Cohere Transcribe 03-2026 โ€” CoreML 4-bit (Palettize4)

CoreML conversion of CohereLabs/cohere-transcribe-03-2026 with 4-bit palettization (16 clusters per weight block) and cross-attention K/V pre-computation optimization.

Model Details

Property Value
Base model CohereLabs/cohere-transcribe-03-2026
Compute precision FP16 (activations), 4-bit palettized (weights)
Frontend precision FP32 (STFT/log-mel)
Total size ~1.0 GB
WER (LibriSpeech test-clean, 500 samples) 2.50%
Speed (GPU only) ~48x realtime
Speed (ANE+GPU async) ~72x realtime
Platform macOS 13+ / Apple Silicon

Pipeline Components

Package Size Role
cohere_frontend.mlpackage 1.5 MB Audio โ†’ log-mel features (FP32)
cohere_encoder.mlpackage 893 MB Conformer encoder (palettize4)
cohere_cross_kv_projector.mlpackage 8.1 MB Pre-compute cross-attn K/V once per chunk
cohere_decoder_cached.mlpackage 65 MB Autoregressive decoder with KV cache
cohere_decoder_fullseq_masked.mlpackage 73 MB Full-sequence decoder (for validation)

Usage

# Build Swift CLI
cd swift_runner && swift build -c release

# Run (GPU only)
.build/release/pure_coreml_asr_cli \
  --audio input.mp3 \
  --artifacts-dir ./artifacts \
  --compute gpu

# Run (ANE+GPU heterogeneous, ~72x realtime)
.build/release/pure_coreml_asr_cli \
  --audio input.mp3 \
  --artifacts-dir ./artifacts \
  --compute gpu \
  --compute-split ane_small

Variants

Variant Repo Size WER
4-bit (this) CoreML-4bit 1.0 GB 2.50%
6-bit CoreML-6bit 2.9 GB 2.43%
FP16 CoreML-fp16 ~3.5 GB baseline

License

Apache 2.0 (following base model license)

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