org/abs/2506.02178](https://arxiv.org/abs/2506.02178)


  You must also:


  - Link to the dataset’s official access page  

  - Acknowledge the MCoRec Challenge Organizers as the data providers and Datoid
  LLC as the data owner  



  ## 4. Privacy, Ethics, and Compliance


  You agree to:


  - Not attempt to re-identify individuals  

  - Not use the dataset for biometric surveillance, profiling, or other
  unethical purposes  

  - Not publish or publicly present any examples of video or audio from this
  dataset. If you require sample material for presentation or demonstration
  purposes, please contact us at
  [[email protected]](mailto:[email protected])  

  - Comply with all relevant privacy and data protection laws, including the
  GDPR where applicable  

  - Conduct research using this dataset in accordance with your institution’s
  ethical standards and, if applicable, Institutional Review Board (IRB)
  requirements  



  ## 5. Data Security


  You agree to:


  - Store the dataset in secure, access-controlled environments  

  - Prevent unauthorized access, sharing, or duplication  

  - Refrain from uploading the dataset to public cloud platforms or storage
  services (e.g., Google Drive, Dropbox, GitHub)  



  ## 6. Termination and Enforcement


  - Any violation of this agreement may result in immediate termination of
  access  

  - Upon request or termination, you must permanently delete all copies of the
  dataset  

  - You must report any known or suspected misuse within 24 hours to:
  [[email protected]](mailto:[email protected])  



  ## 7. Disclaimer of Warranty


  The MCoRec Dataset is provided "as is", without warranties of any kind. Datoid
  LLC and the MCoRec Challenge Organizers expressly disclaim:


  - Any warranties of title or non-infringement  

  - Implied warranties of merchantability or fitness for a particular purpose  

  - Any guarantee of dataset accuracy, quality, or usefulness  



  ## 8. Limitation of Liability


  To the fullest extent allowed by law, Datoid LLC and the MCoRec Challenge
  Organizers shall not be liable for any:


  - Indirect, incidental, or consequential damages  

  - Losses related to the use or inability to use the dataset  

  - Claims resulting from third-party use or interpretation of your derivative
  works  

  - Privacy violations resulting from your use or publication of the data  



  ## 9. Indemnification


  You agree to indemnify and hold harmless Datoid LLC, its affiliates, and
  assigns from any direct or third-party legal claims, liabilities, or
  proceedings arising from your misuse, unauthorized use, or violation of this
  agreement, in connection with your performance of tasks or use of the dataset.



  ## 10. Binding Arbitration


  In the event of a legal dispute or claim relating to this Agreement, excluding
  any dispute principally related to Datoid LLC’s intellectual property (which
  will be resolved through litigation before the United States District Court
  for the Western District of Washington), all other disputes will be resolved
  by arbitration in Seattle, Washington, before a single arbitrator. The
  arbitration will be administered by the American Arbitration Association under
  its Commercial Arbitration Rules.



  ## 11. Term and Survival


  This agreement is valid for the duration of the MCoRec Challenge and may
  continue thereafter for non-commercial research purposes unless revoked.  

  All provisions of this Agreement that give rise to a party’s ongoing
  obligation will survive termination of this Agreement.


  ## 12. Severability


  If any provision(s) of this Agreement shall be held to be invalid, illegal, or
  unenforceable by a court or other tribunal of competent jurisdiction, the
  validity, legality, and enforceability of the remaining provisions shall not
  in any way be affected or impaired thereby.


  ## 13. Contact and Licensing Requests


  For questions regarding this agreement or to inquire about commercial
  licensing, please contact:  

  [[email protected]](mailto:[email protected])
extra_gated_fields:
  First Name: text
  Last Name: text
  Date of birth: date_picker
  Country: country
  Affiliation: text
  Job title:
    type: select
    options:
      - Student
      - Research Graduate
      - AI researcher
      - AI developer/engineer
      - Reporter
      - Other
  geo: ip_location
  By signing the end user agrees to the terms of this license agreement intending to be legally bound hereby If the end user is an entity or organization the individual signing this agreement on behalf of the entity or organization represents that he or she is authorized to sign this license agreement on behalf of the entity or organization If the end user does not agree with these terms the end user may not use the database: checkbox
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CHiME-9 Task 1: Multi-Modal Context-aware Recognition (MCoRec)

The MCoRec dataset contains video recording sessions. A single recording session typically features multiple conversations, each involving two or more participants. A 360° camera, placed in the middle of the room, captures the central view which contains all participants. The audio for the sessions is also captured by the microphone integrated into the 360° camera. Each session can involve a maximum of 8 active speakers and up to 4 simultaneous conversations. With multiple concurrent discussions, the speech overlap ratio in the mixed audio can reach 100%. The image below shows an example of a recording session with 6 participants engaged in 3 separate conversations, each consisting of 2 speakers.

The MCoRec dataset consists of 56 recordings for training (released on July 1st 2025), 25 recordings for development (released on July 1st 2025) and 69 recordings for evaluation (will be released on #TBU).

Important: All facial data in the MCoRec dataset is provided with clear, unblurred facial views to enable face-based analysis and recognition tasks.

Privacy Notice: While faces are unblurred in the dataset for research purposes, any public presentations, publications, or demonstrations must not display speaker faces without explicit permission from the dataset authors for privacy protection.

Here is some statistic number of MCoRec dataset.

	# sessions	# conversations	duration	session's duration	# speakers	# speakers per conversation	# conversations per session
train	56	120	5.6 h	6 min	20	2 - 4	1 - 4
dev	25	60	2.5 h	6 min	12	2 - 4	1 - 4
eval	69	157	6.9 h	6 min	24	2 - 4	1 - 4

Training Set

For the training set, beyond the central view, each speaker is also recorded using a smartphone. This smartphone, placed close to and in front of the speaker for a clear facial view, captures an additional video stream. It also records audio from an attached close-talking lapel microphone. This additional egocentric data is provided only for the training set to facilitate system development.

Development and Evaluation Sets

The participants can use the development set to evaluating model performance during system development. It can be used to select the best model checkpoint, tune hyperparameters, and compare different system configurations. However, the dev set must not be used to train the model or update its internal parameters in any way.

Important Note: The smartphone recordings (egocentric videos and close-talking audio from lapel microphones) are only available for the training set. The development and evaluation sets contain only the central 360° video and audio captured by the central camera.

The number of people show in the central view can be more than the number of paticipants which need to be processed. The target participants will be provided by the bbox.

Detailed desciption of data structure and formats

The directory structure is as follows:

Example of session with 4 speakers separated into 2 conversations.

session_id
├── central_video.mp4
├── metadata.json
├── labels # Only provided in the train and dev set
│   ├── speaker_to_cluster.json
│   ├── spk_0.vtt
│   ├── spk_1.vtt
│   ├── spk_2.vtt
│   └── spk_3.vtt
└── speakers
    ├── spk_0
    ├── spk_1
    │   ├── central_crops
    │   │   ├── track_00_bbox.json
    │   │   ├── track_00.json
    │   │   ├── track_00.mp4
    │   │   ├── track_01_bbox.json
    │   │   ├── track_01.json
    │   │   └── track_01.mp4
    │   ├── ego_crops # Only provided in the train set
    │   │   ├── track_00_bbox.json
    │   │   ├── track_00.json
    │   │   └── track_00.mp4
    │   └── ego_video.mp4
    ├── spk_2
    └── spk_3

Each recording session is contained within a directory named with a unique session_id
central_video.mp4: This is the main video file for the session, captured by the 360° camera placed in the center of the room. It provides a view of all individuals present.
- Audio: single channel (from the 360 camera's microphone), 16kHz
- Video: 4K, 25fps.
speakers/: This directory contains subdirectories for each individual target participant (e.g., spk_0, spk_1, ... spk_N) who needs to be processed in the session.
central_crops/: This folder contains data derived from the central_video.mp4 specifically for a speaker. It contains multiple tracks, all from the same speaker but in different timelines. The multiple tracks exist because face recognition is performed first to detect and track faces throughout the video. In some cases, frames may be miss-predicted during face recognition, causing the speaker's face tracking to be split into separate temporal segments, resulting in multiple tracks (where xx represents the track number: 00, 01, 02, etc.).
- track_xx.mp4: These are 224x224 face crop videos of the speaker, where xx is the track number. Technical specifications:
  - Video: H264 (MPEG-4 AVC), 224x224 resolution, 25fps
  - Audio: MPEG AAC Audio (mp4a), 16kHz sample rate, 32 bits per sample
- track_xx_bbox.json: Stores bounding-box points. Each entry uses the frame number (as a string) to map to a bounding box specification. Example:
```
{ 
  "0": { "x1": 3893.5127, "y1": 910.7231, "x2": 4024.8086, "y2": 1088.9075},     
  "1": { "x1": 3893.5127, "y1": 910.7231, "x2": 4024.8086, "y2": 1088.9075},
  "...": { /* additional frames */ }
}
```
- track_xx.json: Contain the metadata of the track like start-end time/frame of the track, track duration Example:
```
{
    "frame_start": 0,
    "frame_end": 10996,
    "start_time": 0.0,
    "end_time": 439.84,
    "duration": 439.84
}
```
ego_crops/: This subfolder is only provided in the training set. It contains data derived from the egocentric video (e.g., ego_video.mp4) captured by the speaker's phone. The source ego_video.mp4, captured by the speaker's smartphone, includes an audio track (single channel 16kHz) recorded by a lapel microphone that was connected to the smartphone and positioned close to the speaker's mouth for enhanced audio clarity. The file structure and formats within ego_crops/ are identical to those found in the central_crops/ folder.
labels/: This subfolder is only provided in the train and dev set. It contains the ground truth information for the session.
- speaker_to_cluster.json: This file likely maps each speaker ID (e.g., spk_0, spk_1) to a conversation cluster or group ID. This helps in identifying which speakers are part of the same conversation within the session, as one session can feature multiple distinct conversations. Example:
```
{
    "spk_0": 0,
    "spk_1": 0,
    "spk_2": 1,
    "spk_3": 1,
    "spk_4": 2,
    "spk_5": 2
}
```
- spk_0.vtt, spk_1.vtt, ... spk_N.vtt: These are WebVTT (Web Video Text Tracks) files, one for each target speaker in the session (identified as spk_0, spk_1, etc.). Each .vtt file contains the time-stamped transcriptions of the speech for that specific speaker. Example:
```
WEBVTT

00:00:03.500 --> 00:00:05.200
Hello everyone,

00:00:06.000 --> 00:00:08.300
Welcome to the meeting.
```
  The .vtt files provide detailed transcriptions of the spoken content. In addition to lexical words, these include annotations for various vocal events such as hesitations (e.g., 'um', 'uh'), laughter (e.g., 'haha', 'hehe'), exclamations, and specific interjections or backchannels (e.g., 'ah', 'oh', 'wow'). Please note that these annotated vocal events will be automatically removed by the official evaluation script before Word Error Rate calculation.

metadata.json:

{
  "spk_0": {...},
  "spk_1": {
      "ego": { # Only provided in the train set
          "video": "speakers/spk_0/ego_video.mp4",
          "uem": {
              "start": 5.6545,
              "end": 367.8575
          },
          "crops": [
              {
                  "video": "speakers/spk_0/ego_crops/track_00.mp4",
                  "crop_metadata": "speakers/spk_0/ego_crops/track_00.json",
                  "bbox": "speakers/spk_0/ego_crops/track_00_bbox.json"
              }
          ]
      },
      "central": {
          "video": "central_video.mp4",
          "uem": {
              "start": 11.6017,
              "end": 373.8117
          },
          "crops": [
              {
                  "video": "speakers/spk_0/central_crops/track_00.mp4",
                  "crop_metadata": "speakers/spk_0/central_crops/track_00.json",
                  "bbox": "speakers/spk_0/central_crops/track_00_bbox.json"
              },
              {
                  "video": "speakers/spk_0/central_crops/track_01.mp4",
                  "crop_metadata": "speakers/spk_0/central_crops/track_01.json",
                  "bbox": "speakers/spk_0/central_crops/track_01_bbox.json"
              }
          ]
      }
  },
  "spk_2": {...},
  "spk_3": {...},
  "spk_4": {...},
  "spk_5": {...}
}

Each speaker-specific object contains detailed information about their associated data from both ego and central views. Note: ego only provided in the train set.
uem: An object with start and end attributes (in seconds), marking the timestamps when the whistle signals indicate the beginning and end of the primary conversation within the recording. This uem can be used to temporally align the corresponding streams from different videos (ego and central view).

The central_video.mp4, ego_video.mp4 (if provied) videos and derived face crops (central_crops/, ego_crops/) are provided with clear, unblurred facial views of the target participants.

Getting the data

MCoRec-Train:
- train_only_central_videos.zip [~46GB]
- train_without_central_videos.zip [~36.2GB]
MCoRec-Dev:
- dev_only_central_videos.zip [~20.6GB]
- dev_without_central_videos.zip [~6.12GB]
MCoRec-Eval (release date TBA):
- eval_only_central_videos.zip [~50GB]
- eval_without_central_videos.zip [~10GB]

For obtaining the the MCoRec dataset, you first need to request access and then download it using your Hugging Face token. Please note that access requires signing a Data Use Agreement.

Request Access:
- Go to the MCoRec dataset repository on Hugging Face.
- Request access to the dataset. You will need a Hugging Face account.
Get Your Hugging Face Token:

If you don't have one, create a Hugging Face User Access Token. Follow the instructions here to obtain your HF_TOKEN. Make sure it has read permissions.
Download and Unzip Dataset Files:

It's recommended to download and unzip the data into a data-bin directory within your mcorec_baseline project folder.

# Navigate to your baseline project directory if you aren't already there
# cd mcorec_baseline 

# Create a data directory (if it doesn't exist)
mkdir -p data-bin
cd data-bin

# Export your Hugging Face token (replace 'your_actual_token_here' with your actual token)
export HF_TOKEN=your_actual_token_here

# Link to download you can copy above.
# Example download the development set dev_without_central_videos.zip
wget --header="Authorization: Bearer $HF_TOKEN" https://huggingface.co/datasets/MCoRecChallenge/MCoRec/resolve/main/dev_without_central_videos.zip

# Unzip the downloaded files
unzip dev_without_central_videos.zip -d data-bin

License and Data Use Agreement

Dataset: MCoRec (Cocktail-Party Multi-Modal Speech Dataset)
Version: July 2025
Owner: Datoid LLC
Maintained by: MCoRec Challenge Organizers
Contact: [email protected]

The MCoRec Dataset contains sensitive multi-speaker, multi-modal recordings, including identifiable speech, video, and facial landmarks. Use of this dataset is governed by the MCoRec Data Use Agreement and is subject to strict privacy and ethical guidelines.

✅ Permitted Uses

Non-commercial academic or educational research
Algorithm development and benchmarking
Publication of results or models, provided proper citation and attribution

❌ Prohibited Uses

Commercial use or monetization
Redistribution of raw data (video, audio, transcripts, face landmarks)
Uploading the dataset to public cloud platforms or file-sharing services
Biometric surveillance, re-identification, or profiling

📚 Required Citation

If you use this dataset in a publication or presentation, cite:

Thai-Binh Nguyen, Ngoc-Quan Pham, Alexander Waibel. Cocktail-Party Audio-Visual Speech Recognition, Interspeech 2025. https://arxiv.org/abs/2506.02178

⚠️ Legal & Ethical Requirements

Comply with all applicable privacy and data protection laws, including the GDPR
Conduct research in accordance with your institution’s ethical standards (e.g., IRB approval if applicable)
Do not attempt to re-identify individuals
Store and handle the dataset in secure, access-controlled environments