From af411843208de1714350edbc8e5e1216e94dccbf Mon Sep 17 00:00:00 2001 From: "github-actions[bot]" <41898282+github-actions[bot]@users.noreply.github.com> Date: Fri, 12 May 2023 19:43:05 +0000 Subject: [PATCH] Format code (#274) Co-authored-by: github-actions[bot] --- extract_feature_print.py | 11 +++-- infer-web.py | 86 ++++++++++++++++++++++++---------------- vc_infer_pipeline.py | 42 +++++++++++++++----- 3 files changed, 90 insertions(+), 49 deletions(-) diff --git a/extract_feature_print.py b/extract_feature_print.py index 5a64b42..f1c4f9a 100644 --- a/extract_feature_print.py +++ b/extract_feature_print.py @@ -18,9 +18,12 @@ from fairseq import checkpoint_utils device = torch.device("cuda" if torch.cuda.is_available() else "cpu") -if torch.cuda.is_available():device="cuda" -elif torch.backends.mps.is_available():device="mps" -else:device="cpu" +if torch.cuda.is_available(): + device = "cuda" +elif torch.backends.mps.is_available(): + device = "mps" +else: + device = "cpu" f = open("%s/extract_f0_feature.log" % exp_dir, "a+") @@ -64,7 +67,7 @@ models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task( model = models[0] model = model.to(device) printt("move model to %s" % device) -if device not in ["mps","cpu"]: +if device not in ["mps", "cpu"]: model = model.half() model.eval() diff --git a/infer-web.py b/infer-web.py index cc9e59f..089eab4 100644 --- a/infer-web.py +++ b/infer-web.py @@ -11,8 +11,8 @@ now_dir = os.getcwd() sys.path.append(now_dir) tmp = os.path.join(now_dir, "TEMP") shutil.rmtree(tmp, ignore_errors=True) -shutil.rmtree("%s/runtime/Lib/site-packages/infer_pack"%(now_dir), ignore_errors=True) -shutil.rmtree("%s/runtime/Lib/site-packages/uvr5_pack"%(now_dir) , ignore_errors=True) +shutil.rmtree("%s/runtime/Lib/site-packages/infer_pack" % (now_dir), ignore_errors=True) +shutil.rmtree("%s/runtime/Lib/site-packages/uvr5_pack" % (now_dir), ignore_errors=True) os.makedirs(tmp, exist_ok=True) os.makedirs(os.path.join(now_dir, "logs"), exist_ok=True) os.makedirs(os.path.join(now_dir, "weights"), exist_ok=True) @@ -121,11 +121,11 @@ names = [] for name in os.listdir(weight_root): if name.endswith(".pth"): names.append(name) -index_paths=[] +index_paths = [] for root, dirs, files in os.walk(index_root, topdown=False): for name in files: if name.endswith(".index") and "trained" not in name: - index_paths.append("%s/%s"%(root,name)) + index_paths.append("%s/%s" % (root, name)) uvr5_names = [] for name in os.listdir(weight_uvr5_root): if name.endswith(".pth"): @@ -156,13 +156,17 @@ def vc_single( load_hubert() if_f0 = cpt.get("f0", 1) file_index = ( - file_index.strip(" ") - .strip('"') - .strip("\n") - .strip('"') - .strip(" ") - .replace("trained", "added") - )if file_index!=""else file_index2 # 防止小白写错，自动帮他替换掉 + ( + file_index.strip(" ") + .strip('"') + .strip("\n") + .strip('"') + .strip(" ") + .replace("trained", "added") + ) + if file_index != "" + else file_index2 + ) # 防止小白写错，自动帮他替换掉 # file_big_npy = ( # file_big_npy.strip(" ").strip('"').strip("\n").strip('"').strip(" ") # ) @@ -184,10 +188,19 @@ def vc_single( resample_sr, f0_file=f0_file, ) - if(resample_sr>=16000 and tgt_sr!=resample_sr): - tgt_sr=resample_sr - index_info="Using index:%s."%file_index if os.path.exists(file_index)else"Index not used." - return "Success.\n %s\nTime:\n npy:%ss, f0:%ss, infer:%ss"%(index_info,times[0],times[1],times[2]), (tgt_sr, audio_opt) + if resample_sr >= 16000 and tgt_sr != resample_sr: + tgt_sr = resample_sr + index_info = ( + "Using index:%s." % file_index + if os.path.exists(file_index) + else "Index not used." + ) + return "Success.\n %s\nTime:\n npy:%ss, f0:%ss, infer:%ss" % ( + index_info, + times[0], + times[1], + times[2], + ), (tgt_sr, audio_opt) except: info = traceback.format_exc() print(info) @@ -237,7 +250,7 @@ def vc_multi( filter_radius, resample_sr, ) - if "Success"in info: + if "Success" in info: try: tgt_sr, audio_opt = opt wavfile.write( @@ -323,7 +336,7 @@ def uvr(model_name, inp_root, save_root_vocal, paths, save_root_ins, agg): # 一个选项卡全局只能有一个音色 def get_vc(sid): global n_spk, tgt_sr, net_g, vc, cpt - if sid == ""or sid==[]: + if sid == "" or sid == []: global hubert_model if hubert_model != 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 print("clean_empty_cache") @@ -371,12 +384,15 @@ def change_choices(): for name in os.listdir(weight_root): if name.endswith(".pth"): names.append(name) - index_paths=[] + index_paths = [] for root, dirs, files in os.walk(index_root, topdown=False): for name in files: if name.endswith(".index") and "trained" not in name: index_paths.append("%s/%s" % (root, name)) - return {"choices": sorted(names), "__type__": "update"},{"choices": sorted(index_paths), "__type__": "update"} + return {"choices": sorted(names), "__type__": "update"}, { + "choices": sorted(index_paths), + "__type__": "update", + } def clean(): @@ -1096,7 +1112,7 @@ with gr.Blocks() as app: value="pm", interactive=True, ) - filter_radius0=gr.Slider( + filter_radius0 = gr.Slider( minimum=0, maximum=7, label=i18n(">=3则使用对harvest音高识别的结果使用中值滤波，数值为滤波半径，使用可以削弱哑音"), @@ -1115,7 +1131,9 @@ with gr.Blocks() as app: choices=sorted(index_paths), interactive=True, ) - refresh_button.click(fn=change_choices, inputs=[], outputs=[sid0, file_index2]) + refresh_button.click( + fn=change_choices, inputs=[], outputs=[sid0, file_index2] + ) # file_big_npy1 = gr.Textbox( # label=i18n("特征文件路径"), # value="E:\\codes\py39\\vits_vc_gpu_train\\logs\\mi-test-1key\\total_fea.npy", @@ -1128,7 +1146,7 @@ with gr.Blocks() as app: value=0.76, interactive=True, ) - resample_sr0=gr.Slider( + resample_sr0 = gr.Slider( minimum=0, maximum=48000, label=i18n("后处理重采样至最终采样率，0为不进行重采样"), @@ -1154,7 +1172,7 @@ with gr.Blocks() as app: # file_big_npy1, index_rate1, filter_radius0, - resample_sr0 + resample_sr0, ], [vc_output1, vc_output2], ) @@ -1174,7 +1192,7 @@ with gr.Blocks() as app: value="pm", interactive=True, ) - filter_radius1=gr.Slider( + filter_radius1 = gr.Slider( minimum=0, maximum=7, label=i18n(">=3则使用对harvest音高识别的结果使用中值滤波，数值为滤波半径，使用可以削弱哑音"), @@ -1205,7 +1223,7 @@ with gr.Blocks() as app: value=1, interactive=True, ) - resample_sr1=gr.Slider( + resample_sr1 = gr.Slider( minimum=0, maximum=48000, label=i18n("后处理重采样至最终采样率，0为不进行重采样"), @@ -1237,7 +1255,7 @@ with gr.Blocks() as app: # file_big_npy2, index_rate2, filter_radius1, - resample_sr1 + resample_sr1, ], [vc_output3], ) @@ -1335,7 +1353,7 @@ with gr.Blocks() as app: but1 = gr.Button(i18n("处理数据"), variant="primary") info1 = gr.Textbox(label=i18n("输出信息"), value="") but1.click( - preprocess_dataset, [trainset_dir4, exp_dir1, sr2,np7], [info1] + preprocess_dataset, [trainset_dir4, exp_dir1, sr2, np7], [info1] ) with gr.Group(): gr.Markdown(value=i18n("step2b: 使用CPU提取音高(如果模型带音高), 使用GPU提取特征(选择卡号)")) @@ -1597,16 +1615,16 @@ with gr.Blocks() as app: butOnnx = gr.Button(i18n("导出Onnx模型"), variant="primary") butOnnx.click(export_onnx, [ckpt_dir, onnx_dir, moevs], infoOnnx) - tab_faq=i18n("常见问题解答") + tab_faq = i18n("常见问题解答") with gr.TabItem(tab_faq): try: - if(tab_faq=="常见问题解答"): - with open("docs/faq.md","r",encoding="utf8")as f:info=f.read() + if tab_faq == "常见问题解答": + with open("docs/faq.md", "r", encoding="utf8") as f: + info = f.read() else: - with open("docs/faq_en.md", "r")as f:info = f.read() - gr.Markdown( - value=info - ) + with open("docs/faq_en.md", "r") as f: + info = f.read() + gr.Markdown(value=info) except: gr.Markdown(traceback.format_exc()) diff --git a/vc_infer_pipeline.py b/vc_infer_pipeline.py index 670e261..9564493 100644 --- a/vc_infer_pipeline.py +++ b/vc_infer_pipeline.py @@ -2,16 +2,18 @@ import numpy as np, parselmouth, torch, pdb from time import time as ttime import torch.nn.functional as F import scipy.signal as signal -import pyworld, os, traceback, faiss,librosa +import pyworld, os, traceback, faiss, librosa from scipy import signal from functools import lru_cache bh, ah = signal.butter(N=5, Wn=48, btype="high", fs=16000) -input_audio_path2wav={} +input_audio_path2wav = {} + + @lru_cache -def cache_harvest_f0(input_audio_path,fs,f0max,f0min,frame_period): - audio=input_audio_path2wav[input_audio_path] +def cache_harvest_f0(input_audio_path, fs, f0max, f0min, frame_period): + audio = input_audio_path2wav[input_audio_path] f0, t = pyworld.harvest( audio, fs=fs, @@ -22,6 +24,7 @@ def cache_harvest_f0(input_audio_path,fs,f0max,f0min,frame_period): f0 = pyworld.stonemask(audio, f0, t, fs) return f0 + class VC(object): def __init__(self, tgt_sr, config): self.x_pad, self.x_query, self.x_center, self.x_max, self.is_half = ( @@ -41,7 +44,16 @@ class VC(object): self.t_max = self.sr * self.x_max # 免查询时长阈值 self.device = config.device - def get_f0(self, input_audio_path,x, p_len, f0_up_key, f0_method,filter_radius, inp_f0=None): + def get_f0( + self, + input_audio_path, + x, + p_len, + f0_up_key, + f0_method, + filter_radius, + inp_f0=None, + ): global input_audio_path2wav time_step = self.window / self.sr * 1000 f0_min = 50 @@ -65,9 +77,9 @@ class VC(object): f0, [[pad_size, p_len - len(f0) - pad_size]], mode="constant" ) elif f0_method == "harvest": - input_audio_path2wav[input_audio_path]=x.astype(np.double) - f0=cache_harvest_f0(input_audio_path,self.sr,f0_max,f0_min,10) - if(filter_radius>2): + input_audio_path2wav[input_audio_path] = x.astype(np.double) + f0 = cache_harvest_f0(input_audio_path, self.sr, f0_max, f0_min, 10) + if filter_radius > 2: f0 = signal.medfilt(f0, 3) f0 *= pow(2, f0_up_key / 12) # with open("test.txt","w")as f:f.write("\n".join([str(i)for i in f0.tolist()])) @@ -255,7 +267,15 @@ class VC(object): sid = torch.tensor(sid, device=self.device).unsqueeze(0).long() pitch, pitchf = None, None if if_f0 == 1: - pitch, pitchf = self.get_f0(input_audio_path,audio_pad, p_len, f0_up_key, f0_method,filter_radius, inp_f0) + pitch, pitchf = self.get_f0( + input_audio_path, + audio_pad, + p_len, + f0_up_key, + f0_method, + filter_radius, + inp_f0, + ) pitch = pitch[:p_len] pitchf = pitchf[:p_len] if self.device == "mps": @@ -328,11 +348,11 @@ class VC(object): )[self.t_pad_tgt : -self.t_pad_tgt] ) audio_opt = np.concatenate(audio_opt) - if(resample_sr>=16000 and tgt_sr!=resample_sr): + if resample_sr >= 16000 and tgt_sr != resample_sr: audio_opt = librosa.resample( audio_opt, orig_sr=tgt_sr, target_sr=resample_sr ) - audio_opt=audio_opt.astype(np.int16) + audio_opt = audio_opt.astype(np.int16) del pitch, pitchf, sid if torch.cuda.is_available(): torch.cuda.empty_cache()