added vocal tremor

dbm_lib/dbm_features/raw_features/movement/__init__.py

@@ -11,3 +11,5 @@ from __future__ import print_function
 import os
 
 DBMLIB_PATH = os.path.dirname(__file__)
+DBMLIB_VTREMOR_LIB = os.path.abspath(os.path.join(DBMLIB_PATH,
+                                                  '../../../../resources/libraries/voice_tremor.praat'))

dbm_lib/dbm_features/raw_features/movement/voice_tremor.py

@@ -0,0 +1,93 @@
+import pandas as pd
+import os
+import glob
+from os.path import join
+import parselmouth
+from parselmouth.praat import call, run_file
+import numpy as np
+import librosa
+import json
+import re
+import logging
+
+from dbm_lib.dbm_features.raw_features.util import util as ut
+from dbm_lib.dbm_features.raw_features.movement import DBMLIB_VTREMOR_LIB
+
+logging.basicConfig(level=logging.INFO)
+logger=logging.getLogger()
+
+vt_dir = 'movement/voice_tremor'
+csv_ext = '_vtremor.csv'
+
+#Executing praat script using parselmouth function
+def tremor_praat(snd_file,r_cfg):
+    """
+        Generating Voice tremor endpoint dataframe
+        Args:
+            snd_file: (.wav) parsed audio file
+            r_cfg: Raw variable configuration file
+        Returns tremor endpoint dataframe
+    """
+    snd = parselmouth.Sound(snd_file)
+    tremor_var = run_file(snd,DBMLIB_VTREMOR_LIB,capture_output=True)
+    new_tremor_var = re.sub('--undefined--', '0', tremor_var[1])
+    res = json.loads(new_tremor_var)
+    tremor_df = pd.DataFrame(res,index=['0',])
+    tremor_df.columns = [r_cfg.mov_freq_trem_freq,r_cfg.mov_amp_trem_freq,r_cfg.mov_freq_trem_index,
+                         r_cfg.mov_amp_trem_index,r_cfg.mov_freq_trem_pindex,r_cfg.mov_amp_trem_pindex]
+    return tremor_df
+
+def prepare_vtrem_output(audio_file, out_loc, r_config, fl_name):
+    """
+        Preparing voice tremor matrix
+        Args:
+            audio_file: (.wav) parsed audio file ; r_config: raw config object
+            out_loc: (str) Output directory for csv ; fl_name: file name
+    """
+    df_tremor = tremor_praat(audio_file, r_config)
+    df_tremor[r_config.err_reason] = 'Pass'# will replace with threshold in future release
+
+    logger.info('Processing Output file {} '.format(out_loc))
+    ut.save_output(df_tremor, out_loc, fl_name, vt_dir, csv_ext)
+
+def prepare_empty_vt(out_loc, fl_name, r_config, error_txt):
+
+    """
+    Preparing empty voice tremor matrix
+    """
+    cols = [r_config.mov_freq_trem_freq, r_config.mov_amp_trem_freq, r_config.mov_freq_trem_index,
+            r_config.mov_amp_trem_index, r_config.mov_freq_trem_pindex, r_config.mov_amp_trem_pindex, r_config.err_reason]
+
+    out_val = [[np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, error_txt]]
+    df_tremor = pd.DataFrame(out_val, columns = cols)
+
+    logger.info('Saving Output file {} '.format(out_loc))
+    ut.save_output(df_tremor, out_loc, fl_name, vt_dir, csv_ext)
+
+def run_vtremor(video_uri, out_dir, r_config):
+    """
+    Processing all patient's for fetching Formant freq
+    ---------------
+    ---------------
+    Args:
+        video_uri: video path; r_config: raw variable config object
+        out_dir: (str) Output directory for processed output
+    """
+    try:
+        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))
+
+                prepare_empty_vt(video_uri, out_loc, fl_name, r_config)
+                return
+            prepare_vtrem_output(audio_file, out_loc, r_config, fl_name)
+    except Exception as e:
+        logger.error('Failed to compute Voice Tremor {} for {}'.format(e,video_uri))
+        prepare_empty_vt(out_loc, fl_name, r_config, e)