fix: all logger format

according to #1159

gui_v1.py

@@ -358,7 +358,7 @@ if __name__ == "__main__":
                     )
                 if event == "start_vc" and self.flag_vc == False:
                     if self.set_values(values) == True:
-                        logger.info("Use CUDA:" + str(torch.cuda.is_available()))
+                        logger.info("Use CUDA: %b", torch.cuda.is_available())
                         self.start_vc()
                         settings = {
                             "pth_path": values["pth_path"],
@@ -625,7 +625,7 @@ if __name__ == "__main__":
                 sola_offset = sola_offset.item()
             else:
                 sola_offset = torch.argmax(cor_nom[0, 0] / cor_den[0, 0])
-            logger.debug("sola_offset =" + str(int(sola_offset)))
+            logger.debug("sola_offset = %d", int(sola_offset))
             self.output_wav[:] = infer_wav[sola_offset : sola_offset + self.block_frame]
             self.output_wav[: self.crossfade_frame] *= self.fade_in_window
             self.output_wav[: self.crossfade_frame] += self.sola_buffer[:]
@@ -665,7 +665,7 @@ if __name__ == "__main__":
                     outdata[:] = self.output_wav[:].repeat(2, 1).t().cpu().numpy()
             total_time = time.perf_counter() - start_time
             self.window["infer_time"].update(int(total_time * 1000))
-            logger.info("Infer time:" + str(total_time))
+            logger.info("Infer time: %.2f", total_time)
 
         def get_devices(self, update: bool = True):
             """获取设备列表"""
@@ -719,10 +719,10 @@ if __name__ == "__main__":
                 output_devices.index(output_device)
             ]
             logger.info(
-                "Input device:" + str(sd.default.device[0]) + ":" + str(input_device)
+                "Input device: %s:%d", str(sd.default.device[0]), input_device
             )
             logger.info(
-                "Output device:" + str(sd.default.device[1]) + ":" + str(output_device)
+                "Output device: %s:%d", str(sd.default.device[1]), output_device
             )
 
     gui = GUI()

infer-web.py

@@ -370,9 +370,7 @@ def extract_f0_feature(gpus, n_p, f0method, if_f0, exp_dir, version19, gpus_rmvp
     yield log
 
 
-def change_sr2(sr2, if_f0_3, version19):
+def get_pretrained_models(path_str, f0_str, sr2):
-    path_str = "" if version19 == "v1" else "_v2"
-    f0_str = "f0" if if_f0_3 else ""
     if_pretrained_generator_exist = os.access(
         "assets/pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2), os.F_OK
     )
@@ -381,13 +379,13 @@ def change_sr2(sr2, if_f0_3, version19):
     )
     if not if_pretrained_generator_exist:
         logger.warn(
-            "assets/pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2),
+            "assets/pretrained%s/%sG%s.pth not exist, will not use pretrained model",
-            "not exist, will not use pretrained model",
+            path_str, f0_str, sr2
         )
     if not if_pretrained_discriminator_exist:
         logger.warn(
-            "assets/pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2),
+            "assets/pretrained%s/%sD%s.pth not exist, will not use pretrained model",
-            "not exist, will not use pretrained model",
+            path_str, f0_str, sr2
         )
     return (
         "assets/pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2)
@@ -399,6 +397,12 @@ def change_sr2(sr2, if_f0_3, version19):
     )
 
 
+def change_sr2(sr2, if_f0_3, version19):
+    path_str = "" if version19 == "v1" else "_v2"
+    f0_str = "f0" if if_f0_3 else ""
+    return get_pretrained_models(path_str, f0_str, sr2)
+
+
 def change_version19(sr2, if_f0_3, version19):
     path_str = "" if version19 == "v1" else "_v2"
     if sr2 == "32k" and version19 == "v1":
@@ -409,72 +413,18 @@ def change_version19(sr2, if_f0_3, version19):
         else {"choices": ["40k", "48k", "32k"], "__type__": "update", "value": sr2}
     )
     f0_str = "f0" if if_f0_3 else ""
-    if_pretrained_generator_exist = os.access(
-        "assets/pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2), os.F_OK
-    )
-    if_pretrained_discriminator_exist = os.access(
-        "assets/pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2), os.F_OK
-    )
-    if not if_pretrained_generator_exist:
-        logger.warn(
-            "assets/pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2),
-            "not exist, will not use pretrained model",
-        )
-    if not if_pretrained_discriminator_exist:
-        logger.warn(
-            "assets/pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2),
-            "not exist, will not use pretrained model",
-        )
     return (
-        "assets/pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2)
+        *get_pretrained_models(path_str, f0_str, sr2),
-        if if_pretrained_generator_exist
-        else "",
-        "assets/pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2)
-        if if_pretrained_discriminator_exist
-        else "",
         to_return_sr2,
     )
 
 
 def change_f0(if_f0_3, sr2, version19):  # f0method8,pretrained_G14,pretrained_D15
     path_str = "" if version19 == "v1" else "_v2"
-    if_pretrained_generator_exist = os.access(
-        "assets/pretrained%s/f0G%s.pth" % (path_str, sr2), os.F_OK
-    )
-    if_pretrained_discriminator_exist = os.access(
-        "assets/pretrained%s/f0D%s.pth" % (path_str, sr2), os.F_OK
-    )
-    if not if_pretrained_generator_exist:
-        logger.warn(
-            "assets/pretrained%s/f0G%s.pth" % (path_str, sr2),
-            "not exist, will not use pretrained model",
-        )
-    if not if_pretrained_discriminator_exist:
-        logger.warn(
-            "assets/pretrained%s/f0D%s.pth" % (path_str, sr2),
-            "not exist, will not use pretrained model",
-        )
-    if if_f0_3:
-        return (
-            {"visible": True, "__type__": "update"},
-            "assets/pretrained%s/f0G%s.pth" % (path_str, sr2)
-            if if_pretrained_generator_exist
-            else "",
-            "assets/pretrained%s/f0D%s.pth" % (path_str, sr2)
-            if if_pretrained_discriminator_exist
-            else "",
-        )
     return (
-        {"visible": False, "__type__": "update"},
+        {"visible": if_f0_3, "__type__": "update"}, *get_pretrained_models(path_str, "f0", sr2)
-        ("assets/pretrained%s/G%s.pth" % (path_str, sr2))
-        if if_pretrained_generator_exist
-        else "",
-        ("assets/pretrained%s/D%s.pth" % (path_str, sr2))
-        if if_pretrained_discriminator_exist
-        else "",
     )
 
-
 # but3.click(click_train,[exp_dir1,sr2,if_f0_3,save_epoch10,total_epoch11,batch_size12,if_save_latest13,pretrained_G14,pretrained_D15,gpus16])
 def click_train(
     exp_dir1,
@@ -561,7 +511,7 @@ def click_train(
     logger.debug("Write filelist done")
     # 生成config#无需生成config
     # cmd = python_cmd + " train_nsf_sim_cache_sid_load_pretrain.py -e mi-test -sr 40k -f0 1 -bs 4 -g 0 -te 10 -se 5 -pg pretrained/f0G40k.pth -pd pretrained/f0D40k.pth -l 1 -c 0"
-    logger.info("Use gpus:", gpus16)
+    logger.info("Use gpus: %s", str(gpus16))
     if pretrained_G14 == "":
         logger.info("No pretrained Generator")
     if pretrained_D15 == "":

infer/lib/infer_pack/models.py

@@ -617,7 +617,7 @@ class SynthesizerTrnMs256NSFsid(nn.Module):
         )
         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
+            "gin_channels: " + gin_channels + ", self.spk_embed_dim: " + self.spk_embed_dim
         )
 
     def remove_weight_norm(self):
@@ -735,7 +735,7 @@ class SynthesizerTrnMs768NSFsid(nn.Module):
         )
         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
+            "gin_channels: " + gin_channels + ", self.spk_embed_dim: " + self.spk_embed_dim
         )
 
     def remove_weight_norm(self):
@@ -850,7 +850,7 @@ class SynthesizerTrnMs256NSFsid_nono(nn.Module):
         )
         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
+            "gin_channels: " + gin_channels + ", self.spk_embed_dim: " + self.spk_embed_dim
         )
 
     def remove_weight_norm(self):
@@ -958,7 +958,7 @@ class SynthesizerTrnMs768NSFsid_nono(nn.Module):
         )
         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
+            "gin_channels: " + gin_channels + ", self.spk_embed_dim: " + self.spk_embed_dim
         )
 
     def remove_weight_norm(self):

infer/lib/infer_pack/models_onnx.py

@@ -621,7 +621,7 @@ 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
+            "gin_channels: " + gin_channels + ", self.spk_embed_dim: " + self.spk_embed_dim
         )
 
     def remove_weight_norm(self):

infer/lib/rmvpe.py

@@ -695,4 +695,4 @@ if __name__ == "__main__":
     # f0 = rmvpe.infer_from_audio(audio, thred=thred)
     # f0 = rmvpe.infer_from_audio(audio, thred=thred)
     t1 = ttime()
-    logger.info(f0.shape, t1 - t0)
+    logger.info("%s %.2f", f0.shape, t1 - t0)

infer/lib/train/data_utils.py

@@ -113,7 +113,7 @@ class TextAudioLoaderMultiNSFsid(torch.utils.data.Dataset):
             try:
                 spec = torch.load(spec_filename)
             except:
-                logger.warn(spec_filename, traceback.format_exc())
+                logger.warn("%s %s", spec_filename, traceback.format_exc())
                 spec = spectrogram_torch(
                     audio_norm,
                     self.filter_length,
@@ -305,7 +305,7 @@ class TextAudioLoader(torch.utils.data.Dataset):
             try:
                 spec = torch.load(spec_filename)
             except:
-                logger.warn(spec_filename, traceback.format_exc())
+                logger.warn("%s %s", spec_filename, traceback.format_exc())
                 spec = spectrogram_torch(
                     audio_norm,
                     self.filter_length,

infer/lib/train/mel_processing.py

@@ -54,9 +54,9 @@ def spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center=False)
     """
     # Validation
     if torch.min(y) < -1.07:
-        logger.debug("min value is ", torch.min(y))
+        logger.debug("min value is %s", str(torch.min(y)))
     if torch.max(y) > 1.07:
-        logger.debug("max value is ", torch.max(y))
+        logger.debug("max value is %s", str(torch.max(y)))
 
     # Window - Cache if needed
     global hann_window

infer/lib/train/utils.py

@@ -35,12 +35,12 @@ def load_checkpoint_d(checkpoint_path, combd, sbd, optimizer=None, load_opt=1):
                 if saved_state_dict[k].shape != state_dict[k].shape:
                     logger.warn(
                         "shape-%s-mismatch. need: %s, get: %s"
-                        % (k, state_dict[k].shape, saved_state_dict[k].shape)
+                        , k, state_dict[k].shape, saved_state_dict[k].shape
                     )  #
                     raise KeyError
             except:
                 # logger.info(traceback.format_exc())
-                logger.info("%s is not in the checkpoint" % k)  # pretrain缺失的
+                logger.info("%s is not in the checkpoint", k)  # pretrain缺失的
                 new_state_dict[k] = v  # 模型自带的随机值
         if hasattr(model, "module"):
             model.module.load_state_dict(new_state_dict, strict=False)
@@ -111,12 +111,12 @@ def load_checkpoint(checkpoint_path, model, optimizer=None, load_opt=1):
             if saved_state_dict[k].shape != state_dict[k].shape:
                 logger.warn(
                     "shape-%s-mismatch|need-%s|get-%s"
-                    % (k, state_dict[k].shape, saved_state_dict[k].shape)
+                    , k, state_dict[k].shape, saved_state_dict[k].shape
                 )  #
                 raise KeyError
         except:
             # logger.info(traceback.format_exc())
-            logger.info("%s is not in the checkpoint" % k)  # pretrain缺失的
+            logger.info("%s is not in the checkpoint", k)  # pretrain缺失的
             new_state_dict[k] = v  # 模型自带的随机值
     if hasattr(model, "module"):
         model.module.load_state_dict(new_state_dict, strict=False)