Datasets:

webxos
/

audioform_dataset

	---
	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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	[![Hugging Face](https://img.shields.io/badge/Hugging_Face-🤗_webxos-FFD21E?style=for-the-badge&logo=huggingface&logoColor=white)](https://huggingface.co/webxos)
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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.