| --- |
| 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 |
| --- |
| |
| [](https://webxos.netlify.app) |
| [](https://github.com/webxos/webxos) |
| [](https://huggingface.co/webxos) |
| [](https://x.com/webxos) |
|
|
| <div style=" |
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| AAA UUUUUUUU UUUUUUUUDDDDDDDDDDDDD IIIIIIIIII OOOOOOOOO FFFFFFFFFFFFFFFFFFFFFF OOOOOOOOO RRRRRRRRRRRRRRRRR MMMMMMMM MMMMMMMM |
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| AAAAAAA AAAAAAA UUUUUUUUU DDDDDDDDDDDDD IIIIIIIIII OOOOOOOOO FFFFFFFFFFF OOOOOOOOO RRRRRRRR RRRRRRRMMMMMMMM MMMMMMMM |
| </pre> |
| </div> |
| |
| ## 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. |
|
|
| ## License |
|
|
| MIT |
|
|
