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 it can sit as a transparent overlay in a composition (e.g. an avatar floating on a background plate).

npx hyperframes remove-background avatar.mp4 -o transparent.webm  # default: VP9 alpha WebM
npx hyperframes remove-background avatar.mp4 -o transparent.mov   # ProRes 4444 (editing)
npx hyperframes remove-background portrait.jpg -o cutout.png      # single-image cutout
npx hyperframes remove-background avatar.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/.

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.

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.

hyperframes-media