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fix code refactoring in hnr

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jordi.hasianta
2022-09-15 21:33:19 +07:00
parent 2498e93e20
commit 4218ea4bef
1 changed files with 49 additions and 103 deletions
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152
opendbm/dbm_lib/dbm_features/raw_features/audio/hnr.py
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@@ -1,5 +1,5 @@
"""
file_name: gne
file_name: hnr
project_name: DBM
created: 2020-20-07
"""
@@ -9,7 +9,6 @@ import logging
import os
from os.path import join
import more_itertools as mit
import numpy as np
import pandas as pd
import parselmouth
@@ -19,115 +18,70 @@ from opendbm.dbm_lib.dbm_features.raw_features.util import util as ut
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger()
gne_dir = "acoustic/glottal_noise"
ff_dir = "acoustic/pitch"
csv_ext = "_gne.csv"
hnr_dir = "acoustic/harmonic_noise"
csv_ext = "_hnr.csv"
error_txt = "error: length less than 0.064"
def gne_ratio(sound):
def hnr_ratio(filepath):
"""
Using parselmouth library fetching glottal noise excitation ratio
Using parselmouth library fetching harmonic noise ratio ratio
Args:
sound: parselmouth object
path: (.wav) audio file location
Returns:
(list) list of gne ratio for each voice frame
(list) list of hnr ratio for each voice frame, min,max and mean hnr
"""
harmonicity_gne = sound.to_harmonicity_gne()
gne_all_bands = harmonicity_gne.values
gne_all_bands = np.where(gne_all_bands == -200, np.NaN, gne_all_bands)
sound = parselmouth.Sound(filepath)
harmonicity = sound.to_harmonicity_ac(time_step=0.001)
gne = np.nanmax(
gne_all_bands
) # following http://www.fon.hum.uva.nl/rob/NKI_TEVA/TEVA/HTML/NKI_TEVA.pdf
return gne
hnr_all_frames = harmonicity.values # [harmonicity.values != -200] nan it (****)
hnr_all_frames = np.where(hnr_all_frames == -200, np.NaN, hnr_all_frames)
return hnr_all_frames.transpose()
def empty_gne(video_uri, out_loc, fl_name, r_config, error_txt, save=True):
def calc_hnr(video_uri, audio_file, out_loc, fl_name, r_config, save=True):
"""
Preparing empty GNE matrix if something fails
"""
cols = ["Frames", r_config.aco_gne, r_config.err_reason]
out_val = [[np.nan, np.nan, error_txt]]
df_gne = pd.DataFrame(out_val, columns=cols)
df_gne["dbm_master_url"] = video_uri
if save:
logger.info("Saving Output file {} ".format(out_loc))
ut.save_output(df_gne, out_loc, fl_name, gne_dir, csv_ext)
return df_gne
def segment_gne(com_speech_sort, voiced_yes, voiced_no, gne_all_frames, audio_file):
"""
calculating gne for each voice segment
"""
snd = parselmouth.Sound(audio_file)
pitch = snd.to_pitch(time_step=0.001)
for idx, vs in enumerate(com_speech_sort):
try:
max_gne = np.NaN
if vs in voiced_yes and len(vs) > 1:
start_time = pitch.get_time_from_frame_number(vs[0])
end_time = pitch.get_time_from_frame_number(vs[-1])
snd_start = int(snd.get_frame_number_from_time(start_time))
snd_end = int(snd.get_frame_number_from_time(end_time))
samples = parselmouth.Sound(snd.as_array()[0][snd_start:snd_end])
max_gne = gne_ratio(samples)
except:
pass
gne_all_frames[idx] = max_gne
return gne_all_frames
def calc_gne(video_uri, audio_file, out_loc, fl_name, r_config, save=True, ff_df=None):
"""
Preparing gne matrix
Preparing harmonic noise matrix
Args:
audio_file: (.wav) parsed audio file
out_loc: (str) Output directory for csv's
"""
dir_path = os.path.join(out_loc, ff_dir)
if os.path.isdir(dir_path) or ff_df is not None:
if ff_df is not None:
voice_seg = ut.process_segment_pitch(ff_df, r_config)
else:
voice_seg = ut.segment_pitch(dir_path, r_config, ff_df=ff_df)
gne_all_frames = [np.NaN] * len(voice_seg[0])
gne_segment_frames = segment_gne(
voice_seg[0], voice_seg[1], voice_seg[2], gne_all_frames, audio_file
)
hnr_all_frames = hnr_ratio(audio_file)
df_hnr = pd.DataFrame(hnr_all_frames, columns=[r_config.aco_hnr])
df_gne = pd.DataFrame(gne_segment_frames, columns=[r_config.aco_gne])
df_gne[
r_config.err_reason
] = "Pass" # will replace with threshold in future release
df_hnr["Frames"] = df_hnr.index
df_hnr["dbm_master_url"] = video_uri
df_hnr[
r_config.err_reason
] = "Pass" # will replace with threshold in future release
df_gne["Frames"] = df_gne.index
df_gne["dbm_master_url"] = video_uri
if save:
logger.info("Processing Output file {} ".format(out_loc))
ut.save_output(df_gne, out_loc, fl_name, gne_dir, csv_ext)
return df_gne
else:
error_txt = "error: pitch freq not available"
return empty_gne(video_uri, out_loc, fl_name, r_config, error_txt, save=save)
if save:
logger.info("Saving Output file {} ".format(out_loc))
ut.save_output(df_hnr, out_loc, fl_name, hnr_dir, csv_ext)
return df_hnr
def run_gne(video_uri, out_dir, r_config, save=True, ff_df=None):
def empty_hnr(video_uri, out_loc, fl_name, r_config, save=True):
"""
Processing all patient's for fetching glottal noise ratio
---------------
---------------
Preparing empty HNR matrix if something fails
"""
cols = ["Frames", r_config.aco_hnr, r_config.err_reason]
out_val = [[np.nan, np.nan, error_txt]]
df_hnr = pd.DataFrame(out_val, columns=cols)
df_hnr["dbm_master_url"] = video_uri
if save:
logger.info("Saving Output file {} ".format(out_loc))
ut.save_output(df_hnr, out_loc, fl_name, hnr_dir, csv_ext)
return df_hnr
def run_hnr(video_uri, out_dir, r_config, save=True):
"""
Processing all patient's for fetching harmonic noise ratio
-------------------
-------------------
Args:
video_uri: video path; r_config: raw variable config object
out_dir: (str) Output directory for processed output
@@ -146,19 +100,11 @@ def run_gne(video_uri, out_dir, r_config, save=True, ff_df=None):
"Output file {} size is less than 0.064sec".format(audio_file)
)
error_txt = "error: length less than 0.064"
df = empty_gne(
video_uri, out_loc, fl_name, r_config, error_txt, save=save
)
df = empty_hnr(video_uri, out_loc, fl_name, r_config, save=save)
else:
df = calc_gne(
video_uri,
audio_file,
out_loc,
fl_name,
r_config,
save=save,
ff_df=ff_df,
df = calc_hnr(
video_uri, audio_file, out_loc, fl_name, r_config, save=save
)
return df
except Exception as e: