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/`. --- ## Text-to-Speech (`tts`) Generate speech audio locally with Kokoro-82M. **No API key required.** ```bash 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. ```bash 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. ```bash 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. 1. Language known and non-English → `--model small --language <code>` (no `.en` suffix) 2. Language known and English → `--model small.en` 3. 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 ```json [ { "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. ```bash 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: ```bash 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) |