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train.py
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train.py
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import os
import wave
import pickle
import pyaudio
import warnings
import numpy as np
from sklearn import preprocessing
from scipy.io.wavfile import read
import python_speech_features as mfcc
from sklearn.mixture import GaussianMixture
from listen import *
from time import time
import speech_recognition as sr
import playsound
from gtts import gTTS
r = sr.Recognizer()
def speak(text):
tts = gTTS(text = text, lang = "en")
filename = "voice.mp3"
tts.save(filename)
playsound.playsound(filename)
os.remove(filename)
def calculate_delta(array):
rows,cols = array.shape
print(rows)
print(cols)
deltas = np.zeros((rows,20))
N = 2
for i in range(rows):
index = []
j = 1
while j <= N:
if i-j < 0:
first =0
else:
first = i-j
if i+j > rows-1:
second = rows-1
else:
second = i+j
index.append((second,first))
j+=1
deltas[i] = ( array[index[0][0]]-array[index[0][1]] + (2 * (array[index[1][0]]-array[index[1][1]])) ) / 10
return deltas
def extract_features(audio,rate):
mfcc_feature = mfcc.mfcc(audio,rate, 0.025, 0.01,20,nfft = 1200, appendEnergy = True)
mfcc_feature = preprocessing.scale(mfcc_feature)
print(mfcc_feature)
delta = calculate_delta(mfcc_feature)
combined = np.hstack((mfcc_feature,delta))
return combined
def record_audio_train():
speak("Please Speak Your Name:")
#Name =(input("Please Enter Your Name:")
Name = listener()
Ques = ["How are you?", "State your class and course","State your roll number","Repeat this line 3 times: Red blood, Blue blood","Any final words?"]
for count in range(5):
FORMAT = pyaudio.paInt16
CHANNELS = 1
RATE = 44100
CHUNK = 512
RECORD_SECONDS = 8
device_index = 2
audio = pyaudio.PyAudio()
print("----------------------record device list---------------------")
info = audio.get_host_api_info_by_index(0)
numdevices = info.get('deviceCount')
# for i in range(0, numdevices):
# if(audio.get_device_info_by_host_api_device_index(0, i).get('maxInputChannels')) > 0:
# print("Input Device id ", i, " - ", audio.get_device_info_by_host_api_device_index(0, i).get('name'))
# print("-------------------------------------------------------------")
index = 0#int(input())
print("recording via index "+str(index))
print(Ques[count])
speak(Ques[count])
stream = audio.open(format=FORMAT, channels=CHANNELS,
rate=RATE, input=True,input_device_index = index,
frames_per_buffer=CHUNK)
print ("recording started")
Recordframes = []
for i in range(0, int(RATE / CHUNK * RECORD_SECONDS)):
data = stream.read(CHUNK)
Recordframes.append(data)
print ("recording stopped")
stream.stop_stream()
stream.close()
audio.terminate()
OUTPUT_FILENAME=Name+"-sample"+str(count)+".wav"
WAVE_OUTPUT_FILENAME=os.path.join("training_set",OUTPUT_FILENAME)
trainedfilelist = open("training_set_addition.txt", 'a')
trainedfilelist.write(OUTPUT_FILENAME+"\n")
waveFile = wave.open(WAVE_OUTPUT_FILENAME, 'wb')
waveFile.setnchannels(CHANNELS)
waveFile.setsampwidth(audio.get_sample_size(FORMAT))
waveFile.setframerate(RATE)
waveFile.writeframes(b''.join(Recordframes))
waveFile.close()
def train_model():
source = "C:\\Users\\aswinik\\Documents\\Assignments\\code_ed-main\\AudioAssessment\\training_set\\"
dest = "C:\\Users\\aswinik\\Documents\\Assignments\\code_ed-main\\AudioAssessment\\trained_models"
train_file = "C:\\Users\\aswinik\\Documents\\Assignments\\code_ed-main\\AudioAssessment\\training_set_addition.txt"
file_paths = open(train_file,'r')
count = 1
features = np.asarray(())
for path in file_paths:
path = path.strip()
print(path)
sr,audio = read(source + path)
print(sr)
vector = extract_features(audio,sr)
if features.size == 0:
features = vector
else:
features = np.vstack((features, vector))
if count == 5:
gmm = GaussianMixture(n_components = 6, max_iter = 200, covariance_type='diag',n_init = 3)
gmm.fit(features)
# dumping the trained gaussian model
picklefile = path.split("-")[0]+".gmm"
print(picklefile)
pickle.dump(gmm,open(dest + picklefile,'wb'))
print('+ modeling completed for speaker:',picklefile," with data point = ",features.shape)
features = np.asarray(())
count = 0
count = count + 1
# record_audio_train()
# train_model()