Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
The AMI Meeting Corpus consists of 100 hours of meeting recordings. The recordings use a range of signals synchronized to a common timeline. These include close-talking and far-field microphones, individual and room-view video cameras, and output from a slide projector and an electronic whiteboard. During the meetings, the participants also have unsynchronized pens available to them that record what is written. The meetings were recorded in English using three different rooms with different acoustic properties, and include mostly non-native speakers.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Dataset Card for the AMI dataset for speaker diarization
The AMI Meeting Corpus consists of 100 hours of meeting recordings. The recordings use a range of signals synchronized to a common timeline. These include close-talking and far-field microphones, individual and room-view video cameras, and output from a slide projector and an electronic whiteboard. During the meetings, the participants also have unsynchronized pens available to them that record what is written. The meetings… See the full description on the dataset page: https://huggingface.co/datasets/diarizers-community/ami.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Dataset Card for "AMIsum"
Dataset Summary
AMIsum is meeting summaryzation dataset based on the AMI Meeting Corpus (https://groups.inf.ed.ac.uk/ami/corpus/). The dataset utilizes the transcripts as the source data and abstract summaries as the target data.
Supported Tasks and Leaderboards
More Information Needed
Languages
English
Dataset Structure
Data Instances
{'transcript': '
Apache License, v2.0https://www.apache.org/licenses/LICENSE-2.0
License information was derived automatically
DSFL Dataset - AMI Disfluency Laughter Events
This dataset contains segmented audio and video clips from AMI Meeting Corpus, which only consisted of disfluencies and laughter events, segmented in both audio and visual modality. This dataset, along with hhoangphuoc/ami-av is created for my research related to Audio-Visual Speech Recognition, which I currently developed at: https://github.com/hhoangphuoc/AVSL For reproducing the work I've done to create this dataset, checkout the… See the full description on the dataset page: https://huggingface.co/datasets/hhoangphuoc/ami-disfluency.
Dataset Summary
This is the processed Audio-Visual Dataset from AMI Meeting Corpus. The dataset was segmented into sentence-level audio/video segments based on the individual [meeting_id]-[speaker_id] transcripts. The purpose of this data is for audio-visual speech recognition task (AVSR), particularly for spontaneous conversational speech. General information about dataset: Total #segments: 83,438 (including either audio/video or both) Dataset({ features: ['id', 'meeting_id'… See the full description on the dataset page: https://huggingface.co/datasets/hhoangphuoc/ami-av.
AMI DisfluencyLaughter Dataset
This dataset contains segmented audio and video clips which extract from AMI Meeting Corpus. The segmented audio/videos created in this dataset are mainly the disfluencies and laughter events, extracted from original recordings. General information about this dataset:
Number of recordings: 35,731 Has audio: True Has video: True Has lip video: True
Dataset({ features: ['id', 'meeting_id', 'speaker_id', 'start_time', 'end_time', 'duration'… See the full description on the dataset page: https://huggingface.co/datasets/hhoangphuoc/ami-dsfl-av.
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Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
The AMI Meeting Corpus consists of 100 hours of meeting recordings. The recordings use a range of signals synchronized to a common timeline. These include close-talking and far-field microphones, individual and room-view video cameras, and output from a slide projector and an electronic whiteboard. During the meetings, the participants also have unsynchronized pens available to them that record what is written. The meetings were recorded in English using three different rooms with different acoustic properties, and include mostly non-native speakers.