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Add max as well as min to G_prime

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Daniel Povey 2022-07-24 06:50:05 +08:00
parent 6290fcb535
commit 48ac7e0bc3
1 changed files with 6 additions and 1 deletions
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7
egs/librispeech/ASR/pruned_transducer_stateless7/optim.py
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@ -952,6 +952,7 @@ param_rms_smooth1: Smoothing proportion for parameter matrix, if assumed rank of
"""
This function returns a modified/smoothed version of the parameter covariance
P_prime.
Args:
group: dict to look up config values
p_shape: The shape of the parameter we are optimizing
@ -1020,10 +1021,14 @@ param_rms_smooth1: Smoothing proportion for parameter matrix, if assumed rank of
# Make sure G_prime has unit mean and no eigenvalue is super small. Note, G_prime
# is already diagonalized, the variable G_prime is just the tensor of eigenvalues.
G_prime_mean = _mean(G_prime, exclude_dims=[0], keepdim=True)
G_prime_smooth = 0.001
G_prime_smooth = 0.0001
# make sure G_prime has no zero eigs, and is unit mean.
G_prime = ((G_prime + eps + G_prime_smooth * G_prime_mean) /
(G_prime_mean * (1+G_prime_smooth) + eps))
# it now has unit mean..
G_prime_max = 400.0
G_prime = 1. / (1./G_prime + 1./G_prime_max) # apply max
G_prime_rms = G_prime.sqrt()
G_prime_scale = G_prime_rms.unsqueeze(-1) * G_prime_rms.unsqueeze(-2)
# P_gnorm is a version of P_prime that is multiplied by G (actually