chore(format): run black on dev (#1638)

Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>

gui_v1.py

@@ -38,7 +38,11 @@ def phase_vocoder(a, b, fade_out, fade_in):
     deltaphase = deltaphase - 2 * np.pi * torch.floor(deltaphase / 2 / np.pi + 0.5)
     w = 2 * np.pi * torch.arange(n // 2 + 1).to(a) + deltaphase
     t = torch.arange(n).unsqueeze(-1).to(a) / n
-    result = a * (fade_out ** 2) + b * (fade_in ** 2) + torch.sum(absab * torch.cos(w * t + phia), -1) * window / n
+    result = (
+        a * (fade_out**2)
+        + b * (fade_in**2)
+        + torch.sum(absab * torch.cos(w * t + phia), -1) * window / n
+    )
     return result
 
 
@@ -633,7 +637,11 @@ if __name__ == "__main__":
                 self.config,
                 self.rvc if hasattr(self, "rvc") else None,
             )
-            self.gui_config.samplerate = self.rvc.tgt_sr if self.gui_config.sr_type == "sr_model" else self.get_device_samplerate()
+            self.gui_config.samplerate = (
+                self.rvc.tgt_sr
+                if self.gui_config.sr_type == "sr_model"
+                else self.get_device_samplerate()
+            )
             self.zc = self.gui_config.samplerate // 100
             self.block_frame = (
                 int(
@@ -690,7 +698,9 @@ if __name__ == "__main__":
                 2 * self.zc, device=self.config.device, dtype=torch.float32
             )
             self.skip_head = self.extra_frame // self.zc
-            self.return_length = (self.block_frame + self.sola_buffer_frame + self.sola_search_frame) // self.zc
+            self.return_length = (
+                self.block_frame + self.sola_buffer_frame + self.sola_search_frame
+            ) // self.zc
             self.fade_in_window: torch.Tensor = (
                 torch.sin(
                     0.5
@@ -824,7 +834,11 @@ if __name__ == "__main__":
             # volume envelop mixing
             if self.gui_config.rms_mix_rate < 1 and self.function == "vc":
                 rms1 = librosa.feature.rms(
-                    y=self.input_wav_res[160 * self.skip_head : 160 * (self.skip_head + self.return_length)]
+                    y=self.input_wav_res[
+                        160
+                        * self.skip_head : 160
+                        * (self.skip_head + self.return_length)
+                    ]
                     .cpu()
                     .numpy(),
                     frame_length=640,
@@ -874,18 +888,21 @@ if __name__ == "__main__":
             infer_wav = infer_wav[sola_offset:]
             if "privateuseone" in str(self.config.device) or not self.gui_config.use_pv:
                 infer_wav[: self.sola_buffer_frame] *= self.fade_in_window
-                infer_wav[: self.sola_buffer_frame] += self.sola_buffer * self.fade_out_window
+                infer_wav[: self.sola_buffer_frame] += (
+                    self.sola_buffer * self.fade_out_window
+                )
             else:
                 infer_wav[: self.sola_buffer_frame] = phase_vocoder(
                     self.sola_buffer,
                     infer_wav[: self.sola_buffer_frame],
                     self.fade_out_window,
-                                                        self.fade_in_window)
+                    self.fade_in_window,
-            self.sola_buffer[:] = infer_wav[self.block_frame : self.block_frame + self.sola_buffer_frame]
-            if sys.platform == "darwin":
-                outdata[:] = (
-                    infer_wav[: self.block_frame].cpu().numpy()[:, np.newaxis]
                 )
+            self.sola_buffer[:] = infer_wav[
+                self.block_frame : self.block_frame + self.sola_buffer_frame
+            ]
+            if sys.platform == "darwin":
+                outdata[:] = infer_wav[: self.block_frame].cpu().numpy()[:, np.newaxis]
             else:
                 outdata[:] = (
                     infer_wav[: self.block_frame].repeat(2, 1).t().cpu().numpy()
@@ -949,6 +966,8 @@ if __name__ == "__main__":
             printt("Output device: %s:%s", str(sd.default.device[1]), output_device)
 
         def get_device_samplerate(self):
-            return int(sd.query_devices(device=sd.default.device[0])['default_samplerate'])
+            return int(
+                sd.query_devices(device=sd.default.device[0])["default_samplerate"]
+            )
 
     gui = GUI()

infer/lib/train/mel_processing.py

@@ -38,6 +38,7 @@ def spectral_de_normalize_torch(magnitudes):
 mel_basis = {}
 hann_window = {}
 
+
 def spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center=False):
     """Convert waveform into Linear-frequency Linear-amplitude spectrogram.
 
@@ -87,6 +88,7 @@ def spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center=False)
     spec = torch.sqrt(spec.real.pow(2) + spec.imag.pow(2) + 1e-6)
     return spec
 
+
 def spec_to_mel_torch(spec, n_fft, num_mels, sampling_rate, fmin, fmax):
     # MelBasis - Cache if needed
     global mel_basis

tools/rvc_for_realtime.py

@@ -62,6 +62,7 @@ class RVC:
         """
         try:
             if config.dml == True:
+
                 def forward_dml(ctx, x, scale):
                     ctx.scale = scale
                     res = x.clone().detach()
@@ -320,6 +321,7 @@ class RVC:
     def get_f0_fcpe(self, x, f0_up_key):
         if hasattr(self, "model_fcpe") == False:
             from torchfcpe import spawn_bundled_infer_model
+
             printt("Loading fcpe model")
             if "privateuseone" in str(self.device):
                 self.device_fcpe = "cpu"
@@ -329,7 +331,7 @@ class RVC:
         f0 = self.model_fcpe.infer(
             x.to(self.device_fcpe).unsqueeze(0).float(),
             sr=16000,
-            decoder_mode='local_argmax',
+            decoder_mode="local_argmax",
             threshold=0.006,
         )
         f0 *= pow(2, f0_up_key / 12)
@@ -384,21 +386,29 @@ class RVC:
         if self.if_f0 == 1:
             f0_extractor_frame = block_frame_16k + 800
             if f0method == "rmvpe":
-                f0_extractor_frame = (
+                f0_extractor_frame = 5120 * ((f0_extractor_frame - 1) // 5120 + 1) - 160
-                    5120 * ((f0_extractor_frame - 1) // 5120 + 1) - 160
+            pitch, pitchf = self.get_f0(
+                input_wav[-f0_extractor_frame:], self.f0_up_key, self.n_cpu, f0method
             )
-            pitch, pitchf = self.get_f0(input_wav[-f0_extractor_frame: ], self.f0_up_key, self.n_cpu, f0method)
             start_frame = block_frame_16k // 160
             end_frame = len(self.cache_pitch) - (pitch.shape[0] - 4) + start_frame
-            self.cache_pitch[:] = np.append(self.cache_pitch[start_frame: end_frame], pitch[3:-1])
+            self.cache_pitch[:] = np.append(
+                self.cache_pitch[start_frame:end_frame], pitch[3:-1]
+            )
             self.cache_pitchf[:] = np.append(
                 self.cache_pitchf[start_frame:end_frame], pitchf[3:-1]
             )
         t4 = ttime()
         p_len = input_wav.shape[0] // 160
         if self.if_f0 == 1:
-            cache_pitch = torch.LongTensor(self.cache_pitch[-p_len: ]).to(self.device).unsqueeze(0)
+            cache_pitch = (
-            cache_pitchf = torch.FloatTensor(self.cache_pitchf[-p_len: ]).to(self.device).unsqueeze(0)
+                torch.LongTensor(self.cache_pitch[-p_len:]).to(self.device).unsqueeze(0)
+            )
+            cache_pitchf = (
+                torch.FloatTensor(self.cache_pitchf[-p_len:])
+                .to(self.device)
+                .unsqueeze(0)
+            )
         feats = F.interpolate(feats.permute(0, 2, 1), scale_factor=2).permute(0, 2, 1)
         feats = feats[:, :p_len, :]
         p_len = torch.LongTensor([p_len]).to(self.device)