Merge branch 'HEAD_1' into ds2_online

deepspeech/exps/deepspeech2/bin/train.py

@@ -55,7 +55,7 @@ if __name__ == "__main__":
     if args.dump_config:
         with open(args.dump_config, 'w') as f:
             print(config, file=f)
-    if config.training.seed != None:
+    if config.training.seed is not None:
         os.environ.setdefault('FLAGS_cudnn_deterministic', 'True')
 
     main(config, args)

deepspeech/exps/deepspeech2/model.py

@@ -12,7 +12,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 """Contains DeepSpeech2 and DeepSpeech2Online model."""
-import os
 import random
 import time
 from collections import defaultdict
@@ -64,7 +63,7 @@ class DeepSpeech2Trainer(Trainer):
 
     def __init__(self, config, args):
         super().__init__(config, args)
-        if config.training.seed != None:
+        if config.training.seed is not None:
             self.set_seed(config.training.seed)
 
     def set_seed(self, seed):

deepspeech/exps/u2/bin/train.py

@@ -52,10 +52,7 @@ if __name__ == "__main__":
     if args.dump_config:
         with open(args.dump_config, 'w') as f:
             print(config, file=f)
-    if config.training.seed != None:
-        os.environ.setdefault('FLAGS_cudnn_deterministic', 'True')
 
-    main(config, args)
     # Setting for profiling
     pr = cProfile.Profile()
     pr.runcall(main, config, args)

deepspeech/exps/u2/model.py

@@ -55,7 +55,7 @@ class U2Trainer(Trainer):
                 log_interval=100,  # steps
                 accum_grad=1,  # accum grad by # steps
                 global_grad_clip=5.0,  # the global norm clip
-                seed=1024, ))
+            ))
         default.optim = 'adam'
         default.optim_conf = CfgNode(
             dict(
@@ -75,12 +75,6 @@ class U2Trainer(Trainer):
 
     def __init__(self, config, args):
         super().__init__(config, args)
-        if config.training.seed != None:
-            self.set_seed(config.training.seed)
-
-    def set_seed(self, seed):
-        np.random.seed(seed)
-        paddle.seed(seed)
 
     def train_batch(self, batch_index, batch_data, msg):
         train_conf = self.config.training

deepspeech/io/sampler.py

@@ -51,7 +51,7 @@ def _batch_shuffle(indices, batch_size, epoch, clipped=False):
     """
     rng = np.random.RandomState(epoch)
     shift_len = rng.randint(0, batch_size - 1)
-    batch_indices = list(zip(*[iter(indices[shift_len:])] * batch_size))
+    batch_indices = list(zip(* [iter(indices[shift_len:])] * batch_size))
     rng.shuffle(batch_indices)
     batch_indices = [item for batch in batch_indices for item in batch]
     assert clipped is False

deepspeech/models/ds2_online/conv.py

@@ -12,9 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 import paddle
-from paddle import nn
 
-from deepspeech.modules.embedding import PositionalEncoding
 from deepspeech.modules.subsampling import Conv2dSubsampling4
 
 

deepspeech/models/ds2_online/deepspeech2.py

@@ -26,7 +26,7 @@ from deepspeech.utils.checkpoint import Checkpoint
 from deepspeech.utils.log import Log
 logger = Log(__name__).getlog()
 
-__all__ = ['DeepSpeech2ModelOnline', 'DeepSpeech2InferModeOnline']
+__all__ = ['DeepSpeech2ModelOnline', 'DeepSpeech2InferModelOnline']
 
 
 class CRNNEncoder(nn.Layer):
@@ -68,7 +68,7 @@ class CRNNEncoder(nn.Layer):
                 rnn_input_size = i_size
             else:
                 rnn_input_size = layernorm_size
-            if use_gru == True:
+            if use_gru is True:
                 self.rnn.append(
                     nn.GRU(
                         input_size=rnn_input_size,
@@ -113,7 +113,7 @@ class CRNNEncoder(nn.Layer):
         if init_state_h_box is not None:
             init_state_list = None
 
-            if self.use_gru == True:
+            if self.use_gru is True:
                 init_state_h_list = paddle.split(
                     init_state_h_box, self.num_rnn_layers, axis=0)
                 init_state_list = init_state_h_list
@@ -139,7 +139,7 @@ class CRNNEncoder(nn.Layer):
             x = self.fc_layers_list[i](x)
             x = F.relu(x)
 
-        if self.use_gru == True:
+        if self.use_gru is True:
             final_chunk_state_h_box = paddle.concat(
                 final_chunk_state_list, axis=0)
             final_chunk_state_c_box = init_state_c_box

examples/aishell/s0/conf/deepspeech2_online.yaml

@@ -46,7 +46,7 @@ model:
 training:
   n_epoch: 50
   lr: 2e-3
-  lr_decay: 0.9  # 0.83
+  lr_decay: 0.91  # 0.83
   weight_decay: 1e-06
   global_grad_clip: 3.0
   log_interval: 100

tests/deepspeech2_online_model_test.py

@@ -143,10 +143,10 @@ class TestDeepSpeech2ModelOnline(unittest.TestCase):
         eouts_lens_by_chk = paddle.add_n(eouts_lens_by_chk_list)
         decode_max_len = eouts.shape[1]
         eouts_by_chk = eouts_by_chk[:, :decode_max_len, :]
-        self.assertEqual(paddle.allclose(eouts_by_chk, eouts, atol=1e-5), True)
+        self.assertEqual(paddle.allclose(eouts_by_chk, eouts), True)
         self.assertEqual(
             paddle.allclose(final_state_h_box, final_state_h_box_chk), True)
-        if use_gru == False:
+        if use_gru is False:
             self.assertEqual(
                 paddle.allclose(final_state_c_box, final_state_c_box_chk), True)
 
@@ -177,7 +177,7 @@ class TestDeepSpeech2ModelOnline(unittest.TestCase):
         self.assertEqual(paddle.allclose(eouts_by_chk, eouts), True)
         self.assertEqual(
             paddle.allclose(final_state_h_box, final_state_h_box_chk), True)
-        if use_gru == False:
+        if use_gru is False:
             self.assertEqual(
                 paddle.allclose(final_state_c_box, final_state_c_box_chk), True)