incremental backup

loss.py

@@ -9,5 +9,13 @@ def CTCLoss(y_true, y_pred):
     input_length = input_length * tf.ones(shape = (batch_len, 1), dtype = "int64")
     label_length = label_length * tf.ones(shape = (batch_len, 1), dtype = "int64")
 
+    #y_pred = tf.math.log_softmax(y_pred)
+
+    #y_true = tf.cast(y_true, dtype = "int64")
+    #y_true_sparse = tf.keras.backend.ctc_label_dense_to_sparse(y_true, label_length)
+
+    #print(y_true_sparse)
+
     loss = keras.backend.ctc_batch_cost(y_true, y_pred, input_length, label_length)
+    #loss = tf.nn.ctc_loss(y_true_sparse, y_pred, input_length, label_length)
     return loss

model.py

@@ -1,3 +1,5 @@
+import tensorflow as tf
+
 from tensorflow.keras import layers as kl
 from tensorflow.keras import models as km
 from tensorflow.keras import losses as ks
@@ -8,6 +10,10 @@ from tensorflow import keras
 
 from loss import CTCLoss
 
+#g = tf.config.experimental.list_physical_devices('GPU')
+#for i in g:
+#  tf.config.experimental.set_memory_growth(i, True)
+
 
 def model(input_dim, output_dim, rnn_layers = 3, rnn_units = 72):
     li = kl.Input((None, input_dim))

preprocessing.py

@@ -38,7 +38,7 @@ def encode_single_sample(wav, label):
 def to_dataset(df, batch_size = 32):
     ds = tf.data.Dataset.from_tensor_slices((list(df["file_name"]),
                                              list(df["normalized_transcription"])))
-    ds = ds.map(encode_single_sample, num_parallel_calls=tf.data.AUTOTUNE) \
+    ds = ds.map(encode_single_sample, num_parallel_calls = tf.data.AUTOTUNE) \
             .padded_batch(batch_size) \
             .prefetch(buffer_size=tf.data.AUTOTUNE)
 

train.py

@@ -12,9 +12,9 @@ data = pd.read_csv("/mnt/tmpfs1/LJSpeech-1.1/metadata.csv",
                    header = None,
                    quoting = 3,
                    names = ['file_name', 'i', 'normalized_transcription'])
-s = int(len(data) // 2 * 0.90)
+s = int(len(data) * 0.90)
 train_data = data[:s]
-valid_data = data[s:len(data) // 2]
+valid_data = data[s:]
 
 train_ds = to_dataset(train_data, batch_size = bs)
 valid_ds = to_dataset(valid_data, batch_size = bs)
@@ -22,13 +22,13 @@ valid_ds = to_dataset(valid_data, batch_size = bs)
 m = model(input_dim = fft_length // 2 + 1,
           output_dim = char_to_num.vocabulary_size())
 
-m.load_weights('model20-latest.keras')
-ckpt1 = kc.ModelCheckpoint('model21-latest.keras',
+m.load_weights('model23-latest.keras')
+ckpt1 = kc.ModelCheckpoint('model24-latest.keras',
                            monitor = 'val_loss',
                            save_best_only = False,
                            verbose = 1)
 
-ckpt2 = kc.ModelCheckpoint('model21-best.keras',
+ckpt2 = kc.ModelCheckpoint('model24-best.keras',
                            monitor = 'val_loss',
                            save_best_only = True,
                            verbose = 1)
@@ -36,6 +36,6 @@ ckpt2 = kc.ModelCheckpoint('model21-best.keras',
 ce1 = ce(valid_ds, m)
 
 m.fit(train_ds,
-      epochs = 8,
+      epochs = 80,
       validation_data = valid_ds,
       callbacks = [ckpt1, ckpt2, ce1])