Merge pull request #922 from IAHispano/formatter/main

chore(format): run black on main

core.py

@@ -487,7 +487,7 @@ def run_extract_script(
                 sample_rate,
                 embedder_model,
                 embedder_model_custom,
-                include_mutes
+                include_mutes,
             ],
         ),
     ]
@@ -525,7 +525,9 @@ def run_train_script(
         from rvc.lib.tools.pretrained_selector import pretrained_selector
 
         if custom_pretrained == False:
-            pg, pd = pretrained_selector(str(rvc_version), str(vocoder), True, int(sample_rate))
+            pg, pd = pretrained_selector(
+                str(rvc_version), str(vocoder), True, int(sample_rate)
+            )
         else:
             if g_pretrained_path is None or d_pretrained_path is None:
                 raise ValueError(
@@ -558,7 +560,7 @@ def run_train_script(
                 overtraining_threshold,
                 cleanup,
                 vocoder,
-                checkpointing
+                checkpointing,
             ],
         ),
     ]
@@ -1858,9 +1860,9 @@ def parse_arguments():
     preprocess_parser.add_argument(
         "--cut_preprocess",
         type=str,
-        choices=['Skip', 'Simple', 'Automatic'],
+        choices=["Skip", "Simple", "Automatic"],
         help="Cut the dataset into smaller segments for faster preprocessing.",
-        default='Automatic',
+        default="Automatic",
         required=True,
     )
     preprocess_parser.add_argument(
@@ -1902,7 +1904,7 @@ def parse_arguments():
         choices=[0.0, 0.1, 0.2, 0.3, 0.4],
         default=0.3,
         required=False,
-    )    
+    )
 
     # Parser for 'extract' mode
     extract_parser = subparsers.add_parser(
@@ -1981,8 +1983,8 @@ def parse_arguments():
         help="Number of silent files to include.",
         choices=range(0, 11),
         default=2,
-        required=True
-    )    
+        required=True,
+    )
 
     # Parser for 'train' mode
     train_parser = subparsers.add_parser("train", help="Train an RVC model.")
@@ -2010,7 +2012,7 @@ def parse_arguments():
         help="Enables memory-efficient training.",
         default=False,
         required=False,
-    )    
+    )
     train_parser.add_argument(
         "--save_every_epoch",
         type=int,

rvc/infer/infer.py

@@ -482,7 +482,7 @@ def setup_network(self):
                 use_f0=self.use_f0,
                 text_enc_hidden_dim=self.text_enc_hidden_dim,
                 is_half=False,
-                vocoder=self.vocoder
+                vocoder=self.vocoder,
             )
             del self.net_g.enc_q
             self.net_g.load_state_dict(self.cpt["weight"], strict=False)

rvc/lib/algorithm/discriminators.py

@@ -21,26 +21,41 @@ class MultiPeriodDiscriminator(torch.nn.Module):
             Defaults to False.
     """
 
-    def __init__(self, version: str, use_spectral_norm: bool = False, checkpointing: bool = False):
+    def __init__(
+        self, version: str, use_spectral_norm: bool = False, checkpointing: bool = False
+    ):
         super(MultiPeriodDiscriminator, self).__init__()
         periods = (
             [2, 3, 5, 7, 11, 17] if version == "v1" else [2, 3, 5, 7, 11, 17, 23, 37]
         )
         self.checkpointing = checkpointing
         self.discriminators = torch.nn.ModuleList(
-            [DiscriminatorS(use_spectral_norm=use_spectral_norm, checkpointing=checkpointing)]
-            + [DiscriminatorP(p, use_spectral_norm=use_spectral_norm, checkpointing=checkpointing) for p in periods]
+            [
+                DiscriminatorS(
+                    use_spectral_norm=use_spectral_norm, checkpointing=checkpointing
+                )
+            ]
+            + [
+                DiscriminatorP(
+                    p, use_spectral_norm=use_spectral_norm, checkpointing=checkpointing
+                )
+                for p in periods
+            ]
         )
 
     def forward(self, y, y_hat):
         y_d_rs, y_d_gs, fmap_rs, fmap_gs = [], [], [], []
         for d in self.discriminators:
             if self.training and self.checkpointing:
+
                 def forward_discriminator(d, y, y_hat):
                     y_d_r, fmap_r = d(y)
                     y_d_g, fmap_g = d(y_hat)
                     return y_d_r, fmap_r, y_d_g, fmap_g
-                y_d_r, fmap_r, y_d_g, fmap_g = checkpoint.checkpoint(forward_discriminator, d, y, y_hat, use_reentrant=False)
+
+                y_d_r, fmap_r, y_d_g, fmap_g = checkpoint.checkpoint(
+                    forward_discriminator, d, y, y_hat, use_reentrant=False
+                )
             else:
                 y_d_r, fmap_r = d(y)
                 y_d_g, fmap_g = d(y_hat)
@@ -82,8 +97,8 @@ def forward(self, x):
         fmap = []
         for conv in self.convs:
             if self.training and self.checkpointing:
-                x = checkpoint.checkpoint(conv, x, use_reentrant = False)
-                x = checkpoint.checkpoint(self.lrelu, x, use_reentrant = False)
+                x = checkpoint.checkpoint(conv, x, use_reentrant=False)
+                x = checkpoint.checkpoint(self.lrelu, x, use_reentrant=False)
             else:
                 x = self.lrelu(conv(x))
             fmap.append(x)
@@ -153,8 +168,8 @@ def forward(self, x):
 
         for conv in self.convs:
             if self.training and self.checkpointing:
-                x = checkpoint.checkpoint(conv, x, use_reentrant = False)
-                x = checkpoint.checkpoint(self.lrelu, x, use_reentrant = False)
+                x = checkpoint.checkpoint(conv, x, use_reentrant=False)
+                x = checkpoint.checkpoint(self.lrelu, x, use_reentrant=False)
             else:
                 x = self.lrelu(conv(x))
             fmap.append(x)

rvc/lib/algorithm/generators/hifigan.py

@@ -7,6 +7,7 @@
 from rvc.lib.algorithm.residuals import LRELU_SLOPE, ResBlock
 from rvc.lib.algorithm.commons import init_weights
 
+
 class HiFiGANGenerator(torch.nn.Module):
     """
     HiFi-GAN Generator module for audio synthesis.
@@ -107,6 +108,7 @@ def remove_weight_norm(self):
         for l in self.resblocks:
             l.remove_weight_norm()
 
+
 class SineGenerator(torch.nn.Module):
     """
     Sine wave generator with optional harmonic overtones and noise.
@@ -220,4 +222,4 @@ def forward(self, f0: torch.Tensor, upsampling_factor: int):
             # Combine sine waves and noise
             sine_waveforms = sine_waves * voiced_mask + noise
 
-        return sine_waveforms, voiced_mask, noise
+        return sine_waveforms, voiced_mask, noise

rvc/lib/algorithm/generators/hifigan_nsf.py

@@ -9,6 +9,7 @@
 from rvc.lib.algorithm.residuals import LRELU_SLOPE, ResBlock
 from rvc.lib.algorithm.commons import init_weights
 
+
 class SourceModuleHnNSF(torch.nn.Module):
     """
     Source Module for generating harmonic and noise components for audio synthesis.
@@ -49,6 +50,7 @@ def forward(self, x: torch.Tensor, upsample_factor: int = 1):
         sine_merge = self.l_tanh(self.l_linear(sine_wavs))
         return sine_merge, None, None
 
+
 class HiFiGANNSFGenerator(torch.nn.Module):
     """
     Generator module based on the Neural Source Filter (NSF) architecture.
@@ -87,9 +89,7 @@ def __init__(
         self.num_upsamples = len(upsample_rates)
         self.checkpointing = checkpointing
         self.f0_upsamp = torch.nn.Upsample(scale_factor=math.prod(upsample_rates))
-        self.m_source = SourceModuleHnNSF(
-            sample_rate=sr, harmonic_num=0
-        )
+        self.m_source = SourceModuleHnNSF(sample_rate=sr, harmonic_num=0)
 
         self.conv_pre = torch.nn.Conv1d(
             initial_channel, upsample_initial_channel, 7, 1, padding=3
@@ -169,7 +169,9 @@ def __init__(
         self.upp = math.prod(upsample_rates)
         self.lrelu_slope = LRELU_SLOPE
 
-    def forward(self, x: torch.Tensor, f0: torch.Tensor, g: Optional[torch.Tensor] = None):
+    def forward(
+        self, x: torch.Tensor, f0: torch.Tensor, g: Optional[torch.Tensor] = None
+    ):
         har_source, _, _ = self.m_source(f0, self.upp)
         har_source = har_source.transpose(1, 2)
 
@@ -198,7 +200,9 @@ def resblock_forward(x, blocks):
 
             # Checkpoint or regular computation for ResBlocks
             if self.training and self.checkpointing:
-                x = checkpoint.checkpoint(resblock_forward, x, blocks, use_reentrant=False)
+                x = checkpoint.checkpoint(
+                    resblock_forward, x, blocks, use_reentrant=False
+                )
             else:
                 x = resblock_forward(x, blocks)
 
@@ -228,4 +232,4 @@ def __prepare_scriptable__(self):
                     and hook.__class__.__name__ == "WeightNorm"
                 ):
                     remove_weight_norm(l)
-        return self
+        return self

rvc/lib/algorithm/generators/refinegan.py

@@ -6,6 +6,7 @@
 
 from rvc.lib.algorithm.commons import get_padding
 
+
 class ResBlock(torch.nn.Module):
     """
     Residual block with multiple dilated convolutions.
@@ -94,6 +95,7 @@ def init_weights(self, m):
             m.weight.data.normal_(0, 0.01)
             m.bias.data.fill_(0.0)
 
+
 class AdaIN(torch.nn.Module):
     """
     Adaptive Instance Normalization layer.
@@ -104,6 +106,7 @@ class AdaIN(torch.nn.Module):
         channels (int): Number of input channels.
         leaky_relu_slope (float, optional): Slope for the Leaky ReLU activation applied after scaling. Defaults to 0.2.
     """
+
     def __init__(
         self,
         *,
@@ -120,6 +123,7 @@ def forward(self, x: torch.Tensor):
 
         return self.activation(x + gaussian)
 
+
 class ParallelResBlock(torch.nn.Module):
     """
     Parallel residual block that applies multiple residual blocks with different kernel sizes in parallel.
@@ -131,6 +135,7 @@ class ParallelResBlock(torch.nn.Module):
         dilation (tuple[int], optional): Tuple of dilation rates for the convolutional layers within the residual blocks. Defaults to (1, 3, 5).
         leaky_relu_slope (float, optional): Slope for the Leaky ReLU activation. Defaults to 0.2.
     """
+
     def __init__(
         self,
         *,
@@ -181,6 +186,7 @@ def remove_parametrizations(self):
         for block in self.blocks:
             block[1].remove_parametrizations()
 
+
 class SineGenerator(torch.nn.Module):
     """
     Definition of sine generator
@@ -261,6 +267,7 @@ def forward(self, f0):
             sine_waves = sine_waves * uv + noise * (1 - uv)
         return sine_waves, uv, noise
 
+
 class SourceModuleHnNSF(torch.nn.Module):
     """
     Source Module for generating harmonic and noise signals.
@@ -274,6 +281,7 @@ class SourceModuleHnNSF(torch.nn.Module):
         add_noise_std (float, optional): Standard deviation of the additive noise. Defaults to 0.003.
         voiced_threshold (int, optional): F0 threshold for voiced/unvoiced classification. Defaults to 0.
     """
+
     def __init__(
         self,
         sampling_rate,
@@ -303,6 +311,7 @@ def forward(self, x: torch.Tensor):
 
         return sine_merge, None, None
 
+
 class RefineGANGenerator(torch.nn.Module):
     """
     RefineGAN generator for audio synthesis.
@@ -321,6 +330,7 @@ class RefineGANGenerator(torch.nn.Module):
         gin_channels (int, optional): Number of channels for the global conditioning input. Defaults to 256.
         checkpointing (bool, optional): Whether to use checkpointing for memory efficiency. Defaults to False.
     """
+
     def __init__(
         self,
         *,
@@ -477,4 +487,4 @@ def remove_parametrizations(self):
             block[1].remove_parametrizations()
 
         for block in self.upsample_conv_blocks:
-            block.remove_parametrizations()
+            block.remove_parametrizations()

rvc/lib/algorithm/synthesizers.py

@@ -132,7 +132,7 @@ def __init__(
                     upsample_initial_channel,
                     upsample_kernel_sizes,
                     gin_channels=gin_channels,
-                    checkpointing=checkpointing
+                    checkpointing=checkpointing,
                 )
         self.enc_q = PosteriorEncoder(
             spec_channels,

rvc/lib/tools/pretrained_selector.py

@@ -1,5 +1,6 @@
 import os
 
+
 def pretrained_selector(version, vocoder, pitch_guidance, sample_rate):
     base_path = os.path.join("rvc", "models", "pretraineds", f"pretrained_{version}")
     f0 = "f0" if pitch_guidance else ""
@@ -19,4 +20,4 @@ def pretrained_selector(version, vocoder, pitch_guidance, sample_rate):
     if os.path.exists(path_g) and os.path.exists(path_d):
         return path_g, path_d
     else:
-        return "", ""
+        return "", ""