Merge pull request #14 from ugnelis/run_saved_model

Added demo.py for running trained model.

demo.py

@@ -0,0 +1,142 @@
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import time
+import logging
+import sys
+import math
+
+import tensorflow as tf
+
+import utils
+
+# Logging configuration.
+logging.basicConfig(format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
+                    level=logging.DEBUG,
+                    stream=sys.stdout)
+
+# Model path.
+MODEL_PATH = "./models/model.ckpt"
+
+# Summary directory.
+SUMMARY_PATH = "./logs/"
+
+# Data directories.
+DATA_DIR = "./data/LibriSpeech/"
+TRAIN_DIR = DATA_DIR + "train-clean-100-wav/"
+TEST_DIR = DATA_DIR + "test-clean-wav/"
+DEV_DIR = DATA_DIR + "dev-clean-wav/"
+
+# Constants.
+SPACE_TOKEN = '<space>'
+SPACE_INDEX = 0
+FIRST_INDEX = ord('a') - 1  # 0 is reserved to space
+
+# Number of features.
+NUM_FEATURES = 13
+
+# Accounting the 0th index + space + blank label = 28 characters
+NUM_CLASSES = ord('z') - ord('a') + 1 + 1 + 1
+
+# Hyper-parameters.
+NUM_EPOCHS = 200
+NUM_HIDDEN = 50
+NUM_LAYERS = 1
+BATCH_SIZE = 1
+
+# Optimizer parameters.
+INITIAL_LEARNING_RATE = 1e-2
+MOMENTUM = 0.9
+
+
+def main(argv):
+    # Read test data files.
+    test_texts = utils.read_text_files(TEST_DIR)
+    test_labels = utils.texts_encoder(test_texts,
+                                      first_index=FIRST_INDEX,
+                                      space_index=SPACE_INDEX,
+                                      space_token=SPACE_TOKEN)
+    test_labels = utils.sparse_tuples_from_sequences(test_labels)
+    test_inputs = utils.read_audio_files(DEV_DIR)
+    test_inputs = utils.standardize_audios(test_inputs)
+    test_sequence_lengths = utils.get_sequence_lengths(test_inputs)
+    test_inputs = utils.make_sequences_same_length(test_inputs, test_sequence_lengths)
+
+    with tf.device('/cpu:0'):
+        config = tf.ConfigProto()
+
+        graph = tf.Graph()
+        with graph.as_default():
+            logging.debug("Starting new TensorFlow graph.")
+            inputs_placeholder = tf.placeholder(tf.float32, [None, None, NUM_FEATURES])
+
+            # SparseTensor placeholder required by ctc_loss op.
+            labels_placeholder = tf.sparse_placeholder(tf.int32)
+
+            # 1d array of size [batch_size].
+            sequence_length_placeholder = tf.placeholder(tf.int32, [None])
+
+            # Defining the cell.
+            cell = tf.contrib.rnn.LSTMCell(NUM_HIDDEN, state_is_tuple=True)
+
+            # Stacking rnn cells.
+            stack = tf.contrib.rnn.MultiRNNCell([cell] * NUM_LAYERS,
+                                                state_is_tuple=True)
+
+            # Creates a recurrent neural network.
+            outputs, _ = tf.nn.dynamic_rnn(stack, inputs_placeholder, sequence_length_placeholder, dtype=tf.float32)
+
+            shape = tf.shape(inputs_placeholder)
+            batch_size, max_time_steps = shape[0], shape[1]
+
+            # Reshaping to apply the same weights over the time steps.
+            outputs = tf.reshape(outputs, [-1, NUM_HIDDEN])
+
+            weights = tf.Variable(tf.truncated_normal([NUM_HIDDEN, NUM_CLASSES], stddev=0.1),
+                                  name='weights')
+            bias = tf.Variable(tf.constant(0., shape=[NUM_CLASSES]),
+                               name='bias')
+
+            # Doing the affine projection.
+            logits = tf.matmul(outputs, weights) + bias
+
+            # Reshaping back to the original shape.
+            logits = tf.reshape(logits, [batch_size, -1, NUM_CLASSES])
+
+            # Time is major.
+            logits = tf.transpose(logits, (1, 0, 2))
+
+            # CTC decoder.
+            decoded, neg_sum_logits = tf.nn.ctc_greedy_decoder(logits, sequence_length_placeholder)
+
+        with tf.Session(config=config, graph=graph) as session:
+            logging.debug("Starting TensorFlow session.")
+
+            # Initialize the weights and biases.
+            tf.global_variables_initializer().run()
+
+            # Saver op to save and restore all the variables.
+            saver = tf.train.Saver()
+
+            # Restore model weights from previously saved model.
+            saver.restore(session, MODEL_PATH)
+
+            test_feed = {inputs_placeholder: test_inputs,
+                         sequence_length_placeholder: test_sequence_lengths}
+            # Decoding.
+            decoded_outputs = session.run(decoded[0], feed_dict=test_feed)
+            dense_decoded = tf.sparse_tensor_to_dense(decoded_outputs, default_value=-1).eval(session=session)
+            test_num = test_texts.shape[0]
+
+            for i, sequence in enumerate(dense_decoded):
+                sequence = [s for s in sequence if s != -1]
+                decoded_text = utils.sequence_decoder(sequence)
+
+                logging.info("Sequence %d/%d", i + 1, test_num)
+                logging.info("Original:\n%s", test_texts[i])
+                logging.info("Decoded:\n%s", decoded_text)
+
+
+if __name__ == '__main__':
+    tf.app.run()