Align

Accurate word timestamps for any transcript.

Word-timestamp refinement for any transcript, on device.

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

Corrects the word-level timings that Apple's SpeechTranscriber and SpeechAnalyzer return, without replacing them. Align observes the same audio the analyzer already receives, runs a small Core ML cascade on the CPU and Neural Engine, and returns the familiar result surface with tightened audioTimeRange values. The models are tiny (560KB compiled Core ML) and refine a typical result in a few milliseconds on device.

Apple: "world" 2.61-3.04s ➜ Align: "world" 2.57-2.98s

Try it

Install

Swift (requirements)

.package(url: "https://github.com/Desert-Ant-Labs/desert-ant-core.git", from: "4.0.0")

Then add the Align product to your target.

JavaScript (requirements)

npm i @desert-ant-labs/align

Files

The compiled .mlmodelc stages (Apple platforms) or .tflite stages (Linux, Windows and Node), plus mel_filters.bin, calibrator.bin, and refiner_config.json, are exactly what the SDK downloads.

LiteRT

See the LiteRT usage guide for how the SDK loads the .tflite pair on Linux, Windows and Node.

Inputs and outputs

• Input: mono audio plus Apple's recognized words with their proposed start/end times.
• Output: the same words with corrected start/end times, or Apple's original time when a correction is not structurally safe.

Accuracy

Measured on v1.0.0 over held-out recordings.

All nine languages

Raw is each proposer's own timing before refinement; the English cell pools Whisper, Parakeet and Apple proposals.

1.1.0 corrects the Apple runtime's log-mel scaling, so on-device results are re-measured for this release; the numbers above are the training-side measurement.

Macro-averaged over the nine languages, so a language with more test data cannot carry the figure on its own.

Public benchmark, English

A 500-clip sample of each official LibriSpeech test-clean and test-other split.

For editing work the p90 matters more than the mean. Large errors are what a viewer notices when a caption slips or a clip cuts mid-word.

Against hand-corrected boundaries

258 word boundaries across 10 recordings, corrected by hand against the waveform rather than by another aligner. The only figure here not measured against machine references.

Languages

English, Spanish, French, Italian, Portuguese, German, Japanese, Korean, and Chinese. A locale outside this set is passed through unchanged.

Limitations

• References are machine forced-alignment estimates, not human annotations, so the figures show a large, consistent reduction of Apple's timing error rather than sample-accurate ground truth.
• A learned correction is not guaranteed to improve every boundary; the structural fallback keeps Apple's timestamp when a correction looks unsafe but cannot catch every plausible-looking error.
• Japanese, Korean, and Chinese were the weakest languages before v1.0.0. They now improve their proposals by 33%, 55%, and 51%.
• Spoken numbers are the weakest remaining case. On a small sample, refinement moved digit boundaries further from the reference than leaving them alone, so treat them as unimproved until a larger sample settles it.
• LiteRT is a third numeric path alongside Core ML, for Linux, Windows and Node. There is no browser build.

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: [email protected].

See THIRD_PARTY_NOTICES.md.

Citation

@software{align_2026,
  title  = {Align: Word-timestamp refinement for any transcript, on device},
  author = {Desert Ant Labs},
  year   = {2026},
  url    = {https://huggingface.co/desert-ant-labs/align},
}

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