import os
import pickle
import warnings
import numpy as np
from FeaturesExtractor import FeaturesExtractor
from hmmlearn import hmm
import pydub
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
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 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):
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 = hmm.GaussianHMM(n_components=3)
males_gmm = hmm.GaussianHMM(n_components=3)
ubm = hmm.GaussianHMM(n_components=3)
# fit features to models
females_gmm.fit(female_voice_features)
males_gmm.fit(male_voice_features)
ubm.fit(np.vstack((female_voice_features, male_voice_features)))
# save models
self.save_gmm(females_gmm, "females")
self.save_gmm(males_gmm, "males")
self.save_gmm(males_gmm, "ubm")
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))
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")
# stack the features
if features.size == 0: features = vector
else: features = np.vstack((features, vector))
except : pass
os.remove(file.split('.')[0] + "_without_silence.wav")
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 + ".hmm"
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()
