metadata

annotations_creators:
  - expert-generated
language:
  - en
  - de
  - es
  - fr
  - it
license:
  - cc-by-4.0
  - mpl-2.0
multilinguality:
  - multilingual
dataset_info:
  - config_name: config
    features:
      - name: audio_id
        dtype: string
      - name: audio
        dtype:
          audio:
            sampling_rate: 16000
      - name: text
        dtype: string

MOCKS: Multilingual Open Custom Keyword Spotting Testset

Table of Contents
Dataset Description
Dataset Structure
Dataset Creation
Considerations for Using the Data
Additional Information

Dataset Description

Paper: MOCKS 1.0: Multilingual Open Custom Keyword Spotting Testset

Dataset Summary

Multilingual Open Custom Keyword Spotting Testset (MOCKS) is a comprehensive audio testset for evaluation and benchmarking Open-Vocabulary Keyword Spotting (OV-KWS) models. It supports multiple OV-KWS problems: both text-based and audio-based keyword spotting, as well as offline and online (streaming) modes. It is based on the LibriSpeech and Mozilla Common Voice datasets and contains almost 50,000 keywords, with audio data available in English, French, German, Italian, and Spanish. The testset was generated using automatically generated alignments used for the extraction of parts of the recordings that were split into keywords and test samples. MOCKS contains both positive and negative examples selected based on phonetic transcriptions that are challenging and should allow for in-depth OV-KWS model evaluation.

Please refer to our paper for further details.

Supported Tasks and Leaderboards

The MOCKS dataset can be used for Open-Vocabulary Keyword Spotting (OV-KWS) task. It supports two OV-KWS types:

Query-by-Text, where keyword is provided by text and needs to be detected on audio stream.
Query-by-Example, where keyword is provided with enrollment audio for detection on audio stream.

It also allows for:

offline keyword detection, where test audio is trimed to contrain only keyword of interest.
online (streaming) keyword detection, where test audio have past and future context besides keyword of interest.

Languages

The MOCKS incorporates 5 languages:

English - primary and largest test set,
German,
Spanish,
French,
Italian.

Dataset Structure

The MOCKS testset is split by language, source dataset and OV-KWS type:

MOCKS
│
└───de
│   └───MCV
│   │   └───test
│   │   │   └───offline
│   │   │   │   │   all.pair.different.tsv
│   │   │   │   │   all.pair.positive.tsv
│   │   │   │   │   all.pair.similar.tsv
│   │   │   │   │   data.tar.gz
│   │   │   │   │   subset.pair.different.tsv
│   │   │   │   │   subset.pair.positive.tsv
│   │   │   │   │   subset.pair.similar.tsv
│   │   │   │
│   │   │   └───online
│   │   │   │   │   all.pair.different.tsv
│   │   │   │   │   ...
│   │   │   │   data.offline.transcription.tsv
│   │   │   │   data.online.transcription.tsv
│
└───en
│   └───LS-clean
│   │   └───test
│   │   │   └───offline
│   │   │   │   │   all.pair.different.tsv
│   │   │   │   │   ...
│   │   │   │   ...
│   │
│   └───LS-other
│   │   └───test
│   │   │   └───offline
│   │   │   │   │   all.pair.different.tsv
│   │   │   │   │   ...
│   │   │   │   ...
│   │
│   └───MCV
│   │   └───test
│   │   │   └───offline
│   │   │   │   │   all.pair.different.tsv
│   │   │   │   │   ...
│   │   │   │   ...
│
└───...

Each split is divided into:

positive examples (all.pair.positive.tsv) - test examples with true keyword, 5000-8000 keywords in each subset,
similar examples (all.pair.similar.tsv) - test examples with similar phrases to keyword selected based on phonetic transcription distance,
different examples (all.pair.different.tsv) - test examples with completaly different prases.

All those files contain columns separated by tab:

keyword_path - path to audio containing keyword phrase.
adversary_keyword_path - path to test audio.
adversary_keyword_timestamp_start - start time in seconds of phrase of interest for given keyword from keyword_path, field only available in offline split.
adversary_keyword_timestamp_end - end time in seconds of phrase of interest for given keyword from keyword_path, field only available in offline split.
label - whether the adversary_keyword_path contain keyword from keyword_path or not (1 - contains keyword, 0 - doesn't contain keyword).

Each split also contains subset of whole data with the same field sctructure to allow faster evaluation (subset.pair.*.tsv).

Also, trascriptions are provided for each audio in:

data_offline_transcription.tsv - transcriptions for offline examples and keyword_path from online scenario,
data_online_transcription.tsv - transcriptions for adversary, test examples from online scenario,

three columns are present within each file:

path_to_keyword/path_to_adversary_keyword - path to audio file,
keyword_transcription/adversary_keyword_transcription - audio transcription,
keyword_phonetic_transcription/adversary_keyword_phonetic_transcription - audio phonetic transcription.

Dataset Creation

The MOCKS testset was created from LibriSpeech and Mozilla Common Voice (MCV) datasets that are publicly available. To create it:

a MFA with publicly available models was used to extract word-level alignments,
an internally-developed, rule-based grapheme-to-phoneme (G2P) algorithm was used to prepare phonetic transcriptions for each sample.

The data is stored in a 16-bit, single-channel WAV format. 16kHz sampling rate is used for LibriSpeech based testset and 48kHz sampling rate for MCV based testset.

The offline testset contains additional 0.1 second at the beginning and end of extracted audio sample to mitigate the cut-speech effect. The online version contrains additional 1 second or so at the beginning and end of extracted audio sample.

The MOCKS testset is gender balanced.

Citation Information

@inproceedings{pudo23_interspeech,
  author={Mikołaj Pudo and Mateusz Wosik and Adam Cieślak and Justyna Krzywdziak and Bożena Łukasiak and Artur Janicki},
  title={{MOCKS} 1.0: Multilingual Open Custom Keyword Spotting Testset},
  year={2023},
  booktitle={Proc. Interspeech 2023},
}