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"""NIPS2017 "Time Domain Neural Audio Style Transfer" code repository
Parag K. Mital
"""
import tensorflow as tf
import librosa
import numpy as np
from scipy.signal import hann
from audio_style_transfer import utils
import argparse
import glob
import os
os.environ["CUDA_VISIBLE_DEVICES"]="-1"
import soundfile as sf
def chop(signal, hop_size=256, frame_size=512):
n_hops = len(signal) // hop_size
s = []
hann_win = hann(frame_size)
for hop_i in range(n_hops):
frame = signal[(hop_i * hop_size):(hop_i * hop_size + frame_size)]
frame = np.pad(frame, (0, frame_size - len(frame)), 'constant')
frame *= hann_win
s.append(frame)
s = np.array(s)
return s
def unchop(frames, hop_size=256, frame_size=512):
signal = np.zeros((frames.shape[0] * hop_size + frame_size,))
for hop_i, frame in enumerate(frames):
signal[(hop_i * hop_size):(hop_i * hop_size + frame_size)] += frame
return signal
def dft_np(signal, hop_size=256, fft_size=512):
s = chop(signal, hop_size, fft_size)
N = s.shape[-1]
k = np.reshape(
np.linspace(0.0, 2 * np.pi / N * (N // 2), N // 2), [1, N // 2])
x = np.reshape(np.linspace(0.0, N - 1, N), [N, 1])
freqs = np.dot(x, k)
real = np.dot(s, np.cos(freqs)) * (2.0 / N)
imag = np.dot(s, np.sin(freqs)) * (2.0 / N)
return real, imag
def idft_np(re, im, hop_size=256, fft_size=512):
N = re.shape[1] * 2
k = np.reshape(
np.linspace(0.0, 2 * np.pi / N * (N // 2), N // 2), [N // 2, 1])
x = np.reshape(np.linspace(0.0, N - 1, N), [1, N])
freqs = np.dot(k, x)
signal = np.zeros((re.shape[0] * hop_size + fft_size,))
recon = np.dot(re, np.cos(freqs)) + np.dot(im, np.sin(freqs))
for hop_i, frame in enumerate(recon):
signal[(hop_i * hop_size):(hop_i * hop_size + fft_size)] += frame
return signal
def unwrap(x):
return np.unwrap(x).astype(np.float32)
def instance_norm(x, epsilon=1e-5):
"""Instance Normalization.
See Ulyanov, D., Vedaldi, A., & Lempitsky, V. (2016).
Instance Normalization: The Missing Ingredient for Fast Stylization,
Retrieved from http://arxiv.org/abs/1607.08022
Parameters
----------
x : TYPE
Description
epsilon : float, optional
Description
"""
with tf.compat.v1.variable_scope('instance_norm'):
mean, var = tf.nn.moments(x=x, axes=[1, 2], keepdims=True)
scale = tf.compat.v1.get_variable(
name='scale',
shape=[x.get_shape()[-1]],
initializer=tf.compat.v1.truncated_normal_initializer(mean=1.0, stddev=0.02))
offset = tf.compat.v1.get_variable(
name='offset',
shape=[x.get_shape()[-1]],
initializer=tf.compat.v1.constant_initializer(0.0))
out = scale * tf.compat.v1.div(x - mean, tf.sqrt(var + epsilon)) + offset
return out
def compute_inputs(x, freqs, n_fft, n_frames, input_features, norm=False):
if norm:
norm_fn = instance_norm
else:
def norm_fn(x):
return x
freqs_tf = tf.constant(freqs, name="freqs", dtype='float32')
inputs = {}
with tf.compat.v1.variable_scope('real'):
inputs['real'] = norm_fn(tf.reshape(
tf.matmul(x, tf.cos(freqs_tf)), [1, 1, n_frames, n_fft // 2]))
with tf.compat.v1.variable_scope('imag'):
inputs['imag'] = norm_fn(tf.reshape(
tf.matmul(x, tf.sin(freqs_tf)), [1, 1, n_frames, n_fft // 2]))
with tf.compat.v1.variable_scope('mags'):
inputs['mags'] = norm_fn(tf.reshape(
tf.sqrt(
tf.maximum(1e-15, inputs['real'] * inputs['real'] + inputs[
'imag'] * inputs['imag'])), [1, 1, n_frames, n_fft // 2]))
with tf.compat.v1.variable_scope('phase'):
inputs['phase'] = norm_fn(tf.atan2(inputs['imag'], inputs['real']))
with tf.compat.v1.variable_scope('unwrapped'):
inputs['unwrapped'] = tf.compat.v1.py_func(
unwrap, [inputs['phase']], tf.float32)
with tf.compat.v1.variable_scope('unwrapped_difference'):
inputs['unwrapped_difference'] = (tf.slice(
inputs['unwrapped'],
[0, 0, 0, 1], [-1, -1, -1, n_fft // 2 - 1]) -
tf.slice(
inputs['unwrapped'],
[0, 0, 0, 0], [-1, -1, -1, n_fft // 2 - 1]))
if 'unwrapped_difference' in input_features:
for k, v in input_features:
if k is not 'unwrapped_difference':
inputs[k] = tf.slice(
v, [0, 0, 0, 0], [-1, -1, -1, n_fft // 2 - 1])
net = tf.concat([inputs[i] for i in input_features], 1)
return inputs, net
def compute_features(content,
style,
input_features,
norm=False,
stride=1,
n_layers=1,
n_filters=4096,
n_fft=1024,
k_h=1,
k_w=11):
n_frames = content.shape[0]
n_samples = content.shape[1]
content_tf = np.ascontiguousarray(content)
style_tf = np.ascontiguousarray(style)
g = tf.Graph()
kernels = []
content_features = []
style_features = []
config_proto = tf.compat.v1.ConfigProto()
with g.as_default(), g.device('/cpu:0'), tf.compat.v1.Session(config=config_proto) as sess:
x = tf.compat.v1.placeholder('float32', [n_frames, n_samples], name="x")
p = np.reshape(
np.linspace(0.0, n_samples - 1, n_samples), [n_samples, 1])
k = np.reshape(
np.linspace(0.0, 2 * np.pi / n_fft * (n_fft // 2), n_fft // 2),
[1, n_fft // 2])
freqs = np.dot(p, k)
inputs, net = compute_inputs(x, freqs, n_fft, n_frames, input_features, norm)
sess.run(tf.compat.v1.initialize_all_variables())
content_feature = net.eval(feed_dict={x: content_tf})
content_features.append(content_feature)
style_feature = inputs['mags'].eval(feed_dict={x: style_tf})
features = np.reshape(style_feature, (-1, n_fft // 2))
style_gram = np.matmul(features.T, features) / (n_frames)
style_features.append(style_gram)
for layer_i in range(n_layers):
if layer_i == 0:
std = np.sqrt(2) * np.sqrt(2.0 / (
(n_fft / 2 + n_filters) * k_w))
kernel = np.random.randn(k_h, k_w, n_fft // 2, n_filters) * std
else:
std = np.sqrt(2) * np.sqrt(2.0 / (
(n_filters + n_filters) * k_w))
kernel = np.random.randn(1, k_w, n_filters, n_filters) * std
kernels.append(kernel)
kernel_tf = tf.constant(
kernel, name="kernel{}".format(layer_i), dtype='float32')
conv = tf.nn.conv2d(
input=net,
filters=kernel_tf,
strides=[1, stride, stride, 1],
padding="VALID",
name="conv{}".format(layer_i))
net = tf.nn.relu(conv)
content_feature = net.eval(feed_dict={x: content_tf})
content_features.append(content_feature)
style_feature = net.eval(feed_dict={x: style_tf})
features = np.reshape(style_feature, (-1, n_filters))
style_gram = np.matmul(features.T, features) / (n_frames)
style_features.append(style_gram)
return content_features, style_features, kernels, freqs
def compute_stylization(kernels,
n_samples,
n_frames,
content_features,
style_gram,
freqs,
input_features,
norm=False,
stride=1,
n_layers=1,
n_fft=1024,
alpha=1e-4,
learning_rate=1e-3,
iterations=100,
optimizer='bfgs'):
result = None
with tf.Graph().as_default():
x = tf.Variable(
np.random.randn(n_frames, n_samples).astype(np.float32) * 1e-3,
name="x")
inputs, net = compute_inputs(x, freqs, n_fft, n_frames, input_features, norm)
content_loss = alpha * 2 * tf.nn.l2_loss(net - content_features[0])
feats = tf.reshape(inputs['mags'], (-1, n_fft // 2))
gram = tf.matmul(tf.transpose(a=feats), feats) / (n_frames)
style_loss = 2 * tf.nn.l2_loss(gram - style_gram[0])
for layer_i in range(n_layers):
kernel_tf = tf.constant(
kernels[layer_i],
name="kernel{}".format(layer_i),
dtype='float32')
conv = tf.nn.conv2d(
input=net,
filters=kernel_tf,
strides=[1, stride, stride, 1],
padding="VALID",
name="conv{}".format(layer_i))
net = tf.nn.relu(conv)
content_loss = content_loss + \
alpha * 2 * tf.nn.l2_loss(net - content_features[layer_i + 1])
_, height, width, number = map(lambda i: i, net.get_shape())
feats = tf.reshape(net, (-1, number))
gram = tf.matmul(tf.transpose(a=feats), feats) / (n_frames)
style_loss = style_loss + 2 * tf.nn.l2_loss(gram - style_gram[
layer_i + 1])
loss = content_loss + style_loss
if optimizer == 'bfgs':
opt = tf.compat.v1.train.AdamOptimizer(
learning_rate=learning_rate).minimize(loss)
# Optimization
with tf.compat.v1.Session() as sess:
sess.run(tf.compat.v1.initialize_all_variables())
print('Started optimization.')
for i in range(iterations):
s, c, l, _ = sess.run([style_loss, content_loss, loss, opt])
print('Style:', s, 'Content:', c, end='\r')
result = x.eval()
else:
opt = tf.compat.v1.train.AdamOptimizer(
learning_rate=learning_rate).minimize(loss)
# Optimization
with tf.compat.v1.Session() as sess:
sess.run(tf.compat.v1.initialize_all_variables())
print('Started optimization.')
for i in range(iterations):
s, c, l, _ = sess.run([style_loss, content_loss, loss, opt])
print('Style:', s, 'Content:', c, end='\r')
result = x.eval()
return result
def run(content_fname,
style_fname,
output_fname,
norm=False,
input_features=['real', 'imag', 'mags'],
n_fft=4096,
n_layers=1,
n_filters=4096,
hop_length=256,
alpha=0.05,
k_w=15,
k_h=3,
optimizer='bfgs',
stride=1,
iterations=300,
sr=22050):
frame_size = n_fft // 2
audio, fs = librosa.load(content_fname, sr=sr)
content = chop(audio, hop_size=hop_length, frame_size=frame_size)
audio, fs = librosa.load(style_fname, sr=sr)
style = chop(audio, hop_size=hop_length, frame_size=frame_size)
print ("loaded")
n_frames = min(content.shape[0], style.shape[0])
n_samples = min(content.shape[1], style.shape[1])
content = content[:n_frames, :n_samples]
style = style[:n_frames, :n_samples]
content_features, style_gram, kernels, freqs = compute_features(
content=content,
style=style,
input_features=input_features,
norm=norm,
stride=stride,
n_fft=n_fft,
n_layers=n_layers,
n_filters=n_filters,
k_w=k_w,
k_h=k_h)
result = compute_stylization(
kernels=kernels,
freqs=freqs,
input_features=input_features,
norm=norm,
n_samples=n_samples,
n_frames=n_frames,
n_fft=n_fft,
content_features=content_features,
style_gram=style_gram,
stride=stride,
n_layers=n_layers,
alpha=alpha,
optimizer=optimizer,
iterations=iterations)
s = unchop(result, hop_size=hop_length, frame_size=frame_size)
sf.write(output_fname, s, sr=sr)
s = utils.limiter(s)
sf.write(output_fname+'.limiter.wav', s, sr=sr)
def batch(content_path, style_path, output_path, model):
content_files = glob.glob('{}/*.wav'.format(content_path))
style_files = glob.glob('{}/*.wav'.format(style_path))
for content_fname in content_files:
for style_fname in style_files:
output_fname = '{}/{}+{}.wav'.format(output_path,
content_fname.split('/')[-1],
style_fname.split('/')[-1])
if os.path.exists(output_fname):
continue
run(content_fname, style_fname, output_fname, model)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('-s', '--style', help='style file', required=True)
parser.add_argument('-c', '--content', help='content file', required=True)
parser.add_argument('-o', '--output', help='output file', required=True)
parser.add_argument(
'-m',
'--mode',
help='mode for training [single] or batch',
default='single')
args = vars(parser.parse_args())
if args['mode'] == 'single':
run(args['content'], args['style'], args['output'])
else:
batch(args['content'], args['style'], args['output'])
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