I think the wenet-kws is a pretty good project, but I run the example and find some problems in it.
2021-12-02 06:53:06,227 DEBUG CV Batch 99/350 loss 0.00088373 acc 1.00000000 history loss 0.00451618
2021-12-02 06:53:07,493 DEBUG CV Batch 99/360 loss 0.01370203 acc 0.99000000 history loss 0.00447106
2021-12-02 06:53:08,590 DEBUG CV Batch 99/370 loss 0.00051790 acc 1.00000000 history loss 0.00438003
Namespace(dst_model='exp/mdtc_small/avg_10.pt', max_epoch=65536, min_epoch=0, num=10, src_path='exp/mdtc_small', val_best=True)
exp/mdtc_small/84.yaml {'cv_loss': 0.004494945056033555, 'epoch': 84, 'lr': 1e-06}
exp/mdtc_small/85.yaml {'cv_loss': 0.00420595490825506, 'epoch': 85, 'lr': 1e-06}
exp/mdtc_small/80.yaml {'cv_loss': 0.004422268416391168, 'epoch': 80, 'lr': 1.953125e-06}
exp/mdtc_small/86.yaml {'cv_loss': 0.00426848801635275, 'epoch': 86, 'lr': 1e-06}
exp/mdtc_small/40.yaml {'cv_loss': 0.005622736757912678, 'epoch': 40, 'lr': 0.000125}
exp/mdtc_small/12.yaml {'cv_loss': 0.014624565885012986, 'epoch': 12, 'lr': 0.0005}
exp/mdtc_small/19.yaml {'cv_loss': 0.01066642991893427, 'epoch': 19, 'lr': 0.0005}
exp/mdtc_small/67.yaml {'cv_loss': 0.004372600030651831, 'epoch': 67, 'lr': 7.8125e-06}
exp/mdtc_small/3.yaml {'cv_loss': 0.021692731020592233, 'epoch': 3, 'lr': 0.001}
exp/mdtc_small/64.yaml {'cv_loss': 0.004502788117951097, 'epoch': 64, 'lr': 1.5625e-05}
exp/mdtc_small/79.yaml {'cv_loss': 0.00467467649684301, 'epoch': 79, 'lr': 1.953125e-06}
exp/mdtc_small/61.yaml {'cv_loss': 0.005073365263766749, 'epoch': 61, 'lr': 1.5625e-05}
exp/mdtc_small/54.yaml {'cv_loss': 0.00454669024873601, 'epoch': 54, 'lr': 3.125e-05}
exp/mdtc_small/58.yaml {'cv_loss': 0.004749447677743553, 'epoch': 58, 'lr': 3.125e-05}
exp/mdtc_small/49.yaml {'cv_loss': 0.005313400925359438, 'epoch': 49, 'lr': 6.25e-05}
exp/mdtc_small/1.yaml {'cv_loss': 0.026169386569775077, 'epoch': 1, 'lr': 0.001}
exp/mdtc_small/29.yaml {'cv_loss': 0.008564605423066796, 'epoch': 29, 'lr': 0.00025}
exp/mdtc_small/11.yaml {'cv_loss': 0.015435188782970496, 'epoch': 11, 'lr': 0.0005}
exp/mdtc_small/63.yaml {'cv_loss': 0.0047399615163172355, 'epoch': 63, 'lr': 1.5625e-05}
exp/mdtc_small/82.yaml {'cv_loss': 0.004607287351367818, 'epoch': 82, 'lr': 1e-06}
exp/mdtc_small/83.yaml {'cv_loss': 0.004391176717157161, 'epoch': 83, 'lr': 1e-06}
exp/mdtc_small/97.yaml {'cv_loss': 0.0043467484049330655, 'epoch': 97, 'lr': 1e-06}
exp/mdtc_small/28.yaml {'cv_loss': 0.007137542134704971, 'epoch': 28, 'lr': 0.00025}
exp/mdtc_small/98.yaml {'cv_loss': 0.004537665521999014, 'epoch': 98, 'lr': 1e-06}
exp/mdtc_small/52.yaml {'cv_loss': 0.004649514840120405, 'epoch': 52, 'lr': 3.125e-05}
exp/mdtc_small/56.yaml {'cv_loss': 0.00448786976629719, 'epoch': 56, 'lr': 3.125e-05}
exp/mdtc_small/39.yaml {'cv_loss': 0.005829682733898623, 'epoch': 39, 'lr': 0.000125}
exp/mdtc_small/71.yaml {'cv_loss': 0.004532397123410156, 'epoch': 71, 'lr': 3.90625e-06}
exp/mdtc_small/65.yaml {'cv_loss': 0.004342411889022515, 'epoch': 65, 'lr': 7.8125e-06}
exp/mdtc_small/35.yaml {'cv_loss': 0.007356636884470326, 'epoch': 35, 'lr': 0.00025}
exp/mdtc_small/92.yaml {'cv_loss': 0.004499009573627954, 'epoch': 92, 'lr': 1e-06}
exp/mdtc_small/73.yaml {'cv_loss': 0.004482646395611368, 'epoch': 73, 'lr': 3.90625e-06}
exp/mdtc_small/72.yaml {'cv_loss': 0.004230009907933799, 'epoch': 72, 'lr': 3.90625e-06}
exp/mdtc_small/25.yaml {'cv_loss': 0.007332921293077766, 'epoch': 25, 'lr': 0.00025}
exp/mdtc_small/62.yaml {'cv_loss': 0.005137007407078835, 'epoch': 62, 'lr': 1.5625e-05}
exp/mdtc_small/13.yaml {'cv_loss': 0.010610442829166993, 'epoch': 13, 'lr': 0.0005}
exp/mdtc_small/55.yaml {'cv_loss': 0.004722100089821484, 'epoch': 55, 'lr': 3.125e-05}
exp/mdtc_small/99.yaml {'cv_loss': 0.004347019938513233, 'epoch': 99, 'lr': 1e-06}
exp/mdtc_small/26.yaml {'cv_loss': 0.007253867137949056, 'epoch': 26, 'lr': 0.00025}
exp/mdtc_small/21.yaml {'cv_loss': 0.010421987414134434, 'epoch': 21, 'lr': 0.0005}
exp/mdtc_small/32.yaml {'cv_loss': 0.0077251731347400005, 'epoch': 32, 'lr': 0.00025}
best val scores = [0.00420595 0.00423001 0.00423915 0.00424814 0.00426849 0.00432958
0.00434241 0.00434298 0.00434621 0.00434675]
selected epochs = [85 72 77 81 86 89 65 87 95 97]
['exp/mdtc_small/85.pt', 'exp/mdtc_small/72.pt', 'exp/mdtc_small/77.pt', 'exp/mdtc_small/81.pt', 'exp/mdtc_small/86.pt', 'exp/mdtc_small/89.pt', 'exp/mdtc_small/65.pt', 'exp/mdtc_small/87.pt', 'exp/mdtc_small/95.pt', 'exp/mdtc_small/97.pt']
Processing exp/mdtc_small/85.pt
Processing exp/mdtc_small/72.pt
Processing exp/mdtc_small/77.pt
Processing exp/mdtc_small/81.pt
Processing exp/mdtc_small/86.pt
Processing exp/mdtc_small/89.pt
Processing exp/mdtc_small/65.pt
Processing exp/mdtc_small/87.pt
Processing exp/mdtc_small/95.pt
Processing exp/mdtc_small/97.pt
Saving to exp/mdtc_small/avg_10.pt
kernel_size = 5
stack_size = 4
num_stack = 3
hidden_dim = 32
Receptive Fields: 184
2021-12-02 06:53:14,000 INFO Checkpoint: loading from checkpoint exp/mdtc_small/avg_10.pt for GPU
Progress batch 0
Progress batch 10
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Progress batch 50
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...
Progress batch 280
How to check the frr results of "nihao wenwen" and "hi, xiaowen", there are only one result in print
?
Thanks very much to answer these questions in your spare time! I believe the wenet-kws will be better and better!