.mp3 and .mov conversion successful

2020-12-10 16:46:28 +00:00
parent 3b4d3b5e47 6d977980e7
commit a026ca594d
23 changed files with 1602 additions and 138 deletions
--- a/13
+++ b/13
@@ -12,7 +12,9 @@ RUN apt-get update && apt-get install -y python3-pip \
                   && apt-get install -y libavcodec-dev \
                   && apt-get install -y libavformat-dev \
                   && apt-get install -y libavdevice-dev \
-                   && apt-get install -y libboost-all-dev
+                   && apt-get install -y libboost-all-dev \
+                   && apt-get install -y git \
+                   && apt-get install -y sox
 RUN ln -sfn /usr/bin/pip3 /usr/bin/pip

 COPY . /app
@@ -24,8 +26,15 @@ RUN dpkg --configure -a
 RUN su -c ./install.sh
 RUN echo "Done OpenFace!"

-WORKDIR /app
+RUN echo "Cloning DeepSpeech..."
+WORKDIR /app/pkg
+RUN git clone https://github.com/mozilla/DeepSpeech.git

+WORKDIR /app/pkg/DeepSpeech
+RUN wget https://github.com/mozilla/DeepSpeech/releases/download/v0.9.1/deepspeech-0.9.1-models.pbmm
+RUN wget https://github.com/mozilla/DeepSpeech/releases/download/v0.9.1/deepspeech-0.9.1-models.scorer
+
+WORKDIR /app
 RUN pip install --upgrade pip
 RUN pip install -r requirements.txt
 RUN echo "Requirement txt done!"
--- a/dbm_lib/config/config_raw_feature.py
+++ b/dbm_lib/config/config_raw_feature.py
@@ -223,3 +223,49 @@ class ConfigRawReader(object):
            self.mov_Hpose_Roll = config['raw_feature']['mov_Hpose_Roll']
            self.mov_Hpose_Dist = config['raw_feature']['mov_Hpose_Dist']
            
+            self.mov_freq_trem_freq = config['raw_feature']['mov_freq_trem_freq']
+            self.mov_freq_trem_index = config['raw_feature']['mov_freq_trem_index']
+            self.mov_freq_trem_pindex = config['raw_feature']['mov_freq_trem_pindex']
+            self.mov_amp_trem_freq = config['raw_feature']['mov_amp_trem_freq']
+            self.mov_amp_trem_index = config['raw_feature']['mov_amp_trem_index']
+            self.mov_amp_trem_pindex = config['raw_feature']['mov_amp_trem_pindex']
+
+            self.fac_tremor_median_5 = config['raw_feature']['fac_tremor_median_5']
+            self.fac_tremor_median_12 = config['raw_feature']['fac_tremor_median_12']
+            self.fac_tremor_median_8 = config['raw_feature']['fac_tremor_median_8']
+            self.fac_tremor_median_48 = config['raw_feature']['fac_tremor_median_48']
+            self.fac_tremor_median_54 = config['raw_feature']['fac_tremor_median_54']
+            self.fac_tremor_median_28 = config['raw_feature']['fac_tremor_median_28']
+            self.fac_tremor_median_51 = config['raw_feature']['fac_tremor_median_51']
+            self.fac_tremor_median_66 = config['raw_feature']['fac_tremor_median_66']
+            self.fac_tremor_median_57 = config['raw_feature']['fac_tremor_median_57']
+
+            self.mov_leye_x = config['raw_feature']['mov_leye_x']
+            self.mov_leye_y = config['raw_feature']['mov_leye_y']
+            self.mov_leye_z = config['raw_feature']['mov_leye_z']
+            self.mov_reye_x = config['raw_feature']['mov_reye_x']
+            self.mov_reye_y = config['raw_feature']['mov_reye_y']
+            self.mov_reye_z = config['raw_feature']['mov_reye_z']
+            self.mov_eleft_disp = config['raw_feature']['mov_eleft_disp']
+            self.mov_eright_disp = config['raw_feature']['mov_eright_disp']
+
+            #NLP features
+            self.nlp_transcribe = config['raw_feature']['nlp_transcribe']
+            self.nlp_numSentences = config['raw_feature']['nlp_numSentences']
+            self.nlp_singPronPerAns = config['raw_feature']['nlp_singPronPerAns']
+            self.nlp_singPronPerSen = config['raw_feature']['nlp_singPronPerSen']
+            self.nlp_pastTensePerAns = config['raw_feature']['nlp_pastTensePerAns']
+            self.nlp_pastTensePerSen = config['raw_feature']['nlp_pastTensePerSen']
+            self.nlp_pronounsPerAns = config['raw_feature']['nlp_pronounsPerAns']
+            self.nlp_pronounsPerSen = config['raw_feature']['nlp_pronounsPerSen']
+            self.nlp_verbsPerAns = config['raw_feature']['nlp_verbsPerAns']
+            self.nlp_verbsPerSen = config['raw_feature']['nlp_verbsPerSen']
+            self.nlp_adjectivesPerAns = config['raw_feature']['nlp_adjectivesPerAns']
+            self.nlp_adjectivesPerSen = config['raw_feature']['nlp_adjectivesPerSen']
+            self.nlp_nounsPerAns = config['raw_feature']['nlp_nounsPerAns']
+            self.nlp_nounsPerSen = config['raw_feature']['nlp_nounsPerSen']
+            self.nlp_sentiment_mean = config['raw_feature']['nlp_sentiment_mean']
+            self.nlp_mattr = config['raw_feature']['nlp_mattr']
+            self.nlp_wordsPerMin = config['raw_feature']['nlp_wordsPerMin']
+            self.nlp_totalTime = config['raw_feature']['nlp_totalTime']
+         
--- a/dbm_lib/controller/process_feature.py
+++ b/dbm_lib/controller/process_feature.py
@@ -7,7 +7,9 @@ created: 2020-20-07
 from dbm_lib.dbm_features.raw_features.audio import intensity, pitch_freq, hnr, gne, voice_frame_score, formant_freq
 from dbm_lib.dbm_features.raw_features.audio import pause_segment, jitter, shimmer, mfcc
 from dbm_lib.dbm_features.raw_features.video import face_asymmetry, face_au, face_emotion_expressivity, face_landmark
-from dbm_lib.dbm_features.raw_features.movement import head_motion, eye_blink
+from dbm_lib.dbm_features.raw_features.movement import head_motion, eye_blink, eye_gaze, voice_tremor, facial_tremor
+from dbm_lib.dbm_features.raw_features.nlp import transcribe, speech_features
+

 import subprocess
 import logging
@@ -117,12 +119,39 @@ def process_movement(video_uri, out_dir, dbm_group, r_config, dlib_model):
        return

    logger.info('Processing movement variables from data in {}'.format(video_uri))
+
    logger.info('processing head movement....')
    head_motion.run_head_movement(video_uri, out_dir, r_config)
   
    logger.info('processing eye blink....')
    eye_blink.run_eye_blink(video_uri, out_dir, r_config, dlib_model)

+    logger.info('processing eye gaze....')
+    eye_gaze.run_eye_gaze(video_uri, out_dir, r_config)
+    
+    logger.info('processing voice tremor....')
+    voice_tremor.run_vtremor(video_uri, out_dir, r_config)
+
+    logger.info('processing facial tremor....')
+    facial_tremor.fac_tremor_process(video_uri, out_dir, r_config, model_output=True)
+
+    
+def process_nlp(video_uri, out_dir, dbm_group, tran_tog, r_config, deep_path):
+    """
+    processing nlp features
+    Args:
+        video_uri: video path; out_dir: raw variable output dir
+        dbm_group: list of features to process; r_config: raw feature config object
+        deep_path: deep speech build path
+    """
+    if dbm_group != None and len(dbm_group)>0 and 'speech' not in dbm_group:
+        return
+    
+    logger.info('Processing nlp variables from data in {}'.format(video_uri))
+    transcribe.run_transcribe(video_uri, out_dir, r_config, deep_path)
+    speech_features.run_speech_feature(video_uri, out_dir, r_config, tran_tog)
+    
+
 def remove_file(file_path):
    """
    removing wav file
@@ -133,5 +162,3 @@ def remove_file(file_path):

    if len(wav_file)> 0:
        os.remove(wav_file[0])
-    
-    
--- a/dbm_lib/dbm_features/raw_features/audio/formant_freq.py
+++ b/dbm_lib/dbm_features/raw_features/audio/formant_freq.py
@@ -31,7 +31,11 @@ def formant_list(formant,snd):
    Returns:
        List of first through fourth formant for each frame
    """
-    f1_list, f2_list, f3_list, f4_list = ([], ) * 4
+    f1_list = []
+    f2_list = []
+    f3_list = []
+    f4_list = []
+    
    dur = snd.duration-0.02
    dur_round = round(dur, 2)
    
--- a/dbm_lib/dbm_features/raw_features/movement/init.py
+++ b/dbm_lib/dbm_features/raw_features/movement/init.py
@@ -11,3 +11,7 @@ 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'))
+DBMLIB_FTREMOR_CONFIG = os.path.abspath(os.path.join(DBMLIB_PATH, '../../../../resources/features/facial/config.json'))
+
--- a/dbm_lib/dbm_features/raw_features/movement/eye_gaze.py
+++ b/dbm_lib/dbm_features/raw_features/movement/eye_gaze.py
@@ -0,0 +1,148 @@
+"""
+file_name: eye_gaze
+project_name: DBM
+created: 2020-30-11
+"""
+
+import os
+import glob
+import pandas as pd
+import numpy as np
+from scipy.spatial import distance
+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()
+
+eye_pose_dir = 'movement/gaze'
+eye_pose_ext = '_eyegaze.csv'
+
+def eye_motion_df(l_disp, r_disp, error_list, r_config):
+    """
+    Generating eye movement dataframe
+
+    Args:
+        l_disp: displacement list(left eye); l_disp: displacement list(right eye)
+        r_config: raw variable config file object
+
+    Reutrns:
+        Final eye displacement dataframe
+    """
+    df_eye_left = pd.DataFrame(l_disp, columns=[r_config.mov_eleft_disp])
+    df_eye_right = pd.DataFrame(r_disp, columns=[r_config.mov_eright_disp])
+    
+    df_eye_motion = pd.concat([df_eye_left, df_eye_right], axis=1, sort=False)
+    df_eye_motion[r_config.err_reason] = error_list
+    return df_eye_motion
+
+def filter_motion(df_of, df_disp, col_l, col_r, r_config):
+    """
+    Filtering final eye movement dataframe
+    
+    Args:
+        df_of: Openface raw out dataframe; col_r: right eye column
+        col_l: left eye column; r_config: raw variable config file object
+    """
+    
+    df_of = df_of[col_l + col_r + [' confidence']]
+    df_of.loc[(df_of[' confidence'].astype(float) < 0.8), col_l + col_r] = np.nan
+    
+    df_filter =  df_of[col_l + col_r]
+    df_filter.columns = [r_config.mov_leye_x, r_config.mov_leye_y, r_config.mov_leye_z,
+                      r_config.mov_reye_x, r_config.mov_reye_y, r_config.mov_reye_z]
+    
+    df_motion = pd.concat([df_filter, df_disp], axis=1, sort=False)
+    return df_motion
+
+def eye_disp(of_results, col, r_config):
+    """
+    Computing head velocity frame by frame
+
+    Args:
+        of_results: Openface raw out dataframe
+        r_config: Face config file object
+
+    Reutrns:
+        Final head velocity frame by frame output
+    """
+    distance_list = []
+    error_list = []
+    
+    of_results = of_results[col+ [' confidence']]
+    for index, row in of_results.iterrows():
+        dst = np.nan
+        
+        if index == 0 or float(row[' confidence']) < 0.8:  #Threshold < 0.8
+            distance_list.append(dst)
+            
+            if float(row[' confidence']) < 0.8:
+                error_list.append('confidence less than 80%')
+                
+            else:
+                error_list.append('Pass')
+            continue
+            
+        if index > 0:
+            
+            point_x = (of_results[col[0]][index-1], of_results[col[1]][index-1], of_results[col[2]][index-1])
+            point_y = (row[col[0]],row[col[1]],row[col[2]])
+            try:
+                dst = distance.euclidean(point_x, point_y)
+            except:
+                pass
+            
+            distance_list.append(abs(dst))
+            error_list.append('Pass')
+            
+    return distance_list, error_list
+        
+def calc_eye_mov(video_uri, df_of, out_loc, fl_name, r_config):
+    """
+    Computing eye motion variables
+    Args:
+        df_of: Openface dataframe
+        out_loc: Output path for saving output csv's
+        fl_name: file name for output csv
+        r_config: raw variable config file object
+        
+    """
+    
+    col_l = [ ' gaze_0_x', ' gaze_0_y', ' gaze_0_z'] 
+    col_r = [ ' gaze_1_x', ' gaze_1_y', ' gaze_1_z']
+    
+    gazel_disp, err_l = eye_disp(df_of, col_l, r_config)
+    gazer_disp, err_r = eye_disp(df_of, col_r, r_config)
+    
+    df_disp = eye_motion_df(gazel_disp, gazer_disp, err_l, r_config)
+    df_disp['dbm_master_url'] = video_uri
+    
+    df_motion = filter_motion(df_of, df_disp, col_l, col_r, r_config)
+    ut.save_output(df_motion, out_loc, fl_name, eye_pose_dir, eye_pose_ext)
+    
+def run_eye_gaze(video_uri, out_dir, r_config):
+    """
+    Processing all patient's for getting eye movement artifacts 
+    --------------------------------
+    --------------------------------
+    Args:
+        video_uri: video path; input_dir : input directory for video's
+        out_dir: (str) Output directory for processed output; r_config: raw variable config object
+    """
+    try:
+        
+        #filtering path to generate input & output path
+        input_loc, out_loc, fl_name = ut.filter_path(video_uri, out_dir)
+        of_csv_path = glob.glob(join(out_loc, fl_name + '_OF_features/*.csv'))
+
+        if len(of_csv_path)>0:
+
+            of_csv = of_csv_path[0]
+            df_of = pd.read_csv(of_csv, error_bad_lines=False)
+
+            logger.info('Processing Output file {} '.format(os.path.join(out_loc, fl_name)))
+            calc_eye_mov(video_uri, df_of, out_loc, fl_name, r_config)
+    except Exception as e:
+        logger.error('Failed to process video file')
--- a/dbm_lib/dbm_features/raw_features/movement/facial_tremor.py
+++ b/dbm_lib/dbm_features/raw_features/movement/facial_tremor.py
@@ -0,0 +1,164 @@
+import sys, os, glob, cv2, re
+import pickle, json
+import pandas as pd
+import numpy as np
+import numpy.ma as ma
+import logging
+from os.path import join
+
+from dbm_lib.dbm_features.raw_features.util import util as ut
+from dbm_lib.dbm_features.raw_features.util.math_util import *
+
+from dbm_lib.dbm_features.raw_features.movement import DBMLIB_FTREMOR_CONFIG
+
+logging.basicConfig(level=logging.INFO)
+logger=logging.getLogger()
+
+ft_dir = 'movement/facial_tremor'
+csv_ext = '_fac_tremor.csv'
+model_ext = '_fac_model.csv'
+fac_features_ext = '_fac_features.csv'
+
+def compute_features(out_dir, df_of, r_config):
+    """ Computes features
+
+    Returns: features in vector format
+    """
+    config = json.loads(open(DBMLIB_FTREMOR_CONFIG,'r').read())
+
+    pattern_x = re.compile("l\d+_x")
+    pattern_y = re.compile("l\d+_y")
+
+    # assumption: distance of face to camera remains at roughly static
+
+    # logic break
+    landmark_columns = []
+    for col in df_of.columns:
+        if pattern_x.match(col) or pattern_y.match(col):
+            landmark_columns.append(col)
+
+    df_of= df_of[(df_of[landmark_columns]!= 0).any(axis=1)]
+    df_of.reset_index(inplace=True)
+
+    num_frames = len(df_of)
+    logger.info("Number of frames to be processed: {}".format(str(num_frames)))
+    landmarks = config['landmarks']
+
+    try:
+        if num_frames == 0:
+            error_reason = "No frames with visible face."
+            logger.error(error_reason)
+            return empty_frame(landmarks, r_config, error_reason)
+
+#             if num_frames < 60:
+#                 error_reason = 'Number of frames with visible face < 60. Video too short'
+#                 logger.error(error_reason)
+#                 return empty_frame(landmarks, f_cfg, error_reason)
+
+        first_row = df_of.iloc[0]
+
+        facew = abs(first_row[config['face_width_left']] - first_row[config['face_width_right']])
+        faceh = abs(first_row[config['face_height_left']] - first_row[config['face_height_right']])
+
+        if facew == 0 or faceh == 0:
+            error_reason = 'face width or height = 0. Check landmark values'
+            logger.error(error_reason)
+            return empty_frame(landmarks, r_config)
+
+        fac_disp = calc_displacement_vec(df_of, landmarks, num_frames)
+
+        # if verbose:
+            # logger.info("Displacement output: {}".format(str(fac_disp)))
+
+        fac_disp_median = np.median(fac_disp, axis = 1)
+        fac_disp_mean = np.mean(fac_disp, axis = 1)
+
+        if len(fac_disp.shape)!=2:
+            error_reason = 'fac_disp is not 2D. smth went wrong with disp calc'
+            logger.error(error_reason)
+            return empty_frame(landmarks, r_config, error_reason)
+
+        if len(fac_disp[0])<=1:
+            error_reason = 'Video too short. smth went wrong with disp calc'
+            logger.error(error_reason)
+            return empty_frame(landmarks, r_config, error_reason)
+
+        fac_corr_mat = np.corrcoef(fac_disp, rowvar = True)
+        # extract relevant row from cov matrix
+        ref_lmk_index = [i for i, lmk in enumerate(landmarks) if config['ref_lmk']==lmk]
+        fac_corr = fac_corr_mat[ref_lmk_index][0]
+
+        fac_area = config['ref_area'] / (facew * faceh)
+
+        # if verbose:
+        #     logger.info("Face area: {}".format(fac_area))
+        #     logger.info("Face Displacement Median: {}".format(str(fac_disp_median)))
+        #     logger.info("Face Displacement Mean: {}".format(str(fac_disp_mean)))
+
+        fac_features1 =  np.multiply(fac_area * fac_disp_median, (1. - fac_corr))
+        fac_features2 =  np.multiply(fac_area * fac_disp_mean, (1. - fac_corr))
+
+#         base_fac_features = np.dot(fac_area * fac_disp_median, (1. - fac_corr))
+
+        fac_features_dict = {}
+        for i, landmark in enumerate(landmarks):
+            fac_features_dict['fac_features_mean_{}'.format(landmark)] = [fac_features2[i]]
+            raw_variable_map = 'fac_tremor_median_{}'.format(landmark)
+            fac_features_dict[r_config.base_raw['raw_feature'][raw_variable_map]] = [fac_features1[i]]
+
+            fac_features_dict['fac_disp_median_{}'.format(landmark)] = [fac_disp_median[i]]
+            fac_features_dict['fac_corr_{}'.format(landmark)] = [fac_corr[i]]
+
+        fac_features_dict[r_config.err_reason] = ['']
+        data = pd.DataFrame.from_dict(fac_features_dict)
+        logger.info('Concluded computing tremor features')
+
+        return data
+
+    except Exception as e:
+        logger.error('Error computing tremor features: {}'.format(str(e)))
+        return empty_frame(landmarks, r_config, str(e))
+
+def empty_frame(landmarks, r_config, error_reason):
+    fac_features_dict = {}
+    for i, landmark in enumerate(landmarks):
+        raw_variable_map = 'fac_tremor_median_{}'.format(landmark)
+        fac_features_dict[r_config.base_raw['raw_feature'][raw_variable_map]] = [np.nan]
+
+        fac_features_dict['fac_features_mean_{}'.format(landmark)] = [np.nan]
+        fac_features_dict['fac_disp_median_{}'.format(landmark)] = [np.nan]
+        fac_features_dict['fac_corr_{}'.format(landmark)] = [np.nan]
+
+    fac_features_dict[r_config.err_reason] = [error_reason]
+    empty_frame = pd.DataFrame.from_dict(fac_features_dict)
+    return empty_frame
+
+def fac_tremor_process(video_uri, out_dir, r_config, model_output=False):
+    """
+    processing input videos
+    
+    
+    """
+#     try:
+        
+    input_loc, out_loc, fl_name = ut.filter_path(video_uri, out_dir)
+    of_csv_path = glob.glob(join(out_loc, fl_name + '_OF_video_features/*.csv'))
+
+    if len(of_csv_path)>0:
+        of_csv = of_csv_path[0]
+        df_of = pd.read_csv(of_csv, error_bad_lines=False)
+
+        logger.info('Processing Output file {} '.format(os.path.join(out_loc, fl_name)))
+
+        feats = compute_features(of_csv_path , df_of, r_config)
+        
+#         if model_output:
+#             result = score(feats, r_config)
+#             feats = pd.concat([feats, result], axis=1)
+            
+        ut.save_output(feats, out_loc, fl_name, ft_dir, csv_ext)
+
+
+
+#     except Exception as e:
+        logger.error('Failed to process video file')
--- a/dbm_lib/dbm_features/raw_features/movement/head_motion.py
+++ b/dbm_lib/dbm_features/raw_features/movement/head_motion.py
@@ -184,6 +184,7 @@ def run_head_movement(video_uri, out_dir, r_config):
        input_loc, out_loc, fl_name = ut.filter_path(video_uri, out_dir)
        of_csv_path = glob.glob(join(out_loc, fl_name + '_OF_features/*.csv'))

+
        if len(of_csv_path)>0:

            of_csv = of_csv_path[0]
--- a/dbm_lib/dbm_features/raw_features/movement/voice_tremor.py
+++ b/dbm_lib/dbm_features/raw_features/movement/voice_tremor.py
@@ -0,0 +1,94 @@
+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(os.path.join(out_loc, fl_name)))
+    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(os.path.join(out_loc, fl_name)))
+    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) < 0.5:
+                logger.info('Output file {} size is less than 0.5sec'.format(audio_file))
+                
+                error_txt = 'error: length less than 0.5 sec'
+                prepare_empty_vt(video_uri, out_loc, fl_name, error_txt)
+                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)
--- a/dbm_lib/dbm_features/raw_features/nlp/speech_features.py
+++ b/dbm_lib/dbm_features/raw_features/nlp/speech_features.py
@@ -0,0 +1,51 @@
+"""
+file_name: speech_features
+project_name: DBM
+created: 2020-13-11
+"""
+
+import os
+import numpy as np
+import pandas as pd
+import glob
+from os.path import join
+import logging
+import shutil
+
+from dbm_lib.dbm_features.raw_features.util import util as ut
+from dbm_lib.dbm_features.raw_features.util import nlp_util as n_util
+
+logging.basicConfig(level=logging.INFO)
+logger=logging.getLogger()
+
+speech_dir = 'nlp/speech_feature'
+speech_ext = '_nlp.csv'
+transcribe_ext = 'nlp/transcribe/*_transcribe.csv'
+
+def run_speech_feature(video_uri, out_dir, r_config, tran_tog):
+    """
+    Processing all patient's for fetching nlp features
+    -------------------
+    -------------------
+    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)
+
+        transcribe_path = glob.glob(join(out_loc, transcribe_ext))
+        if len(transcribe_path)>0:
+
+            transcribe_df = pd.read_csv(transcribe_path[0])
+            df_speech= n_util.process_speech(transcribe_df, r_config)
+
+            logger.info('Saving Output file {} '.format(out_loc))
+            ut.save_output(df_speech, out_loc, fl_name, speech_dir, speech_ext)
+
+            if (tran_tog == None) or (tran_tog != 'on'):
+                shutil.rmtree(os.path.dirname(transcribe_path[0]))
+            
+    except Exception as e:
+        logger.error('Failed to process video file')
--- a/dbm_lib/dbm_features/raw_features/nlp/transcribe.py
+++ b/dbm_lib/dbm_features/raw_features/nlp/transcribe.py
@@ -0,0 +1,84 @@
+"""
+file_name: transcribe
+project_name: DBM
+created: 2020-10-11
+"""
+
+import pandas as pd
+import numpy as np
+import librosa
+import glob
+from os.path import join
+import logging
+
+from dbm_lib.dbm_features.raw_features.util import util as ut
+from dbm_lib.dbm_features.raw_features.util import nlp_util as n_util
+
+logging.basicConfig(level=logging.INFO)
+logger=logging.getLogger()
+
+formant_dir = 'nlp/transcribe'
+csv_ext = '_transcribe.csv'
+error_txt = 'error: length less than 0.1'
+
+def calc_transcribe(video_uri, audio_file, out_loc, fl_name, r_config, deep_path, aud_dur):
+    """
+    Preparing Formant freq matrix
+    Args:
+        audio_file: (.wav) parsed audio file; fl_name: input file name
+        out_loc: (str) Output directory; r_config: raw variable config
+    """
+    
+    text = n_util.process_deepspeech(audio_file, deep_path)
+    df_formant = pd.DataFrame([text], columns=[r_config.nlp_transcribe])
+    
+    df_formant.replace('', np.nan, regex=True,inplace=True)
+    df_formant[r_config.nlp_totalTime] = aud_dur
+    df_formant[r_config.err_reason] = 'Pass'# will replace with threshold in future release
+    df_formant['dbm_master_url'] = video_uri
+    
+    logger.info('Saving Output file {} '.format(out_loc))
+    ut.save_output(df_formant, out_loc, fl_name, formant_dir, csv_ext)
+    
+def empty_transcribe(video_uri, out_loc, fl_name, r_config):
+    
+    """
+    Preparing empty formant frequency matrix if something fails
+    """
+    cols = [r_config.nlp_transcribe, r_config.nlp_totalTime, r_config.err_reason]
+    out_val = [[np.nan, np.nan, error_txt]]
+    df_fm = pd.DataFrame(out_val, columns = cols)
+    df_fm['dbm_master_url'] = video_uri
+    
+    logger.info('Saving Output file {} '.format(out_loc))
+    ut.save_output(df_fm, out_loc, fl_name, formant_dir, csv_ext)
+
+def run_transcribe(video_uri, out_dir, r_config, deep_path):
+    
+    """
+    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; deep_path: deepspeech build path
+    """
+    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) < 0.1:
+                logger.info('Output file {} size is less than 0.1 sec'.format(audio_file))
+
+                empty_transcribe(video_uri, out_loc, fl_name, r_config)
+                return
+
+            calc_transcribe(video_uri, audio_file, out_loc, fl_name, r_config, deep_path, aud_dur)
+    except Exception as e:
+        logger.error('Failed to process audio file')
+        
--- a/dbm_lib/dbm_features/raw_features/util/math_util.py
+++ b/dbm_lib/dbm_features/raw_features/util/math_util.py
@@ -0,0 +1,57 @@
+"""
+file_name: facial_tremor
+project_name: cdx_analysis
+created: 2019-03-16
+author: Deshana Desai
+"""
+import sys, os, glob, cv2
+import pandas as pd
+import numpy as np
+
+
+def euclidean_distance(point1, point2):
+    """
+    Compute euclidean distance between points
+    """
+
+    return np.sqrt((point1[0] - point2[0])**2 + (point1[1] - point2[1])**2)
+
+
+# def detect_peaks()
+
+
+def expand_landmarks(landmarks):
+    """
+    util method to expand landmark list:
+    eg: [1,2] -> [['l1_x', 'l1_y'], ['l2_x', 'l2_y']]
+    """
+    return [['l{}_x'.format(l), 'l{}_y'.format(l)] for l in landmarks]
+
+
+
+def calc_displacement_vec(df, landmarks, num_frames):
+    """
+    Calculates displacement vector frame by frame
+    """
+
+    landmarks = expand_landmarks(landmarks)
+
+    disp_vec = np.zeros((len(landmarks), num_frames))
+    prev_point = np.zeros((len(landmarks), 2))
+
+    # initialize
+    for j, pair in enumerate(landmarks):
+        first_row = df.iloc[0]
+        prev_point[j] = (first_row[pair[0]], first_row[pair[1]])
+
+
+    for i in range(num_frames):
+        frame_row = df.iloc[i]
+        for j, pair in enumerate(landmarks):
+            x, y = pair[0], pair[1]
+            current = (frame_row[x], frame_row[y])
+            deviation = euclidean_distance( current, prev_point[j])
+            disp_vec[j][i] = deviation
+            prev_point[j] = current
+
+    return disp_vec
--- a/dbm_lib/dbm_features/raw_features/util/nlp_util.py
+++ b/dbm_lib/dbm_features/raw_features/util/nlp_util.py
@@ -0,0 +1,212 @@
+"""
+file_name: nlp_util
+project_name: DBM
+created: 2020-10-11
+"""
+
+import subprocess
+import json
+import numpy as np
+import pandas as pd
+import os
+import logging
+
+import nltk
+import re
+from lexicalrichness import LexicalRichness
+from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer 
+
+logging.basicConfig(level=logging.INFO)
+logger=logging.getLogger()
+
+#Speech to text using Deepspeech 0.9.1
+def deepspeech(AUDIO_FILE,deep_path):
+    """
+        Extracting text from audio using Deep Speech neural network trained model
+        Returns:
+            Text: text which is extracted from audio
+    """
+    api = 'deepspeech'
+    arg_speech0 = '--model'
+    arg_speech_path0 = os.path.join(deep_path, 'deepspeech-0.9.1-models.pbmm')
+    arg_speech1 = '--scorer'
+    arg_speech_path1 = os.path.join(deep_path, 'deepspeech-0.9.1-models.scorer')
+    arg_audio = "--audio"
+    
+    out = subprocess.Popen([api, arg_speech0, arg_speech_path0, arg_speech1, arg_speech_path1, arg_audio, AUDIO_FILE],
+                           stdout=subprocess.PIPE, 
+                           stderr=subprocess.STDOUT)
+    logger.info('Deepspeech output...... {}'.format(out))
+    try:
+        stdout,stderr = out.communicate()
+    except:
+        return "error", "error"
+    print(stderr)
+    return stdout,stderr
+
+def deep_speech_output_clean(result):
+    """
+        Parsing deep speech output(text)
+        Return: 
+            Text from speech
+    """
+    text = ""
+    if len(result)>0:
+        res_split = str(result[0]).split('\\n')
+        
+        if len(res_split)>0:
+            for i in range(len(res_split)):
+                if 'Inference took' in res_split[i]:
+                    text = res_split[i + 1]
+                    return text
+    return text
+
+def process_deepspeech(audio_file,deep_path):
+    """
+    Transcribing audio to extract text from speech
+    """
+    deep_output = deepspeech(audio_file,deep_path)
+    deep_text= deep_speech_output_clean(deep_output)
+    
+    return deep_text
+
+def nltk_download():
+    
+    try:
+        nltk.data.find('tokenizers/punkt')
+        
+    except LookupError:
+        logger.info('punkt is not available')
+        nltk.download('punkt')
+        
+    try:
+        nltk.data.find('averaged_perceptron_tagger')
+        
+    except LookupError:
+        logger.info('averaged_perceptron_tagger is not available')
+        nltk.download('averaged_perceptron_tagger')
+
+def empty_speech(r_config, master_url, error_txt):
+    """
+    Preparing empty speech matrix with error
+    Args:
+        r_config: raw config file object
+        error_txt: Error message during transcription
+        
+    Returns:
+            Empty dataframe for speech features with error
+    """
+    
+    col = [r_config.nlp_numSentences, r_config.nlp_singPronPerAns, r_config.nlp_singPronPerSen, r_config.nlp_pastTensePerAns,
+          r_config.nlp_pastTensePerSen, r_config.nlp_pronounsPerAns, r_config.nlp_pronounsPerSen, r_config.nlp_verbsPerAns,
+          r_config.nlp_verbsPerSen, r_config.nlp_adjectivesPerAns, r_config.nlp_adjectivesPerSen, r_config.nlp_nounsPerAns,
+          r_config.nlp_nounsPerSen, r_config.nlp_sentiment_mean, r_config.nlp_mattr, r_config.nlp_wordsPerMin,
+          r_config.nlp_totalTime, r_config.err_reason]
+    
+    df_speech = pd.DataFrame([[np.nan] * len(col) + [error_txt]], columns = col)
+    df_speech['dbm_master_url'] = master_url
+    
+    return df_speech
+
+def divide_var(speech_var1, spech_var2):
+    """
+    divide variables
+    """
+    speech_var = np.nan
+    if spech_var2!=0:
+        speech_var = speech_var1/spech_var2
+    return speech_var
+
+def process_speech(transcribe_df,r_config):
+    """
+        Preparing speech features
+        Args:
+            transcribe_df: Transcribed dataframe
+            r_config: raw config file object
+        Returns:
+            Dataframe for speech features
+    """
+    transcribe_df = transcribe_df.replace(np.nan, '', regex=True)
+    err_transcribe = transcribe_df[r_config.err_reason].iloc[0]
+    transcribe = transcribe_df[r_config.nlp_transcribe].iloc[0]
+    total_time = transcribe_df[r_config.nlp_totalTime].iloc[0]
+    master_url = transcribe_df['dbm_master_url'].iloc[0]
+    
+    #clean transcribe
+    transcribe = transcribe.replace(",", "")
+    transcribe = " ".join(re.findall(r"[\w']+|[.!?]", transcribe))
+    
+    if err_transcribe != 'Pass':
+        df_speech = empty_speech(r_config, master_url, error_txt)
+        
+        return df_speech
+    
+    speech_dict = {}
+    nltk_download()
+    
+    sentences = nltk.tokenize.sent_tokenize(transcribe)
+    words_all = nltk.tokenize.word_tokenize(transcribe)
+    num_sentences = len(sentences)
+    
+    speech_dict[r_config.nlp_numSentences] = num_sentences
+    
+    #nlp_singPron
+    i_s = transcribe.count('I')
+    me_s = transcribe.count('me')
+    my_s = transcribe.count('my')
+    sing_count = i_s + me_s + my_s
+    
+    speech_dict[r_config.nlp_singPronPerAns] = sing_count if len(words_all)>0 else np.nan
+    speech_dict[r_config.nlp_singPronPerSen] = divide_var(speech_dict[r_config.nlp_singPronPerAns], num_sentences)
+    
+    tagged = nltk.pos_tag(transcribe.split())
+    tagged_df = pd.DataFrame(tagged, columns=['word', 'pos_tag'])
+    
+    #Past tense per answer
+    all_POSs = tagged_df['pos_tag'].tolist()
+    speech_dict[r_config.nlp_pastTensePerAns] = all_POSs.count('VBD') if len(words_all)>0 else np.nan
+    speech_dict[r_config.nlp_pastTensePerSen] = divide_var(speech_dict[r_config.nlp_pastTensePerAns], num_sentences)
+    
+    #Pronoun per answer
+    pronounsPerAns = all_POSs.count('PRP') + all_POSs.count('PRP$')
+    speech_dict[r_config.nlp_pronounsPerAns] = pronounsPerAns if len(words_all)>0 else np.nan
+    speech_dict[r_config.nlp_pronounsPerSen] = divide_var(speech_dict[r_config.nlp_pronounsPerAns], num_sentences)
+    
+    #Verb per answer
+    verbPerAns = all_POSs.count('VB') + all_POSs.count('VBD') + all_POSs.count('VBG') \
+                      + all_POSs.count('VBN') + all_POSs.count('VBP') + all_POSs.count('VBZ')
+    speech_dict[r_config.nlp_verbsPerAns] = verbPerAns if len(words_all) > 0 else np.nan
+    speech_dict[r_config.nlp_verbsPerSen] = divide_var(speech_dict[r_config.nlp_verbsPerAns], num_sentences)
+    
+    #Adjective per answer
+    adjectivesAns = all_POSs.count('JJ') + all_POSs.count('JJR') + all_POSs.count('JJS')
+    speech_dict[r_config.nlp_adjectivesPerAns] = adjectivesAns if len(words_all) > 0 else np.nan
+    speech_dict[r_config.nlp_adjectivesPerSen] = divide_var(speech_dict[r_config.nlp_adjectivesPerAns], num_sentences)
+    
+    #Noun per answer
+    nounsAns = all_POSs.count('NN') + all_POSs.count('NNP') + all_POSs.count('NNS')
+    speech_dict[r_config.nlp_nounsPerAns] = nounsAns if len(words_all) > 0 else np.nan
+    speech_dict[r_config.nlp_nounsPerSen] = divide_var(speech_dict[r_config.nlp_nounsPerAns], num_sentences)
+    
+    #Sentiment analysis
+    vader = SentimentIntensityAnalyzer()
+    sentence_valences = []
+    
+    for s in sentences:
+        sentiment_dict = vader.polarity_scores(s) 
+        sentence_valences.append(sentiment_dict['compound'])
+        
+    speech_dict[r_config.nlp_sentiment_mean] = np.mean(sentence_valences) if len(sentence_valences) > 0 else np.nan
+    non_punc = list(value for value in words_all if value not in ['.','!','?'])
+    
+    non_punc_as_str = " ".join(str(non_punc))
+    lex = LexicalRichness(non_punc_as_str)
+    speech_dict[r_config.nlp_mattr] = lex.mattr(window_size=lex.words) if lex.words > 0 else np.nan
+    
+    #Number of words per minute
+    speech_dict[r_config.nlp_wordsPerMin] = divide_var(len(non_punc), total_time)*60
+    speech_dict[r_config.nlp_totalTime] = total_time
+    speech_dict['dbm_master_url'] = master_url
+    
+    df_speech = pd.DataFrame([speech_dict])
+    return df_speech
--- a/dbm_lib/dbm_features/raw_features/util/vad_utilities.py
+++ b/dbm_lib/dbm_features/raw_features/util/vad_utilities.py
@@ -158,7 +158,7 @@ def vad_get_segment_times(sample_rate, frame_duration_ms,
    for frame in frames:
        is_speech = vad.is_speech(frame.bytes, sample_rate)

-        sys.stdout.write('1' if is_speech else '0')
+        #sys.stdout.write('1' if is_speech else '0')
        if not triggered:
            ring_buffer.append((frame, is_speech))
            num_voiced = len([f for f, speech in ring_buffer if speech])
@@ -167,7 +167,7 @@ def vad_get_segment_times(sample_rate, frame_duration_ms,
            # TRIGGERED state.
            if num_voiced > 0.9 * ring_buffer.maxlen:
                triggered = True
-                sys.stdout.write('+(%s)' % (ring_buffer[0][0].timestamp,))
+                #sys.stdout.write('+(%s)' % (ring_buffer[0][0].timestamp,))
                start_times.append(ring_buffer[0][0].timestamp)  # BT
                ring_buffer.clear()
        else:
@@ -179,18 +179,18 @@ def vad_get_segment_times(sample_rate, frame_duration_ms,
            # unvoiced, then enter NOTTRIGGERED and yield whatever
            # audio we've collected.
            if num_unvoiced > 0.9 * ring_buffer.maxlen:
-                sys.stdout.write('-(%s)' % (frame.timestamp + frame.duration))
+                #sys.stdout.write('-(%s)' % (frame.timestamp + frame.duration))
                end_times.append(ring_buffer[0][0].timestamp + frame.duration)  # BT
                triggered = False

    if triggered:  # BT if were in triggered state at end of signal, set output time
-        sys.stdout.write('-(%s)' % (frame.timestamp + frame.duration))
+        #sys.stdout.write('-(%s)' % (frame.timestamp + frame.duration))
        if len(ring_buffer)>0:
            end_times.append(ring_buffer[0][0].timestamp )  # BT
        else:
            # only get here in very rare case that we triggered on 2nd-to-last frame
            end_times.append(frame.timestamp + frame.duration)
-    sys.stdout.write('\n')
+    #sys.stdout.write('\n')

    return(start_times, end_times)

--- a/dbm_lib/dbm_features/raw_features/video/open_face_process.py
+++ b/dbm_lib/dbm_features/raw_features/video/open_face_process.py
@@ -15,7 +15,7 @@ from dbm_lib.dbm_features.raw_features.util import util as ut
 logging.basicConfig(level=logging.INFO)
 logger=logging.getLogger()

-def batch_open_face(filepaths,video_url, input_dir, out_dir, of_path):
+def batch_open_face(filepaths,video_url, input_dir, out_dir, of_path, video_tracking=False):
    """ Computes open_face features for the files in filepaths

    Args:
@@ -32,8 +32,11 @@ def batch_open_face(filepaths,video_url, input_dir, out_dir, of_path):
    --------
        (itreable[str]) list of .csv files
    """    
+    if video_tracking:
+        suffix = '_OF_video_features'
+    else:
+        suffix = '_OF_features'

-    suffix = '_OF_features'
    csv_files = []
    
    for fp in filepaths:
@@ -43,6 +46,8 @@ def batch_open_face(filepaths,video_url, input_dir, out_dir, of_path):
            full_f_name = fl_name + suffix
            output_directory = os.path.join(out_loc, full_f_name)
            
+            if video_tracking and not os.path.exists(os.path.abspath(output_directory)):
+                os.makedirs(os.path.abspath(output_directory))
            csv_files.append(ut.compute_open_face_features(fp,output_directory,of_path))

        except Exception as e:
@@ -50,7 +55,7 @@ def batch_open_face(filepaths,video_url, input_dir, out_dir, of_path):

    return csv_files

-def process_open_face(video_uri, input_dir, out_dir, of_path, dbm_group):
+def process_open_face(video_uri, input_dir, out_dir, of_path, dbm_group,video_tracking):
    """
    Processing all patient's for fetching emotion expressivity
    -------------------
@@ -62,11 +67,14 @@ def process_open_face(video_uri, input_dir, out_dir, of_path, dbm_group):
    """
    try:
        
-        if dbm_group != None and len(dbm_group) == 1 and 'acoustic' in dbm_group:
-            return
+        if dbm_group != None:
+            check_group = ['facial','movement'] #add group here: if you want to use openface output for raw variable calculation
+            check_val = bool(len({*check_group} & {*dbm_group}))
+            if not check_val:
+                return
        
        filepaths = [video_uri]
-        csv_filepaths = batch_open_face(filepaths, video_uri, input_dir, out_dir, of_path)
+        csv_filepaths = batch_open_face(filepaths, video_uri, input_dir, out_dir, of_path, video_tracking)

    except Exception as e:
        logger.error('Failed to process video file')
--- a/pkg/OpenFace/exe/FaceLandmarkVid/FaceLandmarkVid.cpp
+++ b/pkg/OpenFace/exe/FaceLandmarkVid/FaceLandmarkVid.cpp
@@ -155,7 +155,7 @@ int main(int argc, char **argv)
        std::string base_filename = path.substr(path.find_last_of("/\\") + 1);
        base_filename = base_filename.replace(base_filename.find(ext),sizeof(ext)-1,"");
        results.open(out_dir + '/' + base_filename + "_landmark_output.csv");
-//         confidence.open(out_dir + '/' + base_filename +  "_landmark_likelihoods.csv");
+        confidence.open(out_dir + '/' + base_filename +  "_landmark_likelihoods.csv");
        int lx = 0;
        int ly = 0;
        for(lx = 0; lx < 2; lx++){
@@ -163,7 +163,7 @@ int main(int argc, char **argv)

                if (lx == 0){
                    results << "l" << ly << "_x,";
-//                     confidence << "c" << ly <<",";
+                    confidence << "c" << ly <<",";
                }
                if (lx == 1){
                    results << "l" << ly << "_y,";
@@ -172,7 +172,7 @@ int main(int argc, char **argv)
        }
        results << "pose_Tx,pose_Ty,pose_Tz,pose_Rx,pose_Ry,pose_Rz" ;
        results << std::endl; 
-//         confidence << std::endl;
+        confidence << std::endl;
        
 		int counter = 0;
        
@@ -207,7 +207,6 @@ int main(int argc, char **argv)
 			// Gaze tracking, absolute gaze direction
 			cv::Point3f gazeDirection0(0, 0, -1);
 			cv::Point3f gazeDirection1(0, 0, -1);
-            cv::Vec6d pose_estimate(0, 0, 0, 0, 0, 0);

 			// If tracking succeeded and we have an eye model, estimate gaze
 			if (detection_success && face_model.eye_model)
@@ -217,9 +216,9 @@ int main(int argc, char **argv)
 			}

 			// Work out the pose of the head from the tracked model
-			if (detection_success){
-			pose_estimate = LandmarkDetector::GetPose(face_model, sequence_reader.fx, sequence_reader.fy, sequence_reader.cx, sequence_reader.cy);
-            }
+
+			cv::Vec6d pose_estimate = LandmarkDetector::GetPose(face_model, sequence_reader.fx, sequence_reader.fy, sequence_reader.cx, sequence_reader.cy);
+            
            
            
            
@@ -234,11 +233,11 @@ int main(int argc, char **argv)

 			// Displaying the tracking visualizations
 //             std::cout<< "setting observation landmarks"<<std::endl;
-// 			visualizer.SetImage(rgb_image, sequence_reader.fx, sequence_reader.fy, sequence_reader.cx, sequence_reader.cy);
-// 			visualizer.SetObservationLandmarks(face_model.detected_landmarks, face_model.detection_certainty, face_model.GetVisibilities());
-// 			visualizer.SetObservationPose(pose_estimate, face_model.detection_certainty);
-// 			visualizer.SetObservationGaze(gazeDirection0, gazeDirection1, LandmarkDetector::CalculateAllEyeLandmarks(face_model), LandmarkDetector::Calculate3DEyeLandmarks(face_model, sequence_reader.fx, sequence_reader.fy, sequence_reader.cx, sequence_reader.cy), face_model.detection_certainty);
-// 			visualizer.SetFps(fps_tracker.GetFPS());
+			visualizer.SetImage(rgb_image, sequence_reader.fx, sequence_reader.fy, sequence_reader.cx, sequence_reader.cy);
+			visualizer.SetObservationLandmarks(face_model.detected_landmarks, face_model.detection_certainty, face_model.GetVisibilities());
+			visualizer.SetObservationPose(pose_estimate, face_model.detection_certainty);
+			visualizer.SetObservationGaze(gazeDirection0, gazeDirection1, LandmarkDetector::CalculateAllEyeLandmarks(face_model), LandmarkDetector::Calculate3DEyeLandmarks(face_model, sequence_reader.fx, sequence_reader.fy, sequence_reader.cx, sequence_reader.cy), face_model.detection_certainty);
+			visualizer.SetFps(fps_tracker.GetFPS());
            
 //             std::cout << "openfacerec set obs landmarks"<<std::endl;
 //             std::cout<< fps_tracker.GetFPS() <<std::endl;
@@ -264,15 +263,15 @@ int main(int argc, char **argv)
 			}                
 			results <<std::endl;
            
-//             for(i=0;i<68;i++){
-//                 if (i==67){
-//                 confidence << face_model.landmark_likelihoods[0][i];
-//                 }
-//                 else{
-//                 confidence << face_model.landmark_likelihoods[0][i] << ",";
-//                 }
-//             }
-//             confidence <<std::endl;
+            for(i=0;i<68;i++){
+                if (i==67){
+                confidence << face_model.landmark_likelihoods[0][i];
+                }
+                else{
+                confidence << face_model.landmark_likelihoods[0][i] << ",";
+                }
+            }
+            confidence <<std::endl;

 			// detect key presses (due to pecularities of OpenCV, you can get it when displaying images)
 			//char character_press = visualizer.ShowObservation();
@@ -311,4 +310,3 @@ int main(int argc, char **argv)
 	return 0;
 }

-
--- a/process_data.py
+++ b/process_data.py
@@ -21,7 +21,9 @@ from os.path import splitext
 logging.basicConfig(level=logging.INFO)
 logger=logging.getLogger()

+OPENFACE_PATH_VIDEO = 'pkg/OpenFace/build/bin/FaceLandmarkVid'
 OPENFACE_PATH = 'pkg/OpenFace/build/bin/FeatureExtraction'
+DEEP_SPEECH = 'pkg/DeepSpeech'
 DLIB_SHAPE_MODEL = 'pkg/shape_detector/shape_predictor_68_face_landmarks.dat'

 def common_video(video_file, args, r_config):
@@ -34,12 +36,15 @@ def common_video(video_file, args, r_config):
    """
    out_path = os.path.join(args.output_path, 'raw_variables')
    pf.audio_to_wav(video_file)
-    of.process_open_face(video_file, os.path.dirname(video_file), out_path, OPENFACE_PATH, args.dbm_group)

+    of.process_open_face(video_file, os.path.dirname(video_file), out_path, OPENFACE_PATH, args.dbm_group,video_tracking=False)
    pf.process_facial(video_file, out_path, args.dbm_group, r_config)
    pf.process_acoustic(video_file, out_path, args.dbm_group, r_config)
-    pf.remove_file(video_file)
+    pf.process_nlp(video_file, out_path, args.dbm_group, args.tr, r_config, DEEP_SPEECH)  
+    if args.dbm_group == None or len(args.dbm_group)>0 and 'movement' in args.dbm_group:
+        of.process_open_face(video_file, os.path.dirname(video_file), out_path, OPENFACE_PATH_VIDEO, args.dbm_group, video_tracking=True)
    pf.process_movement(video_file, out_path, args.dbm_group, r_config, DLIB_SHAPE_MODEL)
+    pf.remove_file(video_file)

 def process_raw_video_file(args, s_config, r_config):
    """
@@ -81,7 +86,7 @@ def process_raw_audio_file(args, s_config, r_config):

                out_path = os.path.join(args.output_path, 'raw_variables')
                pf.process_acoustic(audio_file[0], out_path, args.dbm_group, r_config)
-
+                pf.process_nlp(audio_file[0], out_path, args.dbm_group, args.tr, r_config, DEEP_SPEECH)
            else:
                logger.info('Enter correct audio(*.wav) file path.')
    except Exception as e:
@@ -109,6 +114,7 @@ def process_raw_video_dir(args, s_config, r_config):
                if file_ext == '.mov':
                    convert_file(vid_file)
                common_video(fname+'.mp4', args, r_config)
+
            except Exception as e:
                logger.error('Failed to process mp4 file.')
                pf.remove_file(vid_file)
@@ -136,6 +142,7 @@ def process_raw_audio_dir(args, s_config, r_config):
                    convert_file(audio)
                out_path = os.path.join(args.output_path, 'raw_variables')
                pf.process_acoustic(fname+'.wav', out_path, args.dbm_group, r_config)
+                pf.process_nlp(fname +'.wav', out_path, args.dbm_group, args.tr, r_config, DEEP_SPEECH)
            except Exception as e:
                logger.error('Failed to process wav file.')

@@ -155,6 +162,8 @@ def convert_file(input_filepath):

    subprocess.check_output(call)

+
+
 def process_derive(args, r_config, d_config, input_type):
    """
    Processing dbm derived variables
@@ -177,6 +186,7 @@ if __name__=="__main__":
    parser.add_argument("--input_path", help="path to the input files", required=True)
    parser.add_argument("--output_path", help="path to the raw and derived variable output", required=True)
    parser.add_argument("--dbm_group", help="list of feature groups", nargs='+')
+    parser.add_argument("--tr", help="speech transcription toogle")

    args = parser.parse_args()
    s_config = config_reader.ConfigReader()
--- a/process_dbm.sh
+++ b/process_dbm.sh
@@ -4,9 +4,10 @@ helpFunction()
 {
   echo ""
   echo "Usage: $0 --input_path parameterA --output_path parameterB --dbm_group parameterC"
-   echo -e "\t--input_path Description of what is parameterA"
-   echo -e "\t--output_path Description of what is parameterB"
-   echo -e "\t-dbm_group Description of what is parameterC"
+   echo -e "\t--input_path: path to the input files"
+   echo -e "\t--output_path: path to the raw and derived variable output"
+   echo -e "\t--dbm_group: list of feature groups"
+   echo -e "\t--tr: Toggle for speech transcription(optional)"
   exit 1 # Exit script after printing help
 }

@@ -15,6 +16,7 @@ while [ $# -gt 0 ]; do
    --input_path=*) input_path="${1#*=}" ;;
    --output_path=*) output_path="${1#*=}" ;;
    --dbm_group=*) dbm_group="${1#*=}" ;;
+    --tr=*) tr="${1#*=}" ;;
    *) helpFunction ;;
  esac
  shift
@@ -55,6 +57,12 @@ fi
 if [[ $dbm_group == *"movement"* ]]; then
    dbm_new="$dbm_new movement"
 fi
+if [[ $dbm_group == *"speech"* ]]; then
+    dbm_new="$dbm_new speech"
+fi
+if [[ $dbm_group == *"speech"* ]] && [[ ${tr} == "on" ]]; then
+    dbm_new="$dbm_new --tr ${tr}"
+fi

 #docker commands to run container
 docker create -ti --name dbm_container dbm bash
@@ -63,9 +71,9 @@ docker cp $input_path dbm_container:/app/raw_data
 docker start dbm_container
 if [ -z "$dbm_new" ]
  then
-    docker exec -it dbm_container /bin/bash -c "python3 process_data.py --input_path /app/raw_data --output_path /app/output"
+    docker exec -it dbm_container /bin/bash -c "python3 -W ignore process_data.py --input_path /app/raw_data --output_path /app/output"
 else
-    docker exec -it dbm_container /bin/bash -c "python3 process_data.py --input_path /app/raw_data --output_path /app/output --dbm_group$dbm_new"
+    docker exec -it dbm_container /bin/bash -c "python3 -W ignore process_data.py --input_path /app/raw_data --output_path /app/output --dbm_group$dbm_new"
 fi

 docker cp dbm_container:/app/output $output_path
--- a/requirements.txt
+++ b/requirements.txt
@@ -8,7 +8,6 @@ sk-video
 watchtower
 opencv-python
 webrtcvad
-pysptk
 imutils
 dlib==19.13.0
 coloredlogs
@@ -20,3 +19,8 @@ more_itertools
 scipy==1.2.0
 pyyaml
 pydub
+deepspeech
+nltk
+lexicalrichness
+vaderSentiment
+textblob
--- a/resources/features/derived_feature.yml
+++ b/resources/features/derived_feature.yml
@@ -2,7 +2,8 @@ derive_feature:

    #DBM Feature Group
    FEATURE_GROUP: ['FAC_ASYM', 'FAC_AU', 'FAC_EXP', 'FAC_LMK', 'ACO_INT', 'ACO_FF', 'ACO_HNR', 'ACO_GNE', 'ACO_FM',
-    'ACO_JITTER','ACO_SHIMMER', 'ACO_PAUSE', 'ACO_VFS', 'ACO_MFCC', 'MOV_HM', 'MOV_HP', 'EYE_BLINK']
+    'ACO_JITTER','ACO_SHIMMER', 'ACO_PAUSE', 'ACO_VFS', 'ACO_MFCC', 'MOV_HM', 'MOV_HP', 'EYE_BLINK', 'NLP_SPEECH',
+    'EYE_GAZE', 'MOV_VT', 'MOV_FT']

    #Feature group output file extensions
    FAC_ASYM_LOC: _facasym
@@ -22,6 +23,10 @@ derive_feature:
    MOV_HM_LOC: _headmov
    MOV_HP_LOC: _headpose
    EYE_BLINK_LOC: _eyeblinks
+    NLP_SPEECH_LOC: _nlp
+    EYE_GAZE_LOC: _eyegaze
+    MOV_VT_LOC: _vtremor
+    MOV_FT_LOC: _fac_tremor

    #Facial category feature group
    FAC_ASYM: ['fac_AsymMaskMouth', 'fac_AsymMaskEyebrow', 'fac_AsymMaskEye', 'fac_AsymMaskCom']
@@ -64,6 +69,18 @@ derive_feature:
    MOV_HM: ['head_vel']
    MOV_HP: ['mov_Hpose_Dist','mov_Hpose_Pitch','mov_Hpose_Yaw','mov_Hpose_Roll']
    EYE_BLINK: ['mov_blink_ear', 'vid_dur', 'mov_blinkdur']
+    MOV_VT: ['mov_freq_trem_freq', 'mov_freq_trem_index', 'mov_freq_trem_pindex', 'mov_amp_trem_freq',
+    'mov_amp_trem_index', 'mov_amp_trem_pindex']
+    MOV_FT: ['fac_tremor_median_5','fac_tremor_median_12','fac_tremor_median_8','fac_tremor_median_48','fac_tremor_median_54','fac_tremor_median_28','fac_tremor_median_51','fac_tremor_median_66','fac_tremor_median_57']
+
+    EYE_GAZE: ['mov_leye_x', 'mov_leye_y', 'mov_leye_z', 'mov_reye_x', 'mov_reye_y', 'mov_reye_z', 'mov_eleft_disp',
+    'mov_eright_disp']
+    
+    #NLP category feature group
+    NLP_SPEECH: ['nlp_numSentences', 'nlp_singPronPerAns', 'nlp_singPronPerSen', 'nlp_pastTensePerAns', 'nlp_pastTensePerSen',
+    'nlp_pronounsPerAns', 'nlp_pronounsPerSen', 'nlp_verbsPerAns', 'nlp_verbsPerSen', 'nlp_adjectivesPerAns', 
+    'nlp_adjectivesPerSen', 'nlp_nounsPerAns', 'nlp_nounsPerSen', 'nlp_sentiment_mean', 'nlp_mattr', 'nlp_wordsPerMin',
+    'nlp_totalTime']

    #Calculation for variables
    # Facial Asymmetry
@@ -248,3 +265,49 @@ derive_feature:
    mov_blink_ear: ['mean', 'std']
    vid_dur: ['count']
    mov_blinkdur: ['mean', 'std']
+
+    mov_freq_trem_freq: ['mean']
+    mov_freq_trem_index: ['mean']
+    mov_freq_trem_pindex: ['mean']
+    mov_amp_trem_freq: ['mean']
+    mov_amp_trem_index: ['mean']
+    mov_amp_trem_pindex: ['mean']
+
+    fac_tremor_median_5: ['mean']
+    fac_tremor_median_12: ['mean']
+    fac_tremor_median_8: ['mean']
+    fac_tremor_median_48: ['mean']
+    fac_tremor_median_54: ['mean']
+    fac_tremor_median_28: ['mean']
+    fac_tremor_median_51: ['mean']
+    fac_tremor_median_66: ['mean']
+    fac_tremor_median_57: ['mean']
+    
+    mov_leye_x: ['mean', 'std']
+    mov_leye_y: ['mean', 'std']
+    mov_leye_z: ['mean', 'std']
+    mov_reye_x: ['mean', 'std']
+    mov_reye_y: ['mean', 'std']
+    mov_reye_z: ['mean', 'std']
+    mov_eleft_disp: ['mean', 'std']
+    mov_eright_disp: ['mean', 'std']
+    
+    #NLP feature
+    nlp_numSentences: ['mean']
+    nlp_singPronPerAns: ['mean']
+    nlp_singPronPerSen: ['mean']
+    nlp_pastTensePerAns: ['mean']
+    nlp_pastTensePerSen: ['mean']
+    nlp_pronounsPerAns: ['mean']
+    nlp_pronounsPerSen: ['mean']
+    nlp_verbsPerAns: ['mean']
+    nlp_verbsPerSen: ['mean']
+    nlp_adjectivesPerAns: ['mean']
+    nlp_adjectivesPerSen: ['mean']
+    nlp_nounsPerAns: ['mean']
+    nlp_nounsPerSen: ['mean']
+    nlp_sentiment_mean: ['mean']
+    nlp_mattr: ['mean']
+    nlp_wordsPerMin: ['mean']
+    nlp_totalTime: ['mean']
+
--- a/resources/features/facial/config.json
+++ b/resources/features/facial/config.json
@@ -0,0 +1 @@
+{"ref_lmk": 28, "ref_area": 350000, "face_width_left": "l15_x", "face_width_right": "l1_x", "face_height_left": "l8_y", "face_height_right": "l27_y", "landmarks": [5, 12, 8, 48, 54, 28, 51, 66, 57], "model_path": "resources/facial/svm_bin_fac_tremor.sav", "feature_order": ["fac_features_mean_5", "fac_features_mean_12", "fac_features_mean_8", "fac_features_mean_48", "fac_features_mean_54", "fac_features_mean_28", "fac_features_mean_51", "fac_features_mean_66", "fac_features_mean_57", "fac_features_median_5", "fac_features_median_12", "fac_features_median_8", "fac_features_median_48", "fac_features_median_54", "fac_features_median_28", "fac_features_median_51", "fac_features_median_66", "fac_features_median_57"]}
--- a/resources/features/raw_feature.yml
+++ b/resources/features/raw_feature.yml
@@ -196,3 +196,52 @@ raw_feature:
    mov_Hpose_Yaw: mov_hposeyaw
    mov_Hpose_Roll: mov_hposeroll
    mov_Hpose_Dist: mov_hposedist
+
+    mov_freq_trem_freq: mov_freqtremfreq
+    mov_freq_trem_index: mov_freqtremindex
+    mov_freq_trem_pindex: mov_freqtrempindex
+    mov_amp_trem_freq: mov_amptremfreq
+    mov_amp_trem_index: mov_amptremindex
+    mov_amp_trem_pindex: mov_amptrempindex
+    
+    fac_tremor_median_5: fac_tremor_median_5
+    fac_tremor_median_12: fac_tremor_median_12
+    fac_tremor_median_8: fac_tremor_median_8
+    fac_tremor_median_48: fac_tremor_median_48
+    fac_tremor_median_54: fac_tremor_median_54
+    fac_tremor_median_28: fac_tremor_median_28
+    fac_tremor_median_51: fac_tremor_median_51
+    fac_tremor_median_66: fac_tremor_median_66
+    fac_tremor_median_57: fac_tremor_median_57
+
+    mov_leye_x: mov_lefteyex
+    mov_leye_y: mov_lefteyey
+    mov_leye_z: mov_lefteyez
+    mov_reye_x: mov_righteyex
+    mov_reye_y: mov_righteyey
+    mov_reye_z: mov_righteyez
+    mov_eleft_disp: mov_leyedisp
+    mov_eright_disp: mov_reyedisp
+
+    #NLP markers
+    nlp_transcribe: nlp_transcribe
+    nlp_numSentences: nlp_numSentences
+    nlp_singPronPerAns: nlp_singPronPerAns
+    nlp_singPronPerSen: nlp_singPronPerSen
+    nlp_pastTensePerAns: nlp_pastTensePerAns
+    nlp_pastTensePerSen: nlp_pastTensePerSen
+    nlp_pronounsPerAns: nlp_pronounsPerAns
+    nlp_pronounsPerSen: nlp_pronounsPerSen
+    nlp_verbsPerAns: nlp_verbsPerAns
+    nlp_verbsPerSen: nlp_verbsPerSen
+    nlp_adjectivesPerAns: nlp_adjectivesPerAns
+    nlp_adjectivesPerSen: nlp_adjectivesPerSen
+    nlp_nounsPerAns: nlp_nounsPerAns
+    nlp_nounsPerSen: nlp_nounsPerSen
+    nlp_sentiment_mean: nlp_sentiment_mean
+    nlp_mattr: nlp_mattr
+    nlp_wordsPerMin: nlp_wordsPerMin
+    nlp_totalTime: nlp_totalTime
+    
+
+
--- a/resources/libraries/voice_tremor.praat
+++ b/resources/libraries/voice_tremor.praat
@@ -0,0 +1,422 @@
+
+######################################
+# Global Settings
+######################################
+sourcedirec$ = "./"; directory of sounds to be analyzed
+minPi = 60; minimal Pitch [Hz]
+maxPi = 350; maximal Pitch [Hz]
+ts = 0.015; analysis time step [s]
+tremthresh = 0.15; minimal autocorr.-coefficient to assume "tremor"
+minTr = 1.5;  minimal tremor frequency [Hz]
+maxTr = 15; maximal tremor frequency [Hz]
+
+
+
+######################################
+# Sound (.wav) in, results (.txt) out
+######################################
+
+# record/load and select the sound to be analyzed!!!
+
+info$ = Info
+name$ = extractWord$(info$, "Object name: ")
+
+slength = Get total duration
+call ftrem
+call atrem
+
+echo 
+...{"FTrF": 'ftrf:2#', "ATrF":'atrf:2',"FTrI":'ftri:3',"ATrI":'atri:3',"FTrP":'ftrp:3',"ATrP":'atrp:3'}
+
+
+
+######################################
+# Frequency Tremor Analysis
+######################################
+procedure ftrem
+   To Pitch (cc)... ts minPi 15 yes 0.03 0.3 0.01 0.35 0.14 maxPi
+
+#Edit
+#pause
+
+# because PRAAT only runs "Subtract linear fit" if the last frame is "voiceless" (!?):
+# numberOfFrames+1 (1)
+   numberOfFrames = Get number of frames
+   x1 = Get time from frame number... 1
+   am_F0 = Get mean... 0 0 Hertz
+
+   Create Matrix... ftrem_0 0 slength numberOfFrames+1 ts x1 1 1 1 1 1 0
+   for i from 1 to numberOfFrames
+      select Pitch 'name$'
+      f0 = Get value in frame... i Hertz
+      select Matrix ftrem_0
+# write zeros to matrix where frames are voiceless
+      if f0 = undefined
+         Set value... 1 i 0
+      else
+         Set value... 1 i f0
+      endif
+   endfor
+
+# remove the linear F0 trend (F0 declination)
+   To Pitch
+   Subtract linear fit... Hertz
+   Rename... ftrem_0_lin
+
+# undo (1)
+   Create Matrix... ftrem 0 slength numberOfFrames ts x1 1 1 1 1 1 0
+   for i from 1 to numberOfFrames
+      select Pitch ftrem_0_lin
+      f0 = Get value in frame... i Hertz
+      select Matrix ftrem
+# write zeros to matrix where frames are voiceless
+      if f0 = undefined
+         Set value... 1 i 0
+      else
+         Set value... 1 i f0
+      endif
+   endfor
+
+   To Pitch
+
+# normalize F0-contour by mean F0
+   select Matrix ftrem
+   Formula... (self-am_F0)/am_F0
+
+# since zeros in the Matrix (unvoiced frames) become normalized to -1 but 
+# unvoiced frames should be zero (if anything)
+# write zeros to matrix where frames are voiceless
+   for i from 1 to numberOfFrames
+      select Pitch ftrem
+      f0 = Get value in frame... i Hertz
+      if f0 = undefined
+         select Matrix ftrem
+         Set value... 1 i 0
+      endif
+   endfor
+
+# to calculate autocorrelation (cc-method):
+   select Matrix ftrem
+   To Sound (slice)... 1
+# calculate Frequency of Frequency Tremor [Hz]
+   To Pitch (cc)... slength minTr 15 yes 0.01 tremthresh 0.01 0.35 0.14 maxTr
+   Rename... ftrem_norm
+
+   ftrf = Get mean... 0 0 Hertz
+
+# calculate Intensity Index of Frequency Tremor [%]
+   select Sound ftrem
+   plus Pitch ftrem_norm
+   To PointProcess (peaks)... yes no
+   Rename... Maxima
+   numberofMaxPoints = Get number of points
+   ftri_max = 0
+   noFMax = 0
+   for iPoint from 1 to numberofMaxPoints
+      select PointProcess Maxima
+      ti = Get time from index... iPoint
+      select Sound ftrem
+      ftri_Point = Get value at time... Average ti Sinc70
+      if ftri_Point = undefined
+         ftri_Point = 0
+         noFMax += 1
+      endif
+      ftri_max += abs(ftri_Point)
+   endfor
+
+select Sound ftrem
+plus PointProcess Maxima
+#Edit
+#pause
+   
+# ftri_max:= (mean) procentual deviation of F0-maxima from mean F0 at ftrf
+   numberofMaxima = numberofMaxPoints - noFMax
+   ftri_max = 100 * ftri_max/numberofMaxima
+
+   select Sound ftrem
+   plus Pitch ftrem_norm
+   To PointProcess (peaks)... no yes
+   Rename... Minima
+   numberofMinPoints = Get number of points
+   ftri_min = 0
+   noFMin = 0
+   for iPoint from 1 to numberofMinPoints
+      select PointProcess Minima
+      ti = Get time from index... iPoint
+      select Sound ftrem
+      ftri_Point = Get value at time... Average ti Sinc70
+      if ftri_Point = undefined
+         ftri_Point = 0
+         noFMin += 1
+      endif
+      ftri_min += abs(ftri_Point)
+   endfor
+
+select Sound ftrem
+plus PointProcess Minima
+#Edit
+#pause
+
+
+# ftri_min:= (mean) procentual deviation of F0-minima from mean F0 at ftrf
+   numberofMinima = numberofMinPoints - noFMin
+   ftri_min = 100 * ftri_min/numberofMinima
+
+   ftri = (ftri_max + ftri_min) / 2
+   
+   ftrp = ftri * ftrf/(ftrf+1)
+
+# uncomment to inspect frequnecy tremor objects:
+# pause
+
+   select Pitch ftrem
+# uncomment if only frequency tremor is to be analyzed:
+#   plus Pitch 'name$'
+   plus Matrix ftrem_0
+   plus Pitch ftrem_0
+   plus Pitch ftrem_0_lin
+   plus Matrix ftrem
+   plus Sound ftrem
+   plus Pitch ftrem_norm
+   plus PointProcess Maxima
+   plus PointProcess Minima
+   Remove
+
+endproc
+
+
+######################################
+# Amplitude Tremor Analysis
+######################################
+procedure atrem
+   select Sound 'name$'
+# uncomment if only amplitude tremor is to be analyzed:
+#   To Pitch (cc)... ts minPi 15 yes 0.03 0.3 0.01 0.35 0.14 maxPi
+#   select Sound 'name$'
+   plus Pitch 'name$'
+   To PointProcess (cc)
+   select Sound 'name$'
+   plus PointProcess 'name$'_'name$'
+
+# amplitudes are integrals of intensity over periods -- not intensity maxima
+   To AmplitudeTier (period)... 0 0 0.0001 0.02 1.7
+
+#Edit
+#pause
+
+# from here on out: prepare to autocorrelate AmplitudeTier-data
+# sample AmplitudeTier at (constant) rate ts
+   numbOfAmpPoints = Get number of points
+   first_ampP = Get time from index... 1
+   last_ampP = Get time from index... numbOfAmpPoints
+
+# to be able to -- automatically -- read Amp. values...
+   Down to TableOfReal
+
+   select Pitch 'name$'
+   frameNo1 = Get frame number from time... first_ampP
+   hiframe1 = ceiling(frameNo1)
+   t_hiframe1 = Get time from frame number... hiframe1
+
+   frameNoN = Get frame number from time... last_ampP
+   loframeN = floor(frameNoN)
+
+# number of Amp. points if (re-)sampled at ts
+   numbOfPoints_neu = loframeN - hiframe1 + 1
+
+# to enable autocorrelation of the Amp.-contour: ->Matrix->Sound
+
+   Create Matrix... atrem_nlc 0 slength numbOfPoints_neu+1 ts t_hiframe1 1 1 1 1 1 2
+# get the mean of the amplitude contour in time windows of constant duration
+   for point_neu from 1 to numbOfPoints_neu
+      t = (point_neu-1) * ts + t_hiframe1
+      tl = t - ts/2
+      tu = t + ts/2
+
+      select AmplitudeTier 'name$'_'name$'_'name$'
+      loil = Get low index from time... tl
+      hiil = Get high index from time... tl
+      loiu = Get low index from time... tu
+      hiiu = Get high index from time... tu
+
+      select TableOfReal 'name$'_'name$'_'name$'
+      if loil = 0
+         lotl = 0; time before the first amp. point
+         druck_lol = Get value... hiil 2; amplitude value before the first amp. point
+      else
+         lotl = Get value... loil 1; time value of Amp.Point before tl in the PointProcess [s]
+         druck_lol = Get value... loil 2; amplitude value before tl in the PointProcess [Pa, ranged from 0 to 1]
+      endif
+
+      hitl = Get value... hiil 1
+      druck_hil = Get value... hiil 2; amplitude value after tl in the PointProcess
+
+      lotu = Get value... loiu 1
+      druck_lou = Get value... loiu 2; amplitude value before tu in the PointProcess
+
+      if hiiu = numbOfAmpPoints + 1
+         hitu = slength; time after the last amp. point
+         druck_hiu = Get value... hiil 2; amplitude value after the last amp. point
+      else
+         hitu = Get value... hiiu 1; time value after tu in the PointProcess
+         druck_hiu = Get value... hiiu 2; amplitude value after tu in the PointProcess
+      endif
+
+      nPinter = loiu - loil; = hiiu - hiil; number of amp.-points between tl and tu
+      if nPinter > 0
+         itinter = 0
+         tinter = 0
+         druck_tin = 0
+         deltat = 0
+         for iinter from 1 to nPinter
+            hilft = itinter
+            itinter = Get value... loil+iinter 1
+            idruck_tin = Get value... loil+iinter 2
+
+            ideltat = itinter - hilft
+            druck_tin += idruck_tin * ideltat 
+            tinter += itinter
+            deltat += ideltat
+         endfor
+
+         tin = tinter/nPinter
+         druck_tin = druck_tin/deltat
+      endif
+
+      druck_tl = ((hitl-tl)*druck_lol + (tl-lotl)*druck_hil) / (hitl-lotl)
+      druck_tu = ((hitu-tu)*druck_lou + (tu-lotu)*druck_hiu) / (hitu-lotu)
+
+      if nPinter = 0; loil = loiu; hiil = hiiu
+         druck_mean = (druck_tl + druck_tu) / 2
+      else
+         druck_mean = ((tin-tl)*(druck_tl + druck_tin)/2 + (tu-tin)*(druck_tin + druck_tu)/2) / (tu-tl)
+      endif
+
+      select Matrix atrem_nlc
+      Set value... 1 point_neu druck_mean
+   endfor
+
+   To Pitch
+   am_Int = Get mean... 0 0 Hertz
+
+# because PRAAT classifies frequencies in Pitch objects <=0 as "voiceless" and 
+# therefore parts with extreme INTENSITIES would be considered as "voiceless"
+# (irrelevant) after "Subtract linear fit" (1)
+# "1" is added to the original Pa-values (ranged from 0 to 1)
+   select Matrix atrem_nlc
+   Formula... self+1
+
+# because PRAAT only runs "Subtract linear fit" if the last frame is "voiceless"...?(2)
+   Set value... 1 numbOfPoints_neu+1 0
+
+# remove the linear amp.-trend (amplitude declination)
+#Formula... self*1000; better for viewing
+   To Pitch
+   Rename... hilf_lincorr
+   Subtract linear fit... Hertz
+   Rename... atrem
+
+# undo (1)...
+   To Matrix
+   Formula... self-1
+
+# normalize Amp. contour by mean Amp.
+   Formula... (self-am_Int)/am_Int
+
+# remove last frame, undo (2)
+   Create Matrix... atrem_besser 0 slength numbOfPoints_neu ts t_hiframe1 1 1 1 1 1 0
+   for point_neu from 1 to numbOfPoints_neu
+      select Matrix atrem
+      spring = Get value in cell... 1 point_neu
+      select Matrix atrem_besser
+      Set value... 1 point_neu spring
+   endfor
+
+# to calculate autocorrelation (cc-method)
+   To Sound (slice)... 1
+# calculate Frequency of Ampitude Tremor [Hz]
+   To Pitch (cc)... slength minTr 15 yes 0.01 tremthresh 0.01 0.35 0.14 maxTr
+   Rename... atrem_norm
+
+   atrf = Get mean... 0 0 Hertz
+
+# calculate Intensity Index of Amplitude Tremor [%]
+   select Sound atrem_besser
+   plus Pitch atrem_norm
+   To PointProcess (peaks)... yes no
+   Rename... Maxima
+   numberofMaxPoints = Get number of points
+   atri_max = 0
+   noAMax = 0
+   for iPoint from 1 to numberofMaxPoints
+      select PointProcess Maxima
+      ti = Get time from index... iPoint
+      select Sound atrem_besser
+      atri_Point = Get value at time... 0 ti Sinc70
+      if atri_Point = undefined
+         atri_Point = 0
+         noAMax += 1
+      endif
+      atri_max += abs(atri_Point)
+   endfor
+
+select Sound atrem_besser
+plus PointProcess Maxima
+#Edit
+#pause
+
+# atri_max:= (mean) procentual deviation of Amp. maxima from mean Amp.[Pa] at atrf
+   numberofMaxima = numberofMaxPoints - noAMax
+   atri_max = 100 * atri_max / numberofMaxima
+
+   select Sound atrem_besser
+   plus Pitch atrem_norm
+   To PointProcess (peaks)... no yes
+   Rename... Minima
+   numberofMinPoints = Get number of points
+   atri_min = 0
+   noAMin = 0
+   for iPoint from 1 to numberofMinPoints
+      select PointProcess Minima
+      ti = Get time from index... iPoint
+      select Sound atrem_besser
+      atri_Point = Get value at time... 0 ti Sinc70
+      if atri_Point = undefined
+         atri_Point = 0
+         noAMin += 1
+      endif
+      atri_min += abs(atri_Point)
+   endfor
+
+select Sound atrem_besser
+plus PointProcess Minima
+#Edit
+#pause
+
+# atri_min:= (mean) procentual deviation of Amp. minima from mean Amp.[Pa] at atrf
+   numberofMinima = numberofMinPoints - noAMin
+   atri_min = 100 * atri_min / numberofMinima
+
+   atri = (atri_max + atri_min) / 2
+
+   atrp = atri * atrf/(atrf+1)
+
+# uncomment to inspect amplitude tremor objects:
+# pause
+
+   select Pitch 'name$'
+   plus PointProcess 'name$'_'name$'
+   plus AmplitudeTier 'name$'_'name$'_'name$'
+   plus TableOfReal 'name$'_'name$'_'name$'
+   plus Matrix atrem_nlc
+   plus Pitch atrem_nlc
+   plus Pitch hilf_lincorr
+   plus Pitch atrem
+   plus Matrix atrem
+   plus Matrix atrem_besser
+   plus Sound atrem_besser
+   plus Pitch atrem_norm
+   plus PointProcess Maxima
+   plus PointProcess Minima
+   Remove
+endproc
				`@@ -0,0 +1 @@`
				{"ref_lmk": 28, "ref_area": 350000, "face_width_left": "l15_x", "face_width_right": "l1_x", "face_height_left": "l8_y", "face_height_right": "l27_y", "landmarks": [5, 12, 8, 48, 54, 28, 51, 66, 57], "model_path": "resources/facial/svm_bin_fac_tremor.sav", "feature_order": ["fac_features_mean_5", "fac_features_mean_12", "fac_features_mean_8", "fac_features_mean_48", "fac_features_mean_54", "fac_features_mean_28", "fac_features_mean_51", "fac_features_mean_66", "fac_features_mean_57", "fac_features_median_5", "fac_features_median_12", "fac_features_median_8", "fac_features_median_48", "fac_features_median_54", "fac_features_median_28", "fac_features_median_51", "fac_features_median_66", "fac_features_median_57"]}