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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
) # , 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\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)
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