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feat(skills): add audio visualizer effect with extraction script (#168)
* feat(skills): add audio visualizer effect with extraction script Adds reactive audio visualization patterns for HyperFrames: Script: extract-audio-data.py pre-extracts per-frame RMS amplitude and frequency band data via ffmpeg. Uses a 4096-sample FFT window for clean frequency resolution and per-band normalization across the full track so treble activity is visible alongside louder bass. Patterns: spectrum bars, mirrored waveform, pulsing circle, circular visualizer, background glow — all Canvas 2D driven from the GSAP timeline via tl.call() at each frame. Includes smoothing helper, band count guide, band ordering rules (horizontal: low-left high-right), and combining patterns section. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * refactor(skills): replace prescriptive examples with data model + motion principles Removes five hardcoded draw functions that would get copy-pasted verbatim. Replaces with: - Clear data model docs (what rms and bands mean, how to index) - Rendering approach setup for Canvas 2D, WebGL/Three.js, and DOM - Motion principles (smoothing, value mapping, what makes it feel good) - Spatial mapping conventions (low-left/high-right, etc) The LLM invents the visualization; the skill teaches the data contract and motion constraints. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * fix(skills): off-by-one in band slicing, add data loading, fix trigger - Fix exclusive slice end: high_bin clamped to n_bins (not n_bins-1) so the last FFT bin in each band is included - Add data loading section to skill doc (inline and fetch patterns) - Fix example JSON to show frame 0 at time 0.0 - Update description to trigger when audio is analyzed and music detected Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * refactor(skills): require numpy, fix bugs, clean up skill doc Script rewrite: - numpy is now required (pure-Python DFT was unusable for real files) - Use np.frombuffer instead of struct.unpack (~10x less memory) - Precompute Hann window and band edges (were recalculated every frame) - Extract SAMPLE_RATE as module-level constant - Clamp band bins to prevent max() on empty slice - Validate --fps and --bands inputs Skill doc fixes: - Fix fetch loading example (was null ref on sync for-loop) - Remove redundant Canvas 2D section (was duplicate of Step 3) - Fix opening line (said "Canvas 2D" but doc covers 3 approaches) - Fix undefined W/H in example Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> --------- Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
This commit is contained in:
Vance Ingalls
co-authored by
Claude Opus 4.6
Claude Opus 4.6
parent
5f3488e996
commit
116e6aa8e0
@@ -1,16 +1,17 @@
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---
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name: gsap-effects
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description: Ready-made GSAP animation effects for HyperFrames compositions. Use when adding typewriter text, text reveals, or character-by-character animation to a composition. Reference files contain copy-paste patterns.
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description: Ready-made animation effects for HyperFrames compositions. Use when adding typewriter text, text reveals, character-by-character animation, audio visualizations, spectrum bars, waveform displays, or any reactive audio-driven animation to a composition. Also use when audio has been analyzed or transcribed in the current session and music is detected — the audio visualizer can enhance the composition with reactive visuals. Reference files contain patterns and data contracts.
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---
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# GSAP Effects
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Drop-in animation patterns for HyperFrames compositions. Each effect is a self-contained reference with the HTML, CSS, and GSAP code needed to add it to a composition.
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Drop-in animation patterns for HyperFrames compositions. Each effect is a self-contained reference with the HTML, CSS, and code needed to add it to a composition.
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These effects follow all HyperFrames composition rules — deterministic, no randomness, timelines registered via `window.__timelines`.
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## Available Effects
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| Effect | File | Use when |
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| ---------- | -------------------------------- | ---------------------------------------------------------------------------- |
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| Typewriter | [typewriter.md](./typewriter.md) | Text should appear character by character, with or without a blinking cursor |
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| Effect | File | Use when |
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| ---------------- | -------------------------------------------- | ------------------------------------------------------------------------------------------------------------------- |
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| Typewriter | [typewriter.md](./typewriter.md) | Text should appear character by character, with or without a blinking cursor |
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| Audio Visualizer | [audio-visualizer.md](./audio-visualizer.md) | Reactive bars, waveforms, circles, or glow that respond to audio. Includes extraction script and Canvas 2D patterns |
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# Audio Visualizer
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Reactive audio visualizations for HyperFrames compositions. Pre-extracts amplitude and frequency data from an audio file, then drives rendering from the GSAP timeline.
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## Why Pre-Extraction
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HyperFrames renders frame-by-frame in headless Chrome — there's no audio playing during rendering, so the Web Audio API's real-time `AnalyserNode` won't work. Instead, extract all audio data before the composition runs and bake it as a static JSON array. The composition reads the array by frame index. This is fully deterministic and seekable.
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## Step 1: Extract Audio Data
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```bash
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python skills/gsap-effects/scripts/extract-audio-data.py audio.mp3 -o audio-data.json
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python skills/gsap-effects/scripts/extract-audio-data.py video.mp4 --fps 30 --bands 16 -o audio-data.json
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```
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Requires ffmpeg and numpy (`pip install numpy`).
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| Flag | Default | Description |
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| --------- | --------------- | -------------------------------------------------------- |
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| `--fps` | 30 | Must match the composition/render FPS |
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| `--bands` | 16 | Number of frequency bands (more = finer spectrum detail) |
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| `-o` | audio-data.json | Output path |
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The script uses a 4096-sample FFT window (not the per-frame sample count) to ensure each frequency band maps to distinct FFT bins. Bands are logarithmically spaced from 30Hz to 16kHz — the useful range for music. Each band is normalized independently across the full track so treble activity is visible even when bass is louder in absolute terms.
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## Step 2: Understanding the Data
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```json
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{
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"duration": 180.5,
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"fps": 30,
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"bands": 16,
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"totalFrames": 5415,
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"frames": [
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{ "time": 0.0, "rms": 0.0, "bands": [0.0, 0.0, 0.0, ...] },
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{ "time": 0.0333, "rms": 0.42, "bands": [0.8, 0.6, 0.3, ...] }
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]
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}
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```
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**`rms`** (0-1) — overall loudness of this frame, normalized across the full track. 0 is silence, 1 is the loudest moment in the entire audio. Use this for anything that should respond to overall energy: scaling, pulsing, glow intensity, opacity, movement speed.
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**`bands`** (array of 0-1 values) — frequency magnitudes. Each value is normalized independently for that band across the full track, so a 0.8 in treble means "this is 80% of the loudest this treble band gets anywhere in the audio" — not that treble is as loud as bass in absolute terms. This is what makes all frequency ranges visually active.
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- Index 0 = lowest bass (~30Hz). Index `n-1` = highest treble (~16kHz).
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- Low indices (0-3) react to kick drums, bass lines, sub-bass rumble.
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- Mid indices (4-9) react to vocals, guitars, synths, most melodic content.
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- High indices (10-15) react to hi-hats, cymbals, sibilance, brightness.
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## Loading the Data
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Embed the data in the composition so it's available when the timeline runs.
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```js
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// Option A: inline (small files, under ~500KB)
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const AUDIO_DATA = {
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/* paste audio-data.json contents */
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};
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setupTimeline(AUDIO_DATA);
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// Option B: fetch (large files)
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fetch("audio-data.json")
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.then((r) => r.json())
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.then((data) => {
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setupTimeline(data);
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});
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function setupTimeline(AUDIO_DATA) {
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// Register tl.call() draws here — AUDIO_DATA is guaranteed to be loaded
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for (let f = 0; f < AUDIO_DATA.totalFrames; f++) {
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tl.call(
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() => {
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draw(AUDIO_DATA.frames[f]);
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},
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[],
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f / AUDIO_DATA.fps,
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);
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}
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}
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```
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With fetch, wrap all timeline setup inside the callback so `AUDIO_DATA` is available when the `for` loop reads `totalFrames`. The fetch completes before the renderer's first seek because it waits for `window.__hf` readiness.
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## Step 3: Drive Rendering from the Timeline
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Register a `tl.call()` at every frame interval. Each call reads the pre-computed data and renders. This is deterministic and seekable — scrubbing in the studio works because each frame's draw is tied to a specific timeline position.
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## Rendering Approaches
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The data is framework-agnostic. Here's how to wire it up in each approach.
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### Canvas 2D
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Best for: bars, waveforms, circles, gradients, particles. Most common choice.
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```js
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const canvas = document.querySelector("#viz-canvas");
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const ctx = canvas.getContext("2d");
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for (let f = 0; f < AUDIO_DATA.totalFrames; f++) {
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tl.call(
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() => {
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const frame = AUDIO_DATA.frames[f];
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if (!frame) return;
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ctx.clearRect(0, 0, canvas.width, canvas.height);
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// read frame.rms and frame.bands, draw whatever you want
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},
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[],
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f / AUDIO_DATA.fps,
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);
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}
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```
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### WebGL / Three.js
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HyperFrames has a Three.js adapter that patches `THREE.Clock` for deterministic time. Create your scene normally, then update uniforms or object properties from the audio data each frame.
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```js
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// In your Three.js setup:
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const uniforms = { uBass: { value: 0 }, uMid: { value: 0 }, uRms: { value: 0 } };
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for (let f = 0; f < AUDIO_DATA.totalFrames; f++) {
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tl.call(
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() => {
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const frame = AUDIO_DATA.frames[f];
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if (!frame) return;
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uniforms.uBass.value = Math.max(frame.bands[0], frame.bands[1], frame.bands[2]);
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uniforms.uMid.value = Math.max(frame.bands[6], frame.bands[7], frame.bands[8]);
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uniforms.uRms.value = frame.rms;
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},
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[],
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f / AUDIO_DATA.fps,
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);
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}
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```
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### DOM Elements
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For simpler visualizations (a few bars, a pulsing element), you can animate DOM elements directly. Less performant than Canvas for many elements, but fine for under ~20.
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```js
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const bars = document.querySelectorAll(".bar");
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for (let f = 0; f < AUDIO_DATA.totalFrames; f++) {
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tl.call(
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() => {
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const frame = AUDIO_DATA.frames[f];
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if (!frame) return;
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bars.forEach((bar, i) => {
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bar.style.height = frame.bands[i] * 100 + "%";
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});
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},
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[],
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f / AUDIO_DATA.fps,
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);
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}
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```
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## Spatial Mapping
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When laying out frequency data spatially, follow these conventions so visualizations read naturally:
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- **Horizontal layouts**: low frequencies (bass) on the left, high frequencies (treble) on the right. Iterate the bands array left-to-right.
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- **Vertical layouts**: low frequencies at the bottom, high frequencies at the top.
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- **Circular layouts**: bass starts at the top (12 o'clock) and wraps clockwise. Mirror the bands array for a full circle.
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## Motion Principles
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### Smoothing
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Raw per-frame data changes abruptly. Blend with the previous frame for fluid motion:
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```js
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let prev = null;
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const smoothing = 0.25; // 0 = no smoothing, higher = more lag
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function smooth(f) {
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const raw = AUDIO_DATA.frames[f];
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if (!raw) return prev;
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if (!prev) {
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prev = { rms: raw.rms, bands: [...raw.bands] };
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return prev;
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}
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prev = {
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rms: prev.rms * smoothing + raw.rms * (1 - smoothing),
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bands: raw.bands.map((b, i) => prev.bands[i] * smoothing + b * (1 - smoothing)),
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};
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return prev;
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}
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```
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Lower smoothing (0.1-0.2) feels snappy and responsive — good for percussive music. Higher smoothing (0.3-0.5) feels languid and flowing — good for ambient or orchestral.
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### Value Mapping
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Audio data is 0-1 but visual properties need different ranges. Map with intention:
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- **Scale/size**: multiply by a max value. A bar's height = `bands[i] * maxHeight`. Don't let elements disappear at 0 — add a minimum: `minHeight + bands[i] * (maxHeight - minHeight)`.
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- **Opacity**: low values should still be slightly visible. `0.15 + bands[i] * 0.85` keeps elements present during quiet moments.
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- **Color intensity**: shift between a muted base and a vivid peak. Interpolate HSL lightness or RGB channels based on the value.
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- **Position/offset**: use rms to drive drift or wobble. Small movements (5-20px) feel organic; large movements look chaotic.
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### What Makes It Feel Good
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- **Bass drives the big moves.** Scale, position shifts, and glow should react to low bands. Bass is what makes a visualization feel like it's "hitting."
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- **Treble drives the detail.** Small particle movements, edge shimmer, opacity flicker. Treble adds texture without dominating.
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- **RMS drives global properties.** Background brightness, overall scale, color warmth. It's the "energy level" of the whole frame.
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- **Don't animate everything at once.** Pick 2-3 visual properties to tie to the audio. More than that looks noisy.
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- **Quiet sections should still have life.** A completely static frame during a soft passage looks broken. Keep minimum values above zero.
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## Band Count Guide
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| Bands | Detail level | Good for |
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| ----- | ------------ | ------------------------------------------- |
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| 4 | Low | Simple pulsing, background glow |
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| 8 | Medium | Bar visualizations, basic spectrum |
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| 16 | High | Detailed EQ, circular visualizers (default) |
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| 32 | Very high | Smooth curves, dense radial layouts |
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More bands = larger JSON file. 16 is a good default.
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## Layering
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Layer multiple canvases with CSS z-index for depth:
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```html
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<canvas id="bg-layer" style="position:absolute;top:0;left:0;z-index:1;"></canvas>
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<canvas id="main-layer" style="position:absolute;top:0;left:0;z-index:2;"></canvas>
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```
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A background layer driven by bass/rms and a foreground layer driven by individual bands creates depth without complexity.
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## HyperFrames Integration Notes
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- The `<canvas>` element needs `data-start`, `data-duration`, and `data-track-index` like any other clip
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- Set canvas `width`/`height` attributes to match the composition dimensions (1920x1080)
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- The extraction script FPS must match the render FPS (default: 30)
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- For large audio files, the JSON can be several MB — load via `fetch` rather than inlining
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- Each canvas in the composition needs its own `data-track-index` — don't put multiple canvases on the same track
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@@ -0,0 +1,188 @@
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#!/usr/bin/env python3
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"""
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Extract per-frame audio visualization data from an audio or video file.
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Outputs JSON with RMS amplitude and frequency band data at the target FPS,
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ready to embed in a HyperFrames composition.
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Usage:
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python extract-audio-data.py input.mp3 -o audio-data.json
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python extract-audio-data.py input.mp4 --fps 30 --bands 16 -o audio-data.json
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Requirements:
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- Python 3.9+
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- ffmpeg (for decoding audio)
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- numpy (pip install numpy)
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"""
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import argparse
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import json
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import subprocess
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import sys
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import numpy as np
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# ---------------------------------------------------------------------------
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# FFT parameters
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#
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# A 4096-sample window gives ~10.8 Hz per bin at 44100Hz — enough to resolve
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# low-frequency bands cleanly. The per-frame audio slice (44100/30 = 1470
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# samples at 30fps) is too small and causes low bands to map to the same bins.
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#
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# Frequency range 30Hz–16kHz covers the useful range for music. Below 30Hz is
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# sub-bass most speakers can't reproduce; above 16kHz is noise/harmonics that
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# don't contribute to perceived rhythm or melody.
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# ---------------------------------------------------------------------------
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SAMPLE_RATE = 44100
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FFT_SIZE = 4096
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MIN_FREQ = 30.0
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MAX_FREQ = 16000.0
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def decode_audio(path: str) -> np.ndarray:
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"""Decode audio to mono float32 samples via ffmpeg."""
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cmd = [
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"ffmpeg", "-i", path,
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"-vn", "-ac", "1", "-ar", str(SAMPLE_RATE),
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"-f", "s16le", "-acodec", "pcm_s16le",
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"-loglevel", "error",
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"pipe:1",
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]
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result = subprocess.run(cmd, capture_output=True)
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if result.returncode != 0:
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print(f"ffmpeg error: {result.stderr.decode()}", file=sys.stderr)
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sys.exit(1)
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return np.frombuffer(result.stdout, dtype=np.int16).astype(np.float32) / 32768.0
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def compute_band_edges(n_bands: int) -> np.ndarray:
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"""Logarithmically-spaced frequency band edges from MIN_FREQ to MAX_FREQ."""
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return np.array([
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MIN_FREQ * (MAX_FREQ / MIN_FREQ) ** (i / n_bands)
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for i in range(n_bands + 1)
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])
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def compute_fft_bands(
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windowed: np.ndarray, freq_per_bin: float, n_bins: int,
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band_edges: np.ndarray, n_bands: int,
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) -> np.ndarray:
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"""Compute peak magnitude in logarithmically-spaced frequency bands."""
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magnitudes = np.abs(np.fft.rfft(windowed))
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bands = np.zeros(n_bands)
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for b in range(n_bands):
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low_bin = max(0, int(band_edges[b] / freq_per_bin))
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high_bin = min(n_bins, int(band_edges[b + 1] / freq_per_bin))
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if high_bin <= low_bin:
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high_bin = low_bin + 1
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# Clamp to valid range to avoid empty slices
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low_bin = min(low_bin, n_bins - 1)
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high_bin = min(high_bin, n_bins)
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bands[b] = np.max(magnitudes[low_bin:high_bin])
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return bands
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def extract(path: str, fps: int, n_bands: int) -> dict:
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"""Extract per-frame audio data."""
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print(f"Decoding audio from {path}...", file=sys.stderr)
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samples = decode_audio(path)
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duration = len(samples) / SAMPLE_RATE
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frame_step = SAMPLE_RATE // fps
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total_frames = int(duration * fps)
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print(f"Duration: {duration:.1f}s, {total_frames} frames at {fps}fps", file=sys.stderr)
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print(f"FFT window: {FFT_SIZE} samples ({SAMPLE_RATE / FFT_SIZE:.1f} Hz/bin)", file=sys.stderr)
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print(f"Frequency range: {MIN_FREQ:.0f}-{MAX_FREQ:.0f} Hz, {n_bands} bands", file=sys.stderr)
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# Precompute constants
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hann = np.hanning(FFT_SIZE)
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band_edges = compute_band_edges(n_bands)
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freq_per_bin = SAMPLE_RATE / FFT_SIZE
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n_bins = FFT_SIZE // 2 + 1
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half_fft = FFT_SIZE // 2
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# Pass 1: extract raw values
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rms_values = np.zeros(total_frames)
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band_values = np.zeros((total_frames, n_bands))
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for f in range(total_frames):
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# RMS from the frame's audio slice
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rms_start = f * frame_step
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rms_end = rms_start + frame_step
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frame_slice = samples[rms_start:min(rms_end, len(samples))]
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if len(frame_slice) > 0:
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rms_values[f] = np.sqrt(np.mean(frame_slice ** 2))
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# FFT from a centered 4096-sample window
|
||||
center = rms_start + frame_step // 2
|
||||
win_start = center - half_fft
|
||||
win_end = center + half_fft
|
||||
|
||||
if win_start >= 0 and win_end <= len(samples):
|
||||
window = samples[win_start:win_end] * hann
|
||||
else:
|
||||
# Zero-pad at edges
|
||||
padded = np.zeros(FFT_SIZE)
|
||||
src_start = max(0, win_start)
|
||||
src_end = min(len(samples), win_end)
|
||||
dst_start = src_start - win_start
|
||||
dst_end = dst_start + (src_end - src_start)
|
||||
padded[dst_start:dst_end] = samples[src_start:src_end]
|
||||
window = padded * hann
|
||||
|
||||
band_values[f] = compute_fft_bands(window, freq_per_bin, n_bins, band_edges, n_bands)
|
||||
|
||||
# Pass 2: normalize
|
||||
peak_rms = rms_values.max() if total_frames > 0 else 1.0
|
||||
if peak_rms > 0:
|
||||
rms_values /= peak_rms
|
||||
|
||||
# Per-band normalization so treble is visible alongside louder bass
|
||||
band_peaks = band_values.max(axis=0)
|
||||
band_peaks[band_peaks == 0] = 1.0
|
||||
band_values /= band_peaks
|
||||
|
||||
# Build output
|
||||
frames = []
|
||||
for f in range(total_frames):
|
||||
frames.append({
|
||||
"time": round(f / fps, 4),
|
||||
"rms": round(float(rms_values[f]), 4),
|
||||
"bands": [round(float(b), 4) for b in band_values[f]],
|
||||
})
|
||||
|
||||
return {
|
||||
"duration": round(duration, 4),
|
||||
"fps": fps,
|
||||
"bands": n_bands,
|
||||
"totalFrames": total_frames,
|
||||
"frames": frames,
|
||||
}
|
||||
|
||||
|
||||
def main():
|
||||
parser = argparse.ArgumentParser(description="Extract per-frame audio visualization data")
|
||||
parser.add_argument("input", help="Audio or video file")
|
||||
parser.add_argument("-o", "--output", default="audio-data.json", help="Output JSON path")
|
||||
parser.add_argument("--fps", type=int, default=30, help="Frames per second (default: 30)")
|
||||
parser.add_argument("--bands", type=int, default=16, help="Number of frequency bands (default: 16)")
|
||||
args = parser.parse_args()
|
||||
|
||||
if args.fps < 1:
|
||||
parser.error("--fps must be at least 1")
|
||||
if args.bands < 1:
|
||||
parser.error("--bands must be at least 1")
|
||||
|
||||
data = extract(args.input, args.fps, args.bands)
|
||||
|
||||
with open(args.output, "w") as f:
|
||||
json.dump(data, f)
|
||||
|
||||
print(f"Wrote {args.output} ({data['totalFrames']} frames, {data['bands']} bands)", file=sys.stderr)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Reference in New Issue
Block a user