This is just a follow up. In the mlperf tiny repo in benchmark/training/keyword_spotting there is a python script make_bin_files.py that can be adapted to prepare the input for the models found in the mlperf tiny kws benchmark. Since the preparation applies some filters on the input wav files, a direct conversion back from the npy arrays to the wav files is not possible.
I managed to get successful inference runs using microTVM with kws in the quantized (int8) version. However in the float32 version, the generated aot project does not classify the samples correctly. What could be the reason for that? With the visual wake words VWW benchmark from mlperf tiny, I have tried the same. In this case, both the int8 version and the float32 version work well with microTVM. Also converting the input back to the original photos is also possible, because no filters are applied. Best regards, Benedikt --- [Visit Topic](https://discuss.tvm.apache.org/t/microtvm-mlperf-tiny-input-data/18006/2) to respond. You are receiving this because you enabled mailing list mode. To unsubscribe from these emails, [click here](https://discuss.tvm.apache.org/email/unsubscribe/2d698c0cce5cd16ba88c780f47ca26b79091fbe40e5b47611b1c094d6d659193).
