Ruff autofix E71*

TTS/api.py

@@ -231,7 +231,7 @@ class TTS(nn.Module):
             raise ValueError("Model is not multi-speaker but `speaker` is provided.")
         if not self.is_multi_lingual and language is not None:
             raise ValueError("Model is not multi-lingual but `language` is provided.")
-        if not emotion is None and not speed is None:
+        if emotion is not None and speed is not None:
             raise ValueError("Emotion and speed can only be used with Coqui Studio models. Which is discontinued.")
 
     def tts(

TTS/encoder/utils/generic_utils.py

@@ -34,7 +34,7 @@ class AugmentWAV(object):
                     # ignore not listed directories
                     if noise_dir not in self.additive_noise_types:
                         continue
-                    if not noise_dir in self.noise_list:
+                    if noise_dir not in self.noise_list:
                         self.noise_list[noise_dir] = []
                     self.noise_list[noise_dir].append(wav_file)
 

TTS/tts/models/align_tts.py

@@ -415,7 +415,7 @@ class AlignTTS(BaseTTS):
         """Decide AlignTTS training phase"""
         if isinstance(config.phase_start_steps, list):
             vals = [i < global_step for i in config.phase_start_steps]
-            if not True in vals:
+            if True not in vals:
                 phase = 0
             else:
                 phase = (

TTS/tts/models/vits.py

@@ -1880,7 +1880,7 @@ class Vits(BaseTTS):
         self.forward = _forward
         if training:
             self.train()
-        if not disc is None:
+        if disc is not None:
             self.disc = disc
 
     def load_onnx(self, model_path: str, cuda=False):
@@ -1914,9 +1914,9 @@ class Vits(BaseTTS):
             dtype=np.float32,
         )
         input_params = {"input": x, "input_lengths": x_lengths, "scales": scales}
-        if not speaker_id is None:
+        if speaker_id is not None:
             input_params["sid"] = torch.tensor([speaker_id]).cpu().numpy()
-        if not language_id is None:
+        if language_id is not None:
             input_params["langid"] = torch.tensor([language_id]).cpu().numpy()
 
         audio = self.onnx_sess.run(

TTS/utils/manage.py

@@ -516,7 +516,7 @@ class ModelManager(object):
                     sub_conf[field_names[-1]] = new_path
             else:
                 # field name points to a top-level field
-                if not field_name in config:
+                if field_name not in config:
                     return
                 if isinstance(config[field_name], list):
                     config[field_name] = [new_path]

TTS/vc/models/freevc.py

@@ -164,7 +164,7 @@ class DiscriminatorP(torch.nn.Module):
         super(DiscriminatorP, self).__init__()
         self.period = period
         self.use_spectral_norm = use_spectral_norm
-        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
+        norm_f = weight_norm if use_spectral_norm is False else spectral_norm
         self.convs = nn.ModuleList(
             [
                 norm_f(Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
@@ -201,7 +201,7 @@ class DiscriminatorP(torch.nn.Module):
 class DiscriminatorS(torch.nn.Module):
     def __init__(self, use_spectral_norm=False):
         super(DiscriminatorS, self).__init__()
-        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
+        norm_f = weight_norm if use_spectral_norm is False else spectral_norm
         self.convs = nn.ModuleList(
             [
                 norm_f(Conv1d(1, 16, 15, 1, padding=7)),
@@ -468,7 +468,7 @@ class FreeVC(BaseVC):
         Returns:
             torch.Tensor: Output tensor.
         """
-        if c_lengths == None:
+        if c_lengths is None:
             c_lengths = (torch.ones(c.size(0)) * c.size(-1)).to(c.device)
         if not self.use_spk:
             g = self.enc_spk.embed_utterance(mel)

TTS/vc/modules/freevc/wavlm/wavlm.py

@@ -387,7 +387,7 @@ class ConvFeatureExtractionModel(nn.Module):
                 nn.init.kaiming_normal_(conv.weight)
                 return conv
 
-            assert (is_layer_norm and is_group_norm) == False, "layer norm and group norm are exclusive"
+            assert (is_layer_norm and is_group_norm) is False, "layer norm and group norm are exclusive"
 
             if is_layer_norm:
                 return nn.Sequential(

TTS/vocoder/layers/losses.py

@@ -298,7 +298,7 @@ class GeneratorLoss(nn.Module):
             adv_loss = adv_loss + self.hinge_gan_loss_weight * hinge_fake_loss
 
         # Feature Matching Loss
-        if self.use_feat_match_loss and not feats_fake is None:
+        if self.use_feat_match_loss and feats_fake is not None:
             feat_match_loss = self.feat_match_loss(feats_fake, feats_real)
             return_dict["G_feat_match_loss"] = feat_match_loss
             adv_loss = adv_loss + self.feat_match_loss_weight * feat_match_loss