clips

Takes the sentences of a transcript and ranks contiguous spans, so a longer video can be cut into shorter clips.

Selection is two graphs. A per-sentence selector returns a saliency score and start/end probabilities, which is what proposes candidate spans. A per-span scorer then ranks them. They are separate graphs because they take different inputs: the selector also reads five discourse scalars the scorer has no use for.

On Apple platforms both graphs live in one Core ML package over a shared trunk. LiteRT has no equivalent packaging, so the other platforms ship two files and store the trunk twice.

Files

File	Format	Contents
clips.mlmodelc/	Compiled Core ML, int8	Multifunction package. Function select: ids, mask, disc → saliency, start_p, end_p. Function score: ids, mask → score
clips-selector.tflite	LiteRT, int8 weight-only	The selector, for Android, Linux and Windows
clips-scorer.tflite	LiteRT, int8 weight-only	The scorer, for Android, Linux and Windows
clip_tokenizer.bin	Unigram tokenizer	XLM-R SentencePiece pieces and scores, in the compact binary the runtime reads
clips_meta.json	JSON	Graph widths, discourse-feature order and tokenizer ids a runtime needs
checkpoint/	safetensors + PyTorch	The training checkpoint the exports were built from

Reaching a function in the Core ML package

A file path names the package, not the graph. Loading needs MLModelConfiguration.functionName set to select or score. Without it Core ML loads the package's default function and reports nothing, so both halves of the pipeline end up being the selector.

Windows

The selector runs at 128 tokens, the scorer at 256, both at a fixed batch of 16 sentences.

A single sentence is truncated to 64 tokens before it reaches the selector. That is the length the saliency heads were trained at and it is not the same thing as the graph width; a longer single sentence runs the heads off-distribution.

checkpoint/

The exact checkpoint the exports come from, in the layout the training and export scripts read, rather than a flat repacked .pt that nothing can load. It holds the encoder as safetensors, the two head files, and run_manifest.json describing the run that produced it.

There is no TensorFlow checkpoint. The LiteRT files are converted from PyTorch through StableHLO, so no TF SavedModel exists at any point.

Status

Internal testing. This card carries no quality or latency figures: the evaluation behind this checkpoint has not completed independent review, and an unreviewed number on a public card gets quoted as if it had been.

⚠️ The .tflite files do not work with the Desert Ant SDK yet. Do not build on them.

Found by review after publication, by loading the flatbuffers rather than reasoning about them. The LiteRT exports are real and faithful conversions of the checkpoint, but the SDK's LiteRT backend cannot drive them:

• the graphs name their inputs args_0, args_1, args_2 and their outputs output_0…2; the SDK asks for ids, mask, disc and saliency, start_p, end_p. There is no mapping layer, so the first call fails.
• the graphs take int64 ids and mask; the SDK builds int32.
• the scorer is 256 wide and the SDK's LiteRT backend cannot report a width, so it falls back to 128.

They are left published because they are honest artifacts and someone driving LiteRT directly can use them — the shapes and output order are in clips_meta.json and are verified. They are not a working Android/Linux/Windows path today.

Also unresolved: at these widths this export was measured at ~2.1 GB peak RSS against a 1.6 GB Android budget, and the training repo's own recommendation for LiteRT is fp16 rather than this int8 build.

The two platforms are not equally evidenced. The Core ML package has clips that were generated from it and judged. No clip has ever been read from the LiteRT files, on any platform. Their only gate is a synthetic random-token batch, and that gate's own manifest records is_a_quality_result: false. The two exports also use different int8 schemes, recorded per platform in clips_meta.json.

Requirements

The Core ML package is specification version 9: it requires iOS 18 / macOS 15 / tvOS 18 / visionOS 2 / watchOS 11, read off the compiled artifact. Reaching either graph needs MLModelConfiguration.functionName set to select or score.

Limits

Behaviour worth knowing before you build on it, stated without figures for the reason above:

• It under-emits on short video. Given a short transcript it returns markedly fewer clips than a strong teacher finds worth cutting. If your product needs a guaranteed number of clips from a two-minute video, measure before relying on it.
• It emits some dross, most on podcast-length input. There is no confidence score to filter on yet: Clip.score ranks within one video and is not calibrated across videos.
• The clip limit is a cap, not a quota. Asking for 10 does not 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 can return a different-but-comparable set rather than the same set. Do not treat exact span equality between two builds as a correctness check.
• Non-Latin scripts are under-tested. The evaluation corpus is overwhelmingly Latin-script.
• Duration is a soft prior, not a rule. Clips may come back shorter or longer than a typical Short.

Built on

• FacebookAI/xlm-roberta-base (MIT): the shared encoder trunk and its SentencePiece vocabulary.

See THIRD_PARTY_NOTICES.md.

License

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

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