README.md

metadata

annotations_creators: []
language:
  - en
language_creators: []
license:
  - cc-by-4.0
multilinguality:
  - monolingual
pretty_name: AMI
size_categories: []
source_datasets: []
tags: []
task_categories:
  - automatic-speech-recognition
dataset_info:
  - config_name: ihm
    features:
      - name: meeting_id
        dtype: string
      - name: audio_id
        dtype: string
      - name: text
        dtype: string
      - name: audio
        dtype:
          audio:
            sampling_rate: 16000
      - name: begin_time
        dtype: float32
      - name: end_time
        dtype: float32
      - name: microphone_id
        dtype: string
      - name: speaker_id
        dtype: string
    splits:
      - name: train
        num_bytes: 20710074322.672
        num_examples: 108502
      - name: validation
        num_bytes: 2196244962.512
        num_examples: 13098
      - name: test
        num_bytes: 1587855340.548
        num_examples: 12643
    download_size: 15243022474
    dataset_size: 24494174625.732002
  - config_name: sdm
    features:
      - name: meeting_id
        dtype: string
      - name: audio_id
        dtype: string
      - name: text
        dtype: string
      - name: audio
        dtype:
          audio:
            sampling_rate: 16000
      - name: begin_time
        dtype: float32
      - name: end_time
        dtype: float32
      - name: microphone_id
        dtype: string
      - name: speaker_id
        dtype: string
    splits:
      - name: train
        num_bytes: 13324608404.558
        num_examples: 107319
      - name: validation
        num_bytes: 2176476471.684
        num_examples: 13098
      - name: test
        num_bytes: 1867748118.586
        num_examples: 12643
    download_size: 13768733115
    dataset_size: 17368832994.828
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-*

Dataset Card for AMI

Table of Contents

• Table of Contents
• Dataset Description
  • Dataset Summary
  • Supported Tasks and Leaderboards
  • Languages
• Dataset Structure
  • Data Instances
  • Data Fields
  • Data Splits
• Dataset Creation
  • Curation Rationale
  • Source Data
  • Annotations
  • Personal and Sensitive Information
• Considerations for Using the Data
  • Social Impact of Dataset
  • Discussion of Biases
  • Other Known Limitations
• Additional Information
  • Dataset Curators
  • Licensing Information
  • Citation Information
  • Contributions
• Terms of Usage

Dataset Description

• Homepage: https://groups.inf.ed.ac.uk/ami/corpus/
• Repository: https://github.com/kaldi-asr/kaldi/tree/master/egs/ami/s5
• Paper:
• Leaderboard:
• Point of Contact: jonathan@ed.ac.uk

Dataset Description

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 corresponds to the data-processing of KALDI's AMI S5 recipe. This means text is normalized and the audio data is chunked according to the scripts above! To make the user experience as simply as possible, we provide the already chunked data to the user here so that the following can be done:

Example Usage

from datasets import load_dataset
ds = load_dataset("edinburghcstr/ami", "ihm")

print(ds)

gives:

DatasetDict({
    train: Dataset({
        features: ['meeting_id', 'audio_id', 'text', 'audio', 'begin_time', 'end_time', 'microphone_id', 'speaker_id'],
        num_rows: 108502
    })
    validation: Dataset({
        features: ['meeting_id', 'audio_id', 'text', 'audio', 'begin_time', 'end_time', 'microphone_id', 'speaker_id'],
        num_rows: 13098
    })
    test: Dataset({
        features: ['meeting_id', 'audio_id', 'text', 'audio', 'begin_time', 'end_time', 'microphone_id', 'speaker_id'],
        num_rows: 12643
    })
})

ds["train"][0]

automatically loads the audio into memory:

{'meeting_id': 'EN2001a',
 'audio_id': 'AMI_EN2001a_H00_MEE068_0000557_0000594',
 'text': 'OKAY',
 'audio': {'path': '/cache/dir/path/downloads/extracted/2d75d5b3e8a91f44692e2973f08b4cac53698f92c2567bd43b41d19c313a5280/EN2001a/train_ami_en2001a_h00_mee068_0000557_0000594.wav',
  'array': array([0.        , 0.        , 0.        , ..., 0.00033569, 0.00030518,
         0.00030518], dtype=float32),
  'sampling_rate': 16000},
 'begin_time': 5.570000171661377,
 'end_time': 5.940000057220459,
 'microphone_id': 'H00',
 'speaker_id': 'MEE068'}

The dataset was tested for correctness by fine-tuning a Wav2Vec2-Large model on it, more explicitly the wav2vec2-large-lv60 checkpoint.

As can be seen in this experiments, training the model for less than 2 epochs gives

Result (WER):

"dev"	"eval"
25.27	25.21

as can be seen here.

The results are in-line with results of published papers:

• Hybrid acoustic models for distant and multichannel large vocabulary speech recognition
• Multi-Span Acoustic Modelling using Raw Waveform Signals

You can run run.sh to reproduce the result.

Supported Tasks and Leaderboards

Languages

Dataset Structure

Data Instances

Data Fields

Data Splits

Transcribed Subsets Size

Dataset Creation

Curation Rationale

Source Data

Initial Data Collection and Normalization

Who are the source language producers?

Annotations

Annotation process

Who are the annotators?

Personal and Sensitive Information

Considerations for Using the Data

Social Impact of Dataset

[More Information Needed]

Discussion of Biases

Other Known Limitations

Additional Information

Dataset Curators

Licensing Information

Citation Information

Contributions

Thanks to @sanchit-gandhi, @patrickvonplaten, and @polinaeterna for adding this dataset.

Terms of Usage