Core ML build of
moondream/parakeet-ultra
, a full-precision
post-training of
nvidia/parakeet-tdt-0.6b-v3
. Same architecture, 25 languages, tokenizer and output conventions as
v3; drop-in for the v3 component contract. Encoder is int8 linear per-channel (595 MB); decoder and joint are
re-exported from the Ultra checkpoint; preprocessor and vocabulary are the v3 ones.
Usage (FluidAudio ≥ next release)
let models =tryawaitAsrModels.downloadAndLoad(version: .ultra)
let asr =AsrManager()
tryawait asr.initialize(models: models)
let result =tryawait asr.transcribe(audioFileURL)
swift run fluidaudiocli transcribe audio.wav --model-version ultra
Accuracy (FluidAudio, M-series Mac, full corpora, corpus-level WER)
v3 (Core ML)
ultra (this repo)
LibriSpeech test-clean (2620 files)
2.27 %
2.13 %
LibriSpeech test-other (2939 files)
4.12 %
3.81 %
FLEURS, 24 languages × 100 samples, mean
14.81 %
11.67 %
Ultra wins all 24 FLEURS languages. The int8 encoder scores the same WER as an fp16 export (2.13 % / 3.79 %). Speed
is within a few percent of v3 run back to back on the same machine (test-clean 126.7× vs 128.6× on the ANE).
Files
File
Notes
Encoder.mlmodelc
595 MB, int8 linear per-channel, iOS 17+/macOS 14+, fixed 15 s window (
mel [1,128,1501]
→
encoder [1,1024,188]
)
Decoder.mlmodelc
RNNT prediction net, fp16, iOS 17+
JointDecisionv3.mlmodelc
single-step joint + top-K 64, fp16, iOS 17+
Preprocessor.mlmodelc
v3 mel front-end
parakeet_vocab.json
,
parakeet_v3_vocab.json
v3 vocabulary
Conversion recipe:
FluidInference/mobius
models/stt/parakeet-ultra/coreml
.
License CC-BY-4.0, as the upstream checkpoint.
Runs of FluidInference parakeet-ultra-coreml on huggingface.co
771
Total runs
305
24-hour runs
620
3-day runs
771
7-day runs
771
30-day runs
More Information About parakeet-ultra-coreml huggingface.co Model
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