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aufklarer/Parakeet-TDT-v3-CoreML-INT8

sourceHugging Facecc-by-4.0updated 3mo agoView on Hugging Face

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Model Card

Parakeet TDT v3 — CoreML INT8

CoreML conversion of NVIDIA Parakeet-TDT 0.6B v2 with INT8-quantized encoder for Apple Neural Engine acceleration.

Models

Model	Description	Compute	Quantization
`encoder.mlmodelc`	FastConformer encoder (24L, 1024 hidden)	CPU + Neural Engine	INT8 palettized
`decoder.mlmodelc`	LSTM prediction network (2L, 640 hidden)	CPU + Neural Engine	FP16
`joint.mlmodelc`	TDT dual-head joint (token + duration logits)	CPU + Neural Engine	FP16

Additional Files

File	Description
`vocab.json`	SentencePiece vocabulary (1024 tokens)
`config.json`	Model configuration

Notes

—INT8 vs INT4: INT8 uses 8-bit palettization for the encoder, offering higher accuracy than INT4 at the cost of ~2x encoder weight size.
—Mel preprocessing is done in Swift using Accelerate/vDSP (not CoreML) because torch.stft tracing bakes audio length as a constant, breaking per-feature normalization for variable-length inputs.
—Encoder uses EnumeratedShapes (100–3000 mel frames, covering 1–30s audio) to avoid BNNS crashes with dynamic shapes.

Usage

Used by speech-swift ParakeetASR module:

swift

let model = try await ParakeetASRModel.fromPretrained(modelId: ParakeetASRModel.int8ModelId)
let text = try model.transcribeAudio(samples, sampleRate: 16000)

—Guide: soniqo.audio/guides/parakeet
—Docs: soniqo.audio
—GitHub: soniqo/speech-swift