README.md

---
language:
- en
license: cc-by-4.0
task_categories:
- text-to-speech
- automatic-speech-recognition
tags:
- tts
- speech
- audio
- asr
- english
- conversational
pretty_name: raw-emocean
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
---

# raw-emocean

Large-scale English speech dataset for text-to-speech (TTS) model training. Designed for autoregressive TTS architectures (TADA, CSM, VALL-E style models).

## Dataset Summary

| Metric | Value |
|--------|-------|
| Parquet shards | 7 |
| Segment duration | 3–8 seconds |
| Sample rate | 24,000 Hz (mono) |
| ASR engine | NVIDIA Parakeet TDT 0.6B v3 |
| Format | Parquet with embedded audio |

## Dataset Schema

| Column | Type | Description |
|--------|------|-------------|
| `audio` | `Audio` | Waveform array + sampling rate (24kHz mono) |
| `text` | `string` | ASR transcript (Parakeet TDT 0.6B v3, no normalization) |
| `duration` | `float64` | Segment duration in seconds |
| `snr` | `float64` | Estimated signal-to-noise ratio (dB) |
| `video_id` | `string` | YouTube video identifier |
| `source_url` | `string` | Full source URL |
| `start_time` | `float64` | Segment start offset in source audio (seconds) |
| `end_time` | `float64` | Segment end offset in source audio (seconds) |

## Quick Start

```python
from datasets import load_dataset

# Stream large dataset without downloading entirely
ds = load_dataset("somu9/raw-emocean", split="train", streaming=True)
for sample in ds:
    print(sample["text"])
    break

# Load full dataset
ds = load_dataset("somu9/raw-emocean", split="train")
sample = ds[0]
audio_array = sample["audio"]["array"]       # numpy array
sample_rate = sample["audio"]["sampling_rate"]  # 24000
transcript = sample["text"]

# Filter by duration or SNR
clean = ds.filter(lambda x: x["snr"] > 30 and x["duration"] > 4)
```

## Data Collection Pipeline

```
YouTube Audio
    │
    ▼
yt-dlp download (WAV 24kHz)
    │
    ▼
Voice Activity Detection (Silero VAD)
    │  threshold=0.5, min_speech=500ms, min_silence=300ms
    ▼
Segment Extraction (3–8s segments)
    │
    ▼
Quality Filters
    │  ├─ SNR > 25 dB
    │  ├─ Clipping < 0.1%
    │  ├─ Speaker overlap detection (pitch bimodality)
    │  └─ Music detection (spectral flatness + flux)
    │
    ▼
ASR Transcription (Parakeet TDT 0.6B v3)
    │  batch_size=128, CUDA
    ▼
manifest.csv + audio/*.wav
```

## Quality Assurance

- **SNR filtering**: Segments below 25 dB signal-to-noise ratio are discarded
- **Clipping detection**: Segments with >0.1% of samples at peak amplitude are removed
- **Speaker overlap**: Pitch-based bimodality detection removes segments with simultaneous speakers
- **Music detection**: Spectral flatness and flux analysis removes segments with background music
- **Minimum length**: Segments shorter than 3 seconds are discarded
- **Empty transcript filter**: Segments where ASR produces fewer than 5 characters are removed

## Intended Use

- Pre-training autoregressive TTS models (TADA, CSM, VALL-E, SoundStorm)
- Fine-tuning speech synthesis models
- Speech representation learning
- ASR training data augmentation

## Limitations

- Transcripts are ASR-generated (Parakeet) — not human-verified, expect ~5% error rate
- Audio sourced from YouTube — variable recording conditions across sources
- No speaker identity labels — segments are not diarized
- No emotion or prosody annotations
- English only

## Citation

If you use this dataset, please cite:

```bibtex
@dataset{raw-emocean_2026,
  title = {raw-emocean: English Speech Dataset for TTS Training},
  author = {Dataset Contributors},
  year = {2026},
  url = {https://huggingface.co/datasets/somu9/raw-emocean}
}
```

## License

CC-BY-4.0

---

*Last updated: 2026-04-25 14:27 UTC*