README.md

---
license: cc-by-sa-4.0
task_categories:
- audio-to-audio
- automatic-speech-recognition
language:
- en
tags:
- emotion
- speech
- TTS
- emotion-tagging
---

# Moody Girl: Emotion-Tagged DailyTalk Dataset

This dataset is an emotion-tagged version of the [DailyTalk](https://github.com/keonlee9420/DailyTalk) dataset, enhanced with emotion labels and prosodic features for Text-to-Speech (TTS) and emotion recognition tasks.

## Dataset Description

This dataset contains emotion-labeled speech segments with corresponding text transcriptions. Each segment is tagged with an emotion label and includes comprehensive acoustic features extracted from the audio.

### Data Source
Based on [DailyTalkContiguous](https://github.com/keonlee9420/DailyTalk), which uses stereo recordings where the two speakers in each conversation are placed on the left and right channels respectively.

### Data Generation Process

1. **Audio Segmentation**: Audio files were segmented using the word-level timestamps from the original DailyTalk dataset.

2. **Feature Extraction**: For each audio segment, the following features were extracted:
   - **VAD Features**: Arousal, dominance, valence scores
   - **Prosodic Features**: Energy, speech rate, zero-crossing rate, pitch variation
   - **Audio Features**: RMS energy, fundamental frequency (f0), speech rate

3. **Emotion Tagging**: Each segment was tagged with an emotion label based on the combination of VAD features and prosodic characteristics. Common tags include:
   - depressed
   - shouting
   - whispering
   - soft tone
   - worried
   - calm
   - sad
   - And more...

4. **Categorization**: VAD and prosodic features were bucketed into LOW/MID/HIGH categories for easier analysis.

## Dataset Structure

```
.
├── DailyTalkContiguous/
│   ├── data_stereo/
│   │   ├── 0.wav, 1.wav, ...
│   │   └── 0.json, 1.json, ...
│   └── dailytalk.jsonl
└── transcript.jsonl
```

### transcript.jsonl Format

Each line in `transcript.jsonl` contains a JSON object with the following fields:

- `segment_id`: Unique identifier for the segment
- `audio_file`: Source audio file name
- `channel`: Left or right channel in stereo recording
- `start`: Start time in seconds
- `end`: End time in seconds
- `duration`: Segment duration in seconds
- `vad`: VAD features (arousal, dominance, valence)
- `features`: Audio features (rms, zcr, f0_mean, f0_std, speech_rate)
- `text_raw`: Original transcript text
- `text`: Processed transcript text
- `audio_path`: Full path to audio file
- `vad_bucket`: Bucketed VAD categories
- `prosody_bucket`: Bucketed prosodic categories
- `tag`: Emotion label
- `tagged_text`: Text with emotion tag prepended

Example:
```json
{
  "segment_id": 0,
  "audio_file": "0.wav",
  "channel": "left",
  "start": 1.634,
  "end": 5.63,
  "duration": 3.996,
  "vad": {
    "arousal": 0.002,
    "dominance": 0.0,
    "valence": 0.0
  },
  "features": {
    "rms": 0.106,
    "zcr": 0.087,
    "f0_mean": 152.55,
    "f0_std": 46.69,
    "speech_rate": 4.87
  },
  "text_raw": "I'm figuring out all of my budgets.",
  "text": "I'm figuring out all of my budgets.",
  "audio_path": "DailyTalkContiguous/data_stereo/0.wav",
  "vad_bucket": {
    "arousal": "LOW",
    "dominance": "LOW",
    "valence": "LOW"
  },
  "prosody_bucket": {
    "energy": "MID",
    "rate": "MID",
    "zcr": "LOW",
    "pitch_var": "HIGH"
  },
  "tag": "depressed",
  "tagged_text": "(depressed) I'm figuring out all of my budgets."
}
```

## Usage

```python
import json

# Load the transcript data
with open('transcript.jsonl', 'r') as f:
    segments = [json.loads(line) for line in f]

# Access a segment
segment = segments[0]
print(f"Text: {segment['text']}")
print(f"Emotion: {segment['tag']}")
print(f"Audio file: {segment['audio_path']}")
print(f"Time range: {segment['start']:.2f}s - {segment['end']:.2f}s")
```

## Use Cases

- **Emotion Recognition Training**: Train models to recognize emotions from speech
- **Emotion-Controllable TTS**: Generate speech with specific emotional characteristics
- **Prosody Analysis**: Study the relationship between emotion and speech prosody
- **Data Augmentation**: Use emotion tags for synthetic data generation

## License

This dataset inherits the CC-BY-SA 4.0 license from the original [DailyTalk](https://github.com/keonlee9420/DailyTalk) dataset.

## Citation

If you use this dataset, please cite the original DailyTalk dataset:

```bibtex
@dataset{dailytalk,
  title={DailyTalk: A High-Quality Multi-Turn Dialogue Corpus for Conversational Speech Synthesis},
  author={Lee, Keon and Yang, Hyeongseok and Park, Jiyoun and Choi, Seong-Hoon and Kim, Nam Soo},
  year={2021},
  publisher={GitHub},
  howpublished={\url{https://github.com/keonlee9420/DailyTalk}}
}
```

## Acknowledgments

This dataset is derived from the [DailyTalk](https://github.com/keonlee9420/DailyTalk) project by Keon Lee et al. The original dataset provides word-level timestamps for conversational speech, which made this emotion-tagging extension possible.