idk, hopefully this works

1 files changed, 4 insertions, 9 deletions

diff --git a/swr2_asr/model_deep_speech.py b/swr2_asr/model_deep_speech.py
index f00ebd4..dd07ff9 100644
--- a/swr2_asr/model_deep_speech.py
+++ b/swr2_asr/model_deep_speech.py
@@ -1,3 +1,4 @@
+"""Main definition of model"""
 import torch.nn.functional as F
 from torch import nn
 
@@ -30,9 +31,7 @@ class ResidualCNN(nn.Module):
     ):
         super().__init__()
 
-        self.cnn1 = nn.Conv2d(
-            in_channels, out_channels, kernel, stride, padding=kernel // 2
-        )
+        self.cnn1 = nn.Conv2d(in_channels, out_channels, kernel, stride, padding=kernel // 2)
         self.cnn2 = nn.Conv2d(
             out_channels,
             out_channels,
@@ -110,9 +109,7 @@ class SpeechRecognitionModel(nn.Module):
         # n residual cnn layers with filter size of 32
         self.rescnn_layers = nn.Sequential(
             *[
-                ResidualCNN(
-                    32, 32, kernel=3, stride=1, dropout=dropout, n_feats=n_feats
-                )
+                ResidualCNN(32, 32, kernel=3, stride=1, dropout=dropout, n_feats=n_feats)
                 for _ in range(n_cnn_layers)
             ]
         )
@@ -140,9 +137,7 @@ class SpeechRecognitionModel(nn.Module):
         data = self.cnn(data)
         data = self.rescnn_layers(data)
         sizes = data.size()
-        data = data.view(
-            sizes[0], sizes[1] * sizes[2], sizes[3]
-        )  # (batch, feature, time)
+        data = data.view(sizes[0], sizes[1] * sizes[2], sizes[3])  # (batch, feature, time)
         data = data.transpose(1, 2)  # (batch, time, feature)
         data = self.fully_connected(data)
         data = self.birnn_layers(data)