Hard quantization for training

Also, using stateful GRU to randomize initialization

dnn/training_tf2/dataloader.py

@@ -0,0 +1,26 @@
+import numpy as np
+from tensorflow.keras.utils import Sequence
+
+class LPCNetLoader(Sequence):
+    def __init__(self, data, features, periods, batch_size):
+        self.batch_size = batch_size
+        self.nb_batches = np.minimum(np.minimum(data.shape[0], features.shape[0]), periods.shape[0])//self.batch_size
+        self.data = data[:self.nb_batches*self.batch_size, :]
+        self.features = features[:self.nb_batches*self.batch_size, :]
+        self.periods = periods[:self.nb_batches*self.batch_size, :]
+        self.on_epoch_end()
+
+    def on_epoch_end(self):
+        self.indices = np.arange(self.nb_batches*self.batch_size)
+        np.random.shuffle(self.indices)
+
+    def __getitem__(self, index):
+        data = self.data[self.indices[index*self.batch_size:(index+1)*self.batch_size], :, :]
+        in_data = data[: , :, :3]
+        out_data = data[: , :, 3:4]
+        features = self.features[self.indices[index*self.batch_size:(index+1)*self.batch_size], :, :]
+        periods = self.periods[self.indices[index*self.batch_size:(index+1)*self.batch_size], :, :]
+        return ([in_data, features, periods], out_data)
+
+    def __len__(self):
+        return self.nb_batches