105 lines
3.3 KiB
Python
105 lines
3.3 KiB
Python
"""
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file_name: mfcc
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project_name: DBM
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created: 2020-20-07
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"""
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import pandas as pd
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import os
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import glob
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import parselmouth
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import librosa
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import numpy as np
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import librosa
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from os.path import join
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import logging
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from dbm_lib.dbm_features.raw_features.util import util as ut
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logging.basicConfig(level=logging.INFO)
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logger=logging.getLogger()
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mfcc_dir = 'audio/mfcc'
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csv_ext = '_mfcc.csv'
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error_txt = 'error: length less than 0.064'
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def empty_mfcc(video_uri, out_loc, fl_name, r_config):
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"""
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Preparing empty empty_mfcc matrix if something fails
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"""
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cols = ['Frames', r_config.aco_mfcc1, r_config.aco_mfcc2, r_config.aco_mfcc3, r_config.aco_mfcc4, r_config.aco_mfcc5,
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r_config.aco_mfcc6, r_config.aco_mfcc7, r_config.aco_mfcc8, r_config.aco_mfcc9, r_config.aco_mfcc10,
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r_config.aco_mfcc11, r_config.aco_mfcc12, r_config.err_reason]
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out_val = [[np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan,
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error_txt]]
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df_mfcc = pd.DataFrame(out_val, columns = cols)
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df_mfcc['dbm_master_url'] = video_uri
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logger.info('Saving Output file {} '.format(out_loc))
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ut.save_output(df_mfcc, out_loc, fl_name, mfcc_dir, csv_ext)
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def audio_mfcc(path):
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"""
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Using parselmouth library fetching mfccs
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Args:
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path: (.wav) audio file location
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Returns:
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(list) list of mfccs for each voice frame
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"""
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sound = parselmouth.Sound(path)
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mfcc_object = sound.to_mfcc(time_step=.001,number_of_coefficients=12)
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mfccs = mfcc_object.to_array()
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mfccs = np.delete(mfccs, (0), axis=0)
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return mfccs
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def calc_mfcc(video_uri, audio_file, out_loc, fl_name, r_config):
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"""
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Preparing mfcc matrix
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Args:
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audio_file: (.wav) parsed audio file
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out_loc: output location to save csv
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fl_name: (str) name of audio file
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r_config: config.config_raw_feature.pyConfigFeatureNmReader object
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"""
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dict_ = {}
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mfccs = audio_mfcc(audio_file)
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for i in range(1,13):
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conf_str = r_config.base_raw['raw_feature']
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dict_[conf_str['aco_mfcc' + str(i)]] = mfccs[i-1, :]
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df = pd.DataFrame(dict_)
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df['Frames'] = df.index
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df[r_config.err_reason] = 'Pass'# may replace based on threshold in future release
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df['dbm_master_url'] = video_uri
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ut.save_output(df, out_loc, fl_name, mfcc_dir, csv_ext)
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def run_mfcc(video_uri, out_dir, r_config):
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"""
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Processing all patients to fetch mfccs
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Args:
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video_uri: video path; r_config: raw variable config object
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out_dir: (str) Output directory for processed output
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"""
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try:
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input_loc, out_loc, fl_name = ut.filter_path(video_uri, out_dir)
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aud_filter = glob.glob(join(input_loc, fl_name + '.wav'))
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if len(aud_filter)>0:
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audio_file = aud_filter[0]
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aud_dur = librosa.get_duration(filename=audio_file)
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if float(aud_dur) < 0.064:
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logger.info('Output file {} size is less than 0.064sec'.format(audio_file))
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empty_mfcc(video_uri, out_loc, fl_name, r_config)
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return
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calc_mfcc(video_uri, audio_file, out_loc, fl_name, r_config)
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except Exception as e:
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logger.error('Failed to process audio file') |