English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Machine learning tasks in the audio domain frequently require large datasets with training data. Over the last years, numerous datasets have been made available for various purposes, for example, (Snyder, Chen, & Povey, 2015) and (Ardila et al., 2019). Unfortunately, most of the datasets are stored in widely differing formats. As a consequence, machine learning practitioners have to convert datasets into other formats before they can be used or combined. Furthermore, common tasks like reading, partitioning, or shuffling of datasets have to be developed over and over again for each format and require intimate knowledge of the formats. We purpose Audiomate, a Python toolkit, to solve this problem.

audiomate: A Python package for working with audio datasets