| import multiprocessing
|
| import os
|
| import sys
|
|
|
| from scipy import signal
|
|
|
| now_dir = os.getcwd()
|
| sys.path.append(now_dir)
|
| print(*sys.argv[1:])
|
| 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"
|
| per = float(sys.argv[6])
|
| import os
|
| import traceback
|
|
|
| import librosa
|
| import numpy as np
|
| from scipy.io import wavfile
|
|
|
| from infer.lib.audio import load_audio
|
| from infer.lib.slicer2 import Slicer
|
|
|
| f = open("%s/preprocess.log" % exp_dir, "a+")
|
|
|
|
|
| def println(strr):
|
| print(strr)
|
| f.write("%s\n" % strr)
|
| f.flush()
|
|
|
|
|
| class PreProcess:
|
| def __init__(self, sr, exp_dir, per=3.7):
|
| 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 = per
|
| 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
|
| )
|
| 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)
|
|
|
|
|
| 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\t-> Success" % path)
|
| except:
|
| println("%s\t-> %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, per):
|
| pp = PreProcess(sr, exp_dir, per)
|
| println("start preprocess")
|
| pp.pipeline_mp_inp_dir(inp_root, n_p)
|
| println("end preprocess")
|
|
|
|
|
| if __name__ == "__main__":
|
| preprocess_trainset(inp_root, sr, n_p, exp_dir, per)
|
|
|
