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trainset_preprocess_pipeline_print.py

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+import sys,os,pdb,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]
+import numpy as np,ffmpeg,os,traceback
+from slicer2 import Slicer
+from joblib import Parallel, delayed
+import librosa,traceback
+from  scipy.io import wavfile
+import multiprocessing
+from my_utils import load_audio
+from time import sleep
+
+f = open("%s/preprocess.log"%exp_dir, "a+")
+def printt(strr):
+    print(strr)
+    f.write("%s\n" % strr)
+    f.flush()
+
+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
+        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_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)
+            printt("%s->Suc."%path)
+        except:
+            printt("%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))))]
+            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:
+            printt("Fail. %s"%traceback.format_exc())
+
+if __name__=='__main__':
+    # f = open("logs/log_preprocess.log", "w")
+    printt(sys.argv)
+    ######################################################
+    # inp_root=r"E:\语音音频+标注\米津玄师\src"
+    # inp_root=r"E:\codes\py39\vits_vc_gpu_train\todo-songs"
+    # sr=40000
+    # n_p = 6
+    # exp_dir=r"E:\codes\py39\dataset\mi-test"
+
+    ######################################################
+    printt("start preprocess")
+    pp=PreProcess(sr,exp_dir)
+    pp.pipeline_mp_inp_dir(inp_root,n_p)
+    printt("end preprocess")