Hyperframes Media

Other

Asset preprocessing for HyperFrames compositions — text-to-speech narration (Kokoro), audio/video transcription (Whisper), and background removal for transparent overlays (u2net). Use when generating voiceover from text, transcribing speech for captions, removing the background from a video or image to use as a transparent overlay, choosing a TTS voice or whisper model, or chaining these (TTS → transcribe → captions). Each command downloads its own model on first run.

Install

openclaw skills install hyperframes-media

HyperFrames Media Preprocessing

Three CLI commands that produce assets for compositions: tts (speech), transcribe (timestamps), and remove-background (transparent video). Each downloads a model on first run and caches it under ~/.cache/hyperframes/. Drop the output into the project, then reference it from the composition HTML — see the hyperframes skill for the audio/video element conventions.

Text-to-Speech (`tts`)

Generate speech audio locally with Kokoro-82M. No API key.

npx hyperframes tts "Text here" --voice af_nova --output narration.wav
npx hyperframes tts script.txt --voice bf_emma --output narration.wav
npx hyperframes tts --list                       # all 54 voices

Voice Selection

Match voice to content. Default is af_heart.

Content type	Voice	Why
Product demo	`af_heart`/`af_nova`	Warm, professional
Tutorial / how-to	`am_adam`/`bf_emma`	Neutral, easy to follow
Marketing / promo	`af_sky`/`am_michael`	Energetic or authoritative
Documentation	`bf_emma`/`bm_george`	Clear British English, formal
Casual / social	`af_heart`/`af_sky`	Approachable, natural

Multilingual

Voice IDs encode language in the first letter: a=American English, b=British English, e=Spanish, f=French, h=Hindi, i=Italian, j=Japanese, p=Brazilian Portuguese, z=Mandarin. The CLI auto-detects the phonemizer locale from the prefix — no --lang needed when the voice matches the text.

npx hyperframes tts "La reunión empieza a las nueve" --voice ef_dora --output es.wav
npx hyperframes tts "今日はいい天気ですね" --voice jf_alpha --output ja.wav

Use --lang only to override auto-detection (stylized accents). Valid codes: en-us, en-gb, es, fr-fr, hi, it, pt-br, ja, zh. Non-English phonemization requires espeak-ng system-wide (brew install espeak-ng / apt-get install espeak-ng).

Speed

0.7-0.8 — tutorial, complex content, accessibility
1.0 — natural pace (default)
1.1-1.2 — intros, transitions, upbeat content
1.5+ — rarely appropriate; test carefully

Long Scripts

For more than a few paragraphs, write to a .txt file and pass the path. Inputs over ~5 minutes of speech may benefit from splitting into segments.

Requirements

Python 3.8+ with kokoro-onnx and soundfile (pip install kokoro-onnx soundfile). Model downloads on first use (~311 MB + ~27 MB voices, cached in ~/.cache/hyperframes/tts/).

Transcription (`transcribe`)

Produce a normalized transcript.json with word-level timestamps.

npx hyperframes transcribe audio.mp3
npx hyperframes transcribe video.mp4 --model small --language es
npx hyperframes transcribe subtitles.srt          # import existing
npx hyperframes transcribe subtitles.vtt
npx hyperframes transcribe openai-response.json

Language Rule (Non-Negotiable)

Never use .en models unless the user explicitly states the audio is English. .en models (small.en, medium.en) translate non-English audio into English instead of transcribing it. This silently destroys the original language.

Language known and non-English → --model small --language <code> (no .en suffix)
Language known and English → --model small.en
Language unknown → --model small (no .en, no --language) — whisper auto-detects

Default model is small, not small.en.

Model Sizes

Model	Size	Speed	When to use
`tiny`	75 MB	Fastest	Quick previews, testing pipeline
`base`	142 MB	Fast	Short clips, clear audio
`small`	466 MB	Moderate	Default — most content
`medium`	1.5 GB	Slow	Important content, noisy audio, music
`large-v3`	3.1 GB	Slowest	Production quality

Music with vocals: start at medium minimum; produced tracks often need manual SRT/VTT import. For caption-quality checks (mandatory after every transcription), the cleaning JS, retry rules, and the OpenAI/Groq API import path, see hyperframes/references/transcript-guide.md.

Output Shape

Compositions consume a flat array of word objects. The id field (w0, w1, ...) is added during normalization for stable references in caption overrides; it's optional for backwards compatibility.

[
  { "id": "w0", "text": "Hello", "start": 0.0, "end": 0.5 },
  { "id": "w1", "text": "world.", "start": 0.6, "end": 1.2 }
]

Background Removal (`remove-background`)

Remove the background from a video or image so the subject (typically a person — avatar, presenter, talking head) sits as a transparent overlay in a composition.

npx hyperframes remove-background subject.mp4 -o transparent.webm  # default: VP9 alpha WebM
npx hyperframes remove-background subject.mp4 -o transparent.mov   # ProRes 4444 (editing)
npx hyperframes remove-background portrait.jpg -o cutout.png       # single-image cutout
npx hyperframes remove-background subject.mp4 -o subject.webm \
  --background-output plate.webm                                   # both layers in one pass
npx hyperframes remove-background subject.mp4 -o transparent.webm --device cpu
npx hyperframes remove-background --info                           # detected providers

Uses u2net_human_seg (MIT). First run downloads ~168 MB of weights to ~/.cache/hyperframes/background-removal/models/.

Layer separation (`--background-output`)

Pass --background-output (or -b) to emit a second transparent video alongside the cutout: same source RGB, alpha is 255 − mask instead of mask. The cutout is the subject with a transparent background; the plate is the original surroundings with a transparent hole where the subject was.

File	Alpha is…	Use it for
`-o subject.webm`	The mask — subject opaque, background transparent	Foreground layer, place on top
`--background-output plate.webm`	Inverse — surroundings opaque, subject region transparent	Bottom layer; put text or graphics between this and the subject

Both outputs share the same --quality preset and run from a single inference pass — encode cost roughly doubles, segmentation cost stays the same. Only valid for video inputs and .webm/.mov outputs.

Hole-cut plate, not an inpainted clean plate. The subject region in plate.webm is fully transparent — composite something opaque under it to fill the hole. The single test for whether --background-output is the right tool: will anything ever be visible through the subject's silhouette where the subject used to be?

Use case	Right tool
Text/graphics between the cutout and the plate (this command's reason for existing)	Hole-cut (`--background-output`)
Subject onto an unrelated scene	Just `subject.webm`; ignore the plate
Show the room without the person, alone over no other content	Clean plate — needs an inpainter (LaMa, ProPainter, E2FGVI). Not this command.
Replace the subject with a different subject	Clean plate — same as above

If a user asks for "the room with the person removed" and intends to display it standalone, do not reach for --background-output. Tell them they need an inpainter.

Typical layered composition (the canonical hole-cut use case):

<!-- z=1 the inverse-alpha plate fills everything except the subject region -->
<video
  src="plate.webm"
  data-start="0"
  data-duration="6"
  data-track-index="0"
  muted
  playsinline
></video>

<!-- z=2 graphics / text live between the two layers -->
<h1 id="headline" style="z-index:2; ...">MAKE IT IN HYPERFRAMES</h1>

<!-- z=3 the cutout floats the subject back over the headline -->
<div class="cutout-wrap" style="position:absolute;inset:0;z-index:3">
  <video
    src="subject.webm"
    data-start="0"
    data-duration="6"
    data-track-index="1"
    muted
    playsinline
  ></video>
</div>

This is functionally equivalent to the text-behind-subject pattern below, but you don't need the original presenter.mp4 in the project — the plate replaces it. Useful when you want to ship just the two transparent layers and let the user drop arbitrary content between them.

Output Format

Format	When
`.webm` (VP9 + alpha)	Default. Compositions play this directly via `<video>`.
`.mov` (ProRes 4444)	Editing in DaVinci/Premiere/FCP. Large files.
`.png`	Single-image cutout (still subject, layered over a backdrop).

Chrome decodes VP9 alpha natively, so the .webm plugs into a composition like any other muted-autoplay video — see the hyperframes skill for the <video> track conventions.

Quality presets

--quality fast|balanced|best controls only the VP9 encoder's CRF — segmentation quality is fixed.

Preset	CRF	When
`fast`	30	Iterating, smaller file, looser color match
`balanced`	18	Default. Visually identical for most uses
`best`	12	Master / final delivery. Largest file, tightest match

Compositing patterns — pick the right one

The cutout webm is a re-encoded copy of the source mp4's RGB. That choice has consequences depending on what you put behind it:

Pattern	What's behind the cutout	Result
Cutout over a different scene (most common)	Static image, gradient, or unrelated video	Looks great. The cutout's RGB is the only source of the subject — no doubling, no edge halo. This is what `remove-background` is built for.
Cutout over its own source mp4 (text-behind-subject)	Same mp4 the cutout was generated from	Two RGB sources for the same person. At default `--quality balanced` (crf 18) the doubling is barely visible; at `--quality fast` (crf 30) you'll see a faint color shift / edge halo. Use `--quality best` (crf 12) for masters.
*Cutout over a different* take of the same person**	Footage of the same subject	Will look like two separate people overlapping. Don't do this.

Text-behind-subject (headline behind a presenter):

<video
  src="presenter.mp4"
  id="bg"
  data-start="0"
  data-duration="6"
  data-track-index="0"
  muted
  playsinline
></video>
<h1 id="headline" style="z-index:2; ...">MAKE IT IN HYPERFRAMES</h1>
<div class="cutout-wrap" style="position:absolute;inset:0;z-index:3;opacity:0">
  <video
    src="presenter.webm"
    data-start="0"
    data-duration="6"
    data-track-index="1"
    muted
    playsinline
  ></video>
</div>

Two key rules:

Wrap the cutout video in a non-timed <div> and animate the wrapper's opacity, not the video element's. The framework forces opacity:1 on active clips (any element with data-start/data-duration), so animating the video's opacity directly is silently overridden. The wrapper has no data-* attributes, so it's owned by your CSS/GSAP.
Both videos use data-start="0" and data-media-start="0" so the framework decodes them in sync from t=0. Late-mounting the cutout (data-start=3.3) introduces a seek + warm-up that lands a frame off the base mp4 — visible as one frame of misalignment at the cut.

Then GSAP-flip the wrapper opacity at the cut: tl.set(cutoutWrap, { opacity: 1 }, 3.3).

TTS → Transcribe → Captions

When there's no pre-recorded voiceover, generate one and transcribe it back to get word-level timestamps for captions:

npx hyperframes tts script.txt --voice af_heart --output narration.wav
npx hyperframes transcribe narration.wav   # → transcript.json

Whisper extracts precise word boundaries from the generated audio, so caption timing matches delivery without hand-tuning.