mirror of
https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI.git
synced 2024-12-29 19:15:04 +08:00
121 lines
4.4 KiB
Python
121 lines
4.4 KiB
Python
import os,traceback,sys,parselmouth
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import librosa
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import pyworld
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from scipy.io import wavfile
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import numpy as np,logging
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logging.getLogger('numba').setLevel(logging.WARNING)
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from multiprocessing import Process
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exp_dir = sys.argv[1]
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f = open("%s/extract_f0_feature.log"%exp_dir, "a+")
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def printt(strr):
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print(strr)
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f.write("%s\n" % strr)
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f.flush()
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n_p = int(sys.argv[2])
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f0method = sys.argv[3]
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class FeatureInput(object):
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def __init__(self, samplerate=16000, hop_size=160):
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self.fs = samplerate
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self.hop = hop_size
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self.f0_bin = 256
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self.f0_max = 1100.0
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self.f0_min = 50.0
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self.f0_mel_min = 1127 * np.log(1 + self.f0_min / 700)
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self.f0_mel_max = 1127 * np.log(1 + self.f0_max / 700)
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def compute_f0(self, path,f0_method):
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x, sr = librosa.load(path, self.fs)
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p_len=x.shape[0]//self.hop
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assert sr == self.fs
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if(f0_method=="pm"):
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time_step = 160 / 16000 * 1000
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f0_min = 50
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f0_max = 1100
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f0 = parselmouth.Sound(x, sr).to_pitch_ac(
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time_step=time_step / 1000, voicing_threshold=0.6,
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pitch_floor=f0_min, pitch_ceiling=f0_max).selected_array['frequency']
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pad_size=(p_len - len(f0) + 1) // 2
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if(pad_size>0 or p_len - len(f0) - pad_size>0):
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f0 = np.pad(f0,[[pad_size,p_len - len(f0) - pad_size]], mode='constant')
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elif(f0_method=="harvest"):
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f0, t = pyworld.harvest(
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x.astype(np.double),
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fs=sr,
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f0_ceil=1100,
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frame_period=1000 * self.hop / sr,
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)
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f0 = pyworld.stonemask(x.astype(np.double), f0, t, self.fs)
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elif(f0_method=="dio"):
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f0, t = pyworld.dio(
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x.astype(np.double),
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fs=sr,
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f0_ceil=1100,
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frame_period=1000 * self.hop / sr,
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)
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f0 = pyworld.stonemask(x.astype(np.double), f0, t, self.fs)
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return f0
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def coarse_f0(self, f0):
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f0_mel = 1127 * np.log(1 + f0 / 700)
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f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - self.f0_mel_min) * (
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self.f0_bin - 2
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) / (self.f0_mel_max - self.f0_mel_min) + 1
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# use 0 or 1
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f0_mel[f0_mel <= 1] = 1
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f0_mel[f0_mel > self.f0_bin - 1] = self.f0_bin - 1
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f0_coarse = np.rint(f0_mel).astype(np.int)
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assert f0_coarse.max() <= 255 and f0_coarse.min() >= 1, (
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f0_coarse.max(),
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f0_coarse.min(),
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)
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return f0_coarse
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def go(self,paths,f0_method):
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if (len(paths) == 0): printt("no-f0-todo")
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else:
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printt("todo-f0-%s"%len(paths))
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n=max(len(paths)//5,1)#每个进程最多打印5条
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for idx,(inp_path,opt_path1,opt_path2) in enumerate(paths):
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try:
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if(idx%n==0):printt("f0ing,now-%s,all-%s,-%s"%(idx,len(paths),inp_path))
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if(os.path.exists(opt_path1+".npy")==True and os.path.exists(opt_path2+".npy")==True):continue
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featur_pit = self.compute_f0(inp_path,f0_method)
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np.save(opt_path2,featur_pit,allow_pickle=False,)#nsf
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coarse_pit = self.coarse_f0(featur_pit)
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np.save(opt_path1,coarse_pit,allow_pickle=False,)#ori
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except:
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printt("f0fail-%s-%s-%s" % (idx, inp_path,traceback.format_exc()))
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if __name__=='__main__':
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# exp_dir=r"E:\codes\py39\dataset\mi-test"
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# n_p=16
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# f = open("%s/log_extract_f0.log"%exp_dir, "w")
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printt(sys.argv)
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featureInput = FeatureInput()
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paths=[]
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inp_root= "%s/1_16k_wavs"%(exp_dir)
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opt_root1="%s/2a_f0"%(exp_dir)
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opt_root2="%s/2b-f0nsf"%(exp_dir)
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os.makedirs(opt_root1,exist_ok=True)
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os.makedirs(opt_root2,exist_ok=True)
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for name in sorted(list(os.listdir(inp_root))):
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inp_path="%s/%s"%(inp_root,name)
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if ("spec" in inp_path): continue
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opt_path1="%s/%s"%(opt_root1,name)
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opt_path2="%s/%s"%(opt_root2,name)
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paths.append([inp_path,opt_path1,opt_path2])
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ps=[]
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for i in range(n_p):
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p=Process(target=featureInput.go,args=(paths[i::n_p],f0method,))
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p.start()
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ps.append(p)
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for p in ps:
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p.join()
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