import sys, os, multiprocessing from scipy import signal now_dir = os.getcwd() sys.path.append(now_dir) print(sys.argv) inp_root = sys.argv[1] sr = int(sys.argv[2]) n_p = int(sys.argv[3]) exp_dir = sys.argv[4] noparallel = sys.argv[5] == "True" import numpy as np, os, traceback from lib.slicer2 import Slicer import librosa, traceback from scipy.io import wavfile import multiprocessing from lib.audio import load_audio mutex = multiprocessing.Lock() f = open("%s/preprocess.log" % exp_dir, "a+") def println(strr): mutex.acquire() print(strr) f.write("%s\n" % strr) f.flush() mutex.release() class PreProcess: def __init__(self, sr, exp_dir): self.slicer = Slicer( sr=sr, threshold=-42, min_length=1500, min_interval=400, hop_size=15, max_sil_kept=500, ) self.sr = sr self.bh, self.ah = signal.butter(N=5, Wn=48, btype="high", fs=self.sr) self.per = 3.0 self.overlap = 0.3 self.tail = self.per + self.overlap self.max = 0.9 self.alpha = 0.75 self.exp_dir = exp_dir self.gt_wavs_dir = "%s/0_gt_wavs" % exp_dir self.wavs16k_dir = "%s/1_16k_wavs" % exp_dir os.makedirs(self.exp_dir, exist_ok=True) os.makedirs(self.gt_wavs_dir, exist_ok=True) os.makedirs(self.wavs16k_dir, exist_ok=True) def norm_write(self, tmp_audio, idx0, idx1): tmp_max = np.abs(tmp_audio).max() if tmp_max > 2.5: print("%s-%s-%s-filtered" % (idx0, idx1, tmp_max)) return tmp_audio = (tmp_audio / tmp_max * (self.max * self.alpha)) + ( 1 - self.alpha ) * tmp_audio wavfile.write( "%s/%s_%s.wav" % (self.gt_wavs_dir, idx0, idx1), self.sr, tmp_audio.astype(np.float32), ) tmp_audio = librosa.resample( tmp_audio, orig_sr=self.sr, target_sr=16000 ) # , res_type="soxr_vhq" wavfile.write( "%s/%s_%s.wav" % (self.wavs16k_dir, idx0, idx1), 16000, tmp_audio.astype(np.float32), ) def pipeline(self, path, idx0): try: audio = load_audio(path, self.sr) # zero phased digital filter cause pre-ringing noise... # audio = signal.filtfilt(self.bh, self.ah, audio) audio = signal.lfilter(self.bh, self.ah, audio) idx1 = 0 for audio in self.slicer.slice(audio): i = 0 while 1: start = int(self.sr * (self.per - self.overlap) * i) i += 1 if len(audio[start:]) > self.tail * self.sr: tmp_audio = audio[start : start + int(self.per * self.sr)] self.norm_write(tmp_audio, idx0, idx1) idx1 += 1 else: tmp_audio = audio[start:] idx1 += 1 break self.norm_write(tmp_audio, idx0, idx1) println("%s->Suc." % path) except: println("%s->%s" % (path, traceback.format_exc())) def pipeline_mp(self, infos): for path, idx0 in infos: self.pipeline(path, idx0) def pipeline_mp_inp_dir(self, inp_root, n_p): try: infos = [ ("%s/%s" % (inp_root, name), idx) for idx, name in enumerate(sorted(list(os.listdir(inp_root)))) ] if noparallel: for i in range(n_p): self.pipeline_mp(infos[i::n_p]) else: ps = [] for i in range(n_p): p = multiprocessing.Process( target=self.pipeline_mp, args=(infos[i::n_p],) ) ps.append(p) p.start() for i in range(n_p): ps[i].join() except: println("Fail. %s" % traceback.format_exc()) def preprocess_trainset(inp_root, sr, n_p, exp_dir): pp = PreProcess(sr, exp_dir) println("start preprocess") println(sys.argv) pp.pipeline_mp_inp_dir(inp_root, n_p) println("end preprocess") if __name__ == "__main__": preprocess_trainset(inp_root, sr, n_p, exp_dir)