import os
import numpy as np
import tensorflow as tf
import argparse
from train import train
from infer import infer
from generate import generate


class argument:

    def __init__(self, parser):
        self.parser = parser
        global_args = self.parser.add_argument_group('global')
        global_args.add_argument('--train_dir', type=str, help=
            'training directory')
        global_args.add_argument('--wavegan_latent_dim', type=int, help=
            'number of dimensions of the latent space')
        global_args.add_argument('--wavegan_kernel_len', type=int, help=
            'length of 1D filter kernels')
        global_args.add_argument('--wavegan_dim', type=int, help=
            'dimensionality multiplier for model of G and D')
        global_args.add_argument('--wavegan_batchnorm', action='store_true',
            dest='wavegan_batchnorm', help='enable batchnorm')
        global_args.add_argument('--wavegan_smooth_len', type=int, help=
            'length of the pervious audio used to smooth the connection')
        self.parser.set_defaults(train_dir='./train', wavegan_latent_dim=
            100, wavegan_kernel_len=25, wavegan_dim=64, wavegan_batchnorm=
            False, wavegan_smooth_len=4096)

    def train(self):
        data_args = self.parser.add_argument_group('data for train')
        data_args.add_argument('--data_dir', type=str, help=
            'data directory containing *only* audio files to load')
        data_args.add_argument('--data_sample_rate', type=int, help=
            'number of audio samples per second')
        data_args.add_argument('--data_num_channels', type=int, help=
            'number of audio channels to generate (for >2, must match that of data)'
            )
        data_args.add_argument('--data_first_slice', action='store_true',
            dest='data_first_slice', help=
            'if set, only use the first slice each audio example')
        data_args.add_argument('--data_normalize', action='store_true',
            dest='data_normalize', help=
            'if set, normalize the training examples')
        data_args.add_argument('--data_fast_wav', action='store_true', dest
            ='data_fast_wav', help=
            'if your data is comprised of standard WAV files (16-bit signed PCM or 32-bit float), set to decode audio using scipy (faster) instead of librosa'
            )
        data_args.add_argument('--data_prefetch_gpu_num', type=int, help=
            'if nonnegative, prefetch examples to this GPU (Tensorflow device num)'
            )
        train_args = self.parser.add_argument_group('train')
        train_args.add_argument('--train_batch_size', type=int, help=
            'batch size')
        train_args.add_argument('--train_save_secs', type=int, help=
            'how often to save model')
        train_args.add_argument('--train_summary_secs', type=int, help=
            'how often to report summaries')
        train_args.add_argument('--verbose', action='store_true', dest=
            'verbose', help='if set, print G and D loss to stdout')
        train_args.add_argument('--wavegan_disc_nupdates', type=int, help=
            'number of discriminator updates per generator update')
        train_args.add_argument('--wavegan_disc_phaseshuffle', type=int,
            help='radius of phase shuffle operation')
        self.parser.set_defaults(data_dir=None, data_sample_rate=11025,
            data_num_channels=1, data_first_slice=False, data_normalize=
            False, data_fast_wav=False, data_prefetch_gpu_num=0,
            train_batch_size=64, train_save_secs=300, train_summary_secs=
            120, verbose=False, wavegan_disc_nupdates=1,
            wavegan_disc_phaseshuffle=2)

    def generate(self):
        generate_args = self.parser.add_argument_group('generate')
        generate_args.add_argument('--ckpt_path', type=str, help=
            'use chosen checkpoint to generate. if not chosen, use the lastest checkpoint in train_dir'
            )
        generate_args.add_argument('--wav_out_path', type=str, help=
            'path to output wav file')
        generate_args.add_argument('--wav_out_time', type=int, help=
            'output wav file length in seconds, None means inf')
        self.parser.set_defaults(ckpt_path=None, wav_out_path='./gen.wav',
            wav_out_time=None)

    def main(self):
        self.train()
        self.generate()
        main_args = self.parser.add_argument_group('main')
        main_args.add_argument('mode', type=str, choices=['train', 'infer',
            'generate'], help='choose mode')

    def args_init(self):
        return self.parser.parse_args()


if __name__ == '__main__':
    parser = argparse.ArgumentParser(description=
        'main script for training and generating')
    arg = argument(parser)
    arg.main()
    args = arg.args_init()
    if args.mode == 'train':
        train(args)
    elif args.mode == 'generate':
        infer(args)
        generate(args)
    elif args.mode == 'infer':
        infer(args)

f914f8906e5933a556a20bb3984a0e54d9f530e7
<mask token>


class argument:

    def __init__(self, parser):
        self.parser = parser
        global_args = self.parser.add_argument_group('global')
        global_args.add_argument('--train_dir', type=str, help=
            'training directory')
        global_args.add_argument('--wavegan_latent_dim', type=int, help=
            'number of dimensions of the latent space')
        global_args.add_argument('--wavegan_kernel_len', type=int, help=
            'length of 1D filter kernels')
        global_args.add_argument('--wavegan_dim', type=int, help=
            'dimensionality multiplier for model of G and D')
        global_args.add_argument('--wavegan_batchnorm', action='store_true',
            dest='wavegan_batchnorm', help='enable batchnorm')
        global_args.add_argument('--wavegan_smooth_len', type=int, help=
            'length of the pervious audio used to smooth the connection')
        self.parser.set_defaults(train_dir='./train', wavegan_latent_dim=
            100, wavegan_kernel_len=25, wavegan_dim=64, wavegan_batchnorm=
            False, wavegan_smooth_len=4096)

    def train(self):
        data_args = self.parser.add_argument_group('data for train')
        data_args.add_argument('--data_dir', type=str, help=
            'data directory containing *only* audio files to load')
        data_args.add_argument('--data_sample_rate', type=int, help=
            'number of audio samples per second')
        data_args.add_argument('--data_num_channels', type=int, help=
            'number of audio channels to generate (for >2, must match that of data)'
            )
        data_args.add_argument('--data_first_slice', action='store_true',
            dest='data_first_slice', help=
            'if set, only use the first slice each audio example')
        data_args.add_argument('--data_normalize', action='store_true',
            dest='data_normalize', help=
            'if set, normalize the training examples')
        data_args.add_argument('--data_fast_wav', action='store_true', dest
            ='data_fast_wav', help=
            'if your data is comprised of standard WAV files (16-bit signed PCM or 32-bit float), set to decode audio using scipy (faster) instead of librosa'
            )
        data_args.add_argument('--data_prefetch_gpu_num', type=int, help=
            'if nonnegative, prefetch examples to this GPU (Tensorflow device num)'
            )
        train_args = self.parser.add_argument_group('train')
        train_args.add_argument('--train_batch_size', type=int, help=
            'batch size')
        train_args.add_argument('--train_save_secs', type=int, help=
            'how often to save model')
        train_args.add_argument('--train_summary_secs', type=int, help=
            'how often to report summaries')
        train_args.add_argument('--verbose', action='store_true', dest=
            'verbose', help='if set, print G and D loss to stdout')
        train_args.add_argument('--wavegan_disc_nupdates', type=int, help=
            'number of discriminator updates per generator update')
        train_args.add_argument('--wavegan_disc_phaseshuffle', type=int,
            help='radius of phase shuffle operation')
        self.parser.set_defaults(data_dir=None, data_sample_rate=11025,
            data_num_channels=1, data_first_slice=False, data_normalize=
            False, data_fast_wav=False, data_prefetch_gpu_num=0,
            train_batch_size=64, train_save_secs=300, train_summary_secs=
            120, verbose=False, wavegan_disc_nupdates=1,
            wavegan_disc_phaseshuffle=2)
    <mask token>

    def main(self):
        self.train()
        self.generate()
        main_args = self.parser.add_argument_group('main')
        main_args.add_argument('mode', type=str, choices=['train', 'infer',
            'generate'], help='choose mode')

    def args_init(self):
        return self.parser.parse_args()


<mask token>

<mask token>


class argument:

    def __init__(self, parser):
        self.parser = parser
        global_args = self.parser.add_argument_group('global')
        global_args.add_argument('--train_dir', type=str, help=
            'training directory')
        global_args.add_argument('--wavegan_latent_dim', type=int, help=
            'number of dimensions of the latent space')
        global_args.add_argument('--wavegan_kernel_len', type=int, help=
            'length of 1D filter kernels')
        global_args.add_argument('--wavegan_dim', type=int, help=
            'dimensionality multiplier for model of G and D')
        global_args.add_argument('--wavegan_batchnorm', action='store_true',
            dest='wavegan_batchnorm', help='enable batchnorm')
        global_args.add_argument('--wavegan_smooth_len', type=int, help=
            'length of the pervious audio used to smooth the connection')
        self.parser.set_defaults(train_dir='./train', wavegan_latent_dim=
            100, wavegan_kernel_len=25, wavegan_dim=64, wavegan_batchnorm=
            False, wavegan_smooth_len=4096)

    def train(self):
        data_args = self.parser.add_argument_group('data for train')
        data_args.add_argument('--data_dir', type=str, help=
            'data directory containing *only* audio files to load')
        data_args.add_argument('--data_sample_rate', type=int, help=
            'number of audio samples per second')
        data_args.add_argument('--data_num_channels', type=int, help=
            'number of audio channels to generate (for >2, must match that of data)'
            )
        data_args.add_argument('--data_first_slice', action='store_true',
            dest='data_first_slice', help=
            'if set, only use the first slice each audio example')
        data_args.add_argument('--data_normalize', action='store_true',
            dest='data_normalize', help=
            'if set, normalize the training examples')
        data_args.add_argument('--data_fast_wav', action='store_true', dest
            ='data_fast_wav', help=
            'if your data is comprised of standard WAV files (16-bit signed PCM or 32-bit float), set to decode audio using scipy (faster) instead of librosa'
            )
        data_args.add_argument('--data_prefetch_gpu_num', type=int, help=
            'if nonnegative, prefetch examples to this GPU (Tensorflow device num)'
            )
        train_args = self.parser.add_argument_group('train')
        train_args.add_argument('--train_batch_size', type=int, help=
            'batch size')
        train_args.add_argument('--train_save_secs', type=int, help=
            'how often to save model')
        train_args.add_argument('--train_summary_secs', type=int, help=
            'how often to report summaries')
        train_args.add_argument('--verbose', action='store_true', dest=
            'verbose', help='if set, print G and D loss to stdout')
        train_args.add_argument('--wavegan_disc_nupdates', type=int, help=
            'number of discriminator updates per generator update')
        train_args.add_argument('--wavegan_disc_phaseshuffle', type=int,
            help='radius of phase shuffle operation')
        self.parser.set_defaults(data_dir=None, data_sample_rate=11025,
            data_num_channels=1, data_first_slice=False, data_normalize=
            False, data_fast_wav=False, data_prefetch_gpu_num=0,
            train_batch_size=64, train_save_secs=300, train_summary_secs=
            120, verbose=False, wavegan_disc_nupdates=1,
            wavegan_disc_phaseshuffle=2)

    def generate(self):
        generate_args = self.parser.add_argument_group('generate')
        generate_args.add_argument('--ckpt_path', type=str, help=
            'use chosen checkpoint to generate. if not chosen, use the lastest checkpoint in train_dir'
            )
        generate_args.add_argument('--wav_out_path', type=str, help=
            'path to output wav file')
        generate_args.add_argument('--wav_out_time', type=int, help=
            'output wav file length in seconds, None means inf')
        self.parser.set_defaults(ckpt_path=None, wav_out_path='./gen.wav',
            wav_out_time=None)

    def main(self):
        self.train()
        self.generate()
        main_args = self.parser.add_argument_group('main')
        main_args.add_argument('mode', type=str, choices=['train', 'infer',
            'generate'], help='choose mode')

    def args_init(self):
        return self.parser.parse_args()


<mask token>

<mask token>


class argument:

    def __init__(self, parser):
        self.parser = parser
        global_args = self.parser.add_argument_group('global')
        global_args.add_argument('--train_dir', type=str, help=
            'training directory')
        global_args.add_argument('--wavegan_latent_dim', type=int, help=
            'number of dimensions of the latent space')
        global_args.add_argument('--wavegan_kernel_len', type=int, help=
            'length of 1D filter kernels')
        global_args.add_argument('--wavegan_dim', type=int, help=
            'dimensionality multiplier for model of G and D')
        global_args.add_argument('--wavegan_batchnorm', action='store_true',
            dest='wavegan_batchnorm', help='enable batchnorm')
        global_args.add_argument('--wavegan_smooth_len', type=int, help=
            'length of the pervious audio used to smooth the connection')
        self.parser.set_defaults(train_dir='./train', wavegan_latent_dim=
            100, wavegan_kernel_len=25, wavegan_dim=64, wavegan_batchnorm=
            False, wavegan_smooth_len=4096)

    def train(self):
        data_args = self.parser.add_argument_group('data for train')
        data_args.add_argument('--data_dir', type=str, help=
            'data directory containing *only* audio files to load')
        data_args.add_argument('--data_sample_rate', type=int, help=
            'number of audio samples per second')
        data_args.add_argument('--data_num_channels', type=int, help=
            'number of audio channels to generate (for >2, must match that of data)'
            )
        data_args.add_argument('--data_first_slice', action='store_true',
            dest='data_first_slice', help=
            'if set, only use the first slice each audio example')
        data_args.add_argument('--data_normalize', action='store_true',
            dest='data_normalize', help=
            'if set, normalize the training examples')
        data_args.add_argument('--data_fast_wav', action='store_true', dest
            ='data_fast_wav', help=
            'if your data is comprised of standard WAV files (16-bit signed PCM or 32-bit float), set to decode audio using scipy (faster) instead of librosa'
            )
        data_args.add_argument('--data_prefetch_gpu_num', type=int, help=
            'if nonnegative, prefetch examples to this GPU (Tensorflow device num)'
            )
        train_args = self.parser.add_argument_group('train')
        train_args.add_argument('--train_batch_size', type=int, help=
            'batch size')
        train_args.add_argument('--train_save_secs', type=int, help=
            'how often to save model')
        train_args.add_argument('--train_summary_secs', type=int, help=
            'how often to report summaries')
        train_args.add_argument('--verbose', action='store_true', dest=
            'verbose', help='if set, print G and D loss to stdout')
        train_args.add_argument('--wavegan_disc_nupdates', type=int, help=
            'number of discriminator updates per generator update')
        train_args.add_argument('--wavegan_disc_phaseshuffle', type=int,
            help='radius of phase shuffle operation')
        self.parser.set_defaults(data_dir=None, data_sample_rate=11025,
            data_num_channels=1, data_first_slice=False, data_normalize=
            False, data_fast_wav=False, data_prefetch_gpu_num=0,
            train_batch_size=64, train_save_secs=300, train_summary_secs=
            120, verbose=False, wavegan_disc_nupdates=1,
            wavegan_disc_phaseshuffle=2)

    def generate(self):
        generate_args = self.parser.add_argument_group('generate')
        generate_args.add_argument('--ckpt_path', type=str, help=
            'use chosen checkpoint to generate. if not chosen, use the lastest checkpoint in train_dir'
            )
        generate_args.add_argument('--wav_out_path', type=str, help=
            'path to output wav file')
        generate_args.add_argument('--wav_out_time', type=int, help=
            'output wav file length in seconds, None means inf')
        self.parser.set_defaults(ckpt_path=None, wav_out_path='./gen.wav',
            wav_out_time=None)

    def main(self):
        self.train()
        self.generate()
        main_args = self.parser.add_argument_group('main')
        main_args.add_argument('mode', type=str, choices=['train', 'infer',
            'generate'], help='choose mode')

    def args_init(self):
        return self.parser.parse_args()


if __name__ == '__main__':
    parser = argparse.ArgumentParser(description=
        'main script for training and generating')
    arg = argument(parser)
    arg.main()
    args = arg.args_init()
    if args.mode == 'train':
        train(args)
    elif args.mode == 'generate':
        infer(args)
        generate(args)
    elif args.mode == 'infer':
        infer(args)

import os
import numpy as np
import tensorflow as tf
import argparse
from train import train
from infer import infer
from generate import generate


class argument:

    def __init__(self, parser):
        self.parser = parser
        global_args = self.parser.add_argument_group('global')
        global_args.add_argument('--train_dir', type=str, help=
            'training directory')
        global_args.add_argument('--wavegan_latent_dim', type=int, help=
            'number of dimensions of the latent space')
        global_args.add_argument('--wavegan_kernel_len', type=int, help=
            'length of 1D filter kernels')
        global_args.add_argument('--wavegan_dim', type=int, help=
            'dimensionality multiplier for model of G and D')
        global_args.add_argument('--wavegan_batchnorm', action='store_true',
            dest='wavegan_batchnorm', help='enable batchnorm')
        global_args.add_argument('--wavegan_smooth_len', type=int, help=
            'length of the pervious audio used to smooth the connection')
        self.parser.set_defaults(train_dir='./train', wavegan_latent_dim=
            100, wavegan_kernel_len=25, wavegan_dim=64, wavegan_batchnorm=
            False, wavegan_smooth_len=4096)

    def train(self):
        data_args = self.parser.add_argument_group('data for train')
        data_args.add_argument('--data_dir', type=str, help=
            'data directory containing *only* audio files to load')
        data_args.add_argument('--data_sample_rate', type=int, help=
            'number of audio samples per second')
        data_args.add_argument('--data_num_channels', type=int, help=
            'number of audio channels to generate (for >2, must match that of data)'
            )
        data_args.add_argument('--data_first_slice', action='store_true',
            dest='data_first_slice', help=
            'if set, only use the first slice each audio example')
        data_args.add_argument('--data_normalize', action='store_true',
            dest='data_normalize', help=
            'if set, normalize the training examples')
        data_args.add_argument('--data_fast_wav', action='store_true', dest
            ='data_fast_wav', help=
            'if your data is comprised of standard WAV files (16-bit signed PCM or 32-bit float), set to decode audio using scipy (faster) instead of librosa'
            )
        data_args.add_argument('--data_prefetch_gpu_num', type=int, help=
            'if nonnegative, prefetch examples to this GPU (Tensorflow device num)'
            )
        train_args = self.parser.add_argument_group('train')
        train_args.add_argument('--train_batch_size', type=int, help=
            'batch size')
        train_args.add_argument('--train_save_secs', type=int, help=
            'how often to save model')
        train_args.add_argument('--train_summary_secs', type=int, help=
            'how often to report summaries')
        train_args.add_argument('--verbose', action='store_true', dest=
            'verbose', help='if set, print G and D loss to stdout')
        train_args.add_argument('--wavegan_disc_nupdates', type=int, help=
            'number of discriminator updates per generator update')
        train_args.add_argument('--wavegan_disc_phaseshuffle', type=int,
            help='radius of phase shuffle operation')
        self.parser.set_defaults(data_dir=None, data_sample_rate=11025,
            data_num_channels=1, data_first_slice=False, data_normalize=
            False, data_fast_wav=False, data_prefetch_gpu_num=0,
            train_batch_size=64, train_save_secs=300, train_summary_secs=
            120, verbose=False, wavegan_disc_nupdates=1,
            wavegan_disc_phaseshuffle=2)

    def generate(self):
        generate_args = self.parser.add_argument_group('generate')
        generate_args.add_argument('--ckpt_path', type=str, help=
            'use chosen checkpoint to generate. if not chosen, use the lastest checkpoint in train_dir'
            )
        generate_args.add_argument('--wav_out_path', type=str, help=
            'path to output wav file')
        generate_args.add_argument('--wav_out_time', type=int, help=
            'output wav file length in seconds, None means inf')
        self.parser.set_defaults(ckpt_path=None, wav_out_path='./gen.wav',
            wav_out_time=None)

    def main(self):
        self.train()
        self.generate()
        main_args = self.parser.add_argument_group('main')
        main_args.add_argument('mode', type=str, choices=['train', 'infer',
            'generate'], help='choose mode')

    def args_init(self):
        return self.parser.parse_args()


if __name__ == '__main__':
    parser = argparse.ArgumentParser(description=
        'main script for training and generating')
    arg = argument(parser)
    arg.main()
    args = arg.args_init()
    if args.mode == 'train':
        train(args)
    elif args.mode == 'generate':
        infer(args)
        generate(args)
    elif args.mode == 'infer':
        infer(args)
