mirror of
https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI.git
synced 2025-04-29 16:18:59 +08:00
Update trainset_preprocess_pipeline_print.py
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RVC-Boss
GitHub
parent
4a2c9c062f
commit
2c4ec6db93
@ -1,135 +1,135 @@
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import sys, os, multiprocessing
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from scipy import signal
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now_dir = os.getcwd()
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sys.path.append(now_dir)
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inp_root = sys.argv[1]
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sr = int(sys.argv[2])
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n_p = int(sys.argv[3])
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exp_dir = sys.argv[4]
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noparallel = sys.argv[5] == "True"
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import numpy as np, os, traceback
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from slicer2 import Slicer
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import librosa, traceback
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from scipy.io import wavfile
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import multiprocessing
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from my_utils import load_audio
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mutex = multiprocessing.Lock()
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f = open("%s/preprocess.log" % exp_dir, "a+")
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def println(strr):
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mutex.acquire()
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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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mutex.release()
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class PreProcess:
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def __init__(self, sr, exp_dir):
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self.slicer = Slicer(
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sr=sr,
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threshold=-40,
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min_length=800,
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min_interval=400,
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hop_size=15,
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max_sil_kept=150,
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)
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self.sr = sr
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self.bh, self.ah = signal.butter(N=5, Wn=48, btype="high", fs=self.sr)
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self.per = 3.7
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self.overlap = 0.3
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self.tail = self.per + self.overlap
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self.max = 0.95
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self.alpha = 0.8
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self.exp_dir = exp_dir
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self.gt_wavs_dir = "%s/0_gt_wavs" % exp_dir
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self.wavs16k_dir = "%s/1_16k_wavs" % exp_dir
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os.makedirs(self.exp_dir, exist_ok=True)
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os.makedirs(self.gt_wavs_dir, exist_ok=True)
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os.makedirs(self.wavs16k_dir, exist_ok=True)
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def norm_write(self, tmp_audio, idx0, idx1):
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tmp_audio = (tmp_audio / np.abs(tmp_audio).max() * (self.max * self.alpha)) + (
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1 - self.alpha
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) * tmp_audio
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wavfile.write(
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"%s/%s_%s.wav" % (self.gt_wavs_dir, idx0, idx1),
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self.sr,
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tmp_audio.astype(np.float32),
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)
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tmp_audio = librosa.resample(
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tmp_audio, orig_sr=self.sr, target_sr=16000
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) # , res_type="soxr_vhq"
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wavfile.write(
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"%s/%s_%s.wav" % (self.wavs16k_dir, idx0, idx1),
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16000,
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tmp_audio.astype(np.float32),
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)
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def pipeline(self, path, idx0):
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try:
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audio = load_audio(path, self.sr)
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# zero phased digital filter cause pre-ringing noise...
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# audio = signal.filtfilt(self.bh, self.ah, audio)
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audio = signal.lfilter(self.bh, self.ah, audio)
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idx1 = 0
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for audio in self.slicer.slice(audio):
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i = 0
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while 1:
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start = int(self.sr * (self.per - self.overlap) * i)
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i += 1
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if len(audio[start:]) > self.tail * self.sr:
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tmp_audio = audio[start : start + int(self.per * self.sr)]
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self.norm_write(tmp_audio, idx0, idx1)
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idx1 += 1
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else:
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tmp_audio = audio[start:]
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idx1 += 1
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break
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self.norm_write(tmp_audio, idx0, idx1)
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println("%s->Suc." % path)
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except:
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println("%s->%s" % (path, traceback.format_exc()))
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def pipeline_mp(self, infos):
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for path, idx0 in infos:
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self.pipeline(path, idx0)
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def pipeline_mp_inp_dir(self, inp_root, n_p):
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try:
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infos = [
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("%s/%s" % (inp_root, name), idx)
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for idx, name in enumerate(sorted(list(os.listdir(inp_root))))
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]
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if noparallel:
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for i in range(n_p):
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self.pipeline_mp(infos[i::n_p])
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else:
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ps = []
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for i in range(n_p):
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p = multiprocessing.Process(
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target=self.pipeline_mp, args=(infos[i::n_p],)
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)
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p.start()
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ps.append(p)
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for p in ps:
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p.join()
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except:
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println("Fail. %s" % traceback.format_exc())
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def preprocess_trainset(inp_root, sr, n_p, exp_dir):
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pp = PreProcess(sr, exp_dir)
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println("start preprocess")
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println(sys.argv)
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pp.pipeline_mp_inp_dir(inp_root, n_p)
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println("end preprocess")
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if __name__ == "__main__":
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preprocess_trainset(inp_root, sr, n_p, exp_dir)
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import sys, os, multiprocessing
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from scipy import signal
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now_dir = os.getcwd()
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sys.path.append(now_dir)
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inp_root = sys.argv[1]
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sr = int(sys.argv[2])
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n_p = int(sys.argv[3])
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exp_dir = sys.argv[4]
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noparallel = sys.argv[5] == "True"
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import numpy as np, os, traceback
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from slicer2 import Slicer
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import librosa, traceback
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from scipy.io import wavfile
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import multiprocessing
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from my_utils import load_audio
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mutex = multiprocessing.Lock()
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f = open("%s/preprocess.log" % exp_dir, "a+")
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def println(strr):
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mutex.acquire()
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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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mutex.release()
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class PreProcess:
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def __init__(self, sr, exp_dir):
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self.slicer = Slicer(
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sr=sr,
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threshold=-40,
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min_length=800,
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min_interval=400,
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hop_size=15,
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max_sil_kept=150,
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)
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self.sr = sr
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self.bh, self.ah = signal.butter(N=5, Wn=48, btype="high", fs=self.sr)
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self.per = 3.7
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self.overlap = 0.3
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self.tail = self.per + self.overlap
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self.max = 0.95
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self.alpha = 0.8
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self.exp_dir = exp_dir
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self.gt_wavs_dir = "%s/0_gt_wavs" % exp_dir
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self.wavs16k_dir = "%s/1_16k_wavs" % exp_dir
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os.makedirs(self.exp_dir, exist_ok=True)
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os.makedirs(self.gt_wavs_dir, exist_ok=True)
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os.makedirs(self.wavs16k_dir, exist_ok=True)
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def norm_write(self, tmp_audio, idx0, idx1):
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tmp_audio = (tmp_audio / np.abs(tmp_audio).max() * (self.max * self.alpha)) + (
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1 - self.alpha
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) * tmp_audio
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wavfile.write(
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"%s/%s_%s.wav" % (self.gt_wavs_dir, idx0, idx1),
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self.sr,
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tmp_audio.astype(np.float32),
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)
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tmp_audio = librosa.resample(
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tmp_audio, orig_sr=self.sr, target_sr=16000
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) # , res_type="soxr_vhq"
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wavfile.write(
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"%s/%s_%s.wav" % (self.wavs16k_dir, idx0, idx1),
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16000,
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tmp_audio.astype(np.float32),
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)
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def pipeline(self, path, idx0):
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try:
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audio = load_audio(path, self.sr)
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# zero phased digital filter cause pre-ringing noise...
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# audio = signal.filtfilt(self.bh, self.ah, audio)
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audio = signal.lfilter(self.bh, self.ah, audio)
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idx1 = 0
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for audio in self.slicer.slice(audio):
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i = 0
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while 1:
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start = int(self.sr * (self.per - self.overlap) * i)
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i += 1
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if len(audio[start:]) > self.tail * self.sr:
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tmp_audio = audio[start : start + int(self.per * self.sr)]
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self.norm_write(tmp_audio, idx0, idx1)
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idx1 += 1
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else:
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tmp_audio = audio[start:]
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idx1 += 1
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break
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self.norm_write(tmp_audio, idx0, idx1)
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println("%s->Suc." % path)
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except:
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println("%s->%s" % (path, traceback.format_exc()))
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def pipeline_mp(self, infos):
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for path, idx0 in infos:
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self.pipeline(path, idx0)
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def pipeline_mp_inp_dir(self, inp_root, n_p):
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try:
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infos = [
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("%s/%s" % (inp_root, name), idx)
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for idx, name in enumerate(sorted(list(os.listdir(inp_root))))
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]
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if noparallel:
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for i in range(n_p):
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self.pipeline_mp(infos[i::n_p])
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else:
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ps = []
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for i in range(n_p):
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p = multiprocessing.Process(
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target=self.pipeline_mp, args=(infos[i::n_p],)
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)
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p.start()
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ps.append(p)
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for p in ps:
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p.join()
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except:
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println("Fail. %s" % traceback.format_exc())
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def preprocess_trainset(inp_root, sr, n_p, exp_dir):
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pp = PreProcess(sr, exp_dir)
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println("start preprocess")
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println(sys.argv)
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pp.pipeline_mp_inp_dir(inp_root, n_p)
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println("end preprocess")
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if __name__ == "__main__":
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preprocess_trainset(inp_root, sr, n_p, exp_dir)
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