Hyperframes Media Agent Skill

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/.

Text-to-Speech (tts)

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

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                       # list all 54 voices

Voice Selection

| Content Type | Recommended Voices | 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.

Speed

| Speed | Use Case |

|-------|----------|

| 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

Write to a .txt file and pass the path. Inputs over ~5 minutes 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

Critical Language Rule

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.

Output Shape

[
  { "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 sits as a transparent overlay.

npx hyperframes remove-background subject.mp4 -o transparent.webm  # VP9 alpha WebM
npx hyperframes remove-background subject.mp4 -o transparent.mov   # ProRes 4444
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
npx hyperframes remove-background --info                           # detected providers

Uses u2net_human_seg (MIT). First run downloads ~168 MB of weights.

Layer Separation (--background-output)

Pass --background-output (or -b) to emit a second transparent video with the inverse alpha:

| File | Alpha is... | Use it for |

|------|-------------|------------|

| -o subject.webm | The mask — subject opaque, bg transparent | Foreground layer |

| --background-output plate.webm | Inverse — bg opaque, subject transparent | Bottom layer; put text/graphics between |

Both share the same quality preset and run from a single inference pass.

Output Format

| Format | When |

|--------|------|

| .webm (VP9 + alpha) | Default. Compositions play directly via <video>. |

| .mov (ProRes 4444) | Editing in DaVinci/Premiere/FCP. Large files. |

| .png | Single-image cutout. |

Quality Presets

| Preset | CRF | When |

|--------|-----|------|

| fast | 30 | Iterating, smaller file |

| balanced | 18 | Default. Visually identical for most uses |

| best | 12 | Master / final delivery |

TTS -> Transcribe -> Captions Pipeline

Generate voiceover, get word-level timestamps, and create 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.

Related Skills

| Skill | Purpose |

|-------|---------|

| hyperframes | Composition authoring (HTML, GSAP, captions, variables) |

| hyperframes-cli | CLI dev loop (init, lint, preview, render, doctor) |