AMI Corpus

https://groups.inf.ed.ac.uk/ami/corpus/

To be filled!

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:

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

print(ds)

gives:

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

{'segment_id': 'EN2001a',
 'audio_id': 'AMI_EN2001a_H00_MEE068_0000557_0000594',
 'text': 'OKAY',
 'audio': {'path': '/home/patrick_huggingface_co/.cache/huggingface/datasets/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:

You can run run.sh to reproduce the result.