--- 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](https://arxiv.org/abs/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](https://cdla.io/permissive-1-0) 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](https://zenodo.org/records/8122551), 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](https://huggingface.co/docs/datasets/v2.21.0/index). ```py 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](https://huggingface.co/datasets/benjamin-paine/dinner-party-corpus/blob/main/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}, } ```