├── .gitignore ├── LICENSE ├── ProbabilityLANGwithTEXTANALYSIS0test3-0.py └── README.md /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | wheels/ 23 | *.egg-info/ 24 | .installed.cfg 25 | *.egg 26 | MANIFEST 27 | 28 | # PyInstaller 29 | # Usually these files are written by a python script from a template 30 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 31 | *.manifest 32 | *.spec 33 | 34 | # Installer logs 35 | pip-log.txt 36 | pip-delete-this-directory.txt 37 | 38 | # Unit test / coverage reports 39 | htmlcov/ 40 | .tox/ 41 | .coverage 42 | .coverage.* 43 | .cache 44 | nosetests.xml 45 | coverage.xml 46 | *.cover 47 | .hypothesis/ 48 | .pytest_cache/ 49 | 50 | # Translations 51 | *.mo 52 | *.pot 53 | 54 | # Django stuff: 55 | *.log 56 | local_settings.py 57 | db.sqlite3 58 | 59 | # Flask stuff: 60 | instance/ 61 | .webassets-cache 62 | 63 | # Scrapy stuff: 64 | .scrapy 65 | 66 | # Sphinx documentation 67 | docs/_build/ 68 | 69 | # PyBuilder 70 | target/ 71 | 72 | # Jupyter Notebook 73 | .ipynb_checkpoints 74 | 75 | # pyenv 76 | .python-version 77 | 78 | # celery beat schedule file 79 | celerybeat-schedule 80 | 81 | # SageMath parsed files 82 | *.sage.py 83 | 84 | # Environments 85 | .env 86 | .venv 87 | env/ 88 | venv/ 89 | ENV/ 90 | env.bak/ 91 | venv.bak/ 92 | 93 | # Spyder project settings 94 | .spyderproject 95 | .spyproject 96 | 97 | # Rope project settings 98 | .ropeproject 99 | 100 | # mkdocs documentation 101 | /site 102 | 103 | # mypy 104 | .mypy_cache/ 105 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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Interpretation of Sections 15 and 16. 613 | 614 | If the disclaimer of warranty and limitation of liability provided 615 | above cannot be given local legal effect according to their terms, 616 | reviewing courts shall apply local law that most closely approximates 617 | an absolute waiver of all civil liability in connection with the 618 | Program, unless a warranty or assumption of liability accompanies a 619 | copy of the Program in return for a fee. 620 | 621 | END OF TERMS AND CONDITIONS 622 | 623 | How to Apply These Terms to Your New Programs 624 | 625 | If you develop a new program, and you want it to be of the greatest 626 | possible use to the public, the best way to achieve this is to make it 627 | free software which everyone can redistribute and change under these terms. 628 | 629 | To do so, attach the following notices to the program. It is safest 630 | to attach them to the start of each source file to most effectively 631 | state the exclusion of warranty; and each file should have at least 632 | the "copyright" line and a pointer to where the full notice is found. 633 | 634 | 635 | Copyright (C) 636 | 637 | This program is free software: you can redistribute it and/or modify 638 | it under the terms of the GNU General Public License as published by 639 | the Free Software Foundation, either version 3 of the License, or 640 | (at your option) any later version. 641 | 642 | This program is distributed in the hope that it will be useful, 643 | but WITHOUT ANY WARRANTY; without even the implied warranty of 644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 645 | GNU General Public License for more details. 646 | 647 | You should have received a copy of the GNU General Public License 648 | along with this program. If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /ProbabilityLANGwithTEXTANALYSIS0test3-0.py: -------------------------------------------------------------------------------- 1 | from __future__ import print_function 2 | import sys 3 | def my_except_hook(exctype, value, traceback): 4 | print('There has been an error in the system') 5 | sys.excepthook = my_except_hook 6 | import warnings 7 | if not sys.warnoptions: 8 | warnings.simplefilter("ignore") 9 | import parselmouth 10 | from parselmouth.praat import call, run_file 11 | import glob 12 | import operator 13 | import speech_recognition as sr 14 | from langdetect import detect 15 | from langdetect import detect_langs 16 | import numpy as np # linear algebra 17 | from nltk.sentiment import SentimentAnalyzer 18 | import nltk, re, pprint 19 | from nltk import word_tokenize 20 | from nltk.tokenize import sent_tokenize 21 | from nltk.probability import FreqDist 22 | from nltk.corpus import stopwords 23 | from nltk.stem import PorterStemmer 24 | from nltk.stem.wordnet import WordNetLemmatizer 25 | nltk.download('punkt') 26 | import random 27 | import time 28 | import errno 29 | import csv,sys 30 | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv) 31 | import os 32 | from subprocess import check_output 33 | from sklearn import preprocessing 34 | from sklearn.metrics import cohen_kappa_score 35 | import queue 36 | import sounddevice as sd 37 | import soundfile as sf 38 | import _thread 39 | import pickle 40 | from scipy.stats import binom 41 | from scipy.stats import ks_2samp 42 | from scipy.stats import ttest_ind 43 | from pandas import read_csv 44 | from textblob import TextBlob 45 | from collections import defaultdict 46 | from nltk.classify.util import apply_features, accuracy as eval_accuracy 47 | from nltk.collocations import BigramCollocationFinder 48 | from nltk.metrics import ( 49 | BigramAssocMeasures, 50 | precision as eval_precision, 51 | recall as eval_recall, 52 | f_measure as eval_f_measure, 53 | ) 54 | from textblob import Word 55 | from textblob.wordnet import VERB 56 | from nltk.probability import FreqDist 57 | from nltk.sentiment.util import save_file, timer 58 | 59 | pathy = input("Enter the path to the Auto-Speech_Rater directory: ") 60 | name = input("what is your name? ") 61 | t0 = int(input("Your desired Recording time in seconds: ")) 62 | ran = input("jp for Japanese, ln for Like-Native, n for Native: ") 63 | smr = int(input("sampling rate; 16000 or 42000 or 48000 or 96000 Hz: ")) 64 | bt=int(input("bit depth; 8 or 16 or 24 or 32 bit: ")) 65 | 66 | 67 | pa00=pathy+"/"+"dataset"+"/"+"audioFiles"+"/" 68 | pa0=pathy+"/"+"dataset"+"/"+"audioFiles"+"/"+name+".wav" 69 | pa1=pathy+"/"+"dataset"+"/"+"datanewchi22.csv" 70 | pa2=pathy+"/"+"dataset"+"/"+"stats.csv" 71 | pa3=pathy+"/"+"dataset"+"/"+"datacorrP.csv" 72 | pa4=pathy+"/"+"dataset"+"/"+"datanewchi.csv" 73 | pa5=pathy+"/"+"dataset"+"/"+"datanewchi33.csv" 74 | pa6=pathy+"/"+"dataset"+"/"+"datanewchi33.csv" 75 | pa7=pathy+"/"+"dataset"+"/"+"datanewchi44.csv" 76 | pa8=pathy+"/"+"dataset"+"/"+"essen"+"/"+"MLTRNL.praat" 77 | pa9=pathy+"/"+"dataset"+"/"+"essen"+"/"+"myspsolution.praat" 78 | 79 | rere=pa0 80 | 81 | RECORD_TIME = t0 82 | 83 | def countdown(p,q,w): 84 | i=p 85 | j=q 86 | z=w 87 | k=0 88 | while True: 89 | if(j==-1): 90 | j=59 91 | i -=1 92 | if(j > 9): 93 | print(str(k)+str(i)+ " : " +str(j), "\t", end="\r") 94 | else: 95 | print(str(k)+str(i)+" : " + str(k)+str(j), "\t", end="\r") 96 | time.sleep(1) 97 | j -= 1 98 | if(i==0 and j==-1): 99 | break 100 | if(i==0 and j==-1): 101 | if z==0: 102 | huf="Go ahead!" 103 | print(huf) 104 | if z==1: 105 | huf="Time up!" 106 | # time.sleep(1) 107 | 108 | print("===========================================") 109 | print("HOLD ON!! get ready, 5 seconds to go!") 110 | print("===========================================") 111 | countdown(0,5,0) #countdown(min,sec) 112 | 113 | 114 | q = queue.Queue() 115 | rec_start = int(time.time()) 116 | 117 | dev_info = sd.query_devices(2, 'input') 118 | # samplerate = int(dev_info['default_samplerate']) 119 | samplerate = smr 120 | 121 | def data_callback(input_data, frames, time, status): 122 | if status: 123 | print(status, file=sys.stderr) 124 | q.put(input_data.copy()) 125 | 126 | with sf.SoundFile(rere, mode='x', samplerate=samplerate, channels=2) as file: 127 | with sd.InputStream(samplerate=samplerate, device=2, channels=2, callback=data_callback,blocksize=20500): 128 | rec_time = int(time.time()) - rec_start 129 | _thread.start_new_thread(countdown,(0,t0,1)) 130 | while rec_time <= RECORD_TIME: 131 | file.write(q.get()) 132 | rec_time = int(time.time()) - rec_start 133 | 134 | result_array = np.empty((0, 100)) 135 | path = pa0 136 | files = glob.glob(path) 137 | result_array = np.empty((0, 27)) 138 | 139 | try: 140 | def mysppron(m,p,q): 141 | sound=m 142 | sourcerun=p 143 | path=q 144 | objects= run_file(sourcerun, -20, 2, 0.3, "yes",sound,path, 80, 400, 0.01, capture_output=True) 145 | print (objects[0]) # This will print the info from the sound object, and objects[0] is a parselmouth.Sound object 146 | z1=str( objects[1]) # This will print the info from the textgrid object, and objects[1] is a parselmouth.Data object with a TextGrid inside 147 | z2=z1.strip().split() 148 | z3=int(z2[13]) # will be the integer number 10 149 | z4=float(z2[14]) # will be the floating point number 8.3 150 | db= binom.rvs(n=10,p=z4,size=10000) 151 | a=np.array(db) 152 | b=np.mean(a)*100/10 153 | print ("Pronunciation_posteriori_probability_score_percentage= :%.2f" % (b)) 154 | return; 155 | 156 | def myspp(m,p,q): 157 | sound=m 158 | sourcerun=p 159 | path=q 160 | objects= run_file(sourcerun, -20, 2, 0.3, "yes",sound,path, 80, 400, 0.01, capture_output=True) 161 | print (objects[0]) # This will print the info from the sound object, and objects[0] is a parselmouth.Sound object 162 | z1=str( objects[1]) # This will print the info from the textgrid object, and objects[1] is a parselmouth.Data object with a TextGrid inside 163 | z2=z1.strip().split() 164 | z3=int(z2[13]) # will be the integer number 10 165 | z4=float(z2[14]) # will be the floating point number 8.3 166 | db= binom.rvs(n=10,p=z4,size=10000) 167 | a=np.array(db) 168 | b=np.mean(a)*100/10 169 | return b 170 | 171 | def myspgend(m,p,q): 172 | sound=m 173 | sourcerun=p 174 | path=q 175 | objects= run_file(sourcerun, -20, 2, 0.3, "yes",sound,path, 80, 400, 0.01, capture_output=True) 176 | print (objects[0]) # This will print the info from the sound object, and objects[0] is a parselmouth.Sound object 177 | z1=str( objects[1]) # This will print the info from the textgrid object, and objects[1] is a parselmouth.Data object with a TextGrid inside 178 | z2=z1.strip().split() 179 | z3=float(z2[8]) # will be the integer number 10 180 | z4=float(z2[7]) # will be the floating point number 8.3 181 | 182 | if z4<=114: 183 | g=101 184 | j=3.4 185 | elif z4>114 and z4<=135: 186 | g=128 187 | j=4.35 188 | elif z4>135 and z4<=163: 189 | g=142 190 | j=4.85 191 | elif z4>163 and z4<=197: 192 | g=182 193 | j=2.7 194 | elif z4>197 and z4<=226: 195 | g=213 196 | j=4.5 197 | elif z4>226: 198 | g=239 199 | j=5.3 200 | else: 201 | print("Voice not recognized") 202 | exit() 203 | def teset(a,b,c,d): 204 | d1=np.random.wald(a, 1, 1000) 205 | d2=np.random.wald(b,1,1000) 206 | d3=ks_2samp(d1, d2) 207 | c1=np.random.normal(a,c,1000) 208 | c2=np.random.normal(b,d,1000) 209 | c3=ttest_ind(c1,c2) 210 | y=([d3[0],d3[1],abs(c3[0]),c3[1]]) 211 | return y 212 | nn=0 213 | mm=teset(g,j,z4,z3) 214 | while (mm[3]>0.05 and mm[0]>0.04 or nn<5): 215 | mm=teset(g,j,z4,z3) 216 | nn=nn+1 217 | nnn=nn 218 | if mm[3]<=0.09: 219 | mmm=mm[3] 220 | else: 221 | mmm=0.35 222 | if z4>97 and z4<=114: 223 | print("a Male, mood of speech: Showing no emotion, normal, p-value/sample size= :%.2f" % (mmm), (nnn)) 224 | elif z4>114 and z4<=135: 225 | print("a Male, mood of speech: Reading, p-value/sample size= :%.2f" % (mmm), (nnn)) 226 | elif z4>135 and z4<=163: 227 | print("a Male, mood of speech: speaking passionately, p-value/sample size= :%.2f" % (mmm), (nnn)) 228 | elif z4>163 and z4<=197: 229 | print("a female, mood of speech: Showing no emotion, normal, p-value/sample size= :%.2f" % (mmm), (nnn)) 230 | elif z4>197 and z4<=226: 231 | print("a female, mood of speech: Reading, p-value/sample size= :%.2f" % (mmm), (nnn)) 232 | elif z4>226 and z4<=245: 233 | print("a female, mood of speech: speaking passionately, p-value/sample size= :%.2f" % (mmm), (nnn)) 234 | else: 235 | print("Voice not recognized") 236 | 237 | AUDIO_FILE = (pa0) 238 | r = sr.Recognizer() 239 | with sr.AudioFile(AUDIO_FILE) as source: 240 | audio = r.record(source,duration=15) # read the entire audio file 241 | try: 242 | trans=r.recognize_google(audio,language = "en-US") 243 | #trans=r.recognize_sphinx(audio, language="en-US") 244 | #print("The audio file contains: " + a) 245 | except sr.UnknownValueError: 246 | print("Machine could not understand the audio") 247 | except sr.RequestError as e: 248 | print("Not good internet connection; {0}".format(e)) 249 | 250 | b=detect_langs(trans) 251 | c=detect(trans) 252 | now=len(trans.split()) 253 | 254 | if not c=='en'or now<10: 255 | input("No further result, did you speak in English or the machine heard unnatural-sounding speech Try again,Press any key to exit.") 256 | exit() 257 | 258 | 259 | for soundi in files: 260 | objects= run_file(pa8, -20, 2, 0.3, "yes", soundi, pa00, 80, 400, 0.01, capture_output=True) 261 | #print (objects[0]) # This will print the info from the sound object, and objects[0] is a parselmouth.Sound object 262 | z1=( objects[1]) # This will print the info from the textgrid object, and objects[1] is a parselmouth.Data object with a TextGrid inside 263 | z3=z1.strip().split() 264 | z2=np.array([z3]) 265 | result_array=np.append(result_array,[z3], axis=0) 266 | 267 | np.savetxt(pa1,result_array, fmt='%s',delimiter=',') 268 | 269 | #Data and features analysis 270 | df = pd.read_csv(pa1, 271 | names = ['avepauseduratin','avelongpause','speakingtot','avenumberofwords','articulationrate','inpro','f1norm','mr','q25', 272 | 'q50','q75','std','fmax','fmin','vowelinx1','vowelinx2','formantmean','formantstd','nuofwrds','npause','ins', 273 | 'fillerratio','xx','xxx','totsco','xxban','speakingrate'],na_values='?') 274 | 275 | scoreMLdataset=df.drop(['xxx','xxban'], axis=1) 276 | scoreMLdataset.to_csv(pa7, header=False,index = False) 277 | newMLdataset=df.drop(['avenumberofwords','f1norm','inpro','q25','q75','vowelinx1','nuofwrds','npause','xx','totsco','xxban','speakingrate','fillerratio'], axis=1) 278 | newMLdataset.to_csv(pa5, header=False,index = False) 279 | namess=nms = ['avepauseduratin','avelongpause','speakingtot','articulationrate','mr', 280 | 'q50','std','fmax','fmin','vowelinx2','formantmean','formantstd','ins', 281 | 'xxx'] 282 | df1 = pd.read_csv(pa5, 283 | names = namess) 284 | df33=df1.drop(['xxx'], axis=1) 285 | array = df33.values 286 | array=np.log(array) 287 | x = array[:,0:13] 288 | 289 | print(" ") 290 | print(" ") 291 | print("===========================================") 292 | if ran=="jp": 293 | levv=45 294 | elif ran=="ln": 295 | levv=65 296 | else: 297 | levv=80 298 | p=pa0 299 | c=pa9 300 | a=pa00 301 | bi=myspp(p,c,a) 302 | if bi1: 415 | vall=2-vall 416 | else: 417 | vall=vall 418 | 419 | mom=np.array(["a","a1","a2","b1","b2","c"]) 420 | resA=mom[inx] 421 | momm=np.where(mom==inx) 422 | print(" ") 423 | print(" ") 424 | print("===========================================") 425 | print("Your sprosodic fluency level could be at ",resA," with a confidence level of ", "%.2f" % vall) 426 | print(" ") 427 | print("===========================================") 428 | 429 | except: 430 | print(" ") 431 | print(" ") 432 | print("===========================================") 433 | print("Try again, noisy background or unnatural-sounding speech detected. No result.") 434 | exit() 435 | def percentage1(count, total): 436 | return 100 * count / total 437 | def percentage2(xxx): 438 | return 100 * xxx 439 | 440 | class SentimentAnalyzer(object): 441 | """ 442 | A Sentiment Analysis tool based on machine learning approaches. 443 | """ 444 | 445 | def __init__(self, classifier=None): 446 | self.feat_extractors = defaultdict(list) 447 | self.classifier = classifier 448 | 449 | def all_words(self, documents, labeled=None): 450 | """ 451 | Return all words/tokens from the documents (with duplicates). 452 | :param documents: a list of (words, label) tuples. 453 | :param labeled: if `True`, assume that each document is represented by a 454 | (words, label) tuple: (list(str), str). If `False`, each document is 455 | considered as being a simple list of strings: list(str). 456 | :rtype: list(str) 457 | :return: A list of all words/tokens in `documents`. 458 | """ 459 | all_words = [] 460 | if labeled is None: 461 | labeled = documents and isinstance(documents[0], tuple) 462 | if labeled == True: 463 | for words, sentiment in documents: 464 | all_words.extend(words) 465 | elif labeled == False: 466 | for words in documents: 467 | all_words.extend(words) 468 | return all_words 469 | 470 | 471 | def apply_features(self, documents, labeled=None): 472 | """ 473 | Apply all feature extractor functions to the documents. This is a wrapper 474 | around `nltk.classify.util.apply_features`. 475 | 476 | If `labeled=False`, return featuresets as: 477 | [feature_func(doc) for doc in documents] 478 | If `labeled=True`, return featuresets as: 479 | [(feature_func(tok), label) for (tok, label) in toks] 480 | 481 | :param documents: a list of documents. `If labeled=True`, the method expects 482 | a list of (words, label) tuples. 483 | :rtype: LazyMap 484 | """ 485 | return apply_features(self.extract_features, documents, labeled) 486 | 487 | 488 | def unigram_word_feats(self, words, top_n=None, min_freq=0): 489 | """ 490 | Return most common top_n word features. 491 | 492 | :param words: a list of words/tokens. 493 | :param top_n: number of best words/tokens to use, sorted by frequency. 494 | :rtype: list(str) 495 | :return: A list of `top_n` words/tokens (with no duplicates) sorted by 496 | frequency. 497 | """ 498 | # Stopwords are not removed 499 | unigram_feats_freqs = FreqDist(word for word in words) 500 | return [ 501 | w 502 | for w, f in unigram_feats_freqs.most_common(top_n) 503 | if unigram_feats_freqs[w] > min_freq 504 | ] 505 | 506 | 507 | def bigram_collocation_feats( 508 | self, documents, top_n=None, min_freq=3, assoc_measure=BigramAssocMeasures.pmi 509 | ): 510 | """ 511 | Return `top_n` bigram features (using `assoc_measure`). 512 | Note that this method is based on bigram collocations measures, and not 513 | on simple bigram frequency. 514 | 515 | :param documents: a list (or iterable) of tokens. 516 | :param top_n: number of best words/tokens to use, sorted by association 517 | measure. 518 | :param assoc_measure: bigram association measure to use as score function. 519 | :param min_freq: the minimum number of occurrencies of bigrams to take 520 | into consideration. 521 | 522 | :return: `top_n` ngrams scored by the given association measure. 523 | """ 524 | finder = BigramCollocationFinder.from_documents(documents) 525 | finder.apply_freq_filter(min_freq) 526 | return finder.nbest(assoc_measure, top_n) 527 | 528 | 529 | def classify(self, instance): 530 | """ 531 | Classify a single instance applying the features that have already been 532 | stored in the SentimentAnalyzer. 533 | 534 | :param instance: a list (or iterable) of tokens. 535 | :return: the classification result given by applying the classifier. 536 | """ 537 | instance_feats = self.apply_features([instance], labeled=False) 538 | return self.classifier.classify(instance_feats[0]) 539 | 540 | 541 | def add_feat_extractor(self, function, **kwargs): 542 | """ 543 | Add a new function to extract features from a document. This function will 544 | be used in extract_features(). 545 | Important: in this step our kwargs are only representing additional parameters, 546 | and NOT the document we have to parse. The document will always be the first 547 | parameter in the parameter list, and it will be added in the extract_features() 548 | function. 549 | 550 | :param function: the extractor function to add to the list of feature extractors. 551 | :param kwargs: additional parameters required by the `function` function. 552 | """ 553 | self.feat_extractors[function].append(kwargs) 554 | 555 | 556 | def extract_features(self, document): 557 | """ 558 | Apply extractor functions (and their parameters) to the present document. 559 | We pass `document` as the first parameter of the extractor functions. 560 | If we want to use the same extractor function multiple times, we have to 561 | add it to the extractors with `add_feat_extractor` using multiple sets of 562 | parameters (one for each call of the extractor function). 563 | 564 | :param document: the document that will be passed as argument to the 565 | feature extractor functions. 566 | :return: A dictionary of populated features extracted from the document. 567 | :rtype: dict 568 | """ 569 | all_features = {} 570 | for extractor in self.feat_extractors: 571 | for param_set in self.feat_extractors[extractor]: 572 | feats = extractor(document, **param_set) 573 | all_features.update(feats) 574 | return all_features 575 | 576 | 577 | def train(self, trainer, training_set, save_classifier=None, **kwargs): 578 | """ 579 | Train classifier on the training set, optionally saving the output in the 580 | file specified by `save_classifier`. 581 | Additional arguments depend on the specific trainer used. For example, 582 | a MaxentClassifier can use `max_iter` parameter to specify the number 583 | of iterations, while a NaiveBayesClassifier cannot. 584 | 585 | :param trainer: `train` method of a classifier. 586 | E.g.: NaiveBayesClassifier.train 587 | :param training_set: the training set to be passed as argument to the 588 | classifier `train` method. 589 | :param save_classifier: the filename of the file where the classifier 590 | will be stored (optional). 591 | :param kwargs: additional parameters that will be passed as arguments to 592 | the classifier `train` function. 593 | :return: A classifier instance trained on the training set. 594 | :rtype: 595 | """ 596 | print("Training classifier") 597 | self.classifier = trainer(training_set, **kwargs) 598 | if save_classifier: 599 | save_file(self.classifier, save_classifier) 600 | 601 | return self.classifier 602 | 603 | 604 | def evaluate( 605 | self, 606 | test_set, 607 | classifier=None, 608 | accuracy=True, 609 | f_measure=True, 610 | precision=True, 611 | recall=True, 612 | verbose=False, 613 | ): 614 | """ 615 | Evaluate and print classifier performance on the test set. 616 | 617 | :param test_set: A list of (tokens, label) tuples to use as gold set. 618 | :param classifier: a classifier instance (previously trained). 619 | :param accuracy: if `True`, evaluate classifier accuracy. 620 | :param f_measure: if `True`, evaluate classifier f_measure. 621 | :param precision: if `True`, evaluate classifier precision. 622 | :param recall: if `True`, evaluate classifier recall. 623 | :return: evaluation results. 624 | :rtype: dict(str): float 625 | """ 626 | if classifier is None: 627 | classifier = self.classifier 628 | print("Evaluating {0} results...".format(type(classifier).__name__)) 629 | metrics_results = {} 630 | if accuracy == True: 631 | accuracy_score = eval_accuracy(classifier, test_set) 632 | metrics_results['Accuracy'] = accuracy_score 633 | 634 | gold_results = defaultdict(set) 635 | test_results = defaultdict(set) 636 | labels = set() 637 | for i, (feats, label) in enumerate(test_set): 638 | labels.add(label) 639 | gold_results[label].add(i) 640 | observed = classifier.classify(feats) 641 | test_results[observed].add(i) 642 | 643 | for label in labels: 644 | if precision == True: 645 | precision_score = eval_precision( 646 | gold_results[label], test_results[label] 647 | ) 648 | metrics_results['Precision [{0}]'.format(label)] = precision_score 649 | if recall == True: 650 | recall_score = eval_recall(gold_results[label], test_results[label]) 651 | metrics_results['Recall [{0}]'.format(label)] = recall_score 652 | if f_measure == True: 653 | f_measure_score = eval_f_measure( 654 | gold_results[label], test_results[label] 655 | ) 656 | metrics_results['F-measure [{0}]'.format(label)] = f_measure_score 657 | 658 | # Print evaluation results (in alphabetical order) 659 | if verbose == True: 660 | for result in sorted(metrics_results): 661 | print('{0}: {1}'.format(result, metrics_results[result])) 662 | 663 | return metrics_results 664 | 665 | 666 | 667 | text = trans 668 | txt=trans.split() 669 | lent=len(word_tokenize(trans)) 670 | SentimentAnalyzer(text) 671 | fe=pathy+"/"+"dataset"+"/"+"essen"+"/"+"my_classifier.pickle" 672 | f = open(fe, 'rb') 673 | classifier = pickle.load(f) 674 | qualityy=TextBlob(text) 675 | obsub=qualityy.sentiment 676 | if obsub[1]>0.5: 677 | print("Your speech sounds less logically structured by %: ", "%.2f" % ((obsub[1]-0.5)*100)) 678 | else: 679 | print("Your speech sounds more logically structured by %: ", "%.2f" % ((1-obsub[1])*100)) 680 | f.close() 681 | quaa=qualityy.words 682 | def lexical_diversity(a): 683 | return len(set(trans))/len(trans) 684 | v=percentage2(lexical_diversity(trans)) 685 | if v>=0.145*100 and v<0.231*100 : 686 | langscore="b2" 687 | elif v>=0.231*100: 688 | langscore="c" 689 | elif v>=0.121*100 and v<0.145*100: 690 | langscore="b1" 691 | elif v<0.121*100 and v>=0.09*100: 692 | langscore="a2" 693 | else: 694 | langscore="a1" 695 | 696 | tokenized_text=word_tokenize(text) 697 | #print(tokenized_text) 698 | tokenized_sent=sent_tokenize(text) 699 | stop_words=set(stopwords.words("english")) 700 | filtered_sent=[] 701 | for w in tokenized_text: 702 | if w not in stop_words: 703 | filtered_sent.append(w) 704 | #print("Filterd Sentence:",filtered_sent) 705 | filtered_sent=filtered_sent 706 | ps = PorterStemmer() 707 | stemmed_words=[] 708 | for w in filtered_sent: 709 | stemmed_words.append(ps.stem(w)) 710 | #print("Stemmed Sentence:",stemmed_words) 711 | def lexical_diversity(a): 712 | return len(set(a))/len(a) 713 | def lexical_richness(a,b): 714 | return len(a)/len(b) 715 | vio=percentage2(lexical_diversity(tokenized_text)) 716 | vocano=lexical_richness(filtered_sent,tokenized_text) 717 | if vocano>=0.4 and vocano<0.5: 718 | langscoree="b2" 719 | elif vocano>=0.5: 720 | langscoree="c" 721 | elif vocano>=0.3 and vocano<0.4: 722 | langscoree="b1" 723 | elif vocano<0.3 and vocano>=0.2: 724 | langscoree="a2" 725 | else: 726 | langscoree="a1" 727 | 728 | vvv=percentage2(vocano) 729 | #print(len(set(filtered_sent))) 730 | #print(len(trans)) 731 | #print(len(tokenized_text)) 732 | vocab = filtered_sent 733 | long_words = [w for w in vocab if len(w) >10] 734 | soso=sorted(long_words) 735 | mesu=len(soso)/len(vocab) 736 | if mesu>0.01: 737 | sayy="you used sofisticated words" 738 | else: 739 | sayy=" " 740 | 741 | if resA=="c": 742 | if langscore=="c" or langscore=="b2": 743 | ai="c" 744 | elif langscore=="b1" or langscore=="a2": 745 | ai="b2" 746 | elif langscore=="a1": 747 | ai="b1" 748 | else: 749 | ai="b1" 750 | 751 | if resA=="b2": 752 | if langscore=="c" or langscore=="b2": 753 | ai="b2" 754 | elif langscore=="b1" or langscore=="a2": 755 | ai="b2" 756 | elif langscore=="a1": 757 | ai="b1" 758 | else: 759 | ai="b1" 760 | if resA=="b1": 761 | if langscore=="c" or langscore=="b2": 762 | ai="b2" 763 | elif langscore=="b1" or langscore=="a2": 764 | ai="b1" 765 | elif langscore=="a1": 766 | ai="b1" 767 | else: 768 | ai="a2" 769 | if resA=="a2": 770 | if langscore=="c" or langscore=="b2": 771 | ai="b1" 772 | elif langscore=="b1" or langscore=="a2": 773 | ai="a2" 774 | elif langscore=="a1": 775 | ai="a2" 776 | else: 777 | ai="a1" 778 | if resA=="a1" or resA=="a": 779 | if langscore=="c" or langscore=="b2": 780 | ai="a1" 781 | elif langscore=="b1" or langscore=="a2": 782 | ai="a" 783 | elif langscore=="a1": 784 | ai="a" 785 | else: 786 | ai="a" 787 | if ai=="c": 788 | iBT="26-30" 789 | if ai=="b2": 790 | iBT="18-25" 791 | if ai=="b1": 792 | iBT="10-17" 793 | if ai=="a2": 794 | iBT="less than 9" 795 | 796 | print(" ") 797 | print("===========================================") 798 | print("the lexical richness in specific contexts; confidence level %:",round(v,1)) 799 | print("the lexical richness in in ordinary conversation contexts; confidence level %:",round(vio,1)) 800 | print("the lexical diversity; confidence level %: ",round(vvv,1)) 801 | print(sayy) 802 | print(" ") 803 | print("your use of academic language skill could be at ", langscore) 804 | print("your use of general language skill could be at ", langscoree) 805 | print(" ") 806 | print("===========================================") 807 | print("your TOEFL iBT score could be in this range ", iBT) 808 | print("===========================================") 809 | 810 | fini=input("The general assessment is DONE, press f to continue for a specific assessment or any other key to terminate the programe: ") 811 | 812 | if fini=='f': 813 | s1 = input("Enter key word-1: ") 814 | s2 = input("Enter key word-2: ") 815 | s3 = input("Enter key word-3: ") 816 | v1=a.count(s1) 817 | v2=a.count(s2) 818 | v3=a.count(s3) 819 | ran=[v1,v2,v3] 820 | def totscore(numList): 821 | totscore=0 822 | for i in numList: 823 | if i==0: 824 | score=-1 825 | else: 826 | score=i 827 | totscore=totscore+score 828 | return totscore 829 | print(totscore(ran)) 830 | print(v1) 831 | print(v2) 832 | print(v3) 833 | input("Done,press any key to terminate the program") 834 | else: 835 | exit() 836 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Machine-Learning-Algorithm-for-Voice-Analysis 2 | 3 | ## It is an algorithm analysed the acoustic features of a voice and creates an acoustic classifier - USEFUL for auto-speech-rater 4 | 5 | ### It takes 12 hours to complete the machine training (12000 datapoints and 47 initial features). 6 | 7 | ### To run the algorithm, you need to prepare your dataset in the form of csv. 8 | 9 | ### If you need the algorithm to extract the acoustic features of audios, please contact me https://shahabks.github.io/Mysolution-Lab-AI/ 10 | --------------------------------------------------------------------------------