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License:
CC0-1.0
Steward:
Open Home FoundationDataset ID:
cmid4wbrc00hvnv07e448rwv3
Release Date: 11/24/2025
Format: WEBM
Size: 99.60 MB
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Text to speech dataset for Hungarian, male speaker, approximately 1.5 hours of read speech.
Forbidden Usage
You agree not to attempt to determine the identity of speakers in the dataset
Intended Use
Training and fine-tuning text-to-speech models
This dataset contains approximately 1.4 hours of scripted speech for Hungarian (hu) from a single speaker.
Hungarian is a Ugric language spoken in Hungary and parts of several neighboring countries.
There are no variants defined for this dataset.
The age and gender of the speaker was not reported. Dataset names may be gendered, but were assigned according to the speaker's preference only.
The text corpus comes from Piper Recording Studio, which extends Microsoft's samples TTS scripts for Azure.
Microsoft provides the following recommendations:
To use these example scripts for training, it's recommended that you should do the sanity check to make sure it matches what the voice talent actually speaks in the audio and normalize the text before uploading the data. For example, change '50%' to fifty percent and '$45' to forty-five dollars. Normalization should apply to the scripts that contain digits, symbols, abbreviations, date, and time.
Statistics for the text corpus:
Average/median characters per sentence: 69/61
Average/median words per sentence: 10/10
Hungarian uses an extended Latin alphabet.
Standard alphabet:
Lowercase: a b c d e f g h i j k l m n o p r s t u v w x y z á é í ó ö ú ü ő ű
Uppercase: A B C D E F G H I J K L M N O P R S T U V Y Z Á É Í Ó Ö Ú Ü Ő Ű
5 randomly selected sentences:
Hányszor kell még elmondanom?
Mondja meg ezt valaki!
Kérjük, törölje a letöltési csomagot a letöltés befejezése után azonnal.
Ha az eper nedvességtartalmát eltüntetnénk, készítsünk dupla csokis, epres változatot!
Nem hiszem el, hogy nincs megoldás!
Audio was recorded online using Piper Recording Studio. No post-processing or validation was done to the text or audio.
A pre-trained Piper voice model is available for download.
If you would like to contribute your voice and have us train a Piper text-to-speech model, please contact us at voice@openhomefoundation.org
We would like to thank all contributors, as well as supporters of the Open Home Foundation.
This dataset is released under the Creative Commons Zero (CC-0) license. By downloading this data you agree to not determine the identity of speakers in the dataset.