import os import sys import traceback import parselmouth now_dir = os.getcwd() sys.path.append(now_dir) import logging import numpy as np import pyworld from infer.lib.audio import load_audio logging.getLogger("numba").setLevel(logging.WARNING) exp_dir = sys.argv[1] import torch_directml device = torch_directml.device(torch_directml.default_device()) f = open("%s/extract_f0_feature.log" % exp_dir, "a+") def printt(strr): print(strr) f.write("%s\n" % strr) f.flush() 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 == "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=False, device=device ) 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: 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]) try: featureInput.go(paths, "rmvpe") except: printt("f0_all_fail-%s" % (traceback.format_exc())) # 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()