open source pkg v1

dbm_lib/dbm_features/raw_features/audio/hnr.py

@@ -0,0 +1,92 @@
+"""
+file_name: hnr
+project_name: DBM
+created: 2020-20-07
+"""
+
+import pandas as pd
+import numpy as np
+import os
+import glob
+import parselmouth
+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()
+
+hnr_dir = 'audio/harmonic_noise'
+csv_ext = '_hnr_frame.csv'
+error_txt = 'error: length less than 0.064'
+
+def hnr_ratio(filepath):
+    """
+    Using parselmouth library fetching harmonic noise ratio ratio
+    Args:
+        path: (.wav) audio file location
+    Returns:
+        (list) list of hnr ratio for each voice frame, min,max and mean hnr
+    """
+    sound = parselmouth.Sound(filepath)
+    harmonicity = sound.to_harmonicity_ac(time_step=.001)
+    
+    hnr_all_frames = harmonicity.values#[harmonicity.values != -200] nan it (****)
+    hnr_all_frames = np.where(hnr_all_frames==-200, np.NaN, hnr_all_frames)
+    return hnr_all_frames.transpose()
+
+def calc_hnr(video_uri, audio_file, out_loc, fl_name, r_config):
+    """
+    Preparing harmonic noise matrix
+    Args:
+        audio_file: (.wav) parsed audio file
+        out_loc: (str) Output directory for csv's
+    """
+    
+    hnr_all_frames = hnr_ratio(audio_file)
+    df_hnr = pd.DataFrame(hnr_all_frames, columns=[r_config.aco_hnr])
+    
+    df_hnr['Frames'] = df_hnr.index
+    df_hnr['dbm_master_url'] = video_uri
+    df_hnr[r_config.err_reason] = 'Pass'# will replace with threshold in future release
+    
+    logger.info('Saving Output file {} '.format(out_loc))
+    ut.save_output(df_hnr, out_loc, fl_name, hnr_dir, csv_ext)
+    
+def empty_hnr(video_uri, out_loc, fl_name, r_config):
+    """
+    Preparing empty HNR matrix if something fails
+    """
+    cols = ['Frames', r_config.aco_hnr, r_config.err_reason]
+    out_val = [[np.nan, np.nan, error_txt]]
+    df_hnr = pd.DataFrame(out_val, columns = cols)
+    df_hnr['dbm_master_url'] = video_uri
+    
+    logger.info('Saving Output file {} '.format(out_loc))
+    ut.save_output(df_hnr, out_loc, fl_name, hnr_dir, csv_ext)
+
+def run_hnr(video_uri, out_dir, r_config):
+    """
+    Processing all patient's for fetching harmonic noise ratio
+    -------------------
+    -------------------
+    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_hnr(video_uri, out_loc, fl_name, r_config)
+            return
+
+        calc_hnr(video_uri, audio_file, out_loc, fl_name, r_config)