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Revert model.py so there are no constraints on the output.

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This commit is contained in:
Daniel Povey 2022-10-19 13:41:58 +08:00
parent 45c38dec61
commit d37c159174
1 changed files with 0 additions and 12 deletions
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12
egs/librispeech/ASR/pruned_transducer_stateless7/model.py
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@ -19,12 +19,10 @@ import k2
import torch
import torch.nn as nn
from encoder_interface import EncoderInterface
from scaling import random_clamp
from icefall.utils import add_sos
class Transducer(nn.Module):
"""It implements https://arxiv.org/pdf/1211.3711.pdf
"Sequence Transduction with Recurrent Neural Networks"
@ -142,12 +140,6 @@ class Transducer(nn.Module):
lm = self.simple_lm_proj(decoder_out)
am = self.simple_am_proj(encoder_out)
if self.training:
lm = random_clamp(lm, min=-8.0, max=2.0, prob=0.5,
reflect=0.1)
am = random_clamp(am, min=-5.0, max=5.0, prob=0.5,
reflect=0.1)
with torch.cuda.amp.autocast(enabled=False):
simple_loss, (px_grad, py_grad) = k2.rnnt_loss_smoothed(
lm=lm.float(),
@ -183,10 +175,6 @@ class Transducer(nn.Module):
# prior to do_rnnt_pruning (this is an optimization for speed).
logits = self.joiner(am_pruned, lm_pruned, project_input=False)
if self.training:
logits = random_clamp(logits, -8.0, 2.0, prob=0.5,
reflect=0.1)
with torch.cuda.amp.autocast(enabled=False):
pruned_loss = k2.rnnt_loss_pruned(
logits=logits.float(),