-
-
Notifications
You must be signed in to change notification settings - Fork 47
/
train.py
207 lines (170 loc) · 8.36 KB
/
train.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
import tensorflow as tf
from tensorflow.keras import Sequential
from tensorflow.keras.layers import LSTM, Conv1D, Dense
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.backend import clear_session
from tensorflow.keras.activations import tanh, elu, relu
from tensorflow.keras.models import load_model
import tensorflow.keras.backend as K
from tensorflow.keras.utils import Sequence
import os
from scipy import signal
from scipy.io import wavfile
import numpy as np
import matplotlib.pyplot as plt
import math
import h5py
import argparse
def pre_emphasis_filter(x, coeff=0.95):
return tf.concat([x, x - coeff * x], 1)
def error_to_signal(y_true, y_pred):
"""
Error to signal ratio with pre-emphasis filter:
"""
y_true, y_pred = pre_emphasis_filter(y_true), pre_emphasis_filter(y_pred)
return K.sum(tf.pow(y_true - y_pred, 2), axis=0) / K.sum(tf.pow(y_true, 2), axis=0) + 1e-10
def save_wav(name, data):
wavfile.write(name, 44100, data.flatten().astype(np.float32))
def normalize(data):
data_max = max(data)
data_min = min(data)
data_norm = max(data_max,abs(data_min))
return data / data_norm
def main(args):
'''Ths is a similar Tensorflow/Keras implementation of the LSTM model from the paper:
"Real-Time Guitar Amplifier Emulation with Deep Learning"
https://www.mdpi.com/2076-3417/10/3/766/htm
Uses a stack of two 1-D Convolutional layers, followed by LSTM, followed by
a Dense (fully connected) layer. Three preset training modes are available,
with further customization by editing the code. A Sequential tf.keras model
is implemented here.
Note: RAM may be a limiting factor for the parameter "input_size". The wav data
is preprocessed and stored in RAM, which improves training speed but quickly runs out
if using a large number for "input_size". Reduce this if you are experiencing
RAM issues. Also, you can use the "--split_data" option to divide the data by the
specified amount and train the model on each set. Doing this will allow for a higher
input_size setting (more accurate results).
--training_mode=0 Speed training (default)
--training_mode=1 Accuracy training
--training_mode=2 Extended training (set max_epochs as desired, for example 50+)
'''
name = args.name
if not os.path.exists('models/'+name):
os.makedirs('models/'+name)
else:
print("A model folder with the same name already exists. Please choose a new name.")
return
train_mode = args.training_mode # 0 = speed training,
# 1 = accuracy training
# 2 = extended training
batch_size = args.batch_size
test_size = 0.2
epochs = args.max_epochs
input_size = args.input_size
# TRAINING MODE
if train_mode == 0: # Speed Training
learning_rate = 0.01
conv1d_strides = 12
conv1d_filters = 16
hidden_units = 36
elif train_mode == 1: # Accuracy Training (~10x longer than Speed Training)
learning_rate = 0.01
conv1d_strides = 4
conv1d_filters = 36
hidden_units= 64
else: # Extended Training (~60x longer than Accuracy Training)
learning_rate = 0.0005
conv1d_strides = 3
conv1d_filters = 36
hidden_units= 96
# Create Sequential Model ###########################################
clear_session()
model = Sequential()
model.add(Conv1D(conv1d_filters, 12,strides=conv1d_strides, activation=None, padding='same',input_shape=(input_size,1)))
model.add(Conv1D(conv1d_filters, 12,strides=conv1d_strides, activation=None, padding='same'))
model.add(LSTM(hidden_units))
model.add(Dense(1, activation=None))
model.compile(optimizer=Adam(learning_rate=learning_rate), loss=error_to_signal, metrics=[error_to_signal])
print(model.summary())
# Load and Preprocess Data ###########################################
in_rate, in_data = wavfile.read(args.in_file)
out_rate, out_data = wavfile.read(args.out_file)
X_all = in_data.astype(np.float32).flatten()
X_all = normalize(X_all).reshape(len(X_all),1)
y_all = out_data.astype(np.float32).flatten()
y_all = normalize(y_all).reshape(len(y_all),1)
# If splitting the data for training, do this part
if args.split_data > 1:
num_split = len(X_all) // args.split_data
X = X_all[0:num_split*args.split_data]
y = y_all[0:num_split*args.split_data]
X_data = np.split(X, args.split_data)
y_data = np.split(y, args.split_data)
# Perform training on each split dataset
for i in range(len(X_data)):
print("\nTraining on split data " + str(i+1) + "/" +str(len(X_data)))
X_split = X_data[i]
y_split = y_data[i]
y_ordered = y_split[input_size-1:]
indices = np.arange(input_size) + np.arange(len(X_split)-input_size+1)[:,np.newaxis]
X_ordered = tf.gather(X_split,indices)
shuffled_indices = np.random.permutation(len(X_ordered))
X_random = tf.gather(X_ordered,shuffled_indices)
y_random = tf.gather(y_ordered, shuffled_indices)
# Train Model ###################################################
model.fit(X_random,y_random, epochs=epochs, batch_size=batch_size, validation_split=0.2)
model.save('models/'+name+'/'+name+'.h5')
# If training on the full set of input data in one run, do this part
else:
y_ordered = y_all[input_size-1:]
indices = np.arange(input_size) + np.arange(len(X_all)-input_size+1)[:,np.newaxis]
X_ordered = tf.gather(X_all,indices)
shuffled_indices = np.random.permutation(len(X_ordered))
X_random = tf.gather(X_ordered,shuffled_indices)
y_random = tf.gather(y_ordered, shuffled_indices)
# Train Model ###################################################
model.fit(X_random,y_random, epochs=epochs, batch_size=batch_size, validation_split=test_size)
model.save('models/'+name+'/'+name+'.h5')
# Run Prediction #################################################
print("Running prediction..")
# Get the last 20% of the wav data to run prediction and plot results
y_the_rest, y_last_part = np.split(y_all, [int(len(y_all)*.8)])
x_the_rest, x_last_part = np.split(X_all, [int(len(X_all)*.8)])
y_test = y_last_part[input_size-1:]
indices = np.arange(input_size) + np.arange(len(x_last_part)-input_size+1)[:,np.newaxis]
X_test = tf.gather(x_last_part,indices)
prediction = model.predict(X_test, batch_size=batch_size)
save_wav('models/'+name+'/y_pred.wav', prediction)
save_wav('models/'+name+'/x_test.wav', x_last_part)
save_wav('models/'+name+'/y_test.wav', y_test)
# Add additional data to the saved model (like input_size)
filename = 'models/'+name+'/'+name+'.h5'
f = h5py.File(filename, 'a')
grp = f.create_group("info")
dset = grp.create_dataset("input_size", (1,), dtype='int16')
dset[0] = input_size
f.close()
# Create Analysis Plots ###########################################
if args.create_plots == 1:
print("Plotting results..")
import plot
plot.analyze_pred_vs_actual({ 'output_wav':'models/'+name+'/y_test.wav',
'pred_wav':'models/'+name+'/y_pred.wav',
'input_wav':'models/'+name+'/x_test.wav',
'model_name':name,
'show_plots':1,
'path':'models/'+name
})
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("in_file")
parser.add_argument("out_file")
parser.add_argument("name")
parser.add_argument("--training_mode", type=int, default=0)
parser.add_argument("--batch_size", type=int, default=4096)
parser.add_argument("--max_epochs", type=int, default=1)
parser.add_argument("--create_plots", type=int, default=1)
parser.add_argument("--input_size", type=int, default=100)
parser.add_argument("--split_data", type=int, default=1)
args = parser.parse_args()
main(args)