model_asteroid.py

@@ -369,9 +369,9 @@ class Dual_RNN_model(nn.Cell):
         self.print = ops.Print()
         self.stack = ops.Stack()
 
-        # for p in self.get_parameters():
-        #     if p.ndim > 1:
-        #         mindspore.common.initializer.HeNormal(p)
+        for p in self.get_parameters():
+            if p.ndim > 1:
+                mindspore.common.initializer.HeNormal(p)
     def construct(self, x):
         """ forward """
         '''
@@ -393,9 +393,9 @@ class Dual_RNN_model(nn.Cell):
 if __name__ == "__main__":
     context.set_context(mode=context.GRAPH_MODE, device_target="GPU", device_id=0)
     set_seed(42)
-    rnn = Dual_RNN_model(64, 64, 128, 128, bidirectional=True, norm='gln', num_layers=4, dropout=0.0)
+    rnn = Dual_RNN_model(64, 64, 128, 128, bidirectional=True, norm='gln', num_layers=6, dropout=0.0)
     #encoder = Encoder(16, 512)
     ones = ops.Ones()
-    x = ones((2, 32000), mindspore.float32)
+    x = ones((1, 32000), mindspore.float32)
     out = rnn(x)
     print(rnn)

train_asteroid.py

@@ -42,7 +42,7 @@ parser.add_argument('--sample_rate', default=8000, type=int,
                     help='Sample rate')
 parser.add_argument('--segment', default=4, type=float,  # 取音频的长度，2s。#数据集语音长度要相同
                     help='Segment length (seconds)')
-parser.add_argument('--batch_size', default=2, type=int,  # 需要抛弃的音频长度
+parser.add_argument('--batch_size', default=3, type=int,  # 需要抛弃的音频长度
                     help='Batch size')
 
 # Network architecture
@@ -62,7 +62,7 @@ parser.add_argument('--norm', default='gln', type=str,
                     help='gln = "Global Norm", cln = "Cumulative Norm", ln = "Layer Norm"')
 parser.add_argument('--dropout', default=0.0, type=float,
                     help='dropout')
-parser.add_argument('--num_layers', default=6, type=int,
+parser.add_argument('--num_layers', default=5, type=int,
                     help='Number of Dual-Path-Block')
 parser.add_argument('--K', default=250, type=int,
                     help='The length of chunk')

train_ln_adam.py

@@ -41,7 +41,7 @@ parser.add_argument('--sample_rate', default=8000, type=int,
                     help='Sample rate')
 parser.add_argument('--segment', default=4, type=float,  # 取音频的长度，2s。#数据集语音长度要相同
                     help='Segment length (seconds)')
-parser.add_argument('--batch_size', default=3, type=int,  # 需要抛弃的音频长度
+parser.add_argument('--batch_size', default=2, type=int,  # 需要抛弃的音频长度
                     help='Batch size')
 
 # Network architecture
@@ -61,7 +61,7 @@ parser.add_argument('--norm', default='gln', type=str,
                     help='gln = "Global Norm", cln = "Cumulative Norm", ln = "Layer Norm"')
 parser.add_argument('--dropout', default=0.0, type=float,
                     help='dropout')
-parser.add_argument('--num_layers', default=4, type=int,
+parser.add_argument('--num_layers', default=6, type=int,
                     help='Number of Dual-Path-Block')
 parser.add_argument('--K', default=250, type=int,
                     help='The length of chunk')
@@ -109,8 +109,8 @@ def preprocess(args):
     print("preprocess done")
 
 def main(args):
-    # context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target, save_graphs=True)
-    context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target)
+    context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target, save_graphs=True)
+    # context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target)
 
     if args.run_distribute:
         print("distribute")