Added option to export weights and biases as numpy arrays

speecht-cli

@@ -32,6 +32,7 @@ class CLI:
     self._add_recording_parser()
     self._add_parameter_search_parser()
     self._add_preprocess_parser()
+    self._add_export_parser()
 
   def _create_base_parser(self):
     base_parser = argparse.ArgumentParser(add_help=False)
@@ -52,6 +53,14 @@ class CLI:
     base_parser.set_defaults(feature_type='power')
     return base_parser
 
+  def _add_export_parser(self):
+    export_parser = self.subparsers.add_parser('export', help='Export network details',
+                                                 parents=[self.base_parser])
+    export_parser.add_argument('--weights', dest='export_weights_dir', type=str,
+                               help='Store the weights in numpy arrays')
+    export_parser.add_argument('--input-size', dest='input_size', type=int, default=128,
+                               help='The input size of each sample, depending on what preprocessing was used')
+
   def _add_training_parser(self):
     training_parser = self.subparsers.add_parser('train', help='Train the wav2letter weights.',
                                                  parents=[self.base_parser])
@@ -174,14 +183,20 @@ class CLI:
     import speecht.preprocessing
     return speecht.preprocessing.Preprocessing(flags)
 
+  @staticmethod
+  def _get_export_executor(flags):
+    import speecht.exporting
+    return speecht.exporting.Exporting(flags)
+
   @lazy
   def command_executor(self):
     return {
       'train': self._get_training_executor,
       'evaluate': self._get_evaluation_executor,
       'record': self._get_recording_executor,
       'search': self._get_search_executor,
-      'preprocess': self._get_preprocessing_executor
+      'preprocess': self._get_preprocessing_executor,
+      'export': self._get_export_executor
     }[self.parsed.command](self.parsed)
 
   def run(self):

speecht/exporting.py

@@ -0,0 +1,44 @@
+from pathlib import Path
+
+from speecht.speech_input import SingleInputLoader
+from speecht.speech_model import create_default_model
+
+import tensorflow as tf
+import numpy as np
+
+
+class Exporting:
+
+  def __init__(self, flags):
+    self.flags = flags
+
+  def create_model(self, sess: tf.Session):
+    input_loader = SingleInputLoader(self.flags.input_size)
+    model = create_default_model(self.flags, self.flags.input_size, input_loader)
+    model.restore(sess, self.flags.run_train_dir)
+    return model
+
+  def run(self):
+    with tf.Session() as sess:
+      self.create_model(sess)
+
+      if self.flags.export_weights_dir:
+        path = Path(self.flags.export_weights_dir)
+        if not path.exists():
+          path.mkdir()
+
+        variables = tf.trainable_variables()
+        values = sess.run(variables)
+
+        for variable, value in zip(variables, values):
+          file_path = path / variable.name
+          parent_dir = Path(file_path.parent)
+          if not parent_dir.exists():
+            parent_dir.mkdir()
+
+          # noinspection PyTypeChecker
+          np.save(file_path, value)
+
+        return
+
+      print('Nothing to do.')

speecht/speech_model.py

@@ -307,6 +307,9 @@ def create_default_model(flags, input_size: int, speech_input: BaseInputLoader)
                            max_gradient_norm=flags.max_gradient_norm,
                            momentum=flags.momentum)
     model.add_decoding_ops()
+  elif flags.command == 'export':
+    model.add_training_ops()
+    model.add_decoding_ops()
   else:
     model.add_training_ops()
     model.add_decoding_ops(language_model=flags.language_model,