TashaSkyUp/audio-quality-dataset-nfe4-30-step2

Audio Quality Dataset: NFE 4-30 Step 2

Overview

This dataset publishes synthetic speech artifacts and derived spectrograms used for repo-local audio-quality experiments.

At a glance:

• 2800 synthetic runs
• 200 short English prompt sentences
• 14 NFE settings: 4, 6, 8, ..., 30
• fixed seed 1024

Each row represents one synthetic run and includes:

• prompt text
• raw synthetic WAV
• processed synthetic WAV
• spectrogram PNG
• NFE value
• procedural weak label

Here, NFE means the number of LongCat inference / ODE steps used during synthesis.

Synthetic Data Disclosure

Everything in this dataset is synthetic or derived from synthetic artifacts.

• The prompt text comes from a repo-local TSV used for this experiment.
• Raw speech was generated with meituan-longcat/LongCat-AudioDiT-3.5B.
• Processed speech was produced by applying one ClearVoice MossFormer2_SR_48K pass.
• Spectrogram PNGs were rendered from those processed synthetic audio files.
• No human speech recordings are distributed in this dataset bundle.
• Weak labels are procedural NFE-derived buckets, not human annotations.

What One Row Contains

The manifest rows include:

• run_id
• sentence_id
• category
• text
• nfe
• seed
• weak_label
• sentence_file
• run_root
• raw_output_dir
• processed_output_dir
• raw_wav
• processed_wav
• spectrogram_png

Primary files in the repo:

• manifest.tsv
• manifest.jsonl
• manifest_remote_ready.tsv
• summary.json
• sentences/
• runs/

Under runs/<run_id>/ you get the actual synthetic artifacts for that run.

How The Dataset Was Created

This dataset was built in three stages.

1. Prompt and manifest build

Source prompt sheet:

• tmp/audio_quality_dataset/short_sentences_200.tsv

Prompt structure:

• 20 categories
• 10 sentences per category
• 200 total sentences

Manifest settings:

• NFE start: 4
• NFE stop: 30
• NFE step: 2
• seed: 1024

2. Synthetic audio generation

For each manifest row:

• raw synthetic speech: LongCat-AudioDiT-3.5B
• processed synthetic speech: one ClearVoice MossFormer2_SR_48K pass

3. Spectrogram rendering

Each processed synthetic clip was rendered as:

• grayscale PNG
• 1024 x 512
• fixed dB range [-120, 0]
• n_fft = 1024
• hop = 256

Repo revision for this dataset export:

• 064a6bd4df88b3222459350d74341933dcfda075

Weak Label Semantics

The weak labels are procedural buckets derived only from NFE bands:

• bad_like: nfe <= 12
• mid_band: 14 <= nfe <= 22
• good_like: nfe >= 24

Counts:

• bad_like: 1000
• mid_band: 1000
• good_like: 800

These labels are not human perceptual judgments. They are synthetic proxy labels designed for downstream experiments.

Relationship To The Published Model

This dataset was used to train the published autoencoder:

• TashaSkyUp/audio-quality-ae-spectrogram-patches-gpu3090-best-20260409

That model uses:

• spectrogram_png inputs only
• sentence-level train/validation splitting before patch extraction

It does not use the dataset weak labels for training.

If you build new models from this dataset, splitting by sentence_id is the safer default to avoid text leakage across train and validation.

Limitations

This dataset should not be treated as:

• a corpus of real human speech
• a benchmark with human-rated quality labels
• a general-purpose speech-quality dataset

It primarily captures the behavior of one synthetic generation path:

• LongCat synthesis
• one ClearVoice post-pass
• fixed spectrogram rendering settings

Licensing And Attribution

This dataset is marked license: other because it is a repo-local experiment export and does not assert a new standalone permissive license over the generated artifacts.

Synthetic generation in this workflow depended on:

• LongCat-AudioDiT from Meituan, specifically meituan-longcat/LongCat-AudioDiT-3.5B
• ClearVoice, specifically MossFormer2_SR_48K

This Hugging Face dataset repo is not an official upstream release of either dependency. Check upstream terms before redistributing or reusing generated artifacts at scale.