README.md

---
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.