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audioform_dataset

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Image-to-Text
Text-to-Image
Audio Classification
Modalities:
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Audio
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imagefolder
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audio
image
multimodal
visualization
audio-visualization
3d-visualization
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metadata
license: mit
task_categories:
  - image-to-text
  - text-to-image
  - audio-classification
  - image-classification
  - tabular-classification
tags:
  - audio
  - image
  - multimodal
  - visualization
  - audio-visualization
  - 3d-visualization
  - synthetic
  - proof-of-concept
  - frequency-estimation
  - generative-audio
  - music-visualization

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Audioform_Dataset_v1

This dataset is the very first output from AUDIOFORM — a Three.js powered 3D audio visualization tool that turns audio files into beautiful, timestamped visual frames with rich metadata. AUDIOFORM by webXOS is available for download in the /audioform/ folder of this repo so developers can create their own similar datasets. Audio for is a synthetic harmonic oscilator that runs in HTML, think of it as the "Hello World" / MNIST-style dataset application for audio-to-visual multimodal machine learning.

This dataset contains 10 captured frames from a short uploaded WAV file (played at 1× speed), together with per-frame metadata including dominant frequency, timestamp, and capture info.

Dataset Structure 

audioform_dataset/
├── images/
│   ├── frame_0001.png
│   ├── frame_0002.png
│   └── ... (10 PNG frames total)
├── metadata.csv          # Main metadata file (Hugging Face viewer uses this)
└── README.md

| Column        | Type    | Description                                                                 | Example Value                     |
|---------------|---------|-----------------------------------------------------------------------------|-----------------------------------|
| `file_name`   | string  | Relative path to the visualization PNG (required by Hugging Face)           | `images/frame_0001.png`           |
| `frame_id`    | int     | Sequential frame number (0-based)                                           | 0, 1, 2, …, 9                     |
| `timestamp`   | float   | Time in seconds when the frame was captured from the audio                  | 5.365, 6.219, 9.504               |
| `frequency`   | int     | Dominant / main detected audio frequency at capture time (Hz)               | 0 (in this tiny sample)           |
| `time_scale`  | int     | Playback speed multiplier used during visualization                         | 1                                 |
| `capture_date`| string  | UTC ISO timestamp when the frame was rendered                               | 2026-01-13T19:57:36.427Z          |

See how fast a tiny diffusion model / GAN / LoRA can memorize & regenerate these exact 10 styles. Use the frames as style references for ControlNet, IP-Adapter, or fine-tuning SD to adopt this neon 3D audio-viz aesthetic.

   This dataset shows the **format** AUDIOFORM produces.  
   → Feed it real music, voices, field recordings, synths  
   → Generate 1k–100k+ frames  
   → Add labels (genre, instrument, mood, multiple freq peaks…)  
   → Unlock serious applications:

   - Music video auto-generation  
   - Visual audio classifiers  
   - Audio-conditioned image/video generation  
   - Interactive music → 3D art installations  
   - Novel multimodal music understanding models

Dataset Description

This dataset was generated using AUDIOFORM, a 3D audio visualization system.

  • Total Frames: 10
  • Generation Date: 2026-01-13
  • Audio Type: Uploaded WAV File
  • Time Scaling: 1x

Dataset Structure

  • images/: Contains all captured frames in PNG format
  • metadata.csv: Contains classification data for each frame

Metadata Columns

  • file_name: Relative path to the image file (e.g., images/frame_0001.png) - REQUIRED for Hugging Face
  • frame_id: Unique identifier for each frame
  • timestamp: Time in seconds when frame was captured
  • frequency: Audio frequency at capture time (Hz)
  • time_scale: Playback speed multiplier
  • capture_date: ISO date string of capture

Intended Use

This dataset is intended for training machine learning models on audio visualization patterns, waveform classification, or generative AI tasks.