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Use penalize_abs_values_gt, not ActivationBalancer.

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This commit is contained in:
Daniel Povey 2022-10-22 20:15:21 +08:00
parent 7a55cac346
commit 466176eeff
1 changed files with 9 additions and 15 deletions
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24
egs/librispeech/ASR/pruned_transducer_stateless7/model.py
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@ -18,10 +18,11 @@
import k2
import torch
import torch.nn as nn
import random
from encoder_interface import EncoderInterface
from icefall.utils import add_sos
from scaling import ActivationBalancer
from scaling import penalize_abs_values_gt
class Transducer(nn.Module):
@ -68,18 +69,6 @@ class Transducer(nn.Module):
)
self.simple_lm_proj = nn.Linear(decoder_dim, vocab_size)
self.lm_balancer = ActivationBalancer(vocab_size,
channel_dim=-1,
min_positive=0, max_positive=1,
min_abs=0, max_abs=50.0,
max_factor=0.1)
self.am_balancer = ActivationBalancer(vocab_size,
channel_dim=-1,
min_positive=0, max_positive=1,
min_abs=0, max_abs=30.0,
max_factor=0.1)
def forward(
self,
x: torch.Tensor,
@ -150,8 +139,13 @@ class Transducer(nn.Module):
boundary[:, 2] = y_lens
boundary[:, 3] = x_lens
lm = self.lm_balancer(self.simple_lm_proj(decoder_out))
am = self.am_balancer(self.simple_am_proj(encoder_out))
lm = self.simple_lm_proj(decoder_out)
am = self.simple_am_proj(encoder_out)
if self.training and random.random() < 0.25:
lm = penalize_abs_values_gt(lm, 100.0, 1.0e-04)
if self.training and random.random() < 0.25:
am = penalize_abs_values_gt(am, 30.0, 1.0e-04)
with torch.cuda.amp.autocast(enabled=False):
simple_loss, (px_grad, py_grad) = k2.rnnt_loss_smoothed(