Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

Prevent large values in conv module in wav2vec2_module.py in SSL recipe #1593

Open
wants to merge 1 commit into
base: master
Choose a base branch
from
Open

Prevent large values in conv module in wav2vec2_module.py in SSL recipe #1593

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
7 changes: 7 additions & 0 deletions egs/librispeech/SSL/zipformer/wav2vec2_module.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -22,12 +22,15 @@
from typing import List, Tuple

import numpy as np
import random
from scaling import penalize_abs_values_gt
import torch
import torch.nn as nn
import torch.nn.functional as F
from utils import Fp32GroupNorm, Fp32LayerNorm, TransposeLast



class ConvFeatureExtractionModel(nn.Module):
def __init__(
self,
Expand Down Expand Up @@ -105,4 +108,8 @@ def forward(self, x):
for conv in self.conv_layers:
x = conv(x)

if self.training and random.random() < 0.2:
x = penalize_abs_values_gt(x, limit=1000.0, penalty=1.0e-05,
name=(self.name if hasattr(self, 'name') else 'ConvFeatureExtractionModel'))

return x
2 changes: 1 addition & 1 deletion egs/librispeech/SSL/zipformer/zipformer.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -789,7 +789,7 @@ def forward(
selected_attn_weights = attn_weights[0:1]
if torch.jit.is_scripting() or torch.jit.is_tracing():
pass
elif not self.training and random.random() < float(self.const_attention_rate):
elif self.training and random.random() < float(self.const_attention_rate):
# Make attention weights constant. The intention is to
# encourage these modules to do something similar to an
# averaging-over-time operation.
Expand Down
Loading