Restore the card after the sync flattened it again

README.md

@@ -40,6 +40,7 @@ tags:
 pipeline_tag: automatic-speech-recognition
 ---
 
+<!-- card-header:start (generated from manifest.json, edit below this block) -->
 # Voz
 
 Every word, with the time it was said.
@@ -47,3 +48,203 @@ Every word, with the time it was said.
 On-device speech recognition: transcripts with word-level timestamps, 25 languages.
 
 - **SDKs, install and examples:** https://github.com/Desert-Ant-Labs/desert-ant-core/blob/main/docs/models/voz.md
+
+<!-- card-header:end -->
+
+Transcribes speech to text with word-level timestamps, in 25 languages, with the
+whole graph resident on the Neural Engine. Half an hour of audio takes about
+seven seconds on an M3 Ultra, and peak memory does not grow with the length of
+the recording.
+
+> `"We made the bet to build for iOS"` - each word with a start and an end, so a
+> range is enough to cut on.
+
+## Try it
+
+Ships as an Apple SwiftPM package: **[Desert-Ant-Labs/desert-ant-core](https://github.com/Desert-Ant-Labs/desert-ant-core)**.
+
+- **iOS / iPadOS / Mac Catalyst / macOS / tvOS / visionOS:** the Swift SDK (Swift
+  Package Manager). The models below are downloaded on demand and cached, so
+  nothing is bundled into your app.
+- Apple-only. The SDK drives Core ML directly to keep the graph on the Neural
+  Engine, which has no equivalent on the other backends.
+
+```swift
+let voz = try await Voz()
+let result = try await voz.transcribe(url)
+result.text                       // "We made the bet to build for iOS..."
+result.words.first?.start         // 0.32
+result.words.first?.end           // 0.58
+```
+
+## Files
+
+| File | What it is |
+|---|---|
+| `encoder.mlmodelc` | Acoustic encoder |
+| `mel.mlmodelc` | Audio frontend |
+| `decoder.mlmodelc` | Decoder |
+| `meta.json` | Geometry the runtime reads instead of hardcoding |
+| `vocab.json` | SentencePiece vocabulary |
+| `embedding.f16` | Token embedding table |
+
+Artifact names describe roles rather than the network behind them, so replacing
+the recogniser is a new upload rather than an SDK change.
+
+The models are shipped **compiled** (`.mlmodelc`). Keep them that way: an
+`.mlpackage` is recompiled on every launch and loads far more slowly.
+
+## Architecture
+
+A three-stage Core ML cascade over a log-mel spectrogram, dispatched from Swift:
+
+- **Frontend**: a log-mel spectrogram computed inside Core ML, normalized over the frames
+  that hold audio rather than the whole padded window.
+- **Encoder**: a conformer-style acoustic encoder over a fixed 15 s window, producing one
+  frame every 80 ms.
+- **Decoder**: a transducer that emits a token and a duration at each step, run with sixteen
+  independent windows batched into the lanes of a single dispatch.
+
+Longer audio is cut into consecutive windows at pauses, transcribed independently, and
+joined on the longest run of words two neighbouring windows agree on. Every stage runs on
+the Neural Engine with no CPU or GPU fallback.
+
+## Inputs and outputs
+
+- **Input:** mono audio at any sample rate; the SDK resamples and downmixes.
+- **Output:** the transcript, plus every word with a start and an end in seconds.
+
+## Accuracy
+
+Ten minutes of audio on an M3 Ultra, release build, warm:
+
+| | |
+|---|---|
+| Speed | **2.1 s for 611 s of audio (~290x real time)** on long files; 50-62x transcribing single short utterances, where every clip pays for a full 15 s window |
+| Word error rate | **7.40%** averaged over six Open ASR Leaderboard sets, against 7.00% for Whisper large-v3-turbo |
+| Long-form word error rate | **2.83%** on half an hour of narration scored against the book, against 2.72% for Whisper large-v3-turbo on the same span |
+| Word timestamps | starts 83 ms, ends 95 ms mean absolute error against a forced aligner |
+| Neural Engine residency | 100%, with no CPU or GPU fallback |
+| Size on disk | 467 MB |
+| Load | ~0.2 s warm; ~20 s once per install while Core ML specializes |
+
+### English, on the Open ASR Leaderboard
+
+Scored on the [Open ASR Leaderboard](https://huggingface.co/spaces/hf-audio/open_asr_leaderboard)
+datasets with its own text normalizer. Whisper's figures are the leaderboard's,
+on the same dataset configurations.
+
+| dataset | Voz | Whisper large-v3-turbo |
+|---|---:|---:|
+| LibriSpeech test-clean | 2.19% | 2.13% |
+| LibriSpeech test-other | 3.86% | 3.70% |
+| GigaSpeech | 9.70% | 8.47% |
+| SPGISpeech | 3.86% | 2.79% |
+| Earnings-22 | 12.97% | 11.07% |
+| AMI | 11.84% | **13.87%** |
+| **average** | **7.40%** | **7.00%** |
+
+Close overall, two points better on meetings, behind on prepared and read
+speech. It gets there in 467 MB entirely on the Neural Engine, against 1.6 GB
+for Whisper large-v3-turbo.
+
+**Expect the conversational figures, not the LibriSpeech one.** Read speech in a
+clean recording scores around 2%; meetings, earnings calls and podcast audio
+score 10-13%, and most real material is nearer the second group. Roughly one word
+in ten wanting a look is the honest expectation for a podcast.
+
+VoxPopuli and TEDLIUM are omitted: the leaderboard's scripts name a 628-utterance
+shard of the former, which is not the audio its published figure measures, and
+the latter's config carries no rows.
+
+### Word timestamps
+
+Every word carries a start and an end, scored against [torchaudio's MMS_FA
+forced aligner](https://pytorch.org/audio/stable/generated/torchaudio.pipelines.MMS_FA.html):
+
+| | words | start | end | ends within 80 ms | within 200 ms |
+|---|---:|---:|---:|---:|---:|
+| English, LibriSpeech | 6295 | 83 ms | 95 ms | 60% | 90% |
+| German, FLEURS | 939 | 80 ms | 92 ms | 62% | 92% |
+
+Ends are the harder of the two. The recogniser reports how far to skip after
+each token rather than where a word stops, which overshoots into the pause that
+follows it, so ends are trimmed back using the audio. Forced alignment is itself
+approximate at the tens of milliseconds level, so treat these as agreement with a
+good aligner rather than absolute truth. Much of the residual is the 80 ms frame
+resolution, which is the floor for any word time this export produces.
+
+### Every supported language, on long audio
+
+Ten minutes per language, built by concatenating [FLEURS](https://huggingface.co/datasets/google/fleurs)
+test utterances so that each file crosses about forty analysis boundaries. A
+single FLEURS utterance is shorter than one window and so measures nothing about
+how consecutive windows are joined, which is most of what happens on real
+material. 4.2 hours in total.
+
+| | WER | | WER | | WER |
+|---|---:|---|---:|---|---:|
+| it | 3.31% | cs | 14.27% | hu | 21.26% |
+| pt | 6.08% | sk | 15.42% | et | 21.37% |
+| uk | 6.40% | hr | 16.92% | sv | 21.67% |
+| ru | 6.57% | fi | 18.17% | mt | 22.01% |
+| en | 7.36% | ro | 20.95% | da | 24.32% |
+| de | 8.12% | | | lt | 26.89% |
+| es | 9.01% | | | lv | 30.57% |
+| nl | 9.84% | | | sl | 33.85% |
+| pl | 9.99% | | | el | 39.46% |
+| bg | 12.39% | | | | |
+| fr | 12.78% | | | | |
+
+Aggregate 16.58%, median 283x real time. Throughput varies only 12% across
+languages, because cost follows how much audio there is rather than what is in
+it.
+
+Read the spread before choosing a language. The nine best are usable as-is; the
+tail above 20% will cost more to correct than to retype for many uses, and
+reflects where the underlying recogniser is already known to be weak rather than
+anything specific to this export. This is read speech in clean recordings, so
+treat it as a ranking rather than as a number to expect on your own audio.
+
+## Languages
+
+Bulgarian, Croatian, Czech, Danish, Dutch, English, Estonian, Finnish, French, German, Greek,
+Hungarian, Italian, Latvian, Lithuanian, Maltese, Polish, Portuguese, Romanian, Russian,
+Slovak, Slovenian, Spanish, Swedish, and Ukrainian.
+
+Accuracy varies widely across them; see the per-language table above before choosing one.
+
+## Built on
+
+- [Parakeet TDT 0.6B v3](https://huggingface.co/nvidia/parakeet-tdt-0.6b-v3) - NVIDIA
+  (CC BY 4.0): the base recogniser, converted to Core ML and compressed for this export.
+  Weight values are otherwise unchanged.
+- [FLEURS](https://huggingface.co/datasets/google/fleurs) (CC BY 4.0): evaluation audio for
+  the per-language table above.
+
+See [`THIRD_PARTY_NOTICES.md`](THIRD_PARTY_NOTICES.md). FLEURS is not redistributed here.
+
+<!-- card-footer:start (generated from manifest.json, edit above this block) -->
+## License
+
+[Desert Ant Labs Source-Available License](https://license.desertant.com/1.0). 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`](THIRD_PARTY_NOTICES.md).
+
+## Citation
+
+```bibtex
+@software{voz_2026,
+  title  = {Voz: On-device speech recognition: transcripts with word-level timestamps, 25 languages},
+  author = {Desert Ant Labs},
+  year   = {2026},
+  url    = {https://huggingface.co/desert-ant-labs/voz},
+}
+```
+
+---
+
+© 2026 Desert Ant Labs · <https://desertant.com>
+<!-- card-footer:end -->