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https://github.com/aidygus/LinVAM.git
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302 lines
11 KiB
Python
302 lines
11 KiB
Python
"""Script for auto tuning keyword spotting thresholds in pocketsphinx"""
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from __future__ import print_function
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import sys
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import select
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import os
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import termios
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import contextlib
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import time
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import re
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import numpy as np
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from pocketsphinx.pocketsphinx import *
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from sphinxbase.sphinxbase import *
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# keyphrases found in kwlist
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WORDS = []
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# test case containing multiple occurances
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# of words to be used as training audio
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TEST_CASE = []
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# Threshold values
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FREQUENCY = []
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# End frame of each word in input speech
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NO_OF_FRAMES = []
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# Recorded speech input
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OUTPUT_FILENAME = 'testing_audio.wav'
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def preprocess_files(dic_path, kwlist_path):
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"""
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Function to generate required lists and call tuning functions
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"""
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global WORDS, TEST_CASE, FREQUENCY
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# words found in dictinary
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_content = []
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with open(dic_path) as _f:
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_content = _f.readlines()
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_content = [x.strip() for x in _content]
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with open(kwlist_path) as _f:
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WORDS = _f.readlines()
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WORDS = [x.strip()[:x.strip().rfind(' ')] for x in WORDS]
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print (WORDS)
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# Loop to find out initial thresholds based on phonetics provided in dictionary
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for i, _ in enumerate(WORDS):
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# starting position of first phone for a word
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init_pos = 0
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# Count number of phones based on frequency of spaces
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spaces = 0
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# In case there is more than one word in a keyphrase, add phones for all words
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for _m in re.finditer(' ', WORDS[i]):
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indices = [j for j, s in enumerate(_content) if WORDS[i][init_pos:_m.start()]+'\t' in s]
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spaces = _content[indices[0]].count(' ') + spaces + 1
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init_pos = _m.start()+1
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indices = [j for j, s in enumerate(_content) if WORDS[i][init_pos:]+'\t' in s]
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spaces += _content[indices[0]].count(' ') + 1
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# Normalizing
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if spaces <= 3:
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FREQUENCY.append(spaces)
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else:
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FREQUENCY.append(spaces * 2)
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# Adding random noise in test case for better tuning
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TEST_CASE = ['[RANDOM]', '[RANDOM]']
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TEST_CASE.extend(WORDS)
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TEST_CASE.extend(['[RANDOM]', '[RANDOM]'])
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TEST_CASE.extend(WORDS)
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np.random.shuffle(TEST_CASE)
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print ("HERE IS YOUR TRAINING SET")
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print (TEST_CASE)
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# record audio
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record()
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write_frequency_to_file(kwlist_path)
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# Analysis begins
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actual_tuning(dic_path, kwlist_path, 1)
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print ("Removed many false alarms. New frequency: ")
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print (FREQUENCY)
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print ('Moving on to missed detections')
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actual_tuning(dic_path, kwlist_path, 0)
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print ("Frequency tuned to the best of the script's ability. New frequency: ")
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print (FREQUENCY)
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_missed, _fa = process_threshold(kws_analysis(dic_path, kwlist_path))
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def write_frequency_to_file(kwlist_path):
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"""
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update modified frequencies in kwlist file
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"""
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_f = open(kwlist_path, 'w')
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for i, val in enumerate(FREQUENCY):
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_f.write(WORDS[i] + ' /1e-' + str(val) + '/\n')
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_f.close()
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@contextlib.contextmanager
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def raw_mode(_file):
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"""
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Function handle the button press on successful utterance of word by user
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"""
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old_attrs = termios.tcgetattr(_file.fileno())
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new_attrs = old_attrs[:]
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new_attrs[3] = new_attrs[3] & ~(termios.ECHO | termios.ICANON)
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try:
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termios.tcsetattr(_file.fileno(), termios.TCSADRAIN, new_attrs)
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yield
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finally:
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termios.tcsetattr(_file.fileno(), termios.TCSADRAIN, old_attrs)
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def record():
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"""
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Records user's speech with timestamp for each spoken word
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"""
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global NO_OF_FRAMES
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# rec -c 1 -r 16000 -b 16 recording.wav
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print ("-----SAY THE FOLLOWING OUT LOUD AND PRESS ENTER-----")
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print (TEST_CASE[0])
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os.system('rec -q -c 1 -r 16000 -b 16 ' + OUTPUT_FILENAME + ' &')
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NO_OF_FRAMES.append(0)
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previous = time.time()
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i = 0
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with raw_mode(sys.stdin):
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while True:
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if sys.stdin in select.select([sys.stdin], [], [], 0)[0]:
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_a = sys.stdin.read(1)
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if _a == '\n':
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if i == len(TEST_CASE)-1:
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current = time.time()
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NO_OF_FRAMES.append(NO_OF_FRAMES[i] + (current - previous)*100)
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previous = current
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print ("STOPPING RECORDING")
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time.sleep(2)
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# stop Recording
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os.system('pkill rec')
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print (NO_OF_FRAMES)
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break
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else:
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current = time.time()
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NO_OF_FRAMES.append(NO_OF_FRAMES[i] + (current - previous)*100)
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previous = current
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i = i+1
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print ("-----SAY THE FOLLOWING OUT LOUD AND PRESS ENTER-----")
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print (TEST_CASE[i])
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def actual_tuning(dic_path, kwlist_path, _z):
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"""
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process false alarms and missed detections to tune thresholds
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_z in the paramter is 1 for FA analysis and 0 for missed detection analysis
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"""
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# to store thresholds with minimum mismatches
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minimum_inflection = [FREQUENCY[i] for i, _ in enumerate(WORDS)]
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# to check whether a word's assessment has been finished or not
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processed = [0 for i, _ in enumerate(WORDS)]
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# get frequency of missed detections and false alarms
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_missed, _fa = process_threshold(kws_analysis(dic_path, kwlist_path))
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_least_negative_threshold = 1
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_most_negative_threshold = 49
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# Loop until there is at least one word whose assessment has not finished
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while 0 in processed:
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if _z == 1:
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# If there is a False alarm, increase threshold
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for i, val in enumerate(_fa):
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if FREQUENCY[i] > _least_negative_threshold and processed[i] == 0:
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if val[1] > 0:
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FREQUENCY[i] -= 2
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else:
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processed[i] = 1
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else:
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processed[i] = 1
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else:
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# If there is a missed detection, decrease threshold
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for i, val in enumerate(_missed):
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if FREQUENCY[i] < _most_negative_threshold and processed[i] == 0:
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if val[1] > 0:
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FREQUENCY[i] += 1
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else:
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processed[i] = 1
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else:
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processed[i] = 1
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write_frequency_to_file(kwlist_path)
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print ('UPDATED FREQUENCY:')
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print (FREQUENCY)
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_previous_missed = []
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_previous_missed.extend(_missed)
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_previous_fa = []
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_previous_fa.extend(_fa)
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_missed, _fa = process_threshold(kws_analysis(dic_path, kwlist_path))
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if _z == 1:
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# If current readings show increase in missed detections,
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# go to previous state and stop
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for i, val in enumerate(_missed):
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if val[1] > _previous_missed[i][1] and processed[i] == 0:
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processed[i] = 1
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FREQUENCY[i] += 2
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else:
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# If current readings show increase in false alarms,
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# go to previous state and stop
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for i, val in enumerate(_fa):
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if val[1] > _previous_fa[i][1] and processed[i] == 0:
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processed[i] = 1
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FREQUENCY[i] -= 1
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# If updated thresholds caused better accuracy, save them
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for i, val in enumerate([_fa, _missed][_z == 0]):
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if val[1] < [_previous_fa[i][1], _previous_missed][_z == 0]:
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minimum_inflection[i] = FREQUENCY[i]
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for i, val in enumerate([_fa, _missed][_z == 0]):
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FREQUENCY[i] = minimum_inflection[i]
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write_frequency_to_file(kwlist_path)
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def kws_analysis(dic, kwlist):
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"""
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kws analysis on user speech and updated threshold values
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"""
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analysis_result = []
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modeldir = "/usr/local/share/pocketsphinx/model/"
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# Create a decoder with certain model
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config = Decoder.default_config()
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config.set_string('-hmm', os.path.join(modeldir, 'en-us/en-us'))
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config.set_string('-dict', dic)
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config.set_string('-kws', kwlist)
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config.set_string('-dither', "no")
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config.set_string('-logfn', '/dev/null')
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config.set_string('-featparams', os.path.join(os.path.join(modeldir,
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'en-us/en-us'), "feat.params"))
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stream = open(OUTPUT_FILENAME, "rb")
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# Process audio chunk by chunk. On keyphrase detected perform action and restart search
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decoder = Decoder(config)
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decoder.start_utt()
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timer = 0
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while True:
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buf = stream.read(1024)
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if buf:
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decoder.process_raw(buf, False, False)
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else:
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break
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if decoder.hyp() != None:
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for seg in decoder.seg():
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pass
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analysis_result.append([seg.word.rstrip(), timer/320])
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decoder.end_utt()
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decoder.start_utt()
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timer += 1024
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return analysis_result
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def process_threshold(analysis_result):
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"""
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calculate missed detections and false alarms
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Argument: analysis result = kws result
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"""
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# stores timestamp of words which matche in both speech and kws result
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_indices = []
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missed = [[WORDS[i], 0] for i in range(len(WORDS))]
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false_alarms = [[WORDS[i], 0] for i in range(len(WORDS))]
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i = 0
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for i, val in enumerate(analysis_result):
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# Calculate the timestamp in speech closest to timestamp of word found by kws result
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_index = min(range(len(NO_OF_FRAMES)), key=lambda l: abs(NO_OF_FRAMES[l] - val[1]))
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_indices.append(_index)
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if TEST_CASE[_index-1] == '[RANDOM]':
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position_observer = WORDS.index(val[0])
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false_alarms[position_observer][1] += 1
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print ('FA Found', val[0], ' in place of RANDOM TEXT')
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elif TEST_CASE[_index-1] == val[0]:
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print ('DETECTED CORRECTLY', val[0])
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else:
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print ('FA Found', val[0], ' in place of ', TEST_CASE[_index-1])
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position_original = WORDS.index(TEST_CASE[_index-1])
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position_observer = WORDS.index(val[0])
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missed[position_original][1] += 1
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false_alarms[position_observer][1] += 1
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# If speech had timestamp not mentioned in kws result, then its detection was missed
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for i, val in enumerate(TEST_CASE):
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if i+1 not in _indices and val != '[RANDOM]':
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position_original = WORDS.index(val)
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missed[position_original][1] += 1
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print ('Missed ', val)
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return missed, false_alarms
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if __name__ == '__main__':
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DIC_FILE = "/home/pankaj/catkin_ws/src/pocketsphinx/demo/voice_cmd.dic"
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KWLIST_FILE = "/home/pankaj/catkin_ws/src/pocketsphinx/demo/automated.kwlist"
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if len(sys.argv) == 3:
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DIC_FILE = sys.argv[1]
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KWLIST_FILE = sys.argv[2]
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preprocess_files(DIC_FILE, KWLIST_FILE)
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