added test for energy

espnet2/tts/feats_extract/energy.py

@@ -66,9 +66,13 @@ def get_parameters(self) -> Dict[str, Any]:
         return dict(
             fs=self.fs,
             n_fft=self.n_fft,
-            n_shift=self.hop_length,
+            hop_length=self.hop_length,
             window=self.window,
             win_length=self.win_length,
+            center=self.stft.center,
+            pad_mode=self.stft.pad_mode,
+            normalized=self.stft.normalized,
+            use_token_averaged_energy=self.use_token_averaged_energy
         )
 
     def forward(

test/espnet2/tts/feats_extract/test_energy.py

@@ -0,0 +1,31 @@
+import pytest
+import torch
+
+from espnet2.tts.feats_extract.energy import Energy
+
+
+@pytest.mark.parametrize("use_token_averaged_energy", [False, True])
+def test_forward(use_token_averaged_energy):
+    layer = Energy(
+        n_fft=128,
+        hop_length=64,
+        fs="16k",
+        use_token_averaged_energy=use_token_averaged_energy,
+    )
+    x = torch.randn(2, 256)
+    if not use_token_averaged_energy:
+        layer(x, torch.LongTensor([256, 128]))
+    else:
+        d = torch.LongTensor([[1, 2, 2], [3, 0, 0]])
+        dlens = torch.LongTensor([3, 1])
+        layer(x, torch.LongTensor([256, 128]), durations=d, durations_lengths=dlens)
+
+
+def test_output_size():
+    layer = Energy(n_fft=4, hop_length=1, fs="16k")
+    print(layer.output_size())
+
+
+def test_get_parameters():
+    layer = Energy(n_fft=4, hop_length=1, fs="16k")
+    print(layer.get_parameters())