add seed

deepspeech/exps/deepspeech2/bin/train.py

@@ -12,6 +12,8 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 """Trainer for DeepSpeech2 model."""
+import os
+
 from paddle import distributed as dist
 
 from deepspeech.exps.deepspeech2.config import get_cfg_defaults
@@ -53,5 +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:
+        os.environ.setdefault('FLAGS_cudnn_deterministic', 'True')
 
     main(config, args)

deepspeech/exps/deepspeech2/model.py

@@ -12,6 +12,8 @@
 # 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
 from pathlib import Path
@@ -53,6 +55,7 @@ class DeepSpeech2Trainer(Trainer):
                 weight_decay=1e-6,  # the coeff of weight decay
                 global_grad_clip=5.0,  # the global norm clip
                 n_epoch=50,  # train epochs
+                seed=1024,  #train seed
             ))
 
         if config is not None:
@@ -61,6 +64,13 @@ class DeepSpeech2Trainer(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)
+        random.seed(seed)
+        paddle.seed(seed)
 
     def train_batch(self, batch_index, batch_data, msg):
         start = time.time()

deepspeech/exps/u2/bin/train.py

@@ -52,7 +52,10 @@ 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(
@@ -75,6 +75,12 @@ 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/models/ds2_online/deepspeech2.py

@@ -102,13 +102,13 @@ class CRNNEncoder(nn.Layer):
         Args:
             x (Tensor): [B, feature_size, D]
             x_lens (Tensor): [B]
-            init_state_h_box(Tensor): init_states h for RNN layers, num_rnn_layers * num_directions, batch_size, hidden_size
+            init_state_h_box(Tensor): init_states h for RNN layers: [num_rnn_layers * num_directions, batch_size, hidden_size]
-            init_state_c_box(Tensor): init_states c for RNN layers, num_rnn_layers * num_directions, batch_size, hidden_size
+            init_state_c_box(Tensor): init_states c for RNN layers: [num_rnn_layers * num_directions, batch_size, hidden_size]
-        Returns:
+        Return:
             x (Tensor): encoder outputs, [B, size, D]
             x_lens (Tensor): encoder length, [B]
-            final_state_h_box(Tensor): final_states h for RNN layers, num_rnn_layers * num_directions, batch_size, hidden_size
+            final_state_h_box(Tensor): final_states h for RNN layers: [num_rnn_layers * num_directions, batch_size, hidden_size]
-            final_state_c_box(Tensor): final_states c for RNN layers, num_rnn_layers * num_directions, batch_size, hidden_size
+            final_state_c_box(Tensor): final_states c for RNN layers: [num_rnn_layers * num_directions, batch_size, hidden_size]
         """
         if init_state_h_box is not None:
             init_state_list = None
@@ -142,7 +142,7 @@ class CRNNEncoder(nn.Layer):
         if self.use_gru == True:
             final_chunk_state_h_box = paddle.concat(
                 final_chunk_state_list, axis=0)
-            final_chunk_state_c_box = init_state_c_box  #paddle.zeros_like(final_chunk_state_h_box)
+            final_chunk_state_c_box = init_state_c_box
         else:
             final_chunk_state_h_list = [
                 final_chunk_state_list[i][0] for i in range(self.num_rnn_layers)
@@ -165,10 +165,10 @@ class CRNNEncoder(nn.Layer):
             x_lens (Tensor): [B]
             decoder_chunk_size: The chunk size of decoder
         Returns:
-            eouts_list (List of Tensor): The list of encoder outputs in chunk_size, [B, chunk_size, D] * num_chunks
+            eouts_list (List of Tensor): The list of encoder outputs in chunk_size: [B, chunk_size, D] * num_chunks
-            eouts_lens_list (List of Tensor): The list of  encoder length in chunk_size, [B] * num_chunks
+            eouts_lens_list (List of Tensor): The list of  encoder length in chunk_size: [B] * num_chunks
-            final_state_h_box(Tensor): final_states h for RNN layers, num_rnn_layers * num_directions, batch_size, hidden_size
+            final_state_h_box(Tensor): final_states h for RNN layers: [num_rnn_layers * num_directions, batch_size, hidden_size]
-            final_state_c_box(Tensor): final_states c for RNN layers, num_rnn_layers * num_directions, batch_size, hidden_size
+            final_state_c_box(Tensor): final_states c for RNN layers: [num_rnn_layers * num_directions, batch_size, hidden_size]
         """
         subsampling_rate = self.conv.subsampling_rate
         receptive_field_length = self.conv.receptive_field_length
@@ -215,12 +215,14 @@ class CRNNEncoder(nn.Layer):
 class DeepSpeech2ModelOnline(nn.Layer):
     """The DeepSpeech2 network structure for online.
 
-    :param audio_data: Audio spectrogram data layer.
+    :param audio: Audio spectrogram data layer.
-    :type audio_data: Variable
+    :type audio: Variable
-    :param text_data: Transcription text data layer.
+    :param text: Transcription text data layer.
-    :type text_data: Variable
+    :type text: Variable
     :param audio_len: Valid sequence length data layer.
     :type audio_len: Variable
+    :param feat_size: feature size for audio.
+    :type feat_size: int
     :param dict_size: Dictionary size for tokenized transcription.
     :type dict_size: int
     :param num_conv_layers: Number of stacking convolution layers.

tests/deepspeech2_online_model_test.py

@@ -143,7 +143,7 @@ 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), True)
+        self.assertEqual(paddle.allclose(eouts_by_chk, eouts, atol=1e-5), True)
         self.assertEqual(
             paddle.allclose(final_state_h_box, final_state_h_box_chk), True)
         if use_gru == False: