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Task: TTS
Release Date: 12/6/2025
Format: WEBM
Size: 90.74 MB
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Text to speech dataset for Brazilian Portuguese, 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 1.5 hours of scripted speech for Brazilian Portuguese (pt-BR) from a single speaker.
Brazilian Portuguese is the set of varieties of the Portuguese language native to Brazil.
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: 59.5/49
Average/median words per sentence: 10.4/9
Brazilian Portuguese uses an extended Latin alphabet.
Standard alphabet:
Lowercase: a b c d e f g h i j k l m n o p q r s t u v w x y z à á â ã ç é ê í ó ô õ ú
Uppercase: A B C D E F G H I J K L M N O P Q R S T U V W X Z À É Ó Ô Ú
5 randomly selected sentences:
A pesquisa mostrou que o grupo "excêntrico" apresentou uma melhora de 38% na força das pernas, enquanto no grupo de exercícios tradicionais, o ganho foi de 8%.
Ok, então eu vou assumir o serviço de banheiro.
Eu não tenho para onde ir!
Isso é o que vai tornar tudo mais divertido!
Vamos juntos de coração aberto!
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.