open source pkg v1

dbm_lib/dbm_features/raw_features/audio/mfcc.py

@@ -0,0 +1,102 @@
+"""
+file_name: mfcc
+project_name: DBM
+created: 2020-20-07
+"""
+
+import pandas as pd
+import os
+import glob
+import parselmouth
+import librosa
+import numpy as np
+import librosa
+from os.path import join
+import logging
+
+from dbm_lib.dbm_features.raw_features.util import util as ut
+
+logging.basicConfig(level=logging.INFO)
+logger=logging.getLogger()
+
+mfcc_dir = 'audio/mfcc'
+csv_ext = '_mfcc.csv'
+error_txt = 'error: length less than 0.064'
+
+def empty_mfcc(video_uri, out_loc, fl_name, r_config):
+    
+    """
+    Preparing empty empty_mfcc matrix if something fails
+    """
+    cols = ['Frames', r_config.aco_mfcc1, r_config.aco_mfcc2, r_config.aco_mfcc3, r_config.aco_mfcc4, r_config.aco_mfcc5,
+            r_config.aco_mfcc6, r_config.aco_mfcc7, r_config.aco_mfcc8, r_config.aco_mfcc9, r_config.aco_mfcc10, 
+            r_config.aco_mfcc11, r_config.aco_mfcc12, r_config.err_reason]
+    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, 
+                error_txt]]
+    df_mfcc = pd.DataFrame(out_val, columns = cols)
+    df_mfcc['dbm_master_url'] = video_uri
+    
+    logger.info('Saving Output file {} '.format(out_loc))
+    ut.save_output(df_mfcc, out_loc, fl_name, mfcc_dir, csv_ext)
+
+def audio_mfcc(path):
+    """
+    Using parselmouth library fetching mfccs
+    Args:
+        path: (.wav) audio file location
+    Returns:
+        (list) list of mfccs for each voice frame
+    """
+    sound = parselmouth.Sound(path)
+    mfcc_object = sound.to_mfcc(time_step=.001,number_of_coefficients=12)
+    mfccs = mfcc_object.to_array()
+    mfccs = np.delete(mfccs, (0), axis=0)
+    return mfccs
+    
+def calc_mfcc(video_uri, audio_file, out_loc, fl_name, r_config):
+    """
+    Preparing mfcc matrix
+    Args:
+        audio_file: (.wav) parsed audio file
+        out_loc: output location to save csv
+        fl_name: (str) name of audio file
+        r_config: config.config_raw_feature.pyConfigFeatureNmReader object
+    """
+    dict_ = {}
+    mfccs = audio_mfcc(audio_file)
+    
+    for i in range(1,13):
+        conf_str = r_config.base_raw['raw_feature']
+        dict_[conf_str['aco_mfcc' + str(i)]] = mfccs[i-1, :]
+        
+    df = pd.DataFrame(dict_)
+    df['Frames'] = df.index
+    
+    df[r_config.err_reason] = 'Pass'# may replace based on threshold in future release
+    df['dbm_master_url'] = video_uri
+    
+    ut.save_output(df, out_loc, fl_name, mfcc_dir, csv_ext)
+    
+def run_mfcc(video_uri, out_dir, r_config):
+    """
+    Processing all patients to fetch mfccs
+    
+    Args:
+        video_uri: video path; r_config: raw variable config object
+        out_dir: (str) Output directory for processed output 
+    """
+    input_loc, out_loc, fl_name = ut.filter_path(video_uri, out_dir)
+    aud_filter = glob.glob(join(input_loc, fl_name + '.wav'))
+    if len(aud_filter)>0:
+        
+        audio_file = aud_filter[0]
+        aud_dur = librosa.get_duration(filename=audio_file)
+        
+        if float(aud_dur) < 0.064:
+            logger.info('Output file {} size is less than 0.064sec'.format(audio_file))
+            
+            empty_mfcc(video_uri, out_loc, fl_name, r_config)
+            return
+            
+        calc_mfcc(video_uri, audio_file, out_loc, fl_name, r_config) 
+