import os
import pickle
import warnings
import numpy as np
from FeaturesExtractor import FeaturesExtractor
from hmmlearn import hmm
warnings.filterwarnings("ignore")
import subprocess
import speech_recognition as sr
from pydub import AudioSegment
from subprocess import Popen, PIPE
from pydub.silence import split_on_silence, detect_nonsilent
import keras
class GenderIdentifier:
def __init__(self, females_files_path, males_files_path, females_model_path, males_model_path):
self.females_training_path = females_files_path
self.males_training_path = males_files_path
self.error = 0
self.total_sample = 0
self.features_extractor = FeaturesExtractor()
# load models
self.females_gmm = pickle.load(open(females_model_path, 'rb'))
self.males_gmm = pickle.load(open(males_model_path, 'rb'))
# svm
self.X_train = np.vstack((self.females_gmm, self.males_gmm))
self.y_train = np.hstack(( 0 * np.ones(self.females_gmm.shape[0]), np.ones(self.males_gmm.shape[0])))
print(self.X_train.shape, self.y_train.shape)
# define the keras model
self.model = keras.Sequential()
self.model.add(keras.layers.Dense(39, input_dim=39, activation='relu'))
self.model.add(keras.layers.Dense(13, activation='relu'))
self.model.add(keras.layers.Dense( 2, activation='sigmoid'))
self.model.compile(optimizer = 'adam',
loss = 'binary_crossentropy',
metrics = ['accuracy'])
self.model.fit(self.X_train, keras.utils.to_categorical(self.y_train), epochs = 5)
def ffmpeg_silence_eliminator(self, input_path, output_path):
"""
Eliminate silence from voice file using ffmpeg library.
Args:
input_path (str) : Path to get the original voice file from.
output_path (str) : Path to save the processed file to.
Returns:
(list) : List including True for successful authentication, False otherwise and a percentage value
representing the certainty of the decision.
"""
# filter silence in mp3 file
filter_command = ["ffmpeg", "-i", input_path, "-af", "silenceremove=1:0:0.05:-1:1:-36dB", "-ac", "1", "-ss", "0","-t","90", output_path, "-y"]
out = subprocess.Popen(filter_command, stdout=subprocess.PIPE, stderr=subprocess.STDOUT)
out.wait()
with_silence_duration = os.popen("ffprobe -i '" + input_path + "' -show_format -v quiet | sed -n 's/duration=//p'").read()
no_silence_duration = os.popen("ffprobe -i '" + output_path + "' -show_format -v quiet | sed -n 's/duration=//p'").read()
# print duration specs
try:
print("%-32s %-7s %-50s" % ("ORIGINAL SAMPLE DURATION", ":", float(with_silence_duration)))
print("%-23s %-7s %-50s" % ("SILENCE FILTERED SAMPLE DURATION", ":", float(no_silence_duration)))
except:
print("WaveHandlerError: Cannot convert float to string", with_silence_duration, no_silence_duration)
# convert file to wave and read array
load_command = ["ffmpeg", "-i", output_path, "-f", "wav", "-" ]
p = Popen(load_command, stdin=PIPE, stdout=PIPE, stderr=PIPE)
data = p.communicate()[0]
audio_np = np.frombuffer(data[data.find(b'\x00data')+ 9:], np.int16)
# delete temp silence free file, as we only need the array
#os.remove(output_path)
return audio_np, no_silence_duration
def process(self):
files = self.get_file_paths(self.females_training_path, self.males_training_path)
# read the test directory and get the list of test audio files
for file in files:
self.total_sample += 1
print("%10s %8s %1s" % ("--> TESTING", ":", os.path.basename(file)))
#self.ffmpeg_silence_eliminator(file, file.split('.')[0] + "_without_silence.wav")
# extract MFCC & delta MFCC features from audio
try:
# vector = self.features_extractor.extract_features(file.split('.')[0] + "_without_silence.wav")
vector = self.features_extractor.extract_features(file)
spk_gmm = hmm.GaussianHMM(n_components=16)
spk_gmm.fit(vector)
self.spk_vec = spk_gmm.means_
print(self.spk_vec.shape)
prediction = list(self.model.predict_classes(self.spk_vec))
print(prediction)
if prediction.count(0) <= prediction.count(1) : sc = 1
else : sc = 0
genders = {0: "female", 1: "male"}
winner = genders[sc]
expected_gender = file.split("/")[1][:-1]
print(expected_gender)
print("%10s %6s %1s" % ("+ EXPECTATION",":", expected_gender))
print("%10s %3s %1s" % ("+ IDENTIFICATION", ":", winner))
if winner != expected_gender: self.error += 1
print("----------------------------------------------------")
except : print("Error")
# os.remove(file.split('.')[0] + "_without_silence.wav")
accuracy = ( float(self.total_sample - self.error) / float(self.total_sample) ) * 100
accuracy_msg = "*** Accuracy = " + str(round(accuracy, 3)) + "% ***"
print(accuracy_msg)
def get_file_paths(self, females_training_path, males_training_path):
# get file paths
females = [ os.path.join(females_training_path, f) for f in os.listdir(females_training_path) ]
males = [ os.path.join(males_training_path, f) for f in os.listdir(males_training_path) ]
files = females + males
return files
def identify_gender(self, vector):
ubm_score = self.ubm.score(vector)
# female hypothesis scoring
is_female_log_likelihood = self.females_gmm.score(vector) / ubm_score
# male hypothesis scoring
is_male_log_likelihood = self.males_gmm.score(vector) / ubm_score
print("%10s %5s %1s" % ("+ FEMALE SCORE",":", str(round(is_female_log_likelihood, 3))))
print("%10s %7s %1s" % ("+ MALE SCORE", ":", str(round(is_male_log_likelihood,3))))
if is_male_log_likelihood > is_female_log_likelihood: winner = "male"
else : winner = "female"
return winner
if __name__== "__main__":
gender_identifier = GenderIdentifier("TestingData/females", "TestingData/males", "females.hmm", "males.hmm")
gender_identifier.process()
