Canary is a
FastConformer encoder + Transformer attention encoder-decoder (AED)
ASR model (25 European languages, 16384-token SentencePiece BPE). It is decoded
autoregressively: the transformer decoder cross-attends to the encoder output and
emits tokens greedily until EOS (id 3), with a 1024→16384 projection head.
Files
File
Role
Precision
Preprocessor.mlmodelc
waveform
[1,240000]
→ mel
[1,128,1501]
fp32 (CPU)
EncoderInt4.mlmodelc
mel → encoder
[1,1024,188]
int4 (ANE, iOS18)
DecoderInt4.mlmodelc
autoregressive transformer → hidden
[1,256,1024]
int4 (ANE, iOS18)
Projection.mlmodelc
hidden
[1,1024]
→ logits
[1,16384]
fp16 (ANE)
vocab.json
16384 SentencePiece pieces (
id → piece
)
—
projection_weights.npz
raw projection weights (for Python reference pipelines)
fp16
: exact parity with PyTorch, iOS 17, ~1.8 GB (not included here by default).
int8 per-channel decodes correctly only on CPU (crashes the GPU/ANE MPSGraph
backend), so it is not recommended; use int4 for an ANE-resident small build.
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