add some code

managed_components/78__esp-opus/dnn/training_tf2/dataloader.py

@@ -0,0 +1,49 @@
+import numpy as np
+from tensorflow.keras.utils import Sequence
+from ulaw import lin2ulaw
+
+def lpc2rc(lpc):
+    #print("shape is = ", lpc.shape)
+    order = lpc.shape[-1]
+    rc = 0*lpc
+    for i in range(order, 0, -1):
+        rc[:,:,i-1] = lpc[:,:,-1]
+        ki = rc[:,:,i-1:i].repeat(i-1, axis=2)
+        lpc = (lpc[:,:,:-1] - ki*lpc[:,:,-2::-1])/(1-ki*ki)
+    return rc
+
+class LPCNetLoader(Sequence):
+    def __init__(self, data, features, periods, batch_size, e2e=False, lookahead=2):
+        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.e2e = e2e
+        self.lookahead = lookahead
+        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[: , :, :1]
+        out_data = data[: , :, 1:]
+        features = self.features[self.indices[index*self.batch_size:(index+1)*self.batch_size], :, :-16]
+        periods = self.periods[self.indices[index*self.batch_size:(index+1)*self.batch_size], :, :]
+        outputs = [out_data]
+        inputs = [in_data, features, periods]
+        if self.lookahead > 0:
+            lpc = self.features[self.indices[index*self.batch_size:(index+1)*self.batch_size], 4-self.lookahead:-self.lookahead, -16:]
+        else:
+            lpc = self.features[self.indices[index*self.batch_size:(index+1)*self.batch_size], 4:, -16:]
+        if self.e2e:
+            outputs.append(lpc2rc(lpc))
+        else:
+            inputs.append(lpc)
+        return (inputs, outputs)
+
+    def __len__(self):
+        return self.nb_batches