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I'm new to deep learning and don't know how to change this macine learning code to deep learning code. This is speech recognizer using HMM, machine learning and was written with python. WHERE and HOW can I fix it?? Do I have to make from the scratch?? Source code or adivice will be really appreciate.

What I have tried:

import os
import argparse
import warnings

import numpy as np
from import wavfile

from hmmlearn import hmm
from features import mfcc

# Define a function to parse the input arguments
def build_arg_parser():
parser = argparse.ArgumentParser(description='Trains the HMM-based speech \
recognition system')
parser.add_argument("--input-folder", dest="input_folder", required=True,
help="Input folder containing the audio files for training")
return parser

# Define a class to train the HMM
class ModelHMM(object):
def __init__(self, num_components=4, num_iter=1000):
self.n_components = num_components
self.n_iter = num_iter

self.cov_type = 'diag'
self.model_name = 'GaussianHMM'

self.models = []

self.model = hmm.GaussianHMM(n_components=self.n_components,
covariance_type=self.cov_type, n_iter=self.n_iter)

# 'training_data' is a 2D numpy array where each row is 13-dimensional
def train(self, training_data):
cur_model =

# Run the HMM model for inference on input data
def compute_score(self, input_data):
return self.model.score(input_data)

# Define a function to build a model for each word
def build_models(input_folder):
# Initialize the variable to store all the models
speech_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):

# Extract the label
label = subfolder[subfolder.rfind('/') + 1:]

# Initialize the variables
X = np.array([])

# Create a list of files to be used for training
# We will leave one file per folder for testing
training_files = [x for x in os.listdir(subfolder) if x.endswith('.wav')][:-1]

# Iterate through the training files and build the models
for filename in training_files:
# Extract the current filepath
filepath = os.path.join(subfolder, filename)

# Read the audio signal from the input file
sampling_freq, signal =

# Extract the MFCC features
with warnings.catch_warnings():
features_mfcc = mfcc(signal, sampling_freq)

# Append to the variable X
if len(X) == 0:
X = features_mfcc
X = np.append(X, features_mfcc, axis=0)

# Create the HMM model
model = ModelHMM()

# Train the HMM

# Save the model for the current word
speech_models.append((model, label))

# Reset the variable
model = None

return speech_models

# Define a function to run tests on input files
def run_tests(test_files):
# Classify input data
for test_file in test_files:
# Read input file
sampling_freq, signal =

# Extract MFCC features
with warnings.catch_warnings():
features_mfcc = mfcc(signal, sampling_freq)

# Define variables
max_score = -float('inf')
output_label = None

# Run the current feature vector through all the HMM
# models and pick the one with the highest score
for item in speech_models:
model, label = item
score = model.compute_score(features_mfcc)
if score > max_score:
max_score = score
predicted_label = label

# Print the predicted output
start_index = test_file.find('/') + 1
end_index = test_file.rfind('/')
original_label = test_file[start_index:end_index]
print('\nOriginal: ', original_label)
print('Predicted:', predicted_label)

if __name__=='__main__':
args = build_arg_parser().parse_args()
input_folder = args.input_folder

# Build an HMM model for each word
speech_models = build_models(input_folder)

# Test files -- the 15th file in each subfolder
test_files = []
for root, dirs, files in os.walk(input_folder):
for filename in (x for x in files if '15' in x):
filepath = os.path.join(root, filename)

Posted 12-Oct-17 6:11am
Richard MacCutchan 12-Oct-17 15:44pm
Sorry, we do not do code to order.

This content, along with any associated source code and files, is licensed under The Code Project Open License (CPOL)

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