130 lines
4.3 KiB
Python
130 lines
4.3 KiB
Python
import os, traceback, sys, parselmouth
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now_dir = os.getcwd()
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sys.path.append(now_dir)
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from lib.audio import load_audio
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import pyworld
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import numpy as np, logging
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logging.getLogger("numba").setLevel(logging.WARNING)
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exp_dir = sys.argv[1]
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import torch_directml
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device = torch_directml.device(torch_directml.default_device())
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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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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 = load_audio(path, self.fs)
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# p_len = x.shape[0] // self.hop
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if f0_method == "rmvpe":
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if hasattr(self, "model_rmvpe") == False:
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from lib.rmvpe import RMVPE
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print("loading rmvpe model")
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self.model_rmvpe = RMVPE("rmvpe.pt", is_half=False, device=device)
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f0 = self.model_rmvpe.infer_from_audio(x, thred=0.03)
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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(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:
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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:
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printt("f0ing,now-%s,all-%s,-%s" % (idx, len(paths), inp_path))
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if (
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os.path.exists(opt_path1 + ".npy") == True
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and os.path.exists(opt_path2 + ".npy") == True
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):
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continue
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featur_pit = self.compute_f0(inp_path, f0_method)
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np.save(
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opt_path2,
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featur_pit,
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allow_pickle=False,
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) # nsf
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coarse_pit = self.coarse_f0(featur_pit)
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np.save(
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opt_path1,
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coarse_pit,
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allow_pickle=False,
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) # 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:
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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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try:
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featureInput.go(paths, "rmvpe")
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except:
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printt("f0_all_fail-%s" % (traceback.format_exc()))
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# ps = []
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# for i in range(n_p):
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# p = Process(
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# target=featureInput.go,
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# args=(
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# paths[i::n_p],
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# f0method,
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# ),
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# )
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# ps.append(p)
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# p.start()
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# for i in range(n_p):
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# ps[i].join()
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