license:gpl-3.0 size_categories:1K<n<10K format:csv modality:tabular modality:text library:datasets library:pandas library:mlcroissant library:polars region:us
---
license: gpl-3.0
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The data consists of transcriptions of audio tracks from YouTube videos. 

The addresses of the videos were collected using the youtube-data-api-v3. Similarly, transcriptions of the audio tracks of the videos were collected. 

Each video was divided into chunks of 250 words, which on average corresponds to 1.5 minutes of dialogue time. 

Each chunk was marked by the LLaMA 3 70B Instruct model upon request

```1. Extract the speaker's speech and enclose it in [speaker_1_speech] [/speaker_1_speech] tags.
2. Extract another speaker's speech and enclose it in [speaker_2_speech] [/speaker_2_speech] tags.
3. Evaluate the speaker's speech using the Pleasure-Arousal-Dominance (PAD) model, with scores ranging from -10 to 10 for each dimension:
   - Pleasure: -10 (extreme displeasure) to 10 (extreme pleasure)
   - Arousal: -10 (completely calm) to 10 (extremely excited)
   - Dominance: -10 (totally submissive) to 10 (totally dominant)
   Enclose the PAD scores in [speaker_1_pad] [/speaker_1_pad] tags, separated by commas.
4. Evaluate another speaker's speech using the same PAD model and scoring system.
   Enclose the PAD scores in [speaker_2_pad] [/speaker_2_pad] tags, separated by commas.

Text to analyze:
{chunk}

Ensure that you only extract relevant dialogue and provide accurate PAD scores based on the emotional content of the speech. If no relevant dialogue is found, return "No relevant dialogue found."

Example output format:
[speaker_1_speech]speaker's extracted speech[/speaker_1_speech]
[speaker_1_pad]5,-2,7[/speaker_1_pad]
[speaker_2_speech]another speaker's extracted speech[/speaker_2_speech]
[speaker_2_pad]-1,3,-4[/speaker_2_pad]

