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"""*********************************************************************************************""" |
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"""*********************************************************************************************""" |
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import os |
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import sys |
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import pickle |
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import argparse |
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import numpy as np |
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import pandas as pd |
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from tqdm import tqdm |
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from pathlib import Path |
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from joblib import Parallel, delayed |
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from utility.audio import extract_feature, num_mels, num_mfcc, num_freq |
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def boolean_string(s): |
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if s not in ['False', 'True']: |
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raise ValueError('Not a valid boolean string') |
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return s == 'True' |
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def get_preprocess_args(): |
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parser = argparse.ArgumentParser(description='preprocess arguments for LibriSpeech dataset.') |
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parser.add_argument('--data_path', default='./data/timit', type=str, help='Path to raw TIMIT dataset') |
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parser.add_argument('--output_path', default='./data/', type=str, help='Path to store output', required=False) |
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parser.add_argument('--feature_type', default='mel', type=str, help='Feature type ( mfcc / fbank / mel / linear )', required=False) |
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parser.add_argument('--delta', default=True, type=boolean_string, help='Append Delta', required=False) |
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parser.add_argument('--delta_delta', default=False, type=boolean_string, help='Append Delta Delta', required=False) |
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parser.add_argument('--apply_cmvn', default=True, type=boolean_string, help='Apply CMVN on feature', required=False) |
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parser.add_argument('--n_jobs', default=-1, type=int, help='Number of jobs used for feature extraction', required=False) |
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parser.add_argument('--name', default='None', type=str, help='Name of the output directory', required=False) |
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args = parser.parse_args() |
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return args |
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def preprocess(args, dim): |
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for s in ('train', 'dev', 'test'): |
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print('') |
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print(f'Preprocessing {s} data...', end='') |
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todo = list(Path(os.path.join(args.data_path, s.upper())).rglob("*.[wW][aA][vV]")) |
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if len(todo) == 0: todo = list(Path(os.path.join(args.data_path, s)).rglob("*.[wW][aA][vV]")) |
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print(len(todo), f'audio files found in {s} set') |
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if args.name == 'None': |
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output_dir = os.path.join(args.output_path, '_'.join(['timit', str(args.feature_type)+str(dim)])) |
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else: |
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output_dir = os.path.join(args.output_path, args.name) |
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if not os.path.exists(output_dir): os.makedirs(output_dir) |
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cur_path = os.path.join(output_dir, s) |
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if not os.path.exists(cur_path): os.makedirs(cur_path) |
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print('Extracting acoustic feature...', flush=True) |
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tr_x = Parallel(n_jobs=args.n_jobs)(delayed(extract_feature)(str(file), feature=args.feature_type, \ |
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delta=args.delta, delta_delta=args.delta_delta, cmvn=args.apply_cmvn, \ |
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save_feature=os.path.join(cur_path, str(file).split('/')[-1].split('.')[0])) for file in tqdm(todo)) |
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sorted_todo = [os.path.join(s, str(todo[idx]).split('/')[-1].split('.')[0]+'.npy') for idx in reversed(np.argsort(tr_x))] |
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df = pd.DataFrame(data={'file_path':[fp for fp in sorted_todo], 'length':list(reversed(sorted(tr_x))), 'label':None}) |
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df.to_csv(os.path.join(output_dir, s+'.csv')) |
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print('All done, saved at', output_dir, 'exit.') |
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def main(): |
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args = get_preprocess_args() |
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mel_dim = num_mels * (1 + int(args.delta) + int(args.delta_delta)) |
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mfcc_dim = num_mfcc * (1 + int(args.delta) + int(args.delta_delta)) |
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dim = num_freq if args.feature_type == 'linear' else (mfcc_dim if args.feature_type == 'mfcc' else mel_dim) |
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print('Delta: ', args.delta, '. Delta Delta: ', args.delta_delta, '. Cmvn: ', args.apply_cmvn) |
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preprocess(args, dim) |
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if __name__ == '__main__': |
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main() |
