From d37c159174f12171c64df19d68d6be0b6624294d Mon Sep 17 00:00:00 2001 From: Daniel Povey Date: Wed, 19 Oct 2022 13:41:58 +0800 Subject: [PATCH] Revert model.py so there are no constraints on the output. --- .../ASR/pruned_transducer_stateless7/model.py | 12 ------------ 1 file changed, 12 deletions(-) diff --git a/egs/librispeech/ASR/pruned_transducer_stateless7/model.py b/egs/librispeech/ASR/pruned_transducer_stateless7/model.py index 0c1bd9551..ee88a9159 100644 --- a/egs/librispeech/ASR/pruned_transducer_stateless7/model.py +++ b/egs/librispeech/ASR/pruned_transducer_stateless7/model.py @@ -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(),