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---
dataset_info:
- config_name: ihm
features:
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: timestamps_start
sequence: float64
- name: timestamps_end
sequence: float64
- name: speakers
sequence: string
splits:
- name: train
num_bytes: 9326329826
num_examples: 136
- name: validation
num_bytes: 1113896048
num_examples: 18
- name: test
num_bytes: 1044169059
num_examples: 16
download_size: 10267627474
dataset_size: 11484394933
- config_name: sdm
features:
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: timestamps_start
sequence: float64
- name: timestamps_end
sequence: float64
- name: speakers
sequence: string
splits:
- name: train
num_bytes: 9208897240
num_examples: 134
- name: validation
num_bytes: 1113930821
num_examples: 18
- name: test
num_bytes: 1044187355
num_examples: 16
download_size: 10679615636
dataset_size: 11367015416
configs:
- config_name: ihm
data_files:
- split: train
path: ihm/train-*
- split: validation
path: ihm/validation-*
- split: test
path: ihm/test-*
- config_name: sdm
data_files:
- split: train
path: sdm/train-*
- split: validation
path: sdm/validation-*
- split: test
path: sdm/test-*
license: cc-by-4.0
language:
- en
tags:
- speaker-diarization
- voice-activity-detection
- speaker-segmentation
---
# 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
were recorded in English using three different rooms with different acoustic properties, and include mostly
non-native speakers.
**Note**: This dataset has been preprocessed using [diarizers](https://github.com/huggingface/diarizers/tree/main/datasets).
It makes the dataset compatible with the `diarizers` library to fine-tune [pyannote](https://huggingface.co/pyannote/segmentation-3.0) segmentation models.
### Example Usage
```python
from datasets import load_dataset
ds = load_dataset("diarizers-community/ami", "ihm")
print(ds)
```
gives:
```
DatasetDict({
train: Dataset({
features: ['audio', 'timestamps_start', 'timestamps_end', 'speakers'],
num_rows: 136
})
validation: Dataset({
features: ['audio', 'timestamps_start', 'timestamps_end', 'speakers'],
num_rows: 18
})
test: Dataset({
features: ['audio', 'timestamps_start', 'timestamps_end', 'speakers'],
num_rows: 16
})
})
```
## Dataset source
- **Homepage:** https://groups.inf.ed.ac.uk/ami/corpus/
- **Repository:** https://github.com/pyannote/AMI-diarization-setup
- **Point of Contact:** [jonathan@ed.ac.uk](mailto:jonathan@ed.ac.uk)
- **Preprocessed using:** [diarizers](https://github.com/huggingface/diarizers/tree/main/datasets)
## Citation
```
@article{article,
author = {Mccowan, Iain and Carletta, J and Kraaij, Wessel and Ashby, Simone and Bourban, S and Flynn, M and Guillemot, M and Hain, Thomas and Kadlec, J and Karaiskos, V and Kronenthal, M and Lathoud, Guillaume and Lincoln, Mike and Lisowska Masson, Agnes and Post, Wilfried and Reidsma, Dennis and Wellner, P},
year = {2005},
month = {01},
pages = {},
title = {The AMI meeting corpus},
journal = {Int'l. Conf. on Methods and Techniques in Behavioral Research}
}
```
## Contribution
Thanks to [@kamilakesbi](https://huggingface.co/kamilakesbi) and [@sanchit-gandhi](https://huggingface.co/sanchit-gandhi) for adding this dataset.
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