Dataset preprocessing

ㄴ debugging

preprocess_dataset.py

@@ -6,7 +6,7 @@
 import numpy as np
 import argparse
 
-sr = 22050
+sr = 16000
 max_wav_value=32768.0
 trim_fft_size = 1024
 trim_hop_size = 256
@@ -15,7 +15,7 @@
 trim_top_db = 23
 skip_len = 14848
 
-def preprocess_audio(file_list, silence_audio_size, pre_emphasis=True):
+def preprocess_audio(file_list, silence_audio_size, pre_emphasis=False):
     for F in file_list:
         f = open(F, encoding='utf-8')
         R = f.readlines()
@@ -27,10 +27,10 @@ def preprocess_audio(file_list, silence_audio_size, pre_emphasis=True):
             data, sampling_rate = librosa.core.load(wav_file, sr)
             data = data / np.abs(data).max() *0.999
             data_= librosa.effects.trim(data, top_db= trim_top_db, frame_length=trim_fft_size, hop_length=trim_hop_size)[0]
-            data_ = data_*max_wav_value
             if (pre_emphasis):
                 data_ = np.append(data_[0], data_[1:] - 0.97 * data_[:-1])
                 data_ = data_ / np.abs(data_).max() * 0.999
+            data_ = data_ * max_wav_value
             data_ = np.append(data_, [0.]*silence_audio_size)
             data_ = data_.astype(dtype=np.int16)
             write(wav_file, sr, data_)
@@ -58,23 +58,26 @@ def remove_short_audios(file_name):
 if __name__ == "__main__":
     """
     usage
-    python preprocess_audio.py -f=filelists/ljs_audio_text_test_filelist.txt,filelists/ljs_audio_text_train_filelist.txt,filelists/ljs_audio_text_val_filelist.txt -s=5 -p -r
+    python preprocess_dataset.py -f=metadata.csv -s=5 -t -p -r
+    python preprocess_dataset.py -f=metadata.csv
     """
     parser = argparse.ArgumentParser()
     parser.add_argument('-f', '--file_list', type=str,
-                        help='file list to preprocess')
+                        help='Metadata file list to preprocess')
     parser.add_argument('-s', '--silence_padding', type=int, default=0,
                         help='Adding silence padding at the end of each audio, silence audio size is hop_length * silence padding')
     parser.add_argument('-p', '--pre_emphasis', action='store_true',
-                        help="do or don't do pre_emphasis")
+                        help="Doing pre_emphasis")
+    parser.add_argument('-t', '--trimming', action='store_true',
+                        help="Doing trimming audios")
     parser.add_argument('-r', '--remove_short_audios',action='store_true',
-                        help="do or don't remove short audios")
+                        help="Removing short audios in metadata file")
     args = parser.parse_args()
     file_list = args.file_list.split(',')
     silence_audio_size = trim_hop_size * args.silence_padding
     remove_short_audios = args.remove_short_audios
 
-    preprocess_audio(file_list, silence_audio_size)
+    preprocess_audio(file_list, silence_audio_size, args.pre_emphasis)
 
     if(remove_short_audios):
         for f in file_list: