merged master into feature branch
This commit is contained in:
Ubuntu
38
.github/ISSUE_TEMPLATE/bug_report.md
vendored
Normal file
38
.github/ISSUE_TEMPLATE/bug_report.md
vendored
Normal file
@@ -0,0 +1,38 @@
|
||||
---
|
||||
name: Bug report
|
||||
about: Create a report to help us improve
|
||||
title: ''
|
||||
labels: ''
|
||||
assignees: ''
|
||||
|
||||
---
|
||||
|
||||
**Describe the bug**
|
||||
A clear and concise description of what the bug is.
|
||||
|
||||
**To Reproduce**
|
||||
Steps to reproduce the behavior:
|
||||
1. Go to '...'
|
||||
2. Click on '....'
|
||||
3. Scroll down to '....'
|
||||
4. See error
|
||||
|
||||
**Expected behavior**
|
||||
A clear and concise description of what you expected to happen.
|
||||
|
||||
**Screenshots**
|
||||
If applicable, add screenshots to help explain your problem.
|
||||
|
||||
**Desktop (please complete the following information):**
|
||||
- OS: [e.g. iOS]
|
||||
- Browser [e.g. chrome, safari]
|
||||
- Version [e.g. 22]
|
||||
|
||||
**Smartphone (please complete the following information):**
|
||||
- Device: [e.g. iPhone6]
|
||||
- OS: [e.g. iOS8.1]
|
||||
- Browser [e.g. stock browser, safari]
|
||||
- Version [e.g. 22]
|
||||
|
||||
**Additional context**
|
||||
Add any other context about the problem here.
|
||||
@@ -1 +1,41 @@
|
||||
OpenDBM welcomes contributions from anyone. Please see our guidelines((CONTRIBUTING.md)).
|
||||
# Contributing guidelines to openDBM
|
||||
|
||||
Please visit [openDBM](https://aicure.com/opendbm/) page if you have not seen it. If you are enthusiastic to contribute to this toolkit in terms of bug fixes, tutorials, new feature development, enhancing existing features etc. everything should be managed by submitting pull request on Github.
|
||||
|
||||
## What you should know
|
||||
|
||||
- Read [code of conduct](https://github.com/AiCure/open_dbm/blob/master/CODE_OF_CONDUCT.md).
|
||||
- Read [License](https://github.com/AiCure/open_dbm/blob/master/license.txt).
|
||||
- Agree to contribute code under openDBM(GPL v3.0).
|
||||
- Before adding new feature/algorithmn make sure it's not patented.
|
||||
- Before fixing any bug make sure it's still exists and reproducable in master branch.
|
||||
- If you see any issue in existing features make sure to report the issue on openDBM issues page.
|
||||
- After adding new code make sure everything is working as expected.
|
||||
|
||||
## How to contribute
|
||||
|
||||
1. Install Git.
|
||||
2. Register and signin into GitHub.
|
||||
3. Fork openDBM repository https://github.com/AiCure/open_dbm.git (https://help.github.com/articles/fork-a-repo for details)
|
||||
4. Assign a task for yourself. It could be a bugfix or adding new functionality.
|
||||
5. Clone your fork into your local system.
|
||||
6. Navigate to local repository.
|
||||
7. Check that your fork is the 'origin' remote.
|
||||
- Use 'git remote -v' to show current remote
|
||||
- If you do not see any remote, add it using git remote add origin <url of fork branch>
|
||||
8. Add openDBM master repository as 'upstream' remote.
|
||||
- Use 'git remote add upstream https://github.com/AiCure/open_dbm.git' command
|
||||
- Check remote using 'git remote -v'
|
||||
9. Before making any changes better to synchronize local repository with openDBM master
|
||||
- git pull upstream master
|
||||
10. Create new branch where you are going to add bugfix or new features
|
||||
- git checkout -b branch_name
|
||||
11. Make and commit your changes into local repository
|
||||
12. Validate all your commits and make sure everything is working as expected.
|
||||
13. Push your chanhes to new branch(which is a branch of fork repository)
|
||||
- git push origin branch_name
|
||||
14. Create a pull request and add brief information about all your commits.(see https://help.github.com/articles/using-pull-requests for details)
|
||||
|
||||
## Request Approval
|
||||
|
||||
Once reviewer is happy with the code changes, will approve the pull request and merge it with the master branch.
|
||||
|
||||
13
Dockerfile
13
Dockerfile
@@ -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!"
|
||||
|
||||
@@ -222,12 +222,14 @@ class ConfigRawReader(object):
|
||||
self.mov_Hpose_Yaw = config['raw_feature']['mov_Hpose_Yaw']
|
||||
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']
|
||||
@@ -237,3 +239,33 @@ class ConfigRawReader(object):
|
||||
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']
|
||||
|
||||
|
||||
@@ -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, voice_tremor, facial_tremor
|
||||
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
|
||||
@@ -124,12 +126,32 @@ def process_movement(video_uri, out_dir, dbm_group, r_config, dlib_model):
|
||||
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, 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 'nlp' 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)
|
||||
|
||||
|
||||
def remove_file(file_path):
|
||||
"""
|
||||
removing wav file
|
||||
|
||||
@@ -14,3 +14,4 @@ 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'))
|
||||
|
||||
|
||||
148
dbm_lib/dbm_features/raw_features/movement/eye_gaze.py
Normal file
148
dbm_lib/dbm_features/raw_features/movement/eye_gaze.py
Normal file
@@ -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')
|
||||
@@ -82,10 +82,11 @@ def run_vtremor(video_uri, out_dir, r_config):
|
||||
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))
|
||||
if float(aud_dur) < 0.5:
|
||||
logger.info('Output file {} size is less than 0.5sec'.format(audio_file))
|
||||
|
||||
prepare_empty_vt(video_uri, out_loc, fl_name, r_config)
|
||||
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:
|
||||
|
||||
47
dbm_lib/dbm_features/raw_features/nlp/speech_features.py
Normal file
47
dbm_lib/dbm_features/raw_features/nlp/speech_features.py
Normal file
@@ -0,0 +1,47 @@
|
||||
"""
|
||||
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
|
||||
|
||||
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):
|
||||
"""
|
||||
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)
|
||||
|
||||
except Exception as e:
|
||||
logger.error('Failed to process video file')
|
||||
84
dbm_lib/dbm_features/raw_features/nlp/transcribe.py
Normal file
84
dbm_lib/dbm_features/raw_features/nlp/transcribe.py
Normal file
@@ -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')
|
||||
|
||||
212
dbm_lib/dbm_features/raw_features/util/nlp_util.py
Normal file
212
dbm_lib/dbm_features/raw_features/util/nlp_util.py
Normal file
@@ -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
|
||||
"""
|
||||
|
||||
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
|
||||
@@ -67,7 +67,10 @@ def process_open_face(video_uri, input_dir, out_dir, of_path, dbm_group,video_tr
|
||||
"""
|
||||
try:
|
||||
|
||||
if dbm_group != None and len(dbm_group) == 1 and 'acoustic' in dbm_group:
|
||||
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]
|
||||
|
||||
@@ -22,6 +22,7 @@ logger=logging.getLogger()
|
||||
#for ftremor
|
||||
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):
|
||||
@@ -38,7 +39,7 @@ def common_video(video_file, args, r_config):
|
||||
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.process_nlp(video_file, out_path, args.dbm_group, 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)
|
||||
@@ -84,6 +85,7 @@ 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, r_config, DEEP_SPEECH)
|
||||
|
||||
else:
|
||||
logger.info('Enter correct audio(*.wav) file path.')
|
||||
@@ -134,6 +136,8 @@ def process_raw_audio_dir(args, s_config, r_config):
|
||||
|
||||
out_path = os.path.join(args.output_path, 'raw_variables')
|
||||
pf.process_acoustic(audio, out_path, args.dbm_group, r_config)
|
||||
pf.process_nlp(audio, out_path, args.dbm_group, r_config, DEEP_SPEECH)
|
||||
|
||||
except Exception as e:
|
||||
logger.error('Failed to process wav file.')
|
||||
|
||||
|
||||
@@ -55,6 +55,9 @@ fi
|
||||
if [[ $dbm_group == *"movement"* ]]; then
|
||||
dbm_new="$dbm_new movement"
|
||||
fi
|
||||
if [[ $dbm_group == *"nlp"* ]]; then
|
||||
dbm_new="$dbm_new nlp"
|
||||
fi
|
||||
|
||||
#docker commands to run container
|
||||
docker create -ti --name dbm_container dbm bash
|
||||
|
||||
@@ -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
|
||||
|
||||
@@ -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', 'MOV_VT', 'MOV_FT']
|
||||
'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
|
||||
@@ -22,10 +23,11 @@ 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']
|
||||
FAC_AU: ['fac_AU01int', 'fac_AU02int', 'fac_AU04int', 'fac_AU05int', 'fac_AU06int', 'fac_AU07int', 'fac_AU09int',
|
||||
@@ -71,6 +73,15 @@ derive_feature:
|
||||
'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
|
||||
fac_AsymMaskMouth: ['mean', 'std']
|
||||
@@ -254,12 +265,14 @@ 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']
|
||||
@@ -269,3 +282,32 @@ derive_feature:
|
||||
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']
|
||||
|
||||
|
||||
@@ -196,12 +196,14 @@ 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
|
||||
@@ -211,3 +213,35 @@ raw_feature:
|
||||
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
|
||||
|
||||
|
||||
|
||||
|
||||
Reference in New Issue
Block a user