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Datasets:
Ken-Z
/
Latin-Audio

Tasks:
Text-to-Speech
Automatic Speech Recognition
Modalities:
Audio
Languages:
Latin
Tags:
audio
speech-processing
text-to-speech
tts
automatic-speech-recognition
asr
License:
Dataset card Files
xet
Community
1
Ken commited on
Commit
45d6780
·
1 Parent(s): 69a4639

feated: added more audio

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  1. .DS_Store +0 -0
  2. README.md +18 -3
  3. metadata.csv +0 -0
  4. part_0/audio/Amores_1-3_ver2_0006.wav +3 -0
  5. part_0/audio/Aug. Conf. 1.6 part 4 section 9 (Adrian Packel)_0001.wav +3 -0
  6. part_0/audio/Caesar BG 1.5 (Shoaf, Elias, Francese)_0006.wav +3 -0
  7. part_0/audio/Cicero I Cat Section 13 (29-33) (Prince Sterling)_0008.wav +3 -0
  8. part_0/audio/Cicero I Cat Section 4 (8-10) (Prince Sterling)_0013.wav +3 -0
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README.md CHANGED
@@ -20,11 +20,10 @@ tags:
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  ## Dataset Summary
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- Vox Classica is a Latin speech corpus of ~67 hours of audio, segmented into samples each under 30 seconds in length. 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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- - **Curated by:** Kaiyuan (Ken) Zhao
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  - **Language:** Latin (Classical)
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- - **Sources:** Audio primarily derived from the excellent audio collection provided by [Dickinson College Commentaries](https://dcc.dickinson.edu/). Text primarily sourced from the public domain texts available via [The Perseus Project](http://www.perseus.tufts.edu/hopper/).
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  ## Uses
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@@ -40,3 +39,19 @@ Each example in the dataset represents roughly 1-2 sentences and contains the fo
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  ## Dataset Creation
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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 pre-trained 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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  ## Dataset Summary
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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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+ - **Alignment and curation:** Kaiyuan Zhao
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  - **Language:** Latin (Classical)
 
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  ## Uses
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  ## Dataset Creation
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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 pre-trained 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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