Format code (#1162)

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

gui_v1.py

@@ -718,7 +718,9 @@ if __name__ == "__main__":
             sd.default.device[1] = output_device_indices[
                 output_devices.index(output_device)
             ]
-            logger.info("Input device:" + str(sd.default.device[0]) + ":" + str(input_device))
+            logger.info(
+                "Input device:" + str(sd.default.device[0]) + ":" + str(input_device)
+            )
             logger.info(
                 "Output device:" + str(sd.default.device[1]) + ":" + str(output_device)
             )

infer/lib/infer_pack/models.py

@@ -1,5 +1,6 @@
 import math
 import logging
+
 logger = logging.getLogger(__name__)
 
 import numpy as np
@@ -615,7 +616,9 @@ class SynthesizerTrnMs256NSFsid(nn.Module):
             inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels
         )
         self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
-        logger.debug("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
+        logger.debug(
+            "gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim
+        )
 
     def remove_weight_norm(self):
         self.dec.remove_weight_norm()
@@ -731,7 +734,9 @@ class SynthesizerTrnMs768NSFsid(nn.Module):
             inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels
         )
         self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
-        logger.debug("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
+        logger.debug(
+            "gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim
+        )
 
     def remove_weight_norm(self):
         self.dec.remove_weight_norm()
@@ -844,7 +849,9 @@ class SynthesizerTrnMs256NSFsid_nono(nn.Module):
             inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels
         )
         self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
-        logger.debug("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
+        logger.debug(
+            "gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim
+        )
 
     def remove_weight_norm(self):
         self.dec.remove_weight_norm()
@@ -950,7 +957,9 @@ class SynthesizerTrnMs768NSFsid_nono(nn.Module):
             inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels
         )
         self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
-        logger.debug("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
+        logger.debug(
+            "gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim
+        )
 
     def remove_weight_norm(self):
         self.dec.remove_weight_norm()

infer/lib/infer_pack/models_onnx.py

@@ -1,5 +1,6 @@
 import math
 import logging
+
 logger = logging.getLogger(__name__)
 
 import numpy as np
@@ -619,7 +620,9 @@ class SynthesizerTrnMsNSFsidM(nn.Module):
         )
         self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
         self.speaker_map = None
-        logger.debug("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
+        logger.debug(
+            "gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim
+        )
 
     def remove_weight_norm(self):
         self.dec.remove_weight_norm()

infer/lib/infer_pack/onnx_inference.py

@@ -4,6 +4,7 @@ import onnxruntime
 import soundfile
 
 import logging
+
 logger = logging.getLogger(__name__)
 
 

infer/lib/train/data_utils.py

@@ -1,6 +1,7 @@
 import os
 import traceback
 import logging
+
 logger = logging.getLogger(__name__)
 
 import numpy as np

infer/lib/train/mel_processing.py

@@ -2,6 +2,7 @@ import torch
 import torch.utils.data
 from librosa.filters import mel as librosa_mel_fn
 import logging
+
 logger = logging.getLogger(__name__)
 
 MAX_WAV_VALUE = 32768.0

infer/modules/train/train.py

@@ -1,6 +1,7 @@
 import os
 import sys
 import logging
+
 logger = logging.getLogger(__name__)
 
 now_dir = os.getcwd()

infer/modules/uvr5/mdxnet.py

@@ -1,5 +1,6 @@
 import os
 import logging
+
 logger = logging.getLogger(__name__)
 
 import librosa

infer/modules/uvr5/modules.py

@@ -1,6 +1,7 @@
 import os
 import traceback
 import logging
+
 logger = logging.getLogger(__name__)
 
 import ffmpeg

infer/modules/uvr5/preprocess.py

@@ -1,5 +1,6 @@
 import os
 import logging
+
 logger = logging.getLogger(__name__)
 
 import librosa

infer/modules/vc/modules.py

@@ -1,5 +1,6 @@
 import traceback
 import logging
+
 logger = logging.getLogger(__name__)
 
 import numpy as np
@@ -52,8 +53,16 @@ class VC:
         if not sid:
             if self.hubert_model is not None:  # 考虑到轮询, 需要加个判断看是否 sid 是由有模型切换到无模型的
                 logger.info("Clean model cache")
-                del self.net_g, self.n_spk, self.vc, self.hubert_model, self.tgt_sr  # ,cpt
+                del (
-                self.hubert_model = self.net_g = self.n_spk = self.vc = self.hubert_model = self.tgt_sr = None
+                    self.net_g,
+                    self.n_spk,
+                    self.vc,
+                    self.hubert_model,
+                    self.tgt_sr,
+                )  # ,cpt
+                self.hubert_model = (
+                    self.net_g
+                ) = self.n_spk = self.vc = self.hubert_model = self.tgt_sr = None
                 if torch.cuda.is_available():
                     torch.cuda.empty_cache()
                 ###楼下不这么折腾清理不干净

infer/modules/vc/pipeline.py

@@ -2,6 +2,7 @@ import os
 import sys
 import traceback
 import logging
+
 logger = logging.getLogger(__name__)
 
 from functools import lru_cache
@@ -267,9 +268,7 @@ class Pipeline(object):
         with torch.no_grad():
             hasp = pitch is not None and pitchf is not None
             arg = (feats, p_len, pitch, pitchf, sid) if hasp else (feats, p_len, sid)
-            audio1 = (
+            audio1 = (net_g.infer(*arg)[0][0, 0]).data.cpu().float().numpy()
-                (net_g.infer(*arg)[0][0, 0]).data.cpu().float().numpy()
-            )
             del hasp, arg
         del feats, p_len, padding_mask
         if torch.cuda.is_available():

infer/modules/vc/utils.py

@@ -2,6 +2,7 @@ import os
 
 from fairseq import checkpoint_utils
 
+
 def get_index_path_from_model(sid):
     return next(
         (

tools/calc_rvc_model_similarity.py

@@ -2,6 +2,7 @@
 # Fill in the path of the model to be queried and the root directory of the reference models, and this script will return the similarity between the model to be queried and all reference models.
 import os
 import logging
+
 logger = logging.getLogger(__name__)
 
 import torch

tools/infer/infer-pm-index256.py

@@ -4,6 +4,7 @@
 """
 import os
 import logging
+
 logger = logging.getLogger(__name__)
 
 import parselmouth

tools/infer/train-index-v2.py

@@ -4,6 +4,7 @@
 import os
 import traceback
 import logging
+
 logger = logging.getLogger(__name__)
 
 from multiprocessing import cpu_count

tools/infer/train-index.py

@@ -3,6 +3,7 @@
 """
 import os
 import logging
+
 logger = logging.getLogger(__name__)
 
 import faiss

tools/rvc_for_realtime.py

@@ -2,6 +2,7 @@ import os
 import sys
 import traceback
 import logging
+
 logger = logging.getLogger(__name__)
 
 from time import time as ttime
@@ -341,5 +342,11 @@ class RVC:
                     .float()
                 )
         t5 = ttime()
-        logger.info("Spent time: fea = %s, index = %s, f0 = %s, model = %s", t2 - t1, t3 - t2, t4 - t3, t5 - t4)
+        logger.info(
+            "Spent time: fea = %s, index = %s, f0 = %s, model = %s",
+            t2 - t1,
+            t3 - t2,
+            t4 - t3,
+            t5 - t4,
+        )
         return infered_audio