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load_models.py
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load_models.py
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'''
fingerprint audio models in a streaming folder.
'''
import librosa, pickle, getpass, time
from pydub import AudioSegment
import speech_recognition as sr
import os, nltk, random, json
from nltk import word_tokenize
from nltk.classify import apply_features, SklearnClassifier, maxent
from sklearn.naive_bayes import BernoulliNB, MultinomialNB
from sklearn.svm import SVC
from sklearn.ensemble import AdaBoostClassifier, RandomForestClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.linear_model import SGDClassifier, LogisticRegression
from sklearn.model_selection import cross_val_score
from sklearn.ensemble import AdaBoostClassifier
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.ensemble import VotingClassifier
from sklearn.pipeline import Pipeline
from sklearn.model_selection import cross_val_score
from sklearn import preprocessing
from sklearn import svm
from sklearn import metrics
from textblob import TextBlob
import numpy as np
def featurize(wavfile):
#initialize features
hop_length = 512
n_fft=2048
#load file
y, sr = librosa.load(wavfile)
#extract mfcc coefficients
mfcc = librosa.feature.mfcc(y=y, sr=sr, hop_length=hop_length, n_mfcc=13)
mfcc_delta = librosa.feature.delta(mfcc)
#extract mean, standard deviation, min, and max value in mfcc frame, do this across all mfccs
mfcc_features=np.array([np.mean(mfcc[0]),np.std(mfcc[0]),np.amin(mfcc[0]),np.amax(mfcc[0]),
np.mean(mfcc[1]),np.std(mfcc[1]),np.amin(mfcc[1]),np.amax(mfcc[1]),
np.mean(mfcc[2]),np.std(mfcc[2]),np.amin(mfcc[2]),np.amax(mfcc[2]),
np.mean(mfcc[3]),np.std(mfcc[3]),np.amin(mfcc[3]),np.amax(mfcc[3]),
np.mean(mfcc[4]),np.std(mfcc[4]),np.amin(mfcc[4]),np.amax(mfcc[4]),
np.mean(mfcc[5]),np.std(mfcc[5]),np.amin(mfcc[5]),np.amax(mfcc[5]),
np.mean(mfcc[6]),np.std(mfcc[6]),np.amin(mfcc[6]),np.amax(mfcc[6]),
np.mean(mfcc[7]),np.std(mfcc[7]),np.amin(mfcc[7]),np.amax(mfcc[7]),
np.mean(mfcc[8]),np.std(mfcc[8]),np.amin(mfcc[8]),np.amax(mfcc[8]),
np.mean(mfcc[9]),np.std(mfcc[9]),np.amin(mfcc[9]),np.amax(mfcc[9]),
np.mean(mfcc[10]),np.std(mfcc[10]),np.amin(mfcc[10]),np.amax(mfcc[10]),
np.mean(mfcc[11]),np.std(mfcc[11]),np.amin(mfcc[11]),np.amax(mfcc[11]),
np.mean(mfcc[12]),np.std(mfcc[12]),np.amin(mfcc[12]),np.amax(mfcc[12]),
np.mean(mfcc_delta[0]),np.std(mfcc_delta[0]),np.amin(mfcc_delta[0]),np.amax(mfcc_delta[0]),
np.mean(mfcc_delta[1]),np.std(mfcc_delta[1]),np.amin(mfcc_delta[1]),np.amax(mfcc_delta[1]),
np.mean(mfcc_delta[2]),np.std(mfcc_delta[2]),np.amin(mfcc_delta[2]),np.amax(mfcc_delta[2]),
np.mean(mfcc_delta[3]),np.std(mfcc_delta[3]),np.amin(mfcc_delta[3]),np.amax(mfcc_delta[3]),
np.mean(mfcc_delta[4]),np.std(mfcc_delta[4]),np.amin(mfcc_delta[4]),np.amax(mfcc_delta[4]),
np.mean(mfcc_delta[5]),np.std(mfcc_delta[5]),np.amin(mfcc_delta[5]),np.amax(mfcc_delta[5]),
np.mean(mfcc_delta[6]),np.std(mfcc_delta[6]),np.amin(mfcc_delta[6]),np.amax(mfcc_delta[6]),
np.mean(mfcc_delta[7]),np.std(mfcc_delta[7]),np.amin(mfcc_delta[7]),np.amax(mfcc_delta[7]),
np.mean(mfcc_delta[8]),np.std(mfcc_delta[8]),np.amin(mfcc_delta[8]),np.amax(mfcc_delta[8]),
np.mean(mfcc_delta[9]),np.std(mfcc_delta[9]),np.amin(mfcc_delta[9]),np.amax(mfcc_delta[9]),
np.mean(mfcc_delta[10]),np.std(mfcc_delta[10]),np.amin(mfcc_delta[10]),np.amax(mfcc_delta[10]),
np.mean(mfcc_delta[11]),np.std(mfcc_delta[11]),np.amin(mfcc_delta[11]),np.amax(mfcc_delta[11]),
np.mean(mfcc_delta[12]),np.std(mfcc_delta[12]),np.amin(mfcc_delta[12]),np.amax(mfcc_delta[12])])
return mfcc_features
def exportfile(newAudio,time1,time2,filename,i):
#Exports to a wav file in the current path.
newAudio2 = newAudio[time1:time2]
g=os.listdir()
if filename[0:-4]+'_'+str(i)+'.wav' in g:
filename2=str(i)+'_segment'+'.wav'
print('making %s'%(filename2))
newAudio2.export(filename2,format="wav")
else:
filename2=str(i)+'.wav'
print('making %s'%(filename2))
newAudio2.export(filename2, format="wav")
return filename2
def audio_time_features(filename):
#recommend >0.50 seconds for timesplit
timesplit=0.50
hop_length = 512
n_fft=2048
y, sr = librosa.load(filename)
duration=float(librosa.core.get_duration(y))
#Now splice an audio signal into individual elements of 100 ms and extract
#all these features per 100 ms
segnum=round(duration/timesplit)
deltat=duration/segnum
timesegment=list()
time=0
for i in range(segnum):
#milliseconds
timesegment.append(time)
time=time+deltat*1000
newAudio = AudioSegment.from_wav(filename)
filelist=list()
for i in range(len(timesegment)-1):
filename=exportfile(newAudio,timesegment[i],timesegment[i+1],filename,i)
filelist.append(filename)
featureslist=np.array([0,0,0,0,
0,0,0,0,
0,0,0,0,
0,0,0,0,
0,0,0,0,
0,0,0,0,
0,0,0,0,
0,0,0,0,
0,0,0,0,
0,0,0,0,
0,0,0,0,
0,0,0,0,
0,0,0,0,
0,0,0,0,
0,0,0,0,
0,0,0,0,
0,0,0,0,
0,0,0,0,
0,0,0,0,
0,0,0,0,
0,0,0,0,
0,0,0,0,
0,0,0,0,
0,0,0,0,
0,0,0,0,
0,0,0,0])
#save 100 ms segments in current folder (delete them after)
for j in range(len(filelist)):
try:
features=featurize(filelist[i])
featureslist=featureslist+features
os.remove(filelist[j])
except:
print('error splicing')
featureslist.append('silence')
os.remove(filelist[j])
#now scale the featureslist array by the length to get mean in each category
featureslist=featureslist/segnum
return featureslist
def convert(file):
if file[-4:] != '.wav':
filename=file[0:-4]+'.wav'
os.system('ffmpeg -i %s -an %s'%(file,filename))
os.remove(file)
elif file[-4:] == '.wav':
filename=file
else:
filename=file
os.remove(file)
return filename
model_dir=os.getcwd()+'/models'
load_dir=os.getcwd()+'/load_dir'
model_list=list()
os.chdir(model_dir)
listdir=os.listdir()
for i in range(len(listdir)):
if listdir[i][-7:]=='.pickle':
model_list.append(listdir[i])
count=0
errorcount=0
try:
os.chdir(load_dir)
except:
os.mkdir(load_dir)
os.chdir(load_dir)
listdir=os.listdir()
print(os.getcwd())
for i in range(len(listdir)):
try:
if listdir[i][-5:] not in ['Store','.json']:
if listdir[i][-4:] != '.wav':
if listdir[i][-5:] != '.json':
filename=convert(listdir[i])
else:
filename=listdir[i]
print(filename)
if filename[0:-4]+'.json' not in listdir:
features=featurize(filename)
features=features.reshape(1,-1)
os.chdir(model_dir)
class_list=list()
model_acc=list()
deviations=list()
modeltypes=list()
for j in range(len(model_list)):
modelname=model_list[j]
i1=modelname.find('_')
name1=modelname[0:i1]
i2=modelname[i1:]
i3=i2.find('_')
name2=i2[0:i3]
loadmodel=open(modelname, 'rb')
model = pickle.load(loadmodel)
loadmodel.close()
output=str(model.predict(features)[0])
print(output)
classname=output
class_list.append(classname)
g=json.load(open(modelname[0:-7]+'.json'))
model_acc.append(g['accuracy'])
deviations.append(g['deviation'])
modeltypes.append(g['modeltype'])
os.chdir(load_dir)
jsonfilename=filename[0:-4]+'.json'
jsonfile=open(jsonfilename,'w')
data={
'filename':filename,
'filetype':'audio file',
'class':class_list,
'model':model_list,
'model accuracies':model_acc,
'model deviations':deviations,
'model types':modeltypes,
'features':features.tolist(),
'count':count,
'errorcount':errorcount,
}
json.dump(data,jsonfile)
jsonfile.close()
count=count+1
except:
errorcount=errorcount+1
count=count+1