Clips

Create short videos and highlight clips.

Short clips and highlights from talking video and audio: podcasts, interviews, meetings. On-device.

• SDKs, install and examples: https://github.com/Desert-Ant-Labs/desert-ant-core/blob/main/docs/models/clips.md

Platforms	iOS, macOS, tvOS, visionOS, Linux, Windows
Weights	v0.1.0

Install

Swift (requirements)

.package(url: "https://github.com/Desert-Ant-Labs/desert-ant-core.git", from: "3.1.0")

Then add the Clips product to your target.

What the model does

Point Clips at a transcript and get the moments worth cutting, ranked best first and never overlapping. Nothing is uploaded, so clipping a back catalogue costs the same as clipping one video.

284MB as a single int8 Core ML package on Apple platforms, or two 283MB LiteRT files elsewhere. 100 languages.

Clips carry no title. Pairing them with words is a separate model, desert-ant-labs/title.

Maximum video length

There's no context window. Clips never reads a transcript whole: sentences run through the selector in batches of 16 and each candidate clip is scored on its own, so length is bounded by time rather than by a token limit. The longest we've run is an 835 sentence podcast.

transcript	iPhone 17 Pro	iPhone 15 Pro
404 sentences, 25 minutes of video, 12 clips	9.19s	10.22s
57 sentences	2.23s
per candidate, encoder only	2.78ms	3.13ms

Measured on device at batch 16, pinned to .cpuAndNeuralEngine.

Files

File	Format	Size	Contents
clips.mlmodelc/	Compiled Core ML, int8 per-channel	284MB	Multifunction package. Function select: ids, mask, disc to saliency, start_p, end_p. Function score: ids, mask to score
clips-selector.tflite	LiteRT, int8 weight-only	283MB	The selector, for Android, Linux and Windows
clips-scorer.tflite	LiteRT, int8 weight-only	283MB	The scorer, for Android, Linux and Windows
clip_tokenizer.bin	Unigram tokenizer	4MB	Tokenizer pieces and scores, in the compact binary the runtimes read
clips_meta.json	JSON	tiny	Graph widths, input roles and feature order a runtime needs

On Apple platforms both graphs live in one Core ML package with the shared weights stored once. LiteRT has no equivalent packaging, so the other platforms ship two files.

The selector graph is 128 tokens wide, the scorer 256, both at a fixed batch of 16 sentences. A sentence longer than 64 tokens is truncated before it reaches the selector.

Core ML functions

Set MLModelConfiguration.functionName to select or score. A path names the package, not the graph, and Core ML loads the default function silently, so without it both halves of your pipeline run the selector.

Input roles on LiteRT

The LiteRT graphs take int64 ids and mask, and name their inputs positionally, args_0 upward, so clips_meta.json carries the mapping. Feed ids and mask swapped and the graph returns finite, plausibly scaled numbers and a pipeline that quietly builds the wrong pool.

Thresholds

Clip.score runs 1 to 5 and is not calibrated between videos. Threshold on percentile instead. An absolute cut at 0.65 returns nothing on 21% of videos, and at 0.75 on half of them. Per-video 95th percentiles range from 0.33 to 0.95.

percentile cut	clips per video	share of the video covered	use
0.8	5.0	17%	highlights
0.3	32.1	74%	auto edit

Limits

• Clip edges are the weak spot. A clip can open on a pronoun whose referent was in the sentence before it, fuse two topics into one, cut before the payoff lands, or swallow a sponsor read.
• Short videos return few clips. A two minute transcript comes back with fewer clips than a server-side editor would cut, and a transcript under three sentences returns none.
• The clip limit is a cap, not a quota. Asking for 10 doesn't mean receiving 10.
• Selection is sensitive to small score changes. Candidate spans around one moment score very close together, so a different runtime, compute unit or quantization returns a different but comparable set.
• Non-Latin scripts are under-tested. Our evaluation corpus is overwhelmingly Latin script.
• Duration is a soft prior. A clip can come back shorter or longer than a typical Short.
• The LiteRT files don't load through the Desert Ant SDK. The graphs take int64 ids where the SDK builds int32. Drive them from a LiteRT runtime directly.
• Android needs headroom. The shipped int8 weight-only build peaks at 2136MB resident, measured on 6,395 real candidate spans. That fits a high-end phone and not a mid-range one, which is why there is no Android SDK yet rather than any limit in the artifact.

Requirements

iOS 18, macOS 15, tvOS 18, visionOS 2, watchOS 11. Android, Linux and Windows through LiteRT, driven directly rather than through the SDK.

License

Desert Ant Labs Source-Available License. Free for most apps, and a commercial license is required at scale. Full terms are at the link. Licensing: licensing@desertant.com.

See THIRD_PARTY_NOTICES.md.

Citation

@software{clips_2026,
  title  = {Clips: Short clips and highlights from talking video and audio: podcasts, interviews, meetings. On-device},
  author = {Desert Ant Labs},
  year   = {2026},
  url    = {https://huggingface.co/desert-ant-labs/clips},
}

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