Datasets:

somu9
/

raw-emocean

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

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:

@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