Cohere Transcribe for Glade
Glade is a native Swift library for on-device speech inference on Apple
silicon. This repository supplies converted model assets and configuration for
Glade's model-specific host runtime. Glade's runtime source is currently private.
Glade conversion of Cohere Transcribe,
pinned to revision 76b8b23e8607f35f0265a23d481b338fb0e26aea.
The upstream model and converted weights use Apache License 2.0.
Requires physical Apple silicon with macOS 27 or iOS 27 and Glade's model-specific
host runtime. These graphs do not accept raw audio directly. The host provides
the log-mel frontend, language prompt, token embeddings, autoregressive cache
management and tokenization. See metadata.json, decoder-graph.json and
prompts.json for graph capacities and prompts. The encoder retains full
bidirectional context within each chunk; this is offline transcription.
The 48-layer encoder uses W8A16 with selected convolution output and relative
position projections retained in FP16. Four encoder stages use the complete
Fourier relative-position formulation. The eight-layer decoder uses W8A16,
shared batch sizes 1/8 and cache capacities 64/128/256/512/1024. It retains all
previous states within a chunk. Host embedding tables use FP32.
Both encoder and decoder prefer ANE. The qualified runtime uses two encoder
requests in flight, up to eight independently decoded chunks, reused output
buffers and quiet-boundary chunks of at most 35 seconds. Every input sample is
retained; no words or attention positions are intentionally dropped.
Measured performance
M3 MacBook Air (16 GB), macOS 27 build 26A428; median warm transcription,
including frontend, encoder and decoder, excluding preparation, audio file I/O
and chunk planning. Three measured iterations after warmup.
The audio is JFK's “We choose to go to the Moon” speech: a 20-second
excerpt and the full 18-minute 15-second recording.
| Speech excerpt |
Warm transcription |
Audio / elapsed |
| 20 seconds |
0.411 s |
48.6× |
| 18 min 15 s |
11.949 s |
91.7× |
The long recording uses 36 chunks. Whisper-normalized WER is 42/2,220 (1.89%),
matching the independent FP32 reference after normalization. A separate trace
recorded 756 ANE predictions and zero GPU intervals in the measured process.
That establishes placement, not ALU utilization. English JFK is the initial
conversion validation; other supported languages have not yet been qualified.
No word timing is supplied by these graphs.
Source .aimodel files specialize on the target device. First preparation can
take several minutes. Architecture-specific AoT artifacts are omitted because
no significant first-load improvement has been demonstrated. Debug source
locations were removed, retaining graph signatures and operation counts.
Download one consistent Hugging Face snapshot and verify its Hub-provided file checksums.
Device specialization and loading
The complete published bundle was measured in one preparation pass on the same
Mac, after invalidating all seven assets through the public Core AI cache API:
| Preparation |
Time |
| Complete bundle, public specialization cache invalidated |
3 min 0.5 s |
| Complete bundle, subsequent cached process |
0.110 s |
Preparation includes model loading and any compilation requested by Core AI,
excluding warmup, transcription and the cache-invalidation call itself. The
first run prepared the subsampler, all four encoder stages, both decoder assets
and every supported static entry point. The cached measurement uses a new
process with the same executable and assets. Both produce the same transcript
as the previously qualified bundle.
The underlying ANE cache state is not fully observable. These measurements
are observed on this Mac, not guaranteed fresh-install times for every device;
OS/device/application changes or cache eviction can require specialization again.
