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