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search.py
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"""NIPS2017 "Time Domain Neural Audio Style Transfer" code repository
Parag K. Mital
"""
import os
import glob
import numpy as np
from audio_style_transfer.models import timedomain, uylanov
def get_path(model, output_path, content_filename, style_filename):
output_dir = os.path.join(output_path, model)
if not os.path.exists(output_dir):
os.makedirs(output_dir)
output_filename = '{}/{}/{}+{}'.format(output_path, model,
content_filename.split('/')[-1],
style_filename.split('/')[-1])
return output_filename
def params():
n_fft = [2048, 4096, 8196]
n_layers = [1, 2, 4]
n_filters = [128, 2048, 4096]
hop_length = [128, 256, 512]
alpha = [0.1, 0.01, 0.005]
k_w = [4, 8, 12]
norm = [True, False]
input_features = [['mags'], ['mags', 'phase'], ['real', 'imag'], ['real', 'imag', 'mags']]
return locals()
def batch(content_path, style_path, output_path, run_timedomain=True, run_uylanov=False):
content_files = glob.glob('{}/*.wav'.format(content_path))
style_files = glob.glob('{}/*.wav'.format(style_path))
content_filename = np.random.choice(content_files)
style_filename = np.random.choice(style_files)
alpha = np.random.choice(params()['alpha'])
n_fft = np.random.choice(params()['n_fft'])
n_layers = np.random.choice(params()['n_layers'])
n_filters = np.random.choice(params()['n_filters'])
hop_length = np.random.choice(params()['hop_length'])
norm = np.random.choice(params()['norm'])
k_w = np.random.choice(params()['k_w'])
# Run the Time Domain Model
if run_timedomain:
for f in params()['input_features']:
fname = get_path('timedomain/input_features={}'.format(",".join(f)),
output_path, content_filename, style_filename)
output_filename = ('{},n_fft={},n_layers={},n_filters={},norm={},'
'hop_length={},alpha={},k_w={}.wav'.format(
fname, n_fft, n_layers, n_filters, norm,
hop_length, alpha, k_w))
print(output_filename)
if not os.path.exists(output_filename):
timedomain.run(content_fname=content_filename,
style_fname=style_filename,
output_fname=output_filename,
n_fft=n_fft,
n_layers=n_layers,
n_filters=n_filters,
hop_length=hop_length,
alpha=alpha,
norm=norm,
k_w=k_w)
if run_uylanov:
# Run Original Uylanov Model
fname = get_path('uylanov', output_path, content_filename, style_filename)
output_filename = ('{},n_fft={},n_layers={},n_filters={},'
'hop_length={},alpha={},k_w={}.wav'.format(
fname, n_fft, n_layers, n_filters, hop_length, alpha,
k_w))
print(output_filename)
if not os.path.exists(output_filename):
uylanov.run(content_filename,
style_filename,
output_filename,
n_fft=n_fft,
n_layers=n_layers,
n_filters=n_filters,
hop_length=hop_length,
alpha=alpha,
k_w=k_w)
# These only produce noise so they are commented
# # Run NSynth Encoder Model
# output_filename = get_path('nsynth-encoder', output_path, content_filename,
# style_filename)
# output_filename = ('{},n_fft={},n_layers={},n_filters={},'
# 'hop_length={},alpha={},k_w={}.wav'.format(
# fname, n_fft, n_layers, n_filters, hop_length, alpha, k_w))
# if not os.path.exists(output_filename):
# nsynth.run(content_filename,
# style_filename,
# output_filename,
# model='encoder',
# n_fft=n_fft,
# n_layers=n_layers,
# n_filters=n_filters,
# hop_length=hop_length,
# alpha=alpha,
# k_w=k_w)
# # Run NSynth Decoder Model
# output_filename = get_path('wavenet-decoder', output_path, content_filename,
# style_filename)
# output_filename = ('{},n_fft={},n_layers={},n_filters={},'
# 'hop_length={},alpha={},k_w={}.wav'.format(
# fname, n_fft, n_layers, n_filters, hop_length, alpha, k_w))
# if not os.path.exists(output_filename):
# nsynth.run(content_filename,
# style_filename,
# output_filename,
# model='decoder',
# n_fft=n_fft,
# n_layers=n_layers,
# n_filters=n_filters,
# hop_length=hop_length,
# alpha=alpha,
# k_w=k_w)
if __name__ == '__main__':
content_path = './target'
style_path = './corpus'
output_path = './results'
batch(content_path, style_path, output_path)