Datasets:
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bfafee8
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d56766c
Changing License
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README.md
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@@ -5,11 +5,12 @@ language:
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- mt
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language_creators:
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- other
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license:
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- cc-by-4.0
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multilinguality:
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- monolingual
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pretty_name:
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size_categories:
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- n<1K
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source_datasets:
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@@ -132,7 +133,7 @@ Neither the MASRI Team or the CIEMPIESS-UNAM Project guarantee the accuracy of t
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### Dataset Curators
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The audio recordings were collected and segmented by students belonging to the social service program ["Desarrollo de Tecnologías del Habla"](http://profesores.fi-b.unam.mx/carlos_mena/servicio.html), it was curated by Carlos Daniel Hernández Mena and its transcriptions were manually performed by Ayrton-Didier Brincat during 2020.
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### Licensing Information
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[CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/). The copyright remains with the original owners of the video.
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As the data is taken from YouTube, we invoke the same argument of "fair use" as in the [Voxlingua107](http://bark.phon.ioc.ee/voxlingua107/) dataset, which is:
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**"While YouTube users own the copyright to their own videos, using the audio in the videos for training speech recognition models has very limited and transformative purpose and qualifies thus as "fair use" of copyrighted materials. YouTube’s terms of service forbid downloading, storing and distribution of videos. However, the aim of this rule is clearly to forbid unfair monetization of the content by third-party sites and applications. Our dataset contains the videos in segmented audio-only form that makes the monetization of the actual distributed content extremely difficult."**
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### Citation Information
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@@ -146,4 +147,4 @@ As the data is taken from YouTube, we invoke the same argument of "fair use" as
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}
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```
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### Contributions
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The authors would like to thank to Alberto Templos Carbajal, Elena Vera and Angélica Gutiérrez for their support to the social service program ["Desarrollo de Tecnologías del Habla"](http://profesores.fi-b.unam.mx/carlos_mena/servicio.html) at the ["Facultad de Ingeniería (FI)"](https://www.ingenieria.unam.mx/) of the [Universidad Nacional Autónoma de México (UNAM)](https://www.unam.mx/). We also thank to the social service students for all the hard work during the audio segmentation.
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- mt
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language_creators:
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- other
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license: cc-by-nc-sa-4.0
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multilinguality:
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- monolingual
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pretty_name: >-
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MASRI-TEST CORPUS: Audio and Transcriptions in Maltese extracted from the
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YouTube channel of the University of Malta.
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size_categories:
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- n<1K
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source_datasets:
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### Dataset Curators
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The audio recordings were collected and segmented by students belonging to the social service program ["Desarrollo de Tecnologías del Habla"](http://profesores.fi-b.unam.mx/carlos_mena/servicio.html), it was curated by Carlos Daniel Hernández Mena and its transcriptions were manually performed by Ayrton-Didier Brincat during 2020.
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### Licensing Information
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[CC-BY-NC-SA-4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/). The copyright remains with the original owners of the video.
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As the data is taken from YouTube, we invoke the same argument of "fair use" as in the [Voxlingua107](http://bark.phon.ioc.ee/voxlingua107/) dataset, which is:
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**"While YouTube users own the copyright to their own videos, using the audio in the videos for training speech recognition models has very limited and transformative purpose and qualifies thus as "fair use" of copyrighted materials. YouTube’s terms of service forbid downloading, storing and distribution of videos. However, the aim of this rule is clearly to forbid unfair monetization of the content by third-party sites and applications. Our dataset contains the videos in segmented audio-only form that makes the monetization of the actual distributed content extremely difficult."**
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### Citation Information
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}
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```
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### Contributions
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The authors would like to thank to Alberto Templos Carbajal, Elena Vera and Angélica Gutiérrez for their support to the social service program ["Desarrollo de Tecnologías del Habla"](http://profesores.fi-b.unam.mx/carlos_mena/servicio.html) at the ["Facultad de Ingeniería (FI)"](https://www.ingenieria.unam.mx/) of the [Universidad Nacional Autónoma de México (UNAM)](https://www.unam.mx/). We also thank to the social service students for all the hard work during the audio segmentation.
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