diff --git a/diffusion/unit2mel.py b/diffusion/unit2mel.py index 19090fe..b423f0c 100644 --- a/diffusion/unit2mel.py +++ b/diffusion/unit2mel.py @@ -24,9 +24,11 @@ def load_model_vocoder( device='cpu', config_path = None ): - if config_path is None: config_file = os.path.join(os.path.split(model_path)[0], 'config.yaml') - else: config_file = config_path - + if config_path is None: + config_file = os.path.join(os.path.split(model_path)[0], 'config.yaml') + else: + config_file = config_path + with open(config_file, "r") as config: args = yaml.safe_load(config) args = DotDict(args) diff --git a/inference/infer_tool.py b/inference/infer_tool.py index 3ae57c7..6ab669d 100644 --- a/inference/infer_tool.py +++ b/inference/infer_tool.py @@ -179,7 +179,8 @@ class Svc(object): else: self.feature_retrieval=False - if self.shallow_diffusion : self.nsf_hifigan_enhance = False + if self.shallow_diffusion : + self.nsf_hifigan_enhance = False if self.nsf_hifigan_enhance: from modules.enhancer import Enhancer self.enhancer = Enhancer('nsf-hifigan', 'pretrain/nsf_hifigan/model',device=self.dev) @@ -442,7 +443,8 @@ class Svc(object): datas = [data] for k,dat in enumerate(datas): per_length = int(np.ceil(len(dat) / audio_sr * self.target_sample)) if clip_seconds!=0 else length - if clip_seconds!=0: print(f'###=====segment clip start, {round(len(dat) / audio_sr, 3)}s======') + if clip_seconds!=0: + print(f'###=====segment clip start, {round(len(dat) / audio_sr, 3)}s======') # padd pad_len = int(audio_sr * pad_seconds) dat = np.concatenate([np.zeros([pad_len]), dat, np.zeros([pad_len])]) diff --git a/inference_main.py b/inference_main.py index d446649..5101f7a 100644 --- a/inference_main.py +++ b/inference_main.py @@ -132,8 +132,10 @@ def main(): key = "auto" if auto_predict_f0 else f"{tran}key" cluster_name = "" if cluster_infer_ratio == 0 else f"_{cluster_infer_ratio}" isdiffusion = "sovits" - if shallow_diffusion : isdiffusion = "sovdiff" - if only_diffusion : isdiffusion = "diff" + if shallow_diffusion : + isdiffusion = "sovdiff" + if only_diffusion : + isdiffusion = "diff" if use_spk_mix: spk = "spk_mix" res_path = f'results/{clean_name}_{key}_{spk}{cluster_name}_{isdiffusion}_{f0p}.{wav_format}' diff --git a/modules/commons.py b/modules/commons.py index 756234c..d8ba67d 100644 --- a/modules/commons.py +++ b/modules/commons.py @@ -134,12 +134,6 @@ def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels): return acts -def convert_pad_shape(pad_shape): - l = pad_shape[::-1] - pad_shape = [item for sublist in l for item in sublist] - return pad_shape - - def shift_1d(x): x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [1, 0]]))[:, :, :-1] return x diff --git a/train.py b/train.py index 202a135..d2356b5 100644 --- a/train.py +++ b/train.py @@ -1,11 +1,7 @@ import logging import multiprocessing -import time - -logging.getLogger('matplotlib').setLevel(logging.WARNING) -logging.getLogger('numba').setLevel(logging.WARNING) - import os +import time import torch import torch.distributed as dist @@ -26,6 +22,9 @@ from models import ( from modules.losses import discriminator_loss, feature_loss, generator_loss, kl_loss from modules.mel_processing import mel_spectrogram_torch, spec_to_mel_torch +logging.getLogger('matplotlib').setLevel(logging.WARNING) +logging.getLogger('numba').setLevel(logging.WARNING) + torch.backends.cudnn.benchmark = True global_step = 0 start_time = time.time() diff --git a/webUI.py b/webUI.py index 5f4f1e3..e3a8cad 100644 --- a/webUI.py +++ b/webUI.py @@ -45,7 +45,8 @@ def upload_mix_append_file(files,sfiles): p = {file:100 for file in file_paths} return file_paths,mix_model_output1.update(value=json.dumps(p,indent=2)) except Exception as e: - if debug: traceback.print_exc() + if debug: + traceback.print_exc() raise gr.Error(e) def mix_submit_click(js,mode): @@ -59,16 +60,19 @@ def mix_submit_click(js,mode): path = mix_model(model_path,mix_rate,mode) return f"成功，文件被保存在了{path}" except Exception as e: - if debug: traceback.print_exc() + if debug: + traceback.print_exc() raise gr.Error(e) def updata_mix_info(files): try: - if files is None : return mix_model_output1.update(value="") + if files is None : + return mix_model_output1.update(value="") p = {file.name:100 for file in files} return mix_model_output1.update(value=json.dumps(p,indent=2)) except Exception as e: - if debug: traceback.print_exc() + if debug: + traceback.print_exc() raise gr.Error(e) def modelAnalysis(model_path,config_path,cluster_model_path,device,enhance,diff_model_path,diff_config_path,only_diffusion,use_spk_mix): @@ -108,7 +112,8 @@ def modelAnalysis(model_path,config_path,cluster_model_path,device,enhance,diff_ msg += i + " " return sid.update(choices = spks,value=spks[0]), msg except Exception as e: - if debug: traceback.print_exc() + if debug: + traceback.print_exc() raise gr.Error(e) @@ -168,7 +173,8 @@ def vc_fn(sid, input_audio, vc_transform, auto_f0,cluster_ratio, slice_db, noise soundfile.write(output_file, _audio, model.target_sample, format="wav") return "Success", output_file except Exception as e: - if debug: traceback.print_exc() + if debug: + traceback.print_exc() raise gr.Error(e) def tts_func(_text,_rate,_voice): @@ -176,7 +182,8 @@ def tts_func(_text,_rate,_voice): # voice = "zh-CN-XiaoyiNeural"#女性，较高音 # voice = "zh-CN-YunxiNeural"#男性 voice = "zh-CN-YunxiNeural"#男性 - if ( _voice == "女" ) : voice = "zh-CN-XiaoyiNeural" + if ( _voice == "女" ) : + voice = "zh-CN-XiaoyiNeural" output_file = _text[0:10]+".wav" # communicate = edge_tts.Communicate(_text, voice) # await communicate.save(output_file)