diff options
Diffstat (limited to 'swr2_asr/train.py')
| -rw-r--r-- | swr2_asr/train.py | 12 |
1 files changed, 6 insertions, 6 deletions
diff --git a/swr2_asr/train.py b/swr2_asr/train.py index 8943f71..6af1e80 100644 --- a/swr2_asr/train.py +++ b/swr2_asr/train.py @@ -83,7 +83,7 @@ class CNNLayerNorm(nn.Module): """Layer normalization built for cnns input""" def __init__(self, n_feats: int): - super(CNNLayerNorm, self).__init__() + super().__init__() self.layer_norm = nn.LayerNorm(n_feats) def forward(self, data): @@ -105,7 +105,7 @@ class ResidualCNN(nn.Module): dropout: float, n_feats: int, ): - super(ResidualCNN, self).__init__() + super().__init__() self.cnn1 = nn.Conv2d( in_channels, out_channels, kernel, stride, padding=kernel // 2 @@ -147,7 +147,7 @@ class BidirectionalGRU(nn.Module): dropout: float, batch_first: bool, ): - super(BidirectionalGRU, self).__init__() + super().__init__() self.bi_gru = nn.GRU( input_size=rnn_dim, @@ -181,7 +181,7 @@ class SpeechRecognitionModel(nn.Module): stride: int = 2, dropout: float = 0.1, ): - super(SpeechRecognitionModel, self).__init__() + super().__init__() n_feats //= 2 self.cnn = nn.Conv2d(1, 32, 3, stride=stride, padding=3 // 2) # n residual cnn layers with filter size of 32 @@ -227,7 +227,7 @@ class SpeechRecognitionModel(nn.Module): return data -class IterMeter(object): +class IterMeter: """keeps track of total iterations""" def __init__(self): @@ -381,7 +381,7 @@ def run( ).to(device) print( - "Num Model Parameters", sum([param.nelement() for param in model.parameters()]) + "Num Model Parameters", sum((param.nelement() for param in model.parameters())) ) optimizer = optim.AdamW(model.parameters(), hparams["learning_rate"]) criterion = nn.CTCLoss(blank=28).to(device) |