See an explanation of the Mel Frequency Cepstral Coefficient
There are various Python libs dealing with speech features (Speech Features, SpeechRecognition)
import os
import argparse
import numpy as np
from scipy.io import wavfile
from hmmlearn import hmm
from features import mfcc
# Function to parse input arguments
def build_arg_parser():
parser = argparse.ArgumentParser(description='Trains the HMM classifier')
parser.add_argument("--input-folder", dest="input_folder", required=True, help="Input folder containing the audio files in subfolders")
return parser
# Class to handle all HMM related processing
class HMMTrainer(object):
def __init__(self, model_name='GaussianHMM', n_components=4, cov_type='diag', n_iter=1000):
self.model_name = model_name
self.n_components = n_components
self.cov_type = cov_type
self.n_iter = n_iter
self.models = []
if self.model_name == 'GaussianHMM':
self.model = hmm.GaussianHMM(n_components=self.n_components,
covariance_type=self.cov_type, n_iter=self.n_iter)
else:
raise TypeError('Invalid model type')
# X is a 2D numpy array where each row is 13D
def train(self, X):
np.seterr(all='ignore')
self.models.append(self.model.fit(X))
# Run the model on input data
def get_score(self, input_data):
return self.model.score(input_data)
if __name__=='__main__':
args = build_arg_parser().parse_args()
input_folder = args.input_folder
hmm_models = []
# Parse the input directory
for dirname in os.listdir(input_folder):
# Get the name of the subfolder
subfolder = os.path.join(input_folder, dirname)
if not os.path.isdir(subfolder):
continue
# Extract the label
label = subfolder[subfolder.rfind('/') + 1:]
# Initialize variables
X = np.array([])
y_words = []
# Iterate through the audio files (leaving 1 file for testing in each class)
for filename in [x for x in os.listdir(subfolder) if x.endswith('.wav')][:-1]:
# Read the input file
filepath = os.path.join(subfolder, filename)
sampling_freq, audio = wavfile.read(filepath)
# Extract MFCC features
mfcc_features = mfcc(audio, sampling_freq)
# Append to the variable X
if len(X) == 0:
X = mfcc_features
else:
X = np.append(X, mfcc_features, axis=0)
# Append the label
y_words.append(label)
print('X.shape =', X.shape)
# Train and save HMM model
hmm_trainer = HMMTrainer()
hmm_trainer.train(X)
hmm_models.append((hmm_trainer, label))
hmm_trainer = None
# Test files
input_files = [
'one.wav',
'two.wav',
'three.wav',
'four.wav'
]
# Classify input data
for input_file in input_files:
# Read input file
sampling_freq, audio = wavfile.read(input_file)
# Extract MFCC features
mfcc_features = mfcc(audio, sampling_freq)
# Define variables
max_score = None
output_label = None
# Iterate through all HMM models and pick
# the one with the highest score
for item in hmm_models:
hmm_model, label = item
score = hmm_model.get_score(mfcc_features)
if score > max_score:
max_score = score
output_label = label
# Print the output
print("\nTrue:", input_file[input_file.find('/')+1:input_file.rfind('/')])
print("Predicted:", output_label)
