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* feat: initial rnn-t * feat: working with BS>1 * feat: add lstm test * feat: test passing hidden * clean: cleanup * feat: specify start * feat: way faster lstm & model * fix: default batch size * feat: optimization * fix: fix metrics * fix: fix feature splicing * feat: cleaner stacktime * clean: remove unused import * clean: remove extra prints * fix: fix tests and happy llvm * feat: have the librispeech dataset in its own dir * clean: unused variable * feat: no longer need numpy for the embedding + slightly more memory efficient lstm * fix: forgot to remove something that broke tests * feat: use relative paths * feat: even faster * feat: remove pointless transposes in StackTime * fix: correct forward * feat: switch to soundfile for loading and fix some leaks * feat: add comment about initial dataset setup * feat: jit more things * feat: default batch size back to 1 larger than 1 is broken again :( and even in the reference implementation it gives worse results
Each model should be a clean single file. They are imported from the top level `models` directory It should be capable of loading weights from the reference imp. We will focus on these 5 models: # Resnet50-v1.5 (classic) -- 8.2 GOPS/input # Retinanet # 3D UNET (upconvs) # RNNT # BERT-large (transformer) They are used in both the training and inference benchmark: https://mlcommons.org/en/training-normal-21/ https://mlcommons.org/en/inference-edge-30/ And we will submit to both. NOTE: we are Edge since we don't have ECC RAM