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BarathwajAnandan/cohere-transcribe-03-2026-CoreML-6bit

sourceHugging Facegpl-3.0updated 6mo agoView on Hugging Face

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Model Card

Cohere Transcribe 03-2026 - CoreML (6-bit Palettized)

This is a CoreML conversion of CohereLabs/cohere-transcribe-03-2026 with 6-bit palettization for on-device speech recognition on Apple Silicon (macOS / iOS).

Model Details

Property	Value
Base model	CohereLabs/cohere-transcribe-03-2026
Format	CoreML `.mlpackage`
Precision	Float16 + 6-bit palettized weights
Compression	2.7× smaller than FP16
Total size	~1.4 GB
Compute	Apple GPU (recommended), CPU, or Neural Engine
Min deployment	macOS 13 / iOS 16

Benchmark Results

Accuracy — LibriSpeech test-clean

Variant	Samples	WER vs Ground Truth	Size
Palettize6 (this model)	2619/2620 (full)	2.58%	1.4 GB
FP16 (baseline)	200	2.63%	3.8 GB
Palettize8	200	2.65%	1.85 GB

Speed — Apple M4 Pro, GPU compute

Metric	Value
Real-time factor	35.9× real-time
30s audio inference	~836 ms

Files

File	Size	Description
`cohere_frontend.mlpackage`	1.5 MB	Audio feature extraction (pre-emphasis + mel spectrogram)
`cohere_encoder.mlpackage`	1.3 GB	Encoder (audio → hidden states)
`cohere_decoder_fullseq_masked.mlpackage`	109 MB	Full-sequence decoder (first token)
`cohere_decoder_cached.mlpackage`	109 MB	KV-cached decoder (autoregressive generation)
`coreml_manifest.json`	292 KB	Pipeline config (tokenizer, chunking, dimensions)

Usage

Swift CLI

bash

pure_coreml_asr_cli \
  --audio input.wav \
  --artifacts-dir <path-to-this-repo> \
  --compute gpu \
  --decoder-mode cached

Swift App Integration

1.Add all .mlpackage files to your Xcode project
2.Load coreml_manifest.json at runtime for tokenizer IDs and pipeline config
3.Use MLModel with .computeUnits = .cpuAndGPU for best performance
4.Process audio in 30-second chunks with 5-second overlap (params in manifest)

Pipeline Architecture

Audio → Frontend (mel spectrogram) → Encoder → Decoder (autoregressive) → Text

The decoder runs in two phases:

1.Full-sequence masked decoder generates the first token from encoder output
2.KV-cached decoder generates subsequent tokens autoregressively

Compression Details

6-bit palettization clusters each weight tensor into 2⁶ = 64 centroids via k-means, storing only 6-bit indices + a small lookup table. This achieves 2.7× compression over FP16 with virtually no accuracy or speed loss on Apple GPU.

Applied to: encoder, full-sequence decoder, and cached decoder. The frontend remains FP32 (tiny model, no benefit from compression).

License

This converted model is released under GPL-3.0.

The upstream base model, CohereLabs/cohere-transcribe-03-2026, is Apache-2.0 licensed. This repo contains the CoreML conversion and quantized derivative artifacts.