README.md

---
language:
- en
license: cc-by-4.0
task_categories:
- text-to-speech
- audio-classification
tags:
- tts
- speech
- emotion
- prosody
pretty_name: emocean
size_categories:
- 1K<n<10K
---

# emocean

Emotionally expressive English TTS dataset with speaker IDs, prosody features, and emotion labels.

## Dataset Summary

| Metric | Value |
|--------|-------|
| Total segments | 2,261 |
| Total duration | 3.39 hours |
| Speakers | 13 |
| Sources | 12 videos |
| Avg segment duration | 5.4s |
| Duration range | 3.0s - 8.0s |
| Sample rate | 24kHz mono |
| Format | Parquet with embedded audio |

## Emotion Distribution

| Emotion | Count | Percentage |
|---------|-------|------------|
| neutral | 1894 | 83.8% |
| happy | 188 | 8.3% |
| sad | 148 | 6.5% |
| disgusted | 22 | 1.0% |
| fearful | 5 | 0.2% |
| angry | 2 | 0.1% |
| surprised | 2 | 0.1% |

## Speaker Distribution

| Speaker | Segments |
|---------|----------|
| lex_fridman | 551 |
| pavel_durov | 397 |
| jeff_kaplan | 359 |
| norman_ohler | 256 |
| paul_rosolie | 122 |
| julia_shaw | 122 |
| jensen_huang | 118 |
| dan_houser | 97 |
| lars_brownworth | 87 |
| michael_levin | 68 |
| peter_steinberger | 35 |
| irving_finkel | 31 |
| david_kirtley | 18 |

## Dataset Structure

| Column | Type | Description |
|--------|------|-------------|
| `audio` | Audio | Waveform + sampling rate (24kHz) |
| `text_verbatim` | string | Verbatim transcript with fillers (umm, uh, [laughter], etc.) |
| `text_verbatim_normalized` | string | Verbatim text with numbers/abbreviations expanded (keeps fillers) |
| `duration` | float | Segment duration in seconds |
| `snr` | float | Signal-to-noise ratio (dB) |
| `speaker_id` | string | Speaker cluster ID (WavLM embeddings) |
| `emotion` | string | Speech emotion label (emotion2vec+ large, 9 categories) |
| `pitch_mean` | float | Mean F0 frequency (Hz) |
| `pitch_std` | float | F0 standard deviation (Hz) |
| `energy_mean` | float | Mean RMS energy |
| `energy_std` | float | RMS energy standard deviation |
| `speaking_rate` | float | Words per second |
| `video_id` | string | YouTube video ID |
| `source_url` | string | Source URL |
| `start_time` | float | Segment start time in source (seconds) |
| `end_time` | float | Segment end time in source (seconds) |

## Usage

```python
from datasets import load_dataset

ds = load_dataset("somu9/emocean", split="train")

# Access a sample
sample = ds[0]
print(sample["audio"])       # {'path': ..., 'array': [...], 'sampling_rate': 24000}
print(sample["text"])
print(sample["emotion"])
print(sample["speaker_id"])

# Filter by emotion
happy = ds.filter(lambda x: x["emotion"] == "happy")

# Filter by speaker
spk0 = ds.filter(lambda x: x["speaker_id"] == "spk_0000")
```

## Collection Pipeline

1. **Download** YouTube audio via yt-dlp
2. **VAD** segmentation (Silero VAD)
3. **Quality filter** — SNR > 25dB, clipping < 0.1%, music score < 0.5, boundary clip detection
4. **Transcribe** (Whisper large-v3)
5. **Enrich** — speaker embeddings (WavLM), prosody extraction, emotion classification (emotion2vec+ large)

## License

CC-BY-4.0

---

*Last updated: 2026-04-23 14:07 UTC*