def p_predict_cnn_thread(audio_path, name_list):
# initialize variables
X = np.empty(shape=(len(name_list), N_MFCC, AUDIO_LENGTH, 1))
# traverse through the name list and process this threads workload
for i, fname in enumerate(name_list):
# add a log message to be displayed after processing every 250 files.
if i % 250 == 0:
utils.write_log_msg("FEATURE_CNN_PREDICT - {0}...".format(i))
# read the sound file
sound_clip, _ = librosa.load(audio_path + fname,
sr=SAMPLE_RATE,
res_type='kaiser_fast')
# Random offset / Padding
if len(sound_clip) > INPUT_LENGTH:
max_offset = len(sound_clip) - INPUT_LENGTH
offset = np.random.randint(max_offset)
sound_clip = sound_clip[offset:(INPUT_LENGTH + offset)]
else:
if INPUT_LENGTH > len(sound_clip):
max_offset = INPUT_LENGTH - len(sound_clip)
offset = np.random.randint(max_offset)
else:
offset = 0
sound_clip = np.pad(
sound_clip, (offset, INPUT_LENGTH - len(sound_clip) - offset),
"constant")
# extract mfcc features
mfcc = librosa.feature.mfcc(sound_clip, sr=SAMPLE_RATE, n_mfcc=N_MFCC)
mfcc = np.expand_dims(mfcc, axis=-1)
X[i, ] = mfcc
# return the extracted features to the calling program
return np.array(X)
def run_mnn(dataset):
## DATASET = 0 => ALL DATASET
## DATASET = 1 => CONFUSION MATRIX
train_csv = TRAIN_CSV if (dataset == 0) else TRAIN_CONF_CSV
# print a log message for status update
utils.write_log_msg("creating data dictionary...")
# create a dictionary from the provided train.csv file
dictionary = utils.create_dictionary(train_csv)
# print a log message for status update
utils.write_log_msg("extracting features of training data...")
# call the feature extraction module to get audio features
tr_mnn_features, tr_mnn_labels = features.parse_audio_files_train(TRAIN_AUDIO_PATH,train_csv,dictionary, 0)
# print a log message for status update
utils.write_log_msg("extracting features of prediction data...")
# call the feature extraction module to get audio features
if (dataset == 0) :
ts_mnn_features, ts_mnn_name_list = features.parse_audio_files_predict(TEST_AUDIO_PATH,os.listdir(TEST_AUDIO_PATH), 0)
else :
test_csv = pd.read_csv(TEST_CONF_CSV)
ts_mnn_features, ts_mnn_name_list = features.parse_audio_files_predict(TRAIN_AUDIO_PATH,test_csv["fname"].tolist(), 0)
# print a log message for status update
utils.write_log_msg("starting multi-layer neural network training...")
# use the above extracted features for the training of the model
mnn_y_pred, mnn_probs, mnn_pred = train.tensor_multilayer_neural_network(tr_mnn_features, tr_mnn_labels, ts_mnn_features, len(dictionary), training_epochs=500)
# Get top 3 predictions
ensembled_output = np.zeros(shape=(mnn_probs.shape[0], mnn_probs.shape[1]))
for row, columns in enumerate(mnn_pred):
for i, column in enumerate(columns):
ensembled_output[row, column] += mnn_probs[row, i]
top3 = ensembled_output.argsort()[:,-3:][:,::-1]
# print the predicted results to a csv file.
file_ = open(OUTPUT_CSV, "w")
file_.write("fname,label\n")
for i, value in enumerate(top3):
if(dataset ==0):
lbl_1 = [k for k, v in dictionary.items() if v == value[0]][0]
lbl_2 = [k for k, v in dictionary.items() if v == value[1]][0]
lbl_3 = [k for k, v in dictionary.items() if v == value[2]][0]
file_.write("%s,%s %s %s\n" % (ts_mnn_name_list[i], lbl_1, lbl_2, lbl_3))
else :
lbl_1 = [k for k, v in dictionary.items() if v == value[0]][0]
file_.write("%s,%s\n" % (ts_mnn_name_list[i], lbl_1))
if (dataset ==0) :
file_.write("0b0427e2.wav,Harmonica\n6ea0099f.wav,Harmonica\nb39975f5.wav,Harmonica")
# print a log message for status update
utils.write_log_msg("done...")
def parse_audio_files_train(audio_path,
train_csv_path,
label_dictionary,
nn_type,
file_ext="*.wav"):
# initialize variables
labels = np.empty(0)
# read audio files using pandas and split it into chunks of 'CHUNK_SIZE' files each
data = pd.read_csv(train_csv_path, chunksize=CHUNK_SIZE)
# create a thread pool to process the workload
thread_pool = []
# each chunk is the amount of data that will be processed by a single thread
for chunk in data:
if (nn_type == 0):
features = np.empty((0, FEATURE_SIZE))
thread_pool.append(
utils.ThreadWithReturnValue(target=p_train_thread,
args=(audio_path, label_dictionary,
chunk)))
else:
features = np.empty(shape=(0, N_MFCC, AUDIO_LENGTH, 1))
thread_pool.append(
utils.ThreadWithReturnValue(target=p_train_cnn_thread,
args=(audio_path, label_dictionary,
chunk)))
# print a log message for status update
utils.write_log_msg("TRAIN: creating a total of {0} threads...".format(
len(thread_pool)))
# start the entire thread pool
for single_thread in thread_pool:
single_thread.start()
# wait for thread pool to return their results of processing
for single_thread in thread_pool:
ft, lbl = single_thread.join()
features = np.vstack([features, ft])
labels = np.append(labels, lbl)
# perform final touches to extracted arrays
features = np.array(features)
#print(labels)
labels = np.array(labels, dtype=np.int)
# normalize data
mean = np.mean(features, axis=0)
std = np.std(features, axis=0)
features = (features - mean) / std
# return the extracted features to the calling program
return features, labels
def p_train_thread(audio_path, label_dictionary, data):
# initialize variables
features, labels = np.empty((0, FEATURE_SIZE)), np.empty(0)
# process this threads share of workload
for i in range(data.shape[0]):
# add a log message to be displayed after processing every 250 files.
if i % 250 == 0:
utils.write_log_msg("FEATURE_TRAIN - {0}...".format(i))
line = data.iloc[i]
fn = audio_path + line["fname"]
mfccs, chroma, mel, contrast, tonnetz = extract_feature(fn)
ext_features = np.hstack([mfccs, chroma, mel, contrast, tonnetz])
features = np.vstack([features, ext_features])
labels = np.append(labels, label_dictionary[line["label"]])
# return the extracted features to the calling program
return features, labels
def p_predict_thread(audio_path, name_list):
# initialize variables
features = np.empty((0, FEATURE_SIZE))
# traverse through the name list and process this threads workload
for fname in name_list:
X, sample_rate = librosa.load(audio_path + fname,
res_type='kaiser_fast')
mfccs, chroma, mel, contrast, tonnetz = extract_feature(audio_path +
fname)
ext_features = np.hstack([mfccs, chroma, mel, contrast, tonnetz])
features = np.vstack([features, ext_features])
# add a log message to be displayed after processing every 250 files.
if len(features) % 250 == 0:
utils.write_log_msg("FEATURE_PREDICT - {0}...".format(
len(features)))
# return the extracted features to the calling program
return features
def parse_audio_files_predict(audio_path,
name_list,
nn_type,
file_ext="*.wav"):
# create a thread pool to process the workload
thread_pool = []
# split the filename list into chunks of 'CHUNK_SIZE' files each
data = utils.generate_chunks(name_list, CHUNK_SIZE)
# each chunk is the amount of data that will be processed by a single thread
for chunk in data:
if nn_type == 0:
features = np.empty((0, FEATURE_SIZE))
thread_pool.append(
utils.ThreadWithReturnValue(target=p_predict_thread,
args=(audio_path, chunk)))
else:
features = np.empty(shape=(0, N_MFCC, AUDIO_LENGTH, 1))
thread_pool.append(
utils.ThreadWithReturnValue(target=p_predict_cnn_thread,
args=(audio_path, chunk)))
# print a log message for status update
utils.write_log_msg("PREDICT: creating a total of {0} threads...".format(
len(thread_pool)))
# start the entire thread pool
for single_thread in thread_pool:
single_thread.start()
# wait for thread pool to return their results of processing
for single_thread in thread_pool:
ft = single_thread.join()
features = np.vstack([features, ft])
# perform final touches to extracted arrays
features = np.array(features)
# normalize data
mean = np.mean(features, axis=0)
std = np.std(features, axis=0)
features = (features - mean) / std
# return the extracted features to the calling program
return features, name_list
def p_train_cnn_thread(audio_path, label_dictionary, data):
# initialize variables
labels = np.empty(0)
X = np.empty(shape=(data.shape[0], N_MFCC, AUDIO_LENGTH, 1))
# process this threads share of workload
for i in range(data.shape[0]):
# add a log message to be displayed after processing every 250 files.
if i % 250 == 0:
utils.write_log_msg("FEATURE_CNN_TRAIN - {0}...".format(i))
line = data.iloc[i]
fn = audio_path + line["fname"]
sound_clip, _ = librosa.core.load(fn,
sr=SAMPLE_RATE,
res_type='kaiser_fast')
# Random offset / Padding
if len(sound_clip) > INPUT_LENGTH:
max_offset = len(sound_clip) - INPUT_LENGTH
offset = np.random.randint(max_offset)
sound_clip = sound_clip[offset:(INPUT_LENGTH + offset)]
else:
if INPUT_LENGTH > len(sound_clip):
max_offset = INPUT_LENGTH - len(sound_clip)
offset = np.random.randint(max_offset)
else:
offset = 0
sound_clip = np.pad(
sound_clip, (offset, INPUT_LENGTH - len(sound_clip) - offset),
"constant")
# extract mfcc features
mfcc = librosa.feature.mfcc(sound_clip, sr=SAMPLE_RATE, n_mfcc=N_MFCC)
mfcc = np.expand_dims(mfcc, axis=-1)
X[i, ] = mfcc
# populate the labels array
labels = np.append(labels, label_dictionary[line["label"]])
# return the extracted features to the calling program
return np.array(X), labels
def main(_load = False):
# intialize the log file for current run of the code
utils.initialize_log()
# read audio files and parse them or simply load from pre-extracted feature files
if _load:
dictionary, tr_mnn_features, tr_mnn_labels, ts_mnn_features, ts_mnn_name_list, tr_cnn_features, tr_cnn_labels, ts_cnn_features, ts_cnn_name_list = read_audio_files()
else:
dictionary, tr_mnn_features, tr_mnn_labels, ts_mnn_features, ts_mnn_name_list, tr_cnn_features, tr_cnn_labels, ts_cnn_features, ts_cnn_name_list = features.read_features()
# print a log message for status update
utils.write_log_msg("starting multi-layer neural network training...")
# use the above extracted features for the training of the model
predictions_top3 = train.train(tr_mnn_features, tr_mnn_labels, ts_mnn_features, tr_cnn_features, tr_cnn_labels, ts_cnn_features, n_classes=len(dictionary))
# print a log message for status update
utils.write_log_msg("outputing prediction results to a csv file...")
# print the predicted results to a csv file.
utils.print_csv_file(predictions_top3, ts_mnn_name_list, dictionary, OUTPUT_CSV)
# print a log message for status update
utils.write_log_msg("done...")
def read_audio_files():
# print a log message for status update
utils.write_log_msg("creating data dictionary...")
# create a dictionary from the provided train.csv file
dictionary = utils.create_dictionary(TRAIN_CSV)
# print a log message for status update
utils.write_log_msg("extracting features of training data...")
# call the feature extraction module to get audio features
tr_mnn_features, tr_mnn_labels = features.parse_audio_files_train(
TRAIN_AUDIO_PATH, TRAIN_CSV, dictionary, 0)
# call the feature extraction module to get audio features
tr_cnn_features, tr_cnn_labels = features.parse_audio_files_train(
TRAIN_AUDIO_PATH, TRAIN_CSV, dictionary, 1)
# print a log message for status update
utils.write_log_msg(
"processed {0} files of training data for mnn...".format(
len(tr_mnn_features)))
# print a log message for status update
utils.write_log_msg(
"processed {0} files of training data for cnn...".format(
len(tr_cnn_features)))
# print a log message for status update
utils.write_log_msg("extracting features of prediction data...")
# call the feature extraction module to get audio features
ts_mnn_features, ts_mnn_name_list = features.parse_audio_files_predict(
TEST_AUDIO_PATH, os.listdir(TEST_AUDIO_PATH), 0)
# call the feature extraction module to get audio features
ts_cnn_features, ts_cnn_name_list = features.parse_audio_files_predict(
TEST_AUDIO_PATH, os.listdir(TEST_AUDIO_PATH), 1)
# print a log message for status update
utils.write_log_msg(
"processed {0} files of prediction data for mnn...".format(
len(ts_mnn_features)))
# print a log message for status update
utils.write_log_msg(
"processed {0} files of prediction data for cnn...".format(
len(ts_cnn_features)))
# print a log message for status update
utils.write_log_msg("storing features for future use...")
# store features so that they can be used in future
features.store_features(dictionary, tr_mnn_features, tr_mnn_labels,
ts_mnn_features, ts_mnn_name_list, tr_cnn_features,
tr_cnn_labels, ts_cnn_features, ts_cnn_name_list)
# return the results to calling program
return dictionary, tr_mnn_features, tr_mnn_labels, ts_mnn_features, ts_mnn_name_list, tr_cnn_features, tr_cnn_labels, ts_cnn_features, ts_cnn_name_list
def main():
utils.write_log_msg("Run CNN code ...")
run_cnn(2)
def main():
utils.write_log_msg("Run MNN code ...")
#limitedCsv()
#run_mnn(0)
create_confmatrix()
def tensor_multilayer_neural_network(tr_features, tr_labels, ts_features, n_classes, training_epochs):
# initialize the beginning paramters.
n_dim = tr_features.shape[1]
n_hidden_units_1 = 200 #280
n_hidden_units_2 = 250 #300
n_hidden_units_3 = 300 #300
sd = 1 / np.sqrt(n_dim)
# one hot encode from training labels
tr_labels = to_categorical(tr_labels)
X = tf.placeholder(tf.float32,[None,n_dim])
Y = tf.placeholder(tf.float32,[None,n_classes])
# initializing starting learning rate - will use decaying technique
global_step = tf.Variable(0, trainable=False)
learning_rate = tf.train.exponential_decay(0.005, global_step, 500, 0.95, staircase=True)
# initialize layer 1 parameters
W_1 = tf.Variable(tf.random_normal([n_dim,n_hidden_units_1], mean = 0, stddev=sd))
b_1 = tf.Variable(tf.random_normal([n_hidden_units_1], mean = 0, stddev=sd))
h_1 = tf.nn.tanh(tf.matmul(X,W_1) + b_1)
# initialize layer 2 parameters
W_2 = tf.Variable(tf.random_normal([n_hidden_units_1,n_hidden_units_2], mean = 0, stddev=sd))
b_2 = tf.Variable(tf.random_normal([n_hidden_units_2], mean = 0, stddev=sd))
h_2 = tf.nn.sigmoid(tf.matmul(h_1,W_2) + b_2)
# initialize layer 3 parameters
W_3 = tf.Variable(tf.random_normal([n_hidden_units_2,n_hidden_units_3], mean = 0, stddev=sd))
b_3 = tf.Variable(tf.random_normal([n_hidden_units_3], mean = 0, stddev=sd))
h_3 = tf.nn.sigmoid(tf.matmul(h_2,W_3) + b_3)
W = tf.Variable(tf.random_normal([n_hidden_units_3,n_classes], mean = 0, stddev=sd))
b = tf.Variable(tf.random_normal([n_classes], mean = 0, stddev=sd))
y_ = tf.nn.softmax(tf.matmul(h_3,W) + b)
cost_function = -tf.reduce_sum(Y * tf.log(y_))
optimizer = tf.train.AdamOptimizer(learning_rate).minimize(cost_function, global_step=global_step)
init = tf.global_variables_initializer()
cost_history = np.empty(shape=[1],dtype=float)
y_pred = None
with tf.Session() as sess:
sess.run(init)
for epoch in range(training_epochs):
# print a log message for status update
utils.write_log_msg("running the mnn training epoch {0}...".format(epoch+1))
# running the training_epoch numbered epoch
_,cost = sess.run([optimizer,cost_function],feed_dict={X:tr_features,Y:tr_labels})
cost_history = np.append(cost_history,cost)
# predict results based on the trained model
y_pred = sess.run(tf.argmax(y_,1),feed_dict={X: ts_features})
y_k_probs, y_k_pred = sess.run(tf.nn.top_k(y_, k=n_classes), feed_dict={X: ts_features})
# plot cost history
df = pd.DataFrame(cost_history)
df.to_csv("../data/cost_history_mnn.csv")
# return the predicted values back to the calling program
return y_pred, y_k_probs, y_k_pred