Fix lint checks

TTS/tts/models/vits.py

@@ -632,7 +632,9 @@ class Vits(BaseTTS):
 
         if self.args.TTS_part_sample_rate:
             self.interpolate_factor = self.config.audio["sample_rate"] / self.args.TTS_part_sample_rate
-            self.audio_resampler = torchaudio.transforms.Resample(orig_freq=self.config.audio["sample_rate"], new_freq=self.args.TTS_part_sample_rate)
+            self.audio_resampler = torchaudio.transforms.Resample(
+                orig_freq=self.config.audio["sample_rate"], new_freq=self.args.TTS_part_sample_rate
+            )
 
     def init_multispeaker(self, config: Coqpit):
         """Initialize multi-speaker modules of a model. A model can be trained either with a speaker embedding layer
@@ -818,7 +820,6 @@ class Vits(BaseTTS):
         y: torch.tensor,
         y_lengths: torch.tensor,
         waveform: torch.tensor,
-        waveform_spec: torch.tensor,
         aux_input={"d_vectors": None, "speaker_ids": None, "language_ids": None},
     ) -> Dict:
         """Forward pass of the model.
@@ -887,19 +888,14 @@ class Vits(BaseTTS):
         # select a random feature segment for the waveform decoder
         z_slice, slice_ids = rand_segments(z, y_lengths, self.spec_segment_size, let_short_samples=True, pad_short=True)
 
-        wav_seg2 = segment(
-            waveform_spec,
-            slice_ids * self.config.audio.hop_length,
-            self.spec_segment_size * self.config.audio.hop_length,
-            pad_short=True,
-        )
         if self.args.TTS_part_sample_rate:
             slice_ids = slice_ids * int(self.interpolate_factor)
             spec_segment_size = self.spec_segment_size * int(self.interpolate_factor)
             if self.args.interpolate_z:
-                z_slice = z_slice.unsqueeze(0) # pylint: disable=not-callable
+                z_slice = z_slice.unsqueeze(0)  # pylint: disable=not-callable
                 z_slice = torch.nn.functional.interpolate(
-                    z_slice, scale_factor=[1, self.interpolate_factor], mode='nearest').squeeze(0)
+                    z_slice, scale_factor=[1, self.interpolate_factor], mode="nearest"
+                ).squeeze(0)
         else:
             spec_segment_size = self.spec_segment_size
 
@@ -912,11 +908,6 @@ class Vits(BaseTTS):
             pad_short=True,
         )
 
-        # print(o.shape, wav_seg.shape, spec_segment_size, self.spec_segment_size)
-        # self.ap.save_wav(wav_seg[0].squeeze(0).detach().cpu().numpy(), "/raid/edresson/dev/wav_GT_44khz.wav", sr=self.ap.sample_rate)
-        # self.ap.save_wav(wav_seg2[0].squeeze(0).detach().cpu().numpy(), "/raid/edresson/dev/wav_GT_22khz.wav", sr=self.args.TTS_part_sample_rate)
-        # self.ap.save_wav(o[0].squeeze(0).detach().cpu().numpy(), "/raid/edresson/dev/wav_gen_44khz_test_model_output.wav", sr=self.ap.sample_rate)
-
         if self.args.use_speaker_encoder_as_loss and self.speaker_manager.speaker_encoder is not None:
             # concate generated and GT waveforms
             wavs_batch = torch.cat((wav_seg, o), dim=0)
@@ -1021,10 +1012,11 @@ class Vits(BaseTTS):
         z = self.flow(z_p, y_mask, g=g, reverse=True)
 
         if self.args.TTS_part_sample_rate and self.args.interpolate_z:
-            z = z.unsqueeze(0) # pylint: disable=not-callable
-            z = torch.nn.functional.interpolate(
-                z, scale_factor=[1, self.interpolate_factor], mode='nearest').squeeze(0)
-            y_mask = sequence_mask(y_lengths * self.interpolate_factor, None).to(y_mask.dtype).unsqueeze(1)  # [B, 1, T_dec_resampled]
+            z = z.unsqueeze(0)  # pylint: disable=not-callable
+            z = torch.nn.functional.interpolate(z, scale_factor=[1, self.interpolate_factor], mode="nearest").squeeze(0)
+            y_mask = (
+                sequence_mask(y_lengths * self.interpolate_factor, None).to(y_mask.dtype).unsqueeze(1)
+            )  # [B, 1, T_dec_resampled]
 
         o = self.waveform_decoder((z * y_mask)[:, :, : self.max_inference_len], g=g)
 
@@ -1101,7 +1093,6 @@ class Vits(BaseTTS):
 
         self._freeze_layers()
 
-        mel_lens = batch["mel_lens"]
         spec_lens = batch["spec_lens"]
 
         if optimizer_idx == 0:
@@ -1121,7 +1112,6 @@ class Vits(BaseTTS):
                 spec,
                 spec_lens,
                 waveform,
-                batch["waveform_spec"],
                 aux_input={"d_vectors": d_vectors, "speaker_ids": speaker_ids, "language_ids": language_ids},
             )
 
@@ -1146,7 +1136,7 @@ class Vits(BaseTTS):
 
             # compute melspec segment
             with autocast(enabled=False):
-       
+
                 if self.args.TTS_part_sample_rate:
                     spec_segment_size = self.spec_segment_size * int(self.interpolate_factor)
                 else:
@@ -1380,7 +1370,6 @@ class Vits(BaseTTS):
         else:
             spec_mel = batch["spec"]
 
-
         batch["mel"] = spec_to_mel(
             spec=spec_mel,
             n_fft=ac.fft_size,
@@ -1390,15 +1379,13 @@ class Vits(BaseTTS):
             fmax=ac.mel_fmax,
         )
 
-        batch["waveform_spec"] = wav
-
         if not self.args.TTS_part_sample_rate:
             assert batch["spec"].shape[2] == batch["mel"].shape[2], f"{batch['spec'].shape[2]}, {batch['mel'].shape[2]}"
 
         # compute spectrogram frame lengths
         batch["spec_lens"] = (batch["spec"].shape[2] * batch["waveform_rel_lens"]).int()
         batch["mel_lens"] = (batch["mel"].shape[2] * batch["waveform_rel_lens"]).int()
-        
+
         if not self.args.TTS_part_sample_rate:
             assert (batch["spec_lens"] - batch["mel_lens"]).sum() == 0
 

tests/tts_tests/test_vits_multilingual_speaker_emb_train.py

@@ -76,12 +76,7 @@ config.model_args.TTS_part_sample_rate = 11025
 config.model_args.interpolate_z = True
 config.model_args.detach_z_vocoder = True
 
-config.model_args.upsample_rates_decoder = [
-            8,
-            8,
-            2,
-            2
-        ]
+config.model_args.upsample_rates_decoder = [8, 8, 2, 2]
 
 config.save_json(config_path)