linter updates

TTS/bin/tune_wavegrad.py

@@ -10,7 +10,6 @@ from TTS.utils.audio import AudioProcessor
 from TTS.utils.io import load_config
 from TTS.vocoder.datasets.preprocess import load_wav_data
 from TTS.vocoder.datasets.wavegrad_dataset import WaveGradDataset
-from TTS.vocoder.models.wavegrad import Wavegrad
 from TTS.vocoder.utils.generic_utils import setup_generator
 
 parser = argparse.ArgumentParser()

TTS/speaker_encoder/model.py

@@ -61,6 +61,7 @@ class SpeakerEncoder(nn.Module):
             d = torch.nn.functional.normalize(d, p=2, dim=1)
         return d
 
+    @torch.no_grad()
     def inference(self, x):
         d = self.layers.forward(x)
         if self.use_lstm_with_projection:

TTS/vocoder/datasets/wavegrad_dataset.py

@@ -111,8 +111,8 @@ class WaveGradDataset(Dataset):
         mel = torch.from_numpy(mel).float().squeeze(0)
         return (mel, audio)
 
-
+    @staticmethod
-    def collate_full_clips(self, batch):
+    def collate_full_clips(batch):
         """This is used in tune_wavegrad.py.
         It pads sequences to the max length."""
         max_mel_length = max([b[0].shape[1] for b in batch]) if len(batch) > 1 else batch[0][0].shape[1]