Merge pull request #780 from Jackwaterveg/ds2_online

修改pre_commit, 注释以及增加ds2的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 is not None:
+        os.environ.setdefault('FLAGS_cudnn_deterministic', 'True')
 
     main(config, args)

deepspeech/exps/deepspeech2/model.py

@@ -12,6 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 """Contains DeepSpeech2 and DeepSpeech2Online model."""
+import random
 import time
 from collections import defaultdict
 from pathlib import Path
@@ -53,6 +54,7 @@ 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 +63,13 @@ class DeepSpeech2Trainer(Trainer):
 
     def __init__(self, config, args):
         super().__init__(config, args)
+        if config.training.seed is not 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/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,
@@ -102,18 +102,18 @@ 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_c_box(Tensor): init_states c for RNN layers, num_rnn_layers * num_directions, batch_size, hidden_size
-        Returns:
+            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]
+        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_c_box(Tensor): final_states c 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]
         """
         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
@@ -139,10 +139,10 @@ 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  #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_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_c_box(Tensor): final_states c for RNN layers, num_rnn_layers * num_directions, batch_size, hidden_size
+            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
+            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]
         """
         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.
-    :type audio_data: Variable
-    :param text_data: Transcription text data layer.
-    :type text_data: Variable
+    :param audio: Audio spectrogram data layer.
+    :type audio: Variable
+    :param text: Transcription text data layer.
+    :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

@@ -146,7 +146,7 @@ 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)
 
@@ -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)