How can I improve my Neural Network accucary ( Speaker Recognition - MFCC ) - machine-learning

Question

Im working on a speaker recognition Neural Network.
What I am doing is taking wav files [ of the Bing Bang Theory first espiode :-) ], than convert it to MFCC coeffs than I make it as an input to an open source api of Neural Network (MLPClassifier) and as output I define a unique vector to each speaker ( Let's say : [1,0,0,0] - sheldon; [0,1,0,0] - Penny; and ect... ), I take 50 random values for testing and the others for fitting ( training )
This is my code, At the begining I got about random accucary for the NN but after some help of amazing guy I improved it to ~42% but I want more :) about 70% :
from sklearn.neural_network import MLPClassifier
import python_speech_features
import scipy.io.wavfile as wav
import numpy as np
from os import listdir
from os.path import isfile, join
from random import shuffle
import matplotlib.pyplot as plt
from tqdm import tqdm
from random import randint
import random
winner = [] # this array count how much Bingo we had when we test the NN
random_winner = []
win_len = 0.04 # in seconds
step = win_len / 2
nfft = 2048
for TestNum in tqdm(range(20)): # in every round we build NN with X,Y that out of them we check 50 after we build the NN
X = []
Y = []
onlyfiles = [f for f in listdir("FinalAudios/") if isfile(join("FinalAudios/", f))] # Files in dir
names = [] # names of the speakers
for file in onlyfiles: # for each wav sound
# UNESSECERY TO UNDERSTAND THE CODE
if " " not in file.split("_")[0]:
names.append(file.split("_")[0])
else:
names.append(file.split("_")[0].split(" ")[0])
only_speakers = [] + names
#print only_speakers
names = list(dict.fromkeys(names)) # names of speakers
print names
vector_names = [] # vector for each name
i = 0
vector_for_each_name = [0] * len(names)
for name in names:
vector_for_each_name[i] += 1
vector_names.append(np.array(vector_for_each_name))
vector_for_each_name[i] -= 1
i += 1
for f in onlyfiles:
if " " not in f.split("_")[0]:
f_speaker = f.split("_")[0]
else:
f_speaker = f.split("_")[0].split(" ")[0]
fs, audio = wav.read("FinalAudios/" + f) # read the file
try:
mfcc_feat = python_speech_features.mfcc(audio, samplerate=fs, winlen=win_len,
winstep=step, nfft=nfft, appendEnergy=False)
flat_list = [item for sublist in mfcc_feat for item in sublist]
X.append(np.array(flat_list))
Y.append(np.array(vector_names[names.index(f_speaker)]))
except IndexError:
pass
Z = list(zip(X, Y))
shuffle(Z) # WE SHUFFLE X,Y TO PERFORM RANDOM ON THE TEST LEVEL
X, Y = zip(*Z)
X = list(X)
Y = list(Y)
X = np.asarray(X)
Y = np.asarray(Y)
Y_test = Y[:50] # CHOOSE 50 FOR TEST, OTHERS FOR TRAIN
X_test = X[:50]
X = X[50:]
Y = Y[50:]
print len(X)
clf = MLPClassifier(solver='lbfgs', alpha=3e-2, hidden_layer_sizes=(50, 20), random_state=2) # create the NN
clf.fit(X, Y) # Train it
print list(clf.predict_proba([X[0]])[0])
print list(Y_test[0])
for sample in range(len(X_test)): # add 1 to winner array if we correct and 0 if not, than in the end it plot it
arr = list(clf.predict([X_test[sample]])[0])
if arr.index(max(arr)) == list(Y_test[sample]).index(1):
winner.append(1)
else:
winner.append(0)
if only_speakers[randint(0, len(only_speakers) - 1)] == only_speakers[randint(0, len(only_speakers) - 1)]:
random_winner.append(1)
else:
random_winner.append(0)
# plot winner
plot_x = []
plot_y = []
for i in range(1, len(winner)):
plot_y.append(sum(winner[0:i])*1.0/len(winner[0:i]))
plot_x.append(i)
plot_random_x = []
plot_random_y = []
for i in range(1, len(random_winner)):
plot_random_y.append(sum(random_winner[0:i])*1.0/len(random_winner[0:i]))
plot_random_x.append(i)
plt.plot(plot_x, plot_y, 'r', label='machine learning')
plt.plot(plot_random_x, plot_random_y, 'b', label='random')
plt.xlabel('Number Of Samples')
# naming the y axis
plt.ylabel('Success Rate')
# giving a title to my graph
plt.title('Success Rate : Random Vs ML!')
# function to show the plot
plt.show()
This is my zip file that contains the code and the audio file : https://ufile.io/eggjm1gw
Somebody have an idea how can I improve my accucary?
Edit :
I improved my data set and put convolution model and got 60% accucarry, which is ok but also not good enoguh
import python_speech_features
import scipy.io.wavfile as wav
import numpy as np
from os import listdir
import os
import shutil
from os.path import isfile, join
from random import shuffle
from matplotlib import pyplot
from tqdm import tqdm
from random import randint
import tensorflow as tf
from ast import literal_eval as str2arr
from tempfile import TemporaryFile
#win_len = 0.04 # in seconds
#step = win_len / 2
#nfft = 2048
win_len = 0.05 # in seconds
step = win_len
nfft = 16384
results = []
outfile_x = None
outfile_y = None
winner = []
for TestNum in tqdm(range(40)): # We check it several times
if not outfile_x: # if path not exist we create it
X = [] # inputs
Y = [] # outputs
onlyfiles = [f for f in listdir("FinalAudios") if isfile(join("FinalAudios", f))] # Files in dir
names = [] # names of the speakers
for file in onlyfiles: # for each wav sound
# UNESSECERY TO UNDERSTAND THE CODE
if " " not in file.split("_")[0]:
names.append(file.split("_")[0])
else:
names.append(file.split("_")[0].split(" ")[0])
only_speakers = [] + names
namesWithoutDuplicate = list(dict.fromkeys(names))
namesWithoutDuplicateCopy = namesWithoutDuplicate[:]
for name in namesWithoutDuplicateCopy: # we remove low samples files
if names.count(name) < 107:
namesWithoutDuplicate.remove(name)
names = namesWithoutDuplicate
print(names) # print it
vector_names = [] # output for each name
i = 0
for name in names:
vector_for_each_name = i
vector_names.append(np.array(vector_for_each_name))
i += 1
for f in onlyfiles: # for all the files
if " " not in f.split("_")[0]:
f_speaker = f.split("_")[0]
else:
f_speaker = f.split("_")[0].split(" ")[0]
if f_speaker in namesWithoutDuplicate:
fs, audio = wav.read("FinalAudios\\" + f) # read the file
try:
# compute MFCC
mfcc_feat = python_speech_features.mfcc(audio, samplerate=fs, winlen=win_len, winstep=step, nfft=nfft, appendEnergy=False)
#flat_list = [item for sublist in mfcc_feat for item in sublist]
# Create output + inputs
for i in mfcc_feat:
X.append(np.array(i))
Y.append(np.array(vector_names[names.index(f_speaker)]))
except IndexError:
pass
else:
if not os.path.exists("TooLowSamples"): # if path not exist we create it
os.makedirs("TooLowSamples")
shutil.move("FinalAudios\\" + f, "TooLowSamples\\" + f)
outfile_x = TemporaryFile()
np.save(outfile_x, X)
outfile_y = TemporaryFile()
np.save(outfile_y, Y)
# ------------------- RANDOMIZATION, UNNECESSARY TO UNDERSTAND THE CODE ------------------- #
else:
outfile_x.seek(0)
X = np.load(outfile_x)
outfile_y.seek(0)
Y = np.load(outfile_y)
Z = list(zip(X, Y))
shuffle(Z) # WE SHUFFLE X,Y TO PERFORM RANDOM ON THE TEST LEVEL
X, Y = zip(*Z)
X = list(X)
Y = list(Y)
lenX = len(X)
# ------------------- RANDOMIZATION, UNNECESSARY TO UNDERSTAND THE CODE ------------------- #
y_test = np.asarray(Y[:4000]) # CHOOSE 100 FOR TEST, OTHERS FOR TRAIN
x_test = np.asarray(X[:4000]) # CHOOSE 100 FOR TEST, OTHERS FOR TRAIN
x_train = np.asarray(X[4000:]) # CHOOSE 100 FOR TEST, OTHERS FOR TRAIN
y_train = np.asarray(Y[4000:]) # CHOOSE 100 FOR TEST, OTHERS FOR TRAIN
x_val = x_train[-4000:] # FROM THE TRAIN CHOOSE 100 FOR VALIDATION
y_val = y_train[-4000:] # FROM THE TRAIN CHOOSE 100 FOR VALIDATION
x_train = x_train[:-4000] # FROM THE TRAIN CHOOSE 100 FOR VALIDATION
y_train = y_train[:-4000] # FROM THE TRAIN CHOOSE 100 FOR VALIDATION
x_train = x_train.reshape(np.append(x_train.shape, (1, 1))) # RESHAPE FOR INPUT
x_test = x_test.reshape(np.append(x_test.shape, (1, 1))) # RESHAPE FOR INPUT
x_val = x_val.reshape(np.append(x_val.shape, (1, 1))) # RESHAPE FOR INPUT
features_shape = x_val.shape
# -------------- OUR TENSOR FLOW NEURAL NETWORK MODEL -------------- #
model = tf.keras.models.Sequential([
tf.keras.layers.Input(name='inputs', shape=(13, 1, 1), dtype='float32'),
tf.keras.layers.Conv2D(32, (3, 3), activation='relu', padding='same', strides=1, name='block1_conv', input_shape=(13, 1, 1)),
tf.keras.layers.MaxPooling2D((3, 3), strides=(2,2), padding='same', name='block1_pool'),
tf.keras.layers.BatchNormalization(name='block1_norm'),
tf.keras.layers.Conv2D(32, (3, 3), activation='relu', padding='same', strides=1, name='block2_conv',
input_shape=(13, 1, 1)),
tf.keras.layers.MaxPooling2D((3, 3), strides=(2, 2), padding='same', name='block2_pool'),
tf.keras.layers.BatchNormalization(name='block2_norm'),
tf.keras.layers.Conv2D(32, (3, 3), activation='relu', padding='same', strides=1, name='block3_conv',
input_shape=(13, 1, 1)),
tf.keras.layers.MaxPooling2D((3, 3), strides=(2, 2), padding='same', name='block3_pool'),
tf.keras.layers.BatchNormalization(name='block3_norm'),
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(64, activation='relu', name='dense'),
tf.keras.layers.BatchNormalization(name='dense_norm'),
tf.keras.layers.Dropout(0.2, name='dropout'),
tf.keras.layers.Dense(10, activation='softmax', name='pred')
])
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
# -------------- OUR TENSOR FLOW NEURAL NETWORK MODEL -------------- #
print("fitting")
history = model.fit(x_train, y_train, epochs=15, validation_data=(x_val, y_val))
print("testing")
results.append(model.evaluate(x_test, y_test)[1])
print(results)
print(sum(results)/len(results))
for i in range(10000):
f_1 = only_speakers[randint(0, len(only_speakers) - 1)]
f_2 = only_speakers[randint(0, len(only_speakers) - 1)]
if " " not in f_1.split("_")[0]:
f_speaker_1 = f_1.split("_")[0]
else:
f_speaker_1 =f_1.split("_")[0].split(" ")[0]
if " " not in f_2.split("_")[0]:
f_speaker_2 = f_2.split("_")[0]
else:
f_speaker_2 =f_2.split("_")[0].split(" ")[0]
if f_speaker_2 == f_speaker_1:
winner.append(1)
else:
winner.append(0)
print(sum(winner)/len(winner))
#]
# if onlyfiles[randint(len(onlyfiles) - 1)] == onlyfiles[randint(len(onlyfiles) - 1)]
#pyplot.plot(history.history['loss'], label='train')
#pyplot.plot(history.history['val_loss'], label='test') Q
#pyplot.legend()
#pyplot.show()

Answer 1

Readin your post these are the following things I could suggest you fix/explore
42% is not that impressive of an accuracy for the task you have at hand, consider the way you are cross-validating e.g. how do you split between a validation, test and training dataset
Your dataset seems very limited. Your task is to identify the speaker. A single episode might not be enough data for this task.
You might want to consider Deep Neural Network libraries such as Keras and Tensorflow. Convolutions is something you can apply directly to the MFC Graph.
If you decide using Tensorflow or Keras consider Triplet-Loss, where you preset a positive and negative example.
Consider reading the current state of the art for your task: https://github.com/grausof/keras-sincnet
Consider reading https://arxiv.org/abs/1503.03832 and adopting it for speech recognition.
The easiest thing you can do to improve your results is adding CNN layers to extract features from the MFCC