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import os
import sys
import traceback
import logging
import numpy as np
from infer.lib.audio import load_audio
logging.getLogger("numba").setLevel(logging.WARNING)
class FeatureInput(object):
def __init__(self, exp_dir, samplerate=16000, hop_size=160, is_half=False):
self.exp_dir = exp_dir
self.logfile = open("%s/extract_f0_feature.log" % exp_dir, "a+")
self.fs = samplerate
self.hop = hop_size
self.is_half = is_half
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 println(self, strr):
print(strr)
self.logfile.write("%s\n" % strr)
self.logfile.flush()
def compute_f0(self, path, f0_method):
x = load_audio(path, self.fs)
# p_len = x.shape[0] // self.hop
if f0_method == "rmvpe":
if hasattr(self, "model_rmvpe") == False:
from infer.lib.rmvpe import RMVPE
print("Loading rmvpe model")
self.model_rmvpe = RMVPE(
"assets/rmvpe/rmvpe.pt", is_half=self.is_half, device="cuda"
)
f0 = self.model_rmvpe.infer_from_audio(x, thred=0.03)
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(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:
self.println("no-f0-todo")
else:
self.println("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:
self.println(
"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:
self.println(
"f0fail-%s-%s-%s" % (idx, inp_path, traceback.format_exc())
)
def run(self):
inp_root = "%s/1_16k_wavs" % (self.exp_dir)
opt_root1 = "%s/2a_f0" % (self.exp_dir)
opt_root2 = "%s/2b-f0nsf" % (self.exp_dir)
os.makedirs(opt_root1, exist_ok=True)
os.makedirs(opt_root2, exist_ok=True)
paths = []
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])
self.go(paths, "rmvpe")
if __name__ == "__main__":
now_dir = os.getcwd()
sys.path.append(now_dir)
n_part = int(sys.argv[1])
i_part = int(sys.argv[2])
os.environ["CUDA_VISIBLE_DEVICES"] = sys.argv[3]
exp_dir = sys.argv[4]
featureInput = FeatureInput(exp_dir)
featureInput.run()
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