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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) | |