README.md

---
license: other
license_name: desert-ant-labs-source-available-1.0
license_link: https://license.desertant.com/1.0
language:
- bg
- cs
- da
- de
- el
- en
- es
- et
- fi
- fr
- hr
- hu
- it
- lt
- lv
- mt
- nl
- pl
- pt
- ro
- ru
- sk
- sl
- sv
- uk
tags:
- speech
- speech-recognition
- transcription
- word-timestamps
- on-device
- core-ml
- apple-neural-engine
- multilingual
pipeline_tag: automatic-speech-recognition
---

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# Voz

Transcribe 10 minutes in 2 seconds.

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

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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.

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## 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>
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