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Datasets:
Ken-Z
/
Latin-Audio

Tasks:
Text-to-Speech
Automatic Speech Recognition
Modalities:
Audio
Text
Formats:
soundfolder
Languages:
Latin
Size:
10K - 100K
Tags:
audio
speech-processing
text-to-speech
tts
automatic-speech-recognition
asr
Libraries:
Datasets
License:
Dataset card Data Studio Files
xet
 Community
1
Ken commited on
Commit
4936eae
·
1 Parent(s): 80fc0b3

feat: added speakers

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README.md DELETED
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- ---
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- license: cc-by-4.0
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- task_categories:
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- - text-to-speech
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- - automatic-speech-recognition
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- language:
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- - la
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- pretty_name: Vox Classica
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- tags:
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- - audio
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- - speech-processing
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- - text-to-speech
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- - tts
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- - automatic-speech-recognition
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- - asr
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- - latin
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- - classical-latin
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- - low-resource
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- ---
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-
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- ## Dataset Summary
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-
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- Vox Classica is a Latin speech corpus of ~73 hours of audio, segmented into short audio clips by sentence. Vox Classica is a large-scale, ML-ready dataset of human-read Classical Latin. It was designed to address the absence of a publicly available human-read Latin corpus large enough for model training.
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-
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- - **Alignment and curation:** Kaiyuan Zhao
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- - **Language:** Latin (Classical)
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-
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- ## Uses
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-
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- This dataset is built for training and evaluating speech processing models for Classical Latin. Its primary intended use is for training and fine-tuning Automatic Speech Recognition (ASR) and Text-to-Speech (TTS) models.
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-
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- ## Dataset Structure
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-
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- Each example in the dataset represents roughly 1-2 sentences and contains the following fields:
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-
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- - **`transcription`**: A `string` containing the gold-standard Latin sentence. The text has been automatically macronized using the [CLTK macronizer](https://docs.cltk.org/en/latest/latin.html#macronizer) to provide correct vowel length information.
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- - **`audio`**: An mp3 feature containing the spoken version of the text.
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-
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- ## Dataset Creation
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-
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- To split the long-form audio recordings and the corresponding "gold standard" texts into short audio clips, the gold text was segmented into individual sentences. To approximate the location of each sentence within the long audio file, a untrained whisper-large-v3 model was used to generate a rough transcript of the entire audio. A fuzzy string matching algorithm was used to find the location of each gold-standard sentence within the noisy Whisper transcript, providing estimated start and end timestamps. The curator personally verified and adjusted the segment endpoints to ensure they precisely aligned with the sentence text.
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-
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- # Credits and Acknowledgements
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-
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- Audio sourced (with proper licensing or explicit informed permission) from:
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-
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- - [Dickinson College Commentaries](https://dcc.dickinson.edu/)
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- - Western Washington University's [Nuntili Latini](https://nuntiilatini.com/)
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- - [Luke Ranieri](https://www.youtube.com/@ScorpioMartianus)
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- - Stefano Vittori
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- - [Latinum](https://www.youtube.com/channel/UCEekt9eu1g-yEGq6XUlRSIg)
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- - [Onagrus](https://www.youtube.com/@Onagrus-qf2ud)
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- - [Satura Lanx](https://www.youtube.com/c/SaturaLanx)
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- - [ThePrinceSterling](https://www.youtube.com/user/ThePrinceSterling)
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- - [Librivox](https://librivox.org/)
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-
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- Additional thanks to Professor Christopher Francese, William Mattingly, and George Backhouse for their advice and mentorship throughout this process.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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