import os
import pickle
import warnings
import numpy as np
from sklearn.mixture import GMM
from FeaturesExtractor import FeaturesExtractor
warnings.filterwarnings("ignore")
class ModelsTrainer:
def __init__(self, females_files_path, males_files_path):
self.females_training_path = females_files_path
self.males_training_path = males_files_path
self.features_extractor = FeaturesExtractor()
def process(self):
females, males = self.get_file_paths(self.females_training_path,
self.males_training_path)
# collect voice features
female_voice_features = self.collect_features(females)
male_voice_features = self.collect_features(males)
# generate gaussian mixture models
females_gmm = GMM(n_components = 16, n_iter = 200, covariance_type='diag', n_init = 3)
males_gmm = GMM(n_components = 16, n_iter = 200, covariance_type='diag', n_init = 3)
# fit features to models
females_gmm.fit(female_voice_features)
males_gmm.fit(male_voice_features)
# save models
self.save_gmm(females_gmm, "females")
self.save_gmm(males_gmm, "males")
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) ]
return females, males
def collect_features(self, files):
"""
Collect voice features from various speakers of the same gender.
Args:
files (list) : List of voice file paths.
Returns:
(array) : Extracted features matrix.
"""
features = np.asarray(())
# extract features for each speaker
for file in files:
print("%5s %10s" % ("PROCESSNG ", file))
# extract MFCC & delta MFCC features from audio
vector = self.features_extractor.extract_features(file)
# stack the features
if features.size == 0: features = vector
else: features = np.vstack((features, vector))
return features
def save_gmm(self, gmm, name):
""" Save Gaussian mixture model using pickle.
Args:
gmm : Gaussian mixture model.
name (str) : File name.
"""
filename = name + ".gmm"
with open(filename, 'wb') as gmm_file:
pickle.dump(gmm, gmm_file)
print ("%5s %10s" % ("SAVING", filename,))
if __name__== "__main__":
models_trainer = ModelsTrainer("TrainingData/females", "TrainingData/males")
models_trainer.process()
