| --- |
| 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 |
| - [Table of Contents](#table-of-contents) |
| - [Dataset Description](#dataset-description) |
| - [Dataset Summary](#dataset-summary) |
| - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) |
| - [Languages](#languages) |
| - [Dataset Structure](#dataset-structure) |
| - [Data Instances](#data-instances) |
| - [Data Fields](#data-fields) |
| - [Data Splits](#data-splits) |
| - [Dataset Creation](#dataset-creation) |
| - [Curation Rationale](#curation-rationale) |
| - [Source Data](#source-data) |
| - [Annotations](#annotations) |
| - [Personal and Sensitive Information](#personal-and-sensitive-information) |
| - [Considerations for Using the Data](#considerations-for-using-the-data) |
| - [Social Impact of Dataset](#social-impact-of-dataset) |
| - [Discussion of Biases](#discussion-of-biases) |
| - [Other Known Limitations](#other-known-limitations) |
| - [Additional Information](#additional-information) |
| - [Dataset Curators](#dataset-curators) |
| - [Licensing Information](#licensing-information) |
| - [Citation Information](#citation-information) |
| - [Contributions](#contributions) |
|
|
| ## Dataset Description |
|
|
| - **Paper:** [MOCKS 1.0: Multilingual Open Custom Keyword Spotting Testset](https://www.isca-speech.org/archive/pdfs/interspeech_2023/pudo23_interspeech.pdf) |
|
|
| ### 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](https://www.isca-speech.org/archive/pdfs/interspeech_2023/pudo23_interspeech.pdf) 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](https://mfa-models.readthedocs.io/en/latest/acoustic/index.html) 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 |
|
|
| ```bibtex |
| @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}, |
| } |
| ``` |
