Datasets:

PleasedPenguin
/

naturalvoice_737h_16k

license: other
language:
  - en
task_categories:
  - text-to-speech
  - automatic-speech-recognition
  - audio-classification
pretty_name: NaturalVoices Restored (16 kHz, Sidon + UTMOS-filtered)
size_categories:
  - 100K<n<1M
tags:
  - speech
  - audio
  - voice
  - speech-restoration
  - sidon
  - utmos
  - english
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
dataset_info:
  features:
    - name: file_name
      dtype: string
    - name: Document
      dtype: string
    - name: Part Number
      dtype: int64
    - name: DNSMOSPro
      dtype: float64
    - name: gender
      dtype: string
    - name: ASR_CONF
      dtype: float64
    - name: SNR
      dtype: float64
    - name: NUM_SPKS
      dtype: int64
    - name: Arousal
      dtype: float64
    - name: Dominance
      dtype: float64
    - name: Valence
      dtype: float64
    - name: Neutral
      dtype: float64
    - name: Angry
      dtype: float64
    - name: Sad
      dtype: float64
    - name: Happy
      dtype: float64
    - name: Emotion
      dtype: string
    - name: total time
      dtype: float64
    - name: start
      dtype: float64
    - name: end
      dtype: float64
    - name: text
      dtype: string
    - name: utmos
      dtype: float64
    - name: utmos_orig
      dtype: float64
    - name: age
      dtype: float64
    - name: wpm
      dtype: float64
    - name: audio
      dtype:
        audio:
          sampling_rate: 16000
  splits:
    - name: train
      num_bytes: 202098673799.464
      num_examples: 494903
  download_size: 169904567123
  dataset_size: 202098673799.464

NaturalVoices — Sidon-Restored, UTMOS-Filtered (16 kHz)

High-quality English speech derived from NaturalVoices_VC_870h (JHU SmileLab), restored with Sidon v0.1 and kept only where restoration measurably improved perceptual quality (UTMOS gate). Each clip ships with rich per-utterance metadata (transcript, speaker age/gender, speaking rate, emotion, and quality scores) so it is ready for TTS / voice-cloning / ASR / paralinguistic research.

494,903 clips · 736.7 hours (clips ≥ 3.0 s)
16 kHz, mono, float32, embedded audio (plays in the HF viewer)
48 kHz twin: PleasedPenguin/naturalvoice_737h_48k — same utterances, different sampling rate

Quick start

from datasets import load_dataset

ds = load_dataset("PleasedPenguin/naturalvoice_737h_16k", split="train")
ex = ds[0]
print(ex["text"], ex["age"], ex["gender"], ex["wpm"], ex["utmos"])
audio = ex["audio"]            # {'array': np.ndarray, 'sampling_rate': 16000}

How it was built

Restore — every source clip is passed through Sidon v0.1 (deterministic feed-forward restoration, no sampling) to a 48 kHz waveform; the 16 kHz version is a clean downsample of that same restored signal.
Quality gate — UTMOS (utmos22_strong) is scored on the original and the restored clip. A clip is kept only if utmos > utmos_orig (restoration actually helped). Both scores are stored so you can re-threshold.
Label — speaker age (audeering wav2vec2-large-robust-24-ft-age-gender) and wpm / speaking rate (Qwen3 Forced Aligner 0.6B: aligned word count over the first-word-onset → last-word-offset span) are added. The original NaturalVoices annotations (emotion, SNR, DNSMOS, etc.) are carried through unchanged.

Columns

Column	Description
`audio`	Sidon-restored waveform (16 kHz, float32)
`text`	Transcript
`file_name`, `Document`, `Part Number`	Clip identifiers
`total time`, `start`, `end`	Clip duration / source offsets (seconds)
`gender`	Speaker gender (original NaturalVoices label)
`age`	Predicted speaker age in years (audeering wav2vec2)
`wpm`	Speaking rate (words per minute), forced-aligned
`utmos`	UTMOS of the restored clip
`utmos_orig`	UTMOS of the original clip (kept only when `utmos > utmos_orig`)
`DNSMOSPro`, `SNR`, `ASR_CONF`, `NUM_SPKS`	Original NaturalVoices quality annotations
`Arousal`, `Dominance`, `Valence`	Original emotion attributes
`Neutral`, `Angry`, `Sad`, `Happy`, `Emotion`	Original emotion labels

Dataset statistics

Speaking rate (wpm) — mean 175, median 171, p5–p95 104–261.

Age — mean 36.5 yr, median 33.7, p10–p90 22.8–55.4.

Gender — model vs. original-label agreement 94.2% (the original gender label is the one shipped in this dataset; the predictor was used only for cross-checking).

Source & license

Derived from NaturalVoices_VC_870h (JHU SmileLab). Audio has been restored/transformed; please honor the terms of the original NaturalVoices release for any redistribution or use. Restoration: Sidon v0.1. Quality metric: UTMOS (tarepan/SpeechMOS).

Citation

If you use this data, please cite the original NaturalVoices dataset:

@misc{du2025naturalvoiceslargescalespontaneousemotional,
      title={NaturalVoices: A Large-Scale, Spontaneous and Emotional Podcast Dataset for Voice Conversion},
      author={Zongyang Du and Shreeram Suresh Chandra and Ismail Rasim Ulgen and Aurosweta Mahapatra and Ali N. Salman and Carlos Busso and Berrak Sisman},
      year={2025},
      eprint={2511.00256},
      archivePrefix={arXiv},
      primaryClass={eess.AS},
      url={https://arxiv.org/abs/2511.00256},
}