# -*- coding: utf-8 -*- # """*********************************************************************************************""" # FileName [ preprocess_mosi.py ] # Synopsis [ preprocessing for the MOSI dataset ] # Author [ Andy T. Liu (Andi611) ] # Copyright [ Copyleft(c), Speech Lab, NTU, Taiwan ] """*********************************************************************************************""" ############### # IMPORTATION # ############### import os import shutil import glob 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.asr import encode_target from utility.audio import extract_feature, mel_dim, num_freq # using CMU SDK to access audio label # for installing mmsdk package, please follow the instructions in: # https://github.com/A2Zadeh/CMU-MultimodalSDK#installation import mmsdk from mmsdk import mmdatasdk as md ################## # BOOLEAB STRING # ################## def boolean_string(s): if s not in ['False', 'True']: raise ValueError('Not a valid boolean string') return s == 'True' def sdk2npy(string): # not data type conversion # convert the name format in CMU sdk to corresponding npy file name split = string.split('[') utterance_name = split[0] number = int(split[1].split(']')[0]) string = utterance_name + '_' + str(number + 1) + '.npy' return string def npy2sdk(string): # not data type conversion # inverse operation of sdk2npy split = string.split('_') number = int(split[-1][:-4]) utterance_name = '_'.join(split[:-1]) string = utterance_name + '[' + str(number - 1) + ']' return string ############################# # PREPROCESS CONFIGURATIONS # ############################# def get_preprocess_args(): parser = argparse.ArgumentParser(description='preprocess arguments for LibriSpeech dataset.') parser.add_argument('--data_path', default='/home/leo/d/datasets/MOSI/Raw/Audio/WAV_16000/Segmented', type=str, help='Path to raw MOSI segmented audio dataset') parser.add_argument('--output_path', default='../data/', type=str, help='Path to store output', required=False) parser.add_argument('--feature_type', default='mel', type=str, help='Feature type ( mfcc / fbank / mel / linear )', 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('--n_tokens', default=5000, type=int, help='Vocabulary size of target', required=False) args = parser.parse_args() return args ####################### # ACOUSTIC PREPROCESS # ####################### def acoustic_preprocess(args, dim): # Extracting features from audio todo = list(Path(args.data_path).glob("*.wav")) print(len(todo),'audio files found in MOSI') assert args.feature_type in ['mel', 'linear', 'fbank'], 'Feature type unsupported' output_dir = os.path.join(args.output_path,'_'.join(['mosi',str(args.feature_type)+str(dim)])) if not os.path.exists(output_dir): os.makedirs(output_dir) print('Extracting acoustic feature...',flush=True) tr_x = Parallel(n_jobs=args.n_jobs)(delayed(extract_feature)(str(file), feature=args.feature_type, cmvn=args.apply_cmvn, \ save_feature=os.path.join(output_dir, str(file).split('/')[-1].replace('.wav',''))) for file in tqdm(todo)) # Loading labels DATASET = md.cmu_mosi try: md.mmdataset(DATASET.labels, args.data_path) except RuntimeError: print("Labels have been downloaded previously.") label_field = 'CMU_MOSI_Opinion_Labels' features = [ label_field, ] recipe = {feat: os.path.join(args.data_path, feat) + '.csd' for feat in features} dataset = md.mmdataset(recipe) dataset.align(label_field) # Check each label has corresponding utterance utterances = os.listdir(output_dir) for segment_sdk in dataset[label_field].keys(): segment_npy = sdk2npy(segment_sdk) try: assert segment_npy in utterances except AssertionError: print('AssertionError: Cannot find corresponding utterance for given label') # Check the inverse is itself by npy2sdk try: assert npy2sdk(segment_npy) == segment_sdk except AssertionError: print('AssertionError: npt2sdk funtion has bug') # sort by len sorted_xlen = [] sorted_y = [] sorted_todo = [] for idx in reversed(np.argsort(tr_x)): filename = str(todo[idx]).split('/')[-1].replace('.wav','.npy') sdkname = npy2sdk(filename) if sdkname in dataset[label_field].keys(): sorted_xlen.append(tr_x[idx]) sorted_y.append(dataset[label_field][sdkname]['features'].reshape(-1)[0]) sorted_todo.append(filename) # Organize label df = pd.DataFrame(data={'file_path':[fp for fp in sorted_todo],'length':list(sorted_xlen),'label':sorted_y}) # Split data into train/dev/test train_split = DATASET.standard_folds.standard_train_fold dev_split = DATASET.standard_folds.standard_valid_fold test_split = DATASET.standard_folds.standard_test_fold npy_dir = os.path.join(output_dir, 'npy') if not os.path.exists(npy_dir): os.mkdir(npy_dir) def classify(file_name): file_name = file_name[0] prefix = '_'.join(file_name.split('_')[:-1]) shutil.move(os.path.join(output_dir,file_name), os.path.join(npy_dir, file_name)) if prefix in train_split: return 'train' elif prefix in dev_split: return 'dev' elif prefix in test_split: return 'test' else: assert 0, 'Error in preprocess_mosi.py:146' belong = np.apply_along_axis(lambda file_name: classify(file_name), 1, df['file_path'].values.reshape(-1, 1)) df.insert(len(df.columns), 'set', belong) train_frame = df[df.set == 'train'] dev_frame = df[df.set == 'dev'] test_frame = df[df.set == 'test'] # dump csv of all splits df.to_csv(os.path.join(output_dir,'all.csv')) train_frame.to_csv(os.path.join(output_dir,'train.csv')) dev_frame.to_csv(os.path.join(output_dir,'dev.csv')) test_frame.to_csv(os.path.join(output_dir,'test.csv')) remain_npy = glob.glob(os.path.join(output_dir, '*.npy')) print('Delete ' + str(len(remain_npy)) + ' unlabeled npy files:') for npy in remain_npy: print('delete ' + npy) os.remove(npy) print('All done, saved at', output_dir, 'exit.') ######## # MAIN # ######## def main(): # get arguments args = get_preprocess_args() dim = num_freq if args.feature_type == 'linear' else mel_dim # Acoustic Feature Extraction acoustic_preprocess(args, dim) if __name__ == '__main__': main()