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import os, traceback, sys, parselmouth | |
now_dir = os.getcwd() | |
sys.path.append(now_dir) | |
from my_utils import load_audio | |
import pyworld | |
from scipy.io import wavfile | |
import numpy as np, logging | |
logging.getLogger("numba").setLevel(logging.WARNING) | |
from multiprocessing import Process | |
exp_dir = sys.argv[1] | |
f = open("%s/extract_f0_feature.log" % exp_dir, "a+") | |
def printt(strr): | |
print(strr) | |
f.write("%s\n" % strr) | |
f.flush() | |
n_p = int(sys.argv[2]) | |
f0method = sys.argv[3] | |
class FeatureInput(object): | |
def __init__(self, samplerate=16000, hop_size=160): | |
self.fs = samplerate | |
self.hop = hop_size | |
self.f0_bin = 256 | |
self.f0_max = 1100.0 | |
self.f0_min = 50.0 | |
self.f0_mel_min = 1127 * np.log(1 + self.f0_min / 700) | |
self.f0_mel_max = 1127 * np.log(1 + self.f0_max / 700) | |
def compute_f0(self, path, f0_method): | |
x = load_audio(path, self.fs) | |
p_len = x.shape[0] // self.hop | |
if f0_method == "pm": | |
time_step = 160 / 16000 * 1000 | |
f0_min = 50 | |
f0_max = 1100 | |
f0 = ( | |
parselmouth.Sound(x, self.fs) | |
.to_pitch_ac( | |
time_step=time_step / 1000, | |
voicing_threshold=0.6, | |
pitch_floor=f0_min, | |
pitch_ceiling=f0_max, | |
) | |
.selected_array["frequency"] | |
) | |
pad_size = (p_len - len(f0) + 1) // 2 | |
if pad_size > 0 or p_len - len(f0) - pad_size > 0: | |
f0 = np.pad( | |
f0, [[pad_size, p_len - len(f0) - pad_size]], mode="constant" | |
) | |
elif f0_method == "harvest": | |
f0, t = pyworld.harvest( | |
x.astype(np.double), | |
fs=self.fs, | |
f0_ceil=self.f0_max, | |
f0_floor=self.f0_min, | |
frame_period=1000 * self.hop / self.fs, | |
) | |
f0 = pyworld.stonemask(x.astype(np.double), f0, t, self.fs) | |
elif f0_method == "dio": | |
f0, t = pyworld.dio( | |
x.astype(np.double), | |
fs=self.fs, | |
f0_ceil=self.f0_max, | |
f0_floor=self.f0_min, | |
frame_period=1000 * self.hop / self.fs, | |
) | |
f0 = pyworld.stonemask(x.astype(np.double), f0, t, self.fs) | |
return f0 | |
def coarse_f0(self, f0): | |
f0_mel = 1127 * np.log(1 + f0 / 700) | |
f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - self.f0_mel_min) * ( | |
self.f0_bin - 2 | |
) / (self.f0_mel_max - self.f0_mel_min) + 1 | |
# use 0 or 1 | |
f0_mel[f0_mel <= 1] = 1 | |
f0_mel[f0_mel > self.f0_bin - 1] = self.f0_bin - 1 | |
f0_coarse = np.rint(f0_mel).astype(np.int) | |
assert f0_coarse.max() <= 255 and f0_coarse.min() >= 1, ( | |
f0_coarse.max(), | |
f0_coarse.min(), | |
) | |
return f0_coarse | |
def go(self, paths, f0_method): | |
if len(paths) == 0: | |
printt("no-f0-todo") | |
else: | |
printt("todo-f0-%s" % len(paths)) | |
n = max(len(paths) // 5, 1) # 每个进程最多打印5条 | |
for idx, (inp_path, opt_path1, opt_path2) in enumerate(paths): | |
try: | |
if idx % n == 0: | |
printt("f0ing,now-%s,all-%s,-%s" % (idx, len(paths), inp_path)) | |
if ( | |
os.path.exists(opt_path1 + ".npy") == True | |
and os.path.exists(opt_path2 + ".npy") == True | |
): | |
continue | |
featur_pit = self.compute_f0(inp_path, f0_method) | |
np.save( | |
opt_path2, | |
featur_pit, | |
allow_pickle=False, | |
) # nsf | |
coarse_pit = self.coarse_f0(featur_pit) | |
np.save( | |
opt_path1, | |
coarse_pit, | |
allow_pickle=False, | |
) # ori | |
except: | |
printt("f0fail-%s-%s-%s" % (idx, inp_path, traceback.format_exc())) | |
if __name__ == "__main__": | |
# exp_dir=r"E:\codes\py39\dataset\mi-test" | |
# n_p=16 | |
# f = open("%s/log_extract_f0.log"%exp_dir, "w") | |
printt(sys.argv) | |
featureInput = FeatureInput() | |
paths = [] | |
inp_root = "%s/1_16k_wavs" % (exp_dir) | |
opt_root1 = "%s/2a_f0" % (exp_dir) | |
opt_root2 = "%s/2b-f0nsf" % (exp_dir) | |
os.makedirs(opt_root1, exist_ok=True) | |
os.makedirs(opt_root2, exist_ok=True) | |
for name in sorted(list(os.listdir(inp_root))): | |
inp_path = "%s/%s" % (inp_root, name) | |
if "spec" in inp_path: | |
continue | |
opt_path1 = "%s/%s" % (opt_root1, name) | |
opt_path2 = "%s/%s" % (opt_root2, name) | |
paths.append([inp_path, opt_path1, opt_path2]) | |
ps = [] | |
for i in range(n_p): | |
p = Process( | |
target=featureInput.go, | |
args=( | |
paths[i::n_p], | |
f0method, | |
), | |
) | |
ps.append(p) | |
p.start() | |
for i in range(n_p): | |
ps[i].join() | |