add new preprocessing script

preprocess/preprocess_any.py

@@ -0,0 +1,118 @@
+# -*- coding: utf-8 -*- #
+"""*********************************************************************************************"""
+#   FileName     [ preprocess_any.py ]
+#   Synopsis     [ preprocess text transcripts and audio speech for any dataset ]
+#   Author       [ Andy T. Liu (Andi611) ]
+#   Copyright    [ Copyleft(c), Speech Lab, NTU, Taiwan ]
+#   Reference    [ https://github.com/Alexander-H-Liu/End-to-end-ASR-Pytorch ]
+"""*********************************************************************************************"""
+
+
+###############
+# IMPORTATION #
+###############
+import os
+import sys
+import pickle
+import argparse
+import numpy as np
+import pandas as pd
+from tqdm import tqdm
+from pathlib import Path
+from joblib import Parallel, delayed
+from utility.audio import extract_feature, num_mels, num_mfcc, num_freq
+
+
+##################
+# BOOLEAB STRING #
+##################
+def boolean_string(s):
+    if s not in ['False', 'True']:
+        raise ValueError('Not a valid boolean string')
+    return s == 'True'
+
+
+#############################
+# PREPROCESS CONFIGURATIONS #
+#############################
+def get_preprocess_args():
+
+    parser = argparse.ArgumentParser(description='preprocess arguments for any dataset.')
+
+    parser.add_argument('--output_path', default='./data/', type=str, help='Path to store output', required=False)
+    parser.add_argument('--audio_extention', default='.flac', type=str, help='audio file type (.wav / .flac / .mp3 / etc)', required=False)
+
+    parser.add_argument('--feature_type', default='fbank', type=str, help='Feature type ( mfcc / fbank / mel / linear )', required=False)
+    parser.add_argument('--delta', default=False, type=boolean_string, help='Append Delta', required=False)
+    parser.add_argument('--delta_delta', default=False, type=boolean_string, help='Append Delta Delta', required=False)
+    parser.add_argument('--apply_cmvn', default=True, type=boolean_string, help='Apply CMVN on feature', required=False)
+
+    parser.add_argument('--n_jobs', default=-1, type=int, help='Number of jobs used for feature extraction', required=False)
+    parser.add_argument('--name', default='None', type=str, help='Name of the output directory', required=False)
+
+    args = parser.parse_args()
+    return args
+
+
+#######################
+# ACOUSTIC PREPROCESS #
+#######################
+def acoustic_preprocess(args, tr_set, dim, audio_extention):
+
+    sets = ['train', 'dev', 'test']
+    for i, s in enumerate(tr_set):
+        print('')
+        print('Preprocessing data in: ', s, end='')
+        todo = list(Path(s).rglob('*' + audio_extention)) # '*.flac'
+        print(len(todo), 'audio files found.')
+
+        if args.name == 'None':
+            output_dir = os.path.join(args.output_path, '_'.join(['NewData', str(args.feature_type)+str(dim)]))
+        else:
+            output_dir = os.path.join(args.output_path, args.name)
+        if not os.path.exists(output_dir): os.makedirs(output_dir)
+        cur_path = os.path.join(output_dir, sets[i])
+        if not os.path.exists(cur_path): os.makedirs(cur_path)
+
+        print('Extracting acoustic feature...', flush=True)
+        tr_x = Parallel(n_jobs=args.n_jobs)(delayed(extract_feature)(str(file), feature=args.feature_type, \
+                                            delta=args.delta, delta_delta=args.delta_delta, cmvn=args.apply_cmvn, \
+                                            save_feature=os.path.join(cur_path, str(file).split('/')[-1].replace(audio_extention, ''))) for file in tqdm(todo))
+
+        # sort by len
+        sorted_todo = [os.path.join(sets[i], str(todo[idx]).split('/')[-1].replace(audio_extention, '.npy')) for idx in reversed(np.argsort(tr_x))]
+        # Dump data
+        df = pd.DataFrame(data={'file_path':[fp for fp in sorted_todo], 'length':list(reversed(sorted(tr_x))), 'label':None})
+        df.to_csv(os.path.join(output_dir, sets[i] + '.csv'))
+
+    print('All done, saved at', output_dir, 'exit.')
+
+
+########
+# MAIN #
+########
+def main():
+
+    # get arguments
+    args = get_preprocess_args()
+    mel_dim = num_mels * (1 + int(args.delta) + int(args.delta_delta))
+    mfcc_dim = num_mfcc * (1 + int(args.delta) + int(args.delta_delta))
+    dim = num_freq if args.feature_type == 'linear' else (mfcc_dim if args.feature_type == 'mfcc' else mel_dim)
+    print('Delta: ', args.delta, '. Delta Delta: ', args.delta_delta, '. Cmvn: ', args.apply_cmvn)
+
+    # Select data sets
+    print('This scipts will preprocess the following data sets :')
+    sets = ['train', 'dev', 'test']
+    for idx, s in enumerate(sets):
+        print('\t', idx, ':', s)
+
+    tr_set = []
+    for i in range(len(sets)):
+        tr_set.append(input(f'Please enter the path to the directory that contains all the \'{sets[i]}\' data (for example data/MyDataset/{sets[i]}): '))
+
+    # Acoustic Feature Extraction & Make Data Table
+    acoustic_preprocess(args, tr_set, dim, args.audio_extention)
+
+
+if __name__ == '__main__':
+    main()