From b3f22dcdef5252d9d69fe92fe6e68e31075204cc Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E6=BA=90=E6=96=87=E9=9B=A8?= <41315874+fumiama@users.noreply.github.com> Date: Sat, 2 Sep 2023 12:09:19 +0800 Subject: [PATCH] fix: all logger format according to #1159 --- gui_v1.py | 10 ++-- infer-web.py | 78 ++++++----------------------- infer/lib/infer_pack/models.py | 8 +-- infer/lib/infer_pack/models_onnx.py | 2 +- infer/lib/rmvpe.py | 2 +- infer/lib/train/data_utils.py | 4 +- infer/lib/train/mel_processing.py | 4 +- infer/lib/train/utils.py | 8 +-- 8 files changed, 33 insertions(+), 83 deletions(-) diff --git a/gui_v1.py b/gui_v1.py index 470f085..4add169 100644 --- a/gui_v1.py +++ b/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() diff --git a/infer-web.py b/infer-web.py index 5532a2c..61ecfdb 100644 --- a/infer-web.py +++ b/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): - path_str = "" if version19 == "v1" else "_v2" - f0_str = "f0" if if_f0_3 else "" +def get_pretrained_models(path_str, f0_str, sr2): 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), - "not exist, will not use pretrained model", + "assets/pretrained%s/%sG%s.pth 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), - "not exist, will not use pretrained model", + "assets/pretrained%s/%sD%s.pth 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) - if if_pretrained_generator_exist - else "", - "assets/pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2) - if if_pretrained_discriminator_exist - else "", + *get_pretrained_models(path_str, f0_str, sr2), 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"}, - ("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 "", + {"visible": if_f0_3, "__type__": "update"}, *get_pretrained_models(path_str, "f0", sr2) ) - # 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 == "": diff --git a/infer/lib/infer_pack/models.py b/infer/lib/infer_pack/models.py index cf21e3b..80127ed 100644 --- a/infer/lib/infer_pack/models.py +++ b/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): diff --git a/infer/lib/infer_pack/models_onnx.py b/infer/lib/infer_pack/models_onnx.py index e55dff4..0580924 100644 --- a/infer/lib/infer_pack/models_onnx.py +++ b/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): diff --git a/infer/lib/rmvpe.py b/infer/lib/rmvpe.py index 64df092..b498857 100644 --- a/infer/lib/rmvpe.py +++ b/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) diff --git a/infer/lib/train/data_utils.py b/infer/lib/train/data_utils.py index 9256b85..7567970 100644 --- a/infer/lib/train/data_utils.py +++ b/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, diff --git a/infer/lib/train/mel_processing.py b/infer/lib/train/mel_processing.py index a92a311..04a11f1 100644 --- a/infer/lib/train/mel_processing.py +++ b/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 diff --git a/infer/lib/train/utils.py b/infer/lib/train/utils.py index 47634ba..c80d800 100644 --- a/infer/lib/train/utils.py +++ b/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)