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Modification about random combine #452
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mod_inputs.append(inputs[num_inputs - 1]) | ||
for i in range(num_inputs): | ||
mod_inputs.append(inputs[i]) |
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Do we need mod_inputs
any more?
We have to watch out, here, because this would invalidate already-trained models. If we have uploaded a trained model from this recipe, I suggest we could re-upload a new one. |
OK, I will re-upload a new one. |
Have you finished uploading the pre-trained models? Please also update RESULTS.md |
@@ -1072,7 +1077,6 @@ class RandomCombine(nn.Module): | |||
def __init__( | |||
self, | |||
num_inputs: int, | |||
num_channels: int, |
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please also update the test code
num_channels=num_channels, |
@@ -1072,7 +1077,6 @@ class RandomCombine(nn.Module): | |||
def __init__( | |||
self, | |||
num_inputs: int, | |||
num_channels: int, |
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Please also remove the following doc:
icefall/egs/librispeech/ASR/pruned_transducer_stateless5/conformer.py
Lines 1067 to 1069 in 8761452
All but the last input will have a linear transform before we | |
randomly combine them; these linear transforms will be initialized | |
to the identity transform. |
Thanks for the reminder. See #467. |
This PR modifies the random combining of different encoder layers.
RandomCombine
class.Experimental results can be found on #431.