|
--- |
|
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}, |
|
} |
|
``` |
