From 9068d5283e730638920edb2bf02e93abfae1e316 Mon Sep 17 00:00:00 2001 From: "github-actions[bot]" <41898282+github-actions[bot]@users.noreply.github.com> Date: Fri, 28 Apr 2023 11:25:20 +0800 Subject: [PATCH] Format code (#188) Co-authored-by: github-actions[bot] --- config.py | 52 +++++++++++++++++++++++------------ gui.py | 8 +++--- infer-web.py | 76 +++++++++++++++++++++++++++++++++------------------ infer_uvr5.py | 10 +++++-- 4 files changed, 95 insertions(+), 51 deletions(-) diff --git a/config.py b/config.py index 6471f2f..3668e79 100644 --- a/config.py +++ b/config.py @@ -64,25 +64,43 @@ if not torch.cuda.is_available(): device = "cpu" is_half = False -gpu_mem=None +gpu_mem = None if device not in ["cpu", "mps"]: - i_device=int(device.split(":")[-1]) + i_device = int(device.split(":")[-1]) gpu_name = torch.cuda.get_device_name(i_device) - if "16" in gpu_name or "P40"in gpu_name.upper() or "1070"in gpu_name or "1080"in gpu_name: + if ( + "16" in gpu_name + or "P40" in gpu_name.upper() + or "1070" in gpu_name + or "1080" in gpu_name + ): print("16系显卡强制单精度") is_half = False - with open("configs/32k.json","r")as f:strr=f.read().replace("true","false") - with open("configs/32k.json","w")as f:f.write(strr) - with open("configs/40k.json","r")as f:strr=f.read().replace("true","false") - with open("configs/40k.json","w")as f:f.write(strr) - with open("configs/48k.json","r")as f:strr=f.read().replace("true","false") - with open("configs/48k.json","w")as f:f.write(strr) - with open("trainset_preprocess_pipeline_print.py","r")as f:strr=f.read().replace("3.7","3.0") - with open("trainset_preprocess_pipeline_print.py","w")as f:f.write(strr) - gpu_mem=int(torch.cuda.get_device_properties(i_device).total_memory/1024/1024/1024+0.4) - if(gpu_mem<=4): - with open("trainset_preprocess_pipeline_print.py","r")as f:strr=f.read().replace("3.7","3.0") - with open("trainset_preprocess_pipeline_print.py","w")as f:f.write(strr) + with open("configs/32k.json", "r") as f: + strr = f.read().replace("true", "false") + with open("configs/32k.json", "w") as f: + f.write(strr) + with open("configs/40k.json", "r") as f: + strr = f.read().replace("true", "false") + with open("configs/40k.json", "w") as f: + f.write(strr) + with open("configs/48k.json", "r") as f: + strr = f.read().replace("true", "false") + with open("configs/48k.json", "w") as f: + f.write(strr) + with open("trainset_preprocess_pipeline_print.py", "r") as f: + strr = f.read().replace("3.7", "3.0") + with open("trainset_preprocess_pipeline_print.py", "w") as f: + f.write(strr) + gpu_mem = int( + torch.cuda.get_device_properties(i_device).total_memory / 1024 / 1024 / 1024 + + 0.4 + ) + if gpu_mem <= 4: + with open("trainset_preprocess_pipeline_print.py", "r") as f: + strr = f.read().replace("3.7", "3.0") + with open("trainset_preprocess_pipeline_print.py", "w") as f: + f.write(strr) from multiprocessing import cpu_count if n_cpu == 0: @@ -99,8 +117,8 @@ else: x_query = 6 x_center = 38 x_max = 41 -if(gpu_mem!=None and gpu_mem<=4): +if gpu_mem != None and gpu_mem <= 4: x_pad = 1 x_query = 5 x_center = 30 - x_max = 32 \ No newline at end of file + x_max = 32 diff --git a/gui.py b/gui.py index f164829..882291a 100644 --- a/gui.py +++ b/gui.py @@ -375,9 +375,7 @@ class GUI: self.crossfade_frame = int(self.config.crossfade_time * self.config.samplerate) self.sola_search_frame = int(0.012 * self.config.samplerate) self.delay_frame = int(0.01 * self.config.samplerate) # 往前预留0.02s - self.extra_frame = int( - self.config.extra_time * self.config.samplerate - ) + self.extra_frame = int(self.config.extra_time * self.config.samplerate) self.rvc = None self.rvc = RVC( self.config.pitch, @@ -408,7 +406,9 @@ class GUI: orig_freq=self.config.samplerate, new_freq=16000, dtype=torch.float32 ) self.resampler2 = tat.Resample( - orig_freq=self.rvc.tgt_sr, new_freq=self.config.samplerate, dtype=torch.float32 + orig_freq=self.rvc.tgt_sr, + new_freq=self.config.samplerate, + dtype=torch.float32, ) thread_vc = threading.Thread(target=self.soundinput) thread_vc.start() diff --git a/infer-web.py b/infer-web.py index 3347626..bc2d63d 100644 --- a/infer-web.py +++ b/infer-web.py @@ -1,11 +1,12 @@ from multiprocessing import cpu_count -import threading,pdb,librosa +import threading, pdb, librosa from time import sleep from subprocess import Popen from time import sleep import torch, os, traceback, sys, warnings, shutil, numpy as np import faiss from random import shuffle + now_dir = os.getcwd() sys.path.append(now_dir) tmp = os.path.join(now_dir, "TEMP") @@ -24,7 +25,7 @@ i18n = I18nAuto() ncpu = cpu_count() ngpu = torch.cuda.device_count() gpu_infos = [] -mem=[] +mem = [] if (not torch.cuda.is_available()) or ngpu == 0: if_gpu_ok = False else: @@ -50,13 +51,21 @@ else: ): # A10#A100#V100#A40#P40#M40#K80#A4500 if_gpu_ok = True # 至少有一张能用的N卡 gpu_infos.append("%s\t%s" % (i, gpu_name)) - mem.append(int(torch.cuda.get_device_properties(i).total_memory/1024/1024/1024+0.4)) + mem.append( + int( + torch.cuda.get_device_properties(i).total_memory + / 1024 + / 1024 + / 1024 + + 0.4 + ) + ) if if_gpu_ok == True and len(gpu_infos) > 0: - gpu_info ="\n".join(gpu_infos) - default_batch_size=min(mem)//2 + gpu_info = "\n".join(gpu_infos) + default_batch_size = min(mem) // 2 else: gpu_info = "很遗憾您这没有能用的显卡来支持您训练" - default_batch_size=1 + default_batch_size = 1 gpus = "-".join([i[0] for i in gpu_infos]) from infer_pack.models import SynthesizerTrnMs256NSFsid, SynthesizerTrnMs256NSFsid_nono from scipy.io import wavfile @@ -236,7 +245,7 @@ def vc_multi( yield traceback.format_exc() -def uvr(model_name, inp_root, save_root_vocal, paths, save_root_ins,agg): +def uvr(model_name, inp_root, save_root_vocal, paths, save_root_ins, agg): infos = [] try: inp_root = inp_root.strip(" ").strip('"').strip("\n").strip('"').strip(" ") @@ -258,23 +267,30 @@ def uvr(model_name, inp_root, save_root_vocal, paths, save_root_ins,agg): paths = [path.name for path in paths] for path in paths: inp_path = os.path.join(inp_root, path) - need_reformat=1 - done=0 + need_reformat = 1 + done = 0 try: info = ffmpeg.probe(inp_path, cmd="ffprobe") - if(info["streams"][0]["channels"]==2 and info["streams"][0]["sample_rate"]=="44100"): - need_reformat=0 + if ( + info["streams"][0]["channels"] == 2 + and info["streams"][0]["sample_rate"] == "44100" + ): + need_reformat = 0 pre_fun._path_audio_(inp_path, save_root_ins, save_root_vocal) - done=1 + done = 1 except: need_reformat = 1 traceback.print_exc() - if(need_reformat==1): - tmp_path="%s/%s.reformatted.wav"%(tmp,os.path.basename(inp_path)) - os.system("ffmpeg -i %s -vn -acodec pcm_s16le -ac 2 -ar 44100 %s -y"%(inp_path,tmp_path)) - inp_path=tmp_path + if need_reformat == 1: + tmp_path = "%s/%s.reformatted.wav" % (tmp, os.path.basename(inp_path)) + os.system( + "ffmpeg -i %s -vn -acodec pcm_s16le -ac 2 -ar 44100 %s -y" + % (inp_path, tmp_path) + ) + inp_path = tmp_path try: - if(done==0):pre_fun._path_audio_(inp_path, save_root_ins, save_root_vocal) + if done == 0: + pre_fun._path_audio_(inp_path, save_root_ins, save_root_vocal) infos.append("%s->Success" % (os.path.basename(inp_path))) yield "\n".join(infos) except: @@ -660,11 +676,11 @@ def train_index(exp_dir1): big_npy = np.concatenate(npys, 0) # np.save("%s/total_fea.npy" % exp_dir, big_npy) # n_ivf = big_npy.shape[0] // 39 - n_ivf = min(int(16 * np.sqrt(big_npy.shape[0])),big_npy.shape[0]// 39) - infos=[] - infos.append("%s,%s"%(big_npy.shape,n_ivf)) + n_ivf = min(int(16 * np.sqrt(big_npy.shape[0])), big_npy.shape[0] // 39) + infos = [] + infos.append("%s,%s" % (big_npy.shape, n_ivf)) yield "\n".join(infos) - index = faiss.index_factory(256, "IVF%s,Flat"%n_ivf) + index = faiss.index_factory(256, "IVF%s,Flat" % n_ivf) # index = faiss.index_factory(256, "IVF%s,PQ128x4fs,RFlat"%n_ivf) infos.append("training") yield "\n".join(infos) @@ -672,13 +688,19 @@ def train_index(exp_dir1): # index_ivf.nprobe = int(np.power(n_ivf,0.3)) index_ivf.nprobe = 1 index.train(big_npy) - faiss.write_index(index, '%s/trained_IVF%s_Flat_nprobe_%s.index'%(exp_dir,n_ivf,index_ivf.nprobe)) + faiss.write_index( + index, + "%s/trained_IVF%s_Flat_nprobe_%s.index" % (exp_dir, n_ivf, index_ivf.nprobe), + ) # faiss.write_index(index, '%s/trained_IVF%s_Flat_FastScan.index'%(exp_dir,n_ivf)) infos.append("adding") yield "\n".join(infos) index.add(big_npy) - faiss.write_index(index, '%s/added_IVF%s_Flat_nprobe_%s.index'%(exp_dir,n_ivf,index_ivf.nprobe)) - infos.append("成功构建索引，added_IVF%s_Flat_nprobe_%s.index"%(n_ivf,index_ivf.nprobe)) + faiss.write_index( + index, + "%s/added_IVF%s_Flat_nprobe_%s.index" % (exp_dir, n_ivf, index_ivf.nprobe), + ) + infos.append("成功构建索引，added_IVF%s_Flat_nprobe_%s.index" % (n_ivf, index_ivf.nprobe)) # faiss.write_index(index, '%s/added_IVF%s_Flat_FastScan.index'%(exp_dir,n_ivf)) # infos.append("成功构建索引，added_IVF%s_Flat_FastScan.index"%(n_ivf)) yield "\n".join(infos) @@ -876,7 +898,7 @@ def train1key( big_npy = np.concatenate(npys, 0) # np.save("%s/total_fea.npy" % exp_dir, big_npy) # n_ivf = big_npy.shape[0] // 39 - n_ivf = min(int(16 * np.sqrt(big_npy.shape[0])),big_npy.shape[0]// 39) + n_ivf = min(int(16 * np.sqrt(big_npy.shape[0])), big_npy.shape[0] // 39) yield get_info_str("%s,%s" % (big_npy.shape, n_ivf)) index = faiss.index_factory(256, "IVF%s,Flat" % n_ivf) yield get_info_str("training index") @@ -1171,7 +1193,7 @@ with gr.Blocks() as app: label="人声提取激进程度", value=10, interactive=True, - visible=False#先不开放调整 + visible=False, # 先不开放调整 ) opt_vocal_root = gr.Textbox( label=i18n("指定输出人声文件夹"), value="opt" @@ -1187,7 +1209,7 @@ with gr.Blocks() as app: opt_vocal_root, wav_inputs, opt_ins_root, - agg + agg, ], [vc_output4], ) diff --git a/infer_uvr5.py b/infer_uvr5.py index 07da7eb..4aada2d 100644 --- a/infer_uvr5.py +++ b/infer_uvr5.py @@ -13,7 +13,7 @@ from scipy.io import wavfile class _audio_pre_: - def __init__(self, agg,model_path, device, is_half): + def __init__(self, agg, model_path, device, is_half): self.model_path = model_path self.device = device self.data = { @@ -139,7 +139,9 @@ class _audio_pre_: wav_instrument = spec_utils.cmb_spectrogram_to_wave(y_spec_m, self.mp) print("%s instruments done" % name) wavfile.write( - os.path.join(ins_root, "instrument_{}_{}.wav".format(name,self.data["agg"])), + os.path.join( + ins_root, "instrument_{}_{}.wav".format(name, self.data["agg"]) + ), self.mp.param["sr"], (np.array(wav_instrument) * 32768).astype("int16"), ) # @@ -155,7 +157,9 @@ class _audio_pre_: wav_vocals = spec_utils.cmb_spectrogram_to_wave(v_spec_m, self.mp) print("%s vocals done" % name) wavfile.write( - os.path.join(vocal_root, "vocal_{}_{}.wav".format(name,self.data["agg"])), + os.path.join( + vocal_root, "vocal_{}_{}.wav".format(name, self.data["agg"]) + ), self.mp.param["sr"], (np.array(wav_vocals) * 32768).astype("int16"), )