README.md

---
annotations_creators:
- expert-generated
language:
- en
- de
- es
- fr
- it
license:
- mit
multilinguality:
- monolingual
dataset_info:
- config_name: config
  features:
  - name: audio_id
    dtype: string
  - name: audio
    dtype:
      audio:
        sampling_rate: 16000
  - name: text
    dtype: string
---


# MOCKS dataset

## 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

- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**

### 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.

[More Information Needed - add link to paper]

### 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

### Data Instances

[More Information Needed]

### Data Fields

[More Information Needed]

### Data Splits

The MOCKS testset is split by language, source dataset and OV-KWS type. Each split is divided into:
- positive examples - test examples with true keyword, 5000-8000 keywords in each subset,
- similar examples - test examples with similar phrases to keyword selected based on phonetic transcription distance,
- different examples - test examples with completaly different prases.

Each split also contains subset of whole data to allow faster evaluation.

## 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.

### Curation Rationale

[More Information Needed]

### Source Data

#### Initial Data Collection and Normalization

[More Information Needed]

#### Who are the source language producers?

[More Information Needed]

### Annotations

#### Annotation process

[More Information Needed]

#### Who are the annotators?

[More Information Needed]

### Personal and Sensitive Information

[More Information Needed]

## Considerations for Using the Data

### Social Impact of Dataset

[More Information Needed]

### Discussion of Biases

The MOCKS testset is speaker gender balanced.

### Other Known Limitations

[More Information Needed]

## Additional Information

### Dataset Curators

[More Information Needed]

### Licensing Information

[More Information Needed]

### Citation Information

```bibtex
@inproceedings{pudo23_interspeech,
  author={Miko\l{}aj Pudo and Mateusz Wosik and Adam Cie\'slak and Justyna Krzywdziak and Bo\.{z}ena \L{}ukasiak and Artur Janicki},
  title={{MOCKS} 1.0: Multilingual Open Custom Keyword Spotting Testset},
  year={in press.},
  booktitle={Proc. Interspeech 2023},
}
```

### Contributions

Thanks to [@github-username](https://github.com/<github-username>) for adding this dataset.