README.md

metadata

dataset_info:
  - config_name: split-channel
    features:
      - name: audio
        dtype: audio
      - name: start_timestamp
        dtype: string
      - name: start_time_s
        dtype: float32
      - name: start_frame
        dtype: uint64
      - name: end_timestamp
        dtype: string
      - name: end_time_s
        dtype: float32
      - name: end_frame
        dtype: uint64
      - name: duration_s
        dtype: float32
      - name: duration_frames
        dtype: uint64
      - name: transcription
        dtype: string
      - name: mother_tongue
        dtype: string
      - name: participant_id
        dtype: string
      - name: session_id
        dtype: string
      - name: device_id
        dtype: string
      - name: device_channel
        dtype: uint8
      - name: device_distance_mm
        dtype: uint16
      - name: device_type
        dtype:
          class_label:
            names:
              '0': close-talk
              '1': far-field
      - name: gender
        dtype:
          class_label:
            names:
              '0': female
              '1': male
      - name: nativeness
        dtype:
          class_label:
            names:
              '0': native
              '1': non-native
    splits:
      - name: train
        num_bytes: 13863370976.5
        num_examples: 132228
      - name: test
        num_bytes: 13192103916.5
        num_examples: 122580
    download_size: 23859943038
    dataset_size: 27055474893
  - config_name: mixed-channel
    features:
      - name: audio
        dtype: audio
      - name: start_timestamp
        dtype: string
      - name: start_time_s
        dtype: float32
      - name: start_frame
        dtype: uint64
      - name: end_timestamp
        dtype: string
      - name: end_time_s
        dtype: float32
      - name: end_frame
        dtype: uint64
      - name: duration_s
        dtype: float32
      - name: duration_frames
        dtype: uint64
      - name: transcription
        dtype: string
      - name: mother_tongue
        dtype: string
      - name: participant_id
        dtype: string
      - name: session_id
        dtype: string
      - name: device_id
        dtype: string
      - name: device_channel
        dtype: uint8
      - name: device_distance_mm
        dtype: uint16
      - name: device_type
        dtype:
          class_label:
            names:
              '0': close-talk
              '1': far-field
      - name: gender
        dtype:
          class_label:
            names:
              '0': female
              '1': male
      - name: nativeness
        dtype:
          class_label:
            names:
              '0': native
              '1': non-native
    splits:
      - name: train
        num_bytes: 2310562016.25
        num_examples: 22038
      - name: test
        num_bytes: 2198683986.25
        num_examples: 20430
    download_size: 3840697632
    dataset_size: 4509246002.5
configs:
  - config_name: split-channel
    data_files:
      - split: train
        path: split-channel/train-*
      - split: test
        path: split-channel/test-*
  - config_name: mixed-channel
    data_files:
      - split: train
        path: mixed-channel/train-*
      - split: test
        path: mixed-channel/test-*
license: cdla-permissive-1.0
task_categories:
  - automatic-speech-recognition
  - audio-classification
language:
  - en
tags:
  - dinner party
  - dipco
pretty_name: DiPCo - Dinner Party Corpus

This repository contains a reorganized, utterance-focused version of the Dinner Party Corpus, released by Amazon, the Center for Language and Speech Processing (CLSP) and Johns Hopkins University in September 2019.

Description

The following description is provided in arXiv 1909.13447:

We present a speech data corpus that simulates a "dinner party" scenario taking place in an everyday home environment. The corpus was created by recording multiple groups of four Amazon employee volunteers having a natural conversation in English around a dining table. The participants were recorded by a single-channel close-talk microphone and by five far-field 7-microphone array devices positioned at different locations in the recording room. The dataset contains the audio recordings and human labeled transcripts of a total of 10 sessions with a duration between 15 and 45 minutes. The corpus was created to advance in the field of noise robust and distant speech processing and is intended to serve as a public research and benchmarking data set.

License

As stated in the paper linked above, section 4, the dataset is released under the CDLA-Permissive license.

Authors

Van Segbroeck, Maarten; Zaid, Ahmed; Kutsenko, Ksenia; Huerta, Cirenia; Nguyen, Tinh; Luo, Xuewen; Hoffmeister, Björn; Trmal, Jan; Omologo, Maurizio; Maas, Roland

Contact Persons

Maas, Roland; Hoffmeister, Björn

Comparison to Base Dataset

• The base dataset was downloaded from Zenodo, this has a compressed size of 12.4GB, and an uncompressed size of 23GB. It is organized in manner to minimize file size and data repetition, with uncut audio and separate label files.
• This dataset has an uncompressed size of 27GB, making it about 15% larger than the uncompressed base dataset. For this size exchange, you gain ease-of-use; all audio is pre-cut to the start and end utterances, and mapped with the appropriate labels directly in Parquet.

How to Use

This repository is made to be used with 🤗Datasets.

from datasets import load_dataset

dataset = load_dataset(
    "benjamin-paine/dinner-party-corpus",
    config_name="split-channel", # 'split-channel' or 'mixed-channel'
    split="train" # 'train' or 'test'
)

for datum in dataset:
    # Do something with the audio
    # datum["audio"]["array"] is the sample waveform at 16khz (see datum["audio"]["sampling_rate"])
    pass

Conversion Script

The script used to convert the data is available in this repository as convert.py.

Citation

@misc{vansegbroeck2019dipcodinnerparty,
      title={DiPCo -- Dinner Party Corpus}, 
      author={Maarten Van Segbroeck and Ahmed Zaid and Ksenia Kutsenko and Cirenia Huerta and Tinh Nguyen and Xuewen Luo and Björn Hoffmeister and Jan Trmal and Maurizio Omologo and Roland Maas},
      year={2019},
      eprint={1909.13447},
      archivePrefix={arXiv},
      primaryClass={eess.AS},
      url={https://arxiv.org/abs/1909.13447}, 
}