The published int8 .pte predated the schema flow: it carried no get_model_schema method and a [1,128,vocab] decode contract, so react-native-executorch rejected it at load ('Cannot match symbolic dynamic with concrete constant'). Re-exported from export-scripts main; encode now declares a 201..480000 range and decode [1,1,vocab], matching the fp32 variant. Verified by transcription.
quantized duplicated precisions.json, which is authoritative for the
quantized/non-quantized partition. default expressed a per-repository
recommendation nothing consumed: the library resolves its own per-platform
default. Both are gone from the schema.
Adds library_name to the model card, and pipeline_tag where it was missing.
Brings the config and card licenses into agreement, and repairs the variant
declarations that did not match the files actually published here.
Encode runs on Vulkan, decode stays on XNNPACK. Exported with the
conv1d-as-conv2d lowering, the slice-based STFT magnitude, hoisted
cross-attention K/V and a head-major KV layout. The int8 build quantizes
every decode-side linear; encode is identical between the two.
Requires the ExecuTorch 1.4.1 Vulkan runtime from the v0.10.0-libs-1.4.1
artifacts.
perf(coreml): run decode on the CPU, not the Neural Engine
decode is a single-token program whose cost on device is dispatch rather than arithmetic, so it was paying Neural Engine overhead on every call under ExecuTorch's ALL default. Pinning only decode to CPU_ONLY (encode stays on ALL) measures 6.7 ms/step against 20.0 on an iPhone 16.
On 14.5s of real speech the whole transcribe is 1.5-3.1x faster depending on size, with a byte-identical transcript: this removes overhead, it does not change numerics.
Exported from export-scripts MR !18, built on main pins (ExecuTorch 1.4.1). Replaces an ExecuTorch 1.2.0 build of the same change; transcripts verified byte-identical on device at all six sizes.
perf(coreml): run decode on the CPU, not the Neural Engine
decode is a single-token program whose cost on device is dispatch rather than arithmetic, so it was paying Neural Engine overhead on every call under ExecuTorch's ALL default. Pinning only decode to CPU_ONLY (encode stays on ALL) measures 6.7 ms/step against 20.0 on an iPhone 16.
On 14.5s of real speech the whole transcribe is 1.5-3.1x faster depending on size, with a byte-identical transcript: this removes overhead, it does not change numerics.
Exported from export-scripts MR !18.
