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Datasets:
zuhri025
/
speech-utterances

Tasks:
Audio Classification
Modalities:
Audio
Text
Formats:
webdataset
Languages:
English
Korean
Portuguese
Size:
100K - 1M
ArXiv:
Tags:
non-speech
vocal-sounds
emotion
human-voice
webdataset
Libraries:
Datasets
WebDataset
License:
Dataset card Data Studio Files
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zuhri025 gijs commited on
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Co-authored-by: Gijs <gijs@users.noreply.huggingface.co>

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  1. .gitattributes +59 -0
  2. DeeplyNonverbalVocalization/0001.tar +3 -0
  3. DeeplyNonverbalVocalization/0002.tar +3 -0
  4. DeeplyNonverbalVocalization/info.md +32 -0
  5. EmoGator/0001.tar +3 -0
  6. EmoGator/0002.tar +3 -0
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  36. EmoGator/0032.tar +3 -0
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  38. EmoGator/info.md +22 -0
  39. Expresso/0001.tar +3 -0
  40. Expresso/0002.tar +3 -0
  41. Expresso/0003.tar +3 -0
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+ # Deeply Nonverbal Vocalization Dataset
2
+
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+ **Source:** https://www.openslr.org/99/
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+
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+ **License:** CC BY-NC-ND 4.0
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+
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+ **Description:** The Vocal Characterizer Dataset is a human nonverbal vocal sound dataset consisting of approximately 56.7 hours of short clips from 1,419 speakers, crowdsourced by the general public in South Korea. This is a subset (~5%) of the full dataset.
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+
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+ **Classes (16):**
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+ - teeth-chattering
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+ - teeth-grinding
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+ - tongue-clicking
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+ - nose-blowing
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+ - coughing
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+ - yawning
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+ - panting
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+ - crying
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+ - laughing
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+ - sneezing
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+ - moaning
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+ - screaming
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+
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+ **Format:** FLAC 48kHz 16-bit mono
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+
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+ **Total samples:** 726
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+ **Total tar files:** 2
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+
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+ **Reference:** Deeply Inc. Vocal Characterizer Dataset
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+ # EmoGator Dataset
2
+
3
+ **Source:** https://github.com/fredbuhl/EmoGator
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+
5
+ **License:** Apache 2.0
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+
7
+ **Description:** The EmoGator dataset consists of 32,130 non-speech vocal bursts (for example: laughter, sighs, moans, and groans) in 30 emotion categories. The dataset contains 357 contributors, 30 vocal burst categories, and 3 instances of each category per contributor, providing a balanced dataset with 90 samples per contributor.
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+
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+ **Emotion Categories (30):**
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+ - Adoration, Amusement, Anger, Awe, Confusion
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+ - Contempt, Contentment, Desire, Disappointment, Disgust
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+ - Distress, Ecstasy, Elation, Embarrassment, Fear
13
+ - Guilt, Interest, Neutral, Pain, Pride
14
+ - Realization, Relief, Romantic Love, Sadness, Serenity
15
+ - Shame, Surprise (Negative), Surprise (Positive), Sympathy, Triumph
16
+
17
+ **Format:** FLAC 48kHz 16-bit mono
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+
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+ **Total samples:** 32130
20
+ **Total tar files:** 33
21
+
22
+ **Reference:** https://arxiv.org/abs/2301.00508
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