Every time I used Opus Clip or Vidyo.ai, the same thought hit me:
I’m paying $20/month to upload my video to someone else’s server,
wait in a queue, and hope their AI finds something useful.

So I built an alternative that runs entirely in the browser.
No file uploads. No subscriptions. No server costs on my end.
The result is ClipGG’s AI Video Highlights tool —
and in this post I’ll walk through exactly how it works technically.

The core problem I was solving

Finding highlights in a long video is genuinely hard to automate well.
The expensive approach: transcribe with Whisper, feed text to GPT-4,
profit. But that requires a backend, API costs, and user uploads.

I wanted zero server involvement.
That meant doing everything with browser APIs.

What actually runs in the browser

The pipeline has four stages:

1. File reading — no upload needed

const arrayBuffer = await file.arrayBuffer()
// The file never leaves the device.
// ArrayBuffer is passed directly to Web Audio API.

2. Audio analysis — Web Audio API + Web Worker

I use OfflineAudioContext to decode audio faster than real-time,
then downsample to 8000–11025 Hz before analysis.
This reduces RAM usage from ~115MB to ~19MB for a 10-minute video.

// Decode in a Web Worker so the UI never freezes
const tempCtx = new OfflineAudioContext(1, 44100, 44100)
const audioBuffer = await tempCtx.decodeAudioData(arrayBuffer)

// Downsample manually — OfflineAudioContext does NOT resample automatically
function downsample(channelData, originalRate, targetRate) {
  const ratio = originalRate / targetRate
  const output = new Float32Array(Math.floor(channelData.length / ratio))
  for (let i = 0; i < output.length; i++) {
    const start = Math.floor(i * ratio)
    const end = Math.min(Math.floor((i + 1) * ratio), channelData.length)
    let sum = 0
    for (let j = start; j < end; j++) sum += channelData[j]
    output[i] = sum / (end - start)
  }
  return output
}

3. Scoring — three audio signals

For each 500ms window I compute:

• RMS (Root Mean Square) — average energy/loudness
• ZCR (Zero Crossing Rate) — distinguishes speech from noise
• Volume Peak — catches sudden loud moments

Then I do relative normalization so a quiet podcast
and a loud gaming stream are scored fairly against themselves:

// Relative normalization — key insight
const normalizedRms = (seg.rms - globalMinRms) / (globalMaxRms - globalMinRms)

Different content types use different weights:

4. Clip selection — diversity + peak centering

The selector groups high-scoring segments into zones,
finds the peak moment in each zone, and centers a 30–90 second
clip around it. A diversity radius of 12 seconds prevents
three clips from covering the same moment.

const combinedSignal =
  (seg.score ?? 0) +
  (seg.energyChange ?? 0) * 2.0 +
  (seg.volumePeak ?? 0) * 1.5

// Center the clip around the strongest combined signal,
// not just the loudest sustained section

The Safari problem I didn’t expect

Safari on iOS can’t decode video containers
via AudioContext.decodeAudioData().
It only accepts clean audio files.

The fix: detect iOS and pre-extract audio with FFmpeg.wasm
before passing it to the Web Audio API:

const isIOS = /iPhone|iPad|iPod/i.test(navigator.userAgent)

if (isIOS) {
  await ffmpeg.exec([
    '-i', 'input_video',
    '-vn',
    '-acodec', 'pcm_s16le',  // WAV — guaranteed to work on all iOS versions
    '-ar', '44100',
    '-ac', '1',
    'audio.wav'
  ])
  // Pass audio.wav to Web Audio instead of the original video
}

WAV/PCM is uncompressed and works reliably on every iOS version.
AAC containers are not.

Export — FFmpeg.wasm with stream copy

Once highlights are found, FFmpeg.wasm cuts the clips:

// Fast path: H.264 + AAC + MP4 = stream copy, no re-encoding
// A 90-second clip exports in ~2–3 seconds
await ffmpeg.exec([
  '-ss', String(clip.start),
  '-i', 'input',
  '-t', String(clip.end - clip.start),
  '-c', 'copy',               // copy bytes, don't re-encode
  '-avoid_negative_ts', 'make_zero',
  '-movflags', '+faststart',
  outputName
])

Non-standard formats (MOV, MKV, AV1) get converted to MP4 first
before the analysis pipeline runs. This also fixed all the
“file won’t export” bugs from iPhone footage.

What I learned

OfflineAudioContext doesn’t resample.
I assumed new OfflineAudioContext(1, length, 8000)
would give me 8kHz audio. It doesn’t.
You get whatever sample rate the source file has.
Downsampling has to be manual.

Transfer, don’t copy ArrayBuffers.
worker.postMessage({ arrayBuffer }, [arrayBuffer])
transfers ownership with zero memory copy.
Without the second argument you’re doubling RAM usage.

-ss before -i for stream copy, after for re-encode.
This one cost me an hour. For -c copy, seek before input
for speed. For re-encoding, seek after input for frame accuracy.

Try it

The tool is live and free at:
👉 https://clipgg.uk/en/ai-video-highlights

Drop a video, pick a mode (Gaming / Podcast / Funny / General),
and get three highlight clips with timestamps in about 30 seconds.

No account. No upload. Works on desktop Chrome, Firefox,
and now iOS Safari too.

Curious what others think about the audio scoring approach —
would love feedback on the algorithm in the comments.