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trainset_preprocess_pipeline_print.py
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trainset_preprocess_pipeline_print.py
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import sys,os,multiprocessing
now_dir=os.getcwd()
sys.path.append(now_dir)
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 slicer2 import Slicer
import librosa,traceback
from scipy.io import wavfile
import multiprocessing
from my_utils import load_audio
mutex = multiprocessing.Lock()
class PreProcess():
def __init__(self,sr,exp_dir):
self.slicer = Slicer(
sr=sr,
threshold=-32,
min_length=800,
min_interval=400,
hop_size=15,
max_sil_kept=150
)
self.sr=sr
self.per=3.7
self.overlap=0.3
self.tail=self.per+self.overlap
self.max=0.95
self.alpha=0.8
self.exp_dir=exp_dir
self.gt_wavs_dir="%s/0_gt_wavs"%exp_dir
self.wavs16k_dir="%s/1_16k_wavs"%exp_dir
self.f = open("%s/preprocess.log"%exp_dir, "a+")
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 println(self, strr):
mutex.acquire()
print(strr)
self.f.write("%s\n" % strr)
self.f.flush()
mutex.release()
def norm_write(self,tmp_audio,idx0,idx1):
tmp_audio = (tmp_audio / np.abs(tmp_audio).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*32768).astype(np.int16))
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*32768).astype(np.int16))
def pipeline(self,path, idx0):
try:
audio = load_audio(path,self.sr)
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:]
break
self.norm_write(tmp_audio, idx0, idx1)
self.println("%s->Suc."%path)
except:
self.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],))
p.start()
ps.append(p)
for p in ps:p.join()
except:
self.println("Fail. %s"%traceback.format_exc())
def preprocess_trainset(inp_root, sr, n_p, exp_dir):
pp=PreProcess(sr,exp_dir)
pp.println("start preprocess")
pp.println(sys.argv)
pp.pipeline_mp_inp_dir(inp_root,n_p)
pp.println("end preprocess")
if __name__=='__main__':
preprocess_trainset(inp_root, sr, n_p, exp_dir)