hyperframes/skills/media-use/references/operations.md

Media operations: agent guidance

media-use resolves and remembers assets. For operating on them: cutting, reframing, stitching, transforming, it does not wrap every action as a bespoke command. Instead it points you at the right local tool (decision OP1). Run the tool, then register the output with resolve --from <output> --type <type> so the result lands in the ledger and the global cache like any other asset.

All tools below are local and free. ffmpeg is assumed present (it backs the engine already).

Cut / trim: keep a slice

ffmpeg -i in.mp4 -ss 00:00:12 -to 00:00:20 -c copy out.mp4   # 0:12–0:20, no re-encode

In-composition trimming usually needs no new file: a clip plays a sub-window via data-media-start + data-duration (see hyperframes-core). Only cut a physical file when exporting/assembling outside the composition.

Reframe / crop: change aspect ratio

# 16:9 -> 9:16, crop centered
ffmpeg -i in.mp4 -vf "crop=ih*9/16:ih,scale=1080:1920" out.mp4

For a non-destructive crop, set a clip-path on the element in the composition itself (render-time, source file untouched) instead of re-encoding with ffmpeg.

Montage / stitch: join clips

printf "file '%s'\n" a.mp4 b.mp4 c.mp4 > list.txt
ffmpeg -f concat -safe 0 -i list.txt -c copy out.mp4

Silence-cut / highlight: trim dead air, grab the best moment

auto-editor in.mp4 --edit audio:threshold=4% -o tight.mp4   # pip install auto-editor
scenedetect -i in.mp4 detect-adaptive list-scenes           # pip install scenedetect

Transforms with a quality choice (process)

These have a local option AND a higher-quality HeyGen-CLI option. Run the local one for free/offline; use the HeyGen CLI when quality matters. Showing the user a side-by-side (local vs HeyGen) is the honest way to let them choose.

Op	Local (free)	HeyGen CLI (quality)
Background removal	hyperframes remove-background in.png (u2net)	heygen background-removal
Upscale	realesrgan-ncnn-vulkan -i in.png -o out.png -s 4	n/a
Lipsync (dub)	n/a	heygen lipsync
Translate	n/a	heygen video-translate

After any op: resolve --from out.ext --type <type> to register the derived asset (it records provenance and auto-promotes to the global cache).

ponytail: media-use doesn't re-wrap ffmpeg/heygen here, that's deliberate (OP1). The value it adds is the ledger + global reuse on the output, via --from. Add a thin process verb only if agents repeatedly fumble these recipes.

Transcription (default: Parakeet, better than whisper.cpp)

transcribe.mjs is the default local transcription path. It runs NVIDIA Parakeet-TDT via parakeet-mlx, which beats whisper.cpp on the Open ASR Leaderboard (avg WER ~6.05% vs 7.44%; on NOISY audio 4.73% vs 5.96%, where whisper-large-v3 hallucinated to 308% WER on meetings) and is 5-10x faster. It emits { text, words:[{text,start,end}] } with word timestamps (merged from Parakeet's sub-word tokens), feeding transcript-cut, captions, and the audio engine directly.

# install once: uv venv ~/.venvs/parakeet && VIRTUAL_ENV=~/.venvs/parakeet uv pip install parakeet-mlx
node <SKILL_DIR>/scripts/transcribe.mjs --input talk.mp4 --out talk.transcribe.json

# equivalently, the hyperframes CLI has Parakeet built in (auto-detects it, whisper fallback):
npx hyperframes transcribe talk.mp4 --engine parakeet   # or --engine auto (default)

VERIFIED on 24GB: accurate, ~3s (cached) for 8s audio. Parakeet covers English + 25 European languages. For other languages, or when parakeet-mlx is not installed, transcribe.mjs auto-falls-back to whisper.cpp (99 languages) via hyperframes transcribe. --engine parakeet|whisper forces one. (Cohere Transcribe tops the leaderboard on paper but its mlx-audio quants produced garbage and ran 40-70x slower on a Mac in testing, so it is not wired in.)

Text-based editing (transcript cut)

transcript-cut.mjs is a compiler, not a wrapper: it turns word timestamps and agent cut decisions into exact kept segments. It is provided even though the rest of this file is guidance-only.

node <SKILL_DIR>/scripts/transcript-cut.mjs \
  --input talk.mp4 \
  --transcript talk.transcribe.json \
  --remove "12.41-15.02,88.3-91.7" \
  --remove-fillers "um,uh,like" \
  --cut-silence 0.8 \
  --out talk.cut.mp4

resolve --from talk.cut.mp4 --type video

Use --plan first when you want to inspect the kept segment JSON before encoding.

Ducking (declare in-composition / bake for export)

B1, declare ducking in the composition. audio-duck.mjs emits GSAP volume keyframes. Paste them into the composition timeline, the source file stays untouched.

node <SKILL_DIR>/scripts/audio-duck.mjs \
  --meta audio_meta.json \
  --target "#bgm" \
  --composition index.html

// auto-duck: #bgm under narration (generated; base volume 0.6)
tl.to("#bgm", { volume: 0.15, duration: 0.15 }, 3.42);
tl.to("#bgm", { volume: 0.6, duration: 0.4 }, 9.87);

B2, bake ducking only for exported or standalone files.

ffmpeg -i bgm.mp3 -i voice.wav \
  -filter_complex "[0][1]sidechaincompress=threshold=0.03:ratio=8:attack=200:release=400[ducked]" \
  -map "[ducked]" bgm.ducked.wav

Declare inside compositions. Bake only for assets leaving the hyperframes pipeline.

Publish loudness

Two-pass loudnorm measures first, then applies the measured values with the target LUFS baked in.

Socials target, -14 LUFS:

ffmpeg -i mix.wav \
  -af loudnorm=I=-14:TP=-1.5:LRA=11:print_format=json \
  -f null -

ffmpeg -i mix.wav \
  -af loudnorm=I=-14:TP=-1.5:LRA=11:measured_I=<input_i>:measured_TP=<input_tp>:measured_LRA=<input_lra>:measured_thresh=<input_thresh>:offset=<target_offset>:linear=true:print_format=summary \
  mix.social.wav

Podcast target, -16 LUFS:

ffmpeg -i mix.wav \
  -af loudnorm=I=-16:TP=-1.5:LRA=11:print_format=json \
  -f null -

ffmpeg -i mix.wav \
  -af loudnorm=I=-16:TP=-1.5:LRA=11:measured_I=<input_i>:measured_TP=<input_tp>:measured_LRA=<input_lra>:measured_thresh=<input_thresh>:offset=<target_offset>:linear=true:print_format=summary \
  mix.podcast.wav

Generate: images (local first, cloud upsell)

resolve --type image retrieves from the HeyGen catalog first; on a miss it GENERATES. Two paths, best-for-the-machine picked automatically:

1. Local (default, free, private): mflux (FLUX-on-MLX). resolve spec-checks AVAILABLE RAM and runs the best FLUX-class model that fits, via scripts/lib/local-models.mjs (imagegen ladder) + mflux-provider.mjs. The RAM ladder (agent sees it via describeModelLadder("imagegen", specs)):

   Tier	Model	Needs (available RAM)	Notes
   medium	FLUX.1 schnell int4	~8GB (--low-ram)	~20s/512px on 24GB. VERIFIED. Fast.
   large	FLUX.2 Klein 4B int4	~32GB	higher quality, full-resident
   xlarge	Qwen-Image	~64GB	top quality, 64GB+ Macs only

   Gotchas baked into the table: the official FLUX repos are HF-gated, so it points at non-gated community 4-bit re-uploads; and --low-ram is MANDATORY at the medium tier (without it a 768x512 run swap-thrashed to 90 minutes on 24GB; with it, 20 seconds).

2. Cloud upsell (better quality): the codex CLI image_gen tool, on the user's ChatGPT subscription (codex owns auth, no key here, no per-call charge). It is the automatic fallback when no local model fits AND the explicit "make it better" choice on any machine. Users who just want codex can ask for it directly. Verified: prompt -> raster -> frozen + ledgered.

--local-only keeps mflux (once cached) and skips codex (network).

Generate: video (local first, HeyGen avatar upsell)

Operate-on-video ships now; GENERATING video is local-first with a HeyGen avatar upsell (decision X3).

• Local (default): LTX 2.3 on MLX via dgrauet/ltx-2-mlx, the videogen ladder in local-models.mjs. Generative clips (t2v / i2v), spec-gated to RAM. Verified on 24GB: 512x320 x 33f with audio.

• HeyGen avatar upsell (better, script-driven): the heygen CLI, NOT the raw API. For a talking-head / avatar video, heygen video create (avatar engine IV by default) beats a generative clip when you want a real presenter. Browser OAuth uses the web-plan/free avatar-video allowance where eligible; API keys follow the normal API billing path:

  # discover an avatar + a starfish voice, then create + wait
  heygen avatar list --ownership public --limit 5
  heygen voice list --engine starfish --limit 5
  heygen video create --wait -d '{
    "type": "avatar",
    "avatar_id": "<avatar-id>",
    "script": "Your narration here.",
    "voice_id": "<voice-id>"
  }'
  

  Avatar videos are deterministic + script-driven (lip-sync from a script or a pre-recorded audio_url), distinct from the generative LTX clips. After it renders, resolve --from <downloaded.mp4> --type video to ledger it.

Image-to-video (animate any still into a talking clip)

heygen video create takes the raw POST /v3/videos body, so switching type from avatar to image animates any image of a person into a lip-synced talking video, with no avatar/photo-avatar creation step first. Point image at a public URL or an uploaded asset_id, and drive speech with a script+voice_id or a pre-recorded audio_url:

heygen video create --wait -d '{
  "type": "image",
  "image": { "type": "url", "url": "https://example.com/person.jpg" },
  "script": "Your narration here.",
  "voice_id": "<voice-id>"
}'

Common optional fields: title, resolution (4k/1080p/720p), aspect_ratio, remove_background, background, voice_settings, motion_prompt + expressiveness (photo-avatar animation), and callback_url/callback_id for webhooks. Don't hardcode these from memory: the CLI self-documents the full, current body with heygen video create --request-schema (a discriminated union keyed on type), so read the schema rather than trusting a stale field list. For a still you'll reuse across many scripts, create a Photo Avatar once instead (below). Ledger the result with resolve --from <downloaded.mp4> --type video. Docs: https://developers.heygen.com/image-to-video.

Other HeyGen generative use cases

All reachable through the installed heygen CLI (verified v0.3.0): either video create -d '{...}' body variants (keyed on type) or the dedicated subcommands. There is no capability gap that would need the raw API, and every command prints its exact input with --request-schema, so this table is a pointer, not a spec. Reach for these when a composition needs a real presenter, a dub, or a designed clip that LTX/avatar don't cover:

Use case	Reach it via	What it does	Docs
Photo Avatar / talking photo	avatar create + avatar looks	Create a reusable avatar from one still, then drive it via video create type avatar	photo-avatar
Digital Twin (video avatar)	video create (type:"avatar")	Lifelike avatar trained from real footage; speaks any script	generate-avatar-video
Cinematic avatar (prompt-gen)	video create (type:"cinematic_avatar")	Prompt + 1-3 avatar/reference assets → generated clip (Seedance); no script/voice	avatar-v
Video translation	heygen video-translate	Translate + voice-clone + lip-sync a video across 30+ languages	docs/video-translate
Lipsync / dub	heygen lipsync	Replace or dub audio on an existing video with fresh lip-sync	lipsync-speed, lipsync-precision
Short clips from long video	heygen ai-clipping	Turn long-form footage into ready-to-share short clips with captions	ai-clipping
Voice design / clone	heygen voice	Describe or clone a voice, then use its id as voice_id	docs/voices/design-voices

Doc slugs are under https://developers.heygen.com/. Prefer HyperFrames' own compositions for motion graphics / data-viz / title cards (that's what this repo is for); the HeyGen generative endpoints are for presenter footage, dubs, and image-driven talking clips that HTML compositions can't produce.