def prepare_run(args):
modified_hp = hparams.parse(args.hparams)
os.environ["TF_CPP_MIN_LOG_LEVEL"] = str(args.tf_log_level)
run_name = args.name
log_dir = os.path.join(args.models_dir, "logs-{}".format(run_name))
os.makedirs(log_dir, exist_ok=True)
all_images = get_image_list('train', args.data_root)
all_test_images = get_image_list('val', args.data_root)
modified_hp.add_hparam('all_images', all_images)
modified_hp.add_hparam('all_test_images', all_test_images)
## add speaker-specific parameters
modified_hp.add_hparam('fps', int(args.fps))
modified_hp.add_hparam('T', int(args.window_size * args.fps))
modified_hp.add_hparam('mel_step_size', int(args.window_size * 80))
assert (modified_hp.mel_step_size % modified_hp.outputs_per_step == 0),\
'Mel step size should be a multiple of outputs per step, change either of them to meet this condition'
modified_hp.add_hparam(
'max_iters', modified_hp.mel_step_size // modified_hp.outputs_per_step)
print('Training on {} hours'.format(
len(all_images) / (3600. * modified_hp.fps)))
print('Validating on {} hours'.format(
len(all_test_images) / (3600. * modified_hp.fps)))
return log_dir, modified_hp
def prepare_run(args):
modified_hp = hparams.parse(args.hparams)
os.environ["TF_CPP_MIN_LOG_LEVEL"] = str(args.tf_log_level)
run_name = args.name
log_dir = os.path.join(args.models_dir, "logs-{}".format(run_name))
os.makedirs(log_dir, exist_ok=True)
infolog.init(os.path.join(log_dir, "Terminal_train_log"), run_name, args.slack_url)
return log_dir, modified_hp
def main():
parser = argparse.ArgumentParser(
description=
"Preprocesses audio files from datasets, encodes them as mel spectrograms "
"and writes them to the disk. Audio files are also saved, to be used by the "
"vocoder for training.",
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument("datasets_root", type=Path, help=\
"Path to the directory containing your LibriSpeech/TTS datasets.")
parser.add_argument("-o", "--out_dir", type=Path, default=argparse.SUPPRESS, help=\
"Path to the output directory that will contain the mel spectrograms, the audios and the "
"embeds. Defaults to <datasets_root>/SV2TTS/synthesizer/")
parser.add_argument("-n", "--n_processes", type=int, default=8, help=\
"Number of processes in parallel.")
parser.add_argument("-s", "--skip_existing", action="store_true", help=\
"Whether to overwrite existing files with the same name. Useful if the preprocessing was "
"interrupted.")
parser.add_argument("--hparams", type=str, default="", help=\
"Hyperparameter overrides as a comma-separated list of name-value pairs")
parser.add_argument("-d",
"--datasets",
type=str,
default="librispeech_other")
args = parser.parse_args()
args.datasets = args.datasets.split(",")
# Process the arguments
if not hasattr(args, "out_dir"):
args.out_dir = args.datasets_root.joinpath("SV2TTS", "synthesizer")
# Create directories
assert args.datasets_root.exists()
args.out_dir.mkdir(exist_ok=True, parents=True)
# Preprocess the dataset
print_args(args, parser)
args.hparams = hparams.parse(args.hparams)
preprocess_func = {
"custom": preprocess_custom,
"librispeech_other": preprocess_librispeech,
}
args = vars(args)
for dataset in args.pop("datasets"):
print("Preprocessing %s" % dataset)
preprocess_func[dataset](**args)
def main():
print('initializing preprocessing..')
parser = argparse.ArgumentParser()
parser.add_argument('--base_dir', default='')
parser.add_argument('--hparams', default='',
help='Hyperparameter overrides as a comma-separated list of name=value pairs')
parser.add_argument('--dataset', default='wave_zlm')
parser.add_argument('--language', default='en_US')
parser.add_argument('--voice', default='female')
parser.add_argument('--reader', default='mary_ann')
parser.add_argument('--merge_books', default='False')
parser.add_argument('--book', default='northandsouth')
parser.add_argument('--output', default='./SV2TTS/training_data_zlm')
parser.add_argument('--n_jobs', type=int, default=cpu_count())
args = parser.parse_args()
modified_hp = hparams.parse(args.hparams)
assert args.merge_books in ('False', 'True')
run_preprocess(args, modified_hp)
def main():
class MyFormatter(argparse.ArgumentDefaultsHelpFormatter,
argparse.RawDescriptionHelpFormatter):
pass
parser = argparse.ArgumentParser(
description=
"Creates ground-truth aligned (GTA) spectrograms from the vocoder.",
formatter_class=MyFormatter)
parser.add_argument("datasets_root", type=str, help=\
"Path to the directory containing your SV2TTS directory. If you specify both --in_dir and "
"--out_dir, this argument won't be used.")
parser.add_argument("--model_dir", type=str,
default="synthesizer/saved_models/logs-pretrained/", help=\
"Path to the pretrained model directory.")
parser.add_argument("-i", "--in_dir", type=str, default=argparse.SUPPRESS, help= \
"Path to the synthesizer directory that contains the mel spectrograms, the wavs and the "
"embeds. Defaults to <datasets_root>/SV2TTS/synthesizer/.")
parser.add_argument("-o", "--out_dir", type=str, default=argparse.SUPPRESS, help= \
"Path to the output vocoder directory that will contain the ground truth aligned mel "
"spectrograms. Defaults to <datasets_root>/SV2TTS/vocoder/.")
parser.add_argument(
"--hparams",
default="",
help="Hyperparameter overrides as a comma-separated list of name=value "
"pairs")
parser.add_argument("-gpuid", "--gpu_id", type=str, default='0', help= \
"Select the GPU to run the code")
args = parser.parse_args()
print_args(args, parser)
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu_id
modified_hp = hparams.parse(args.hparams)
if not hasattr(args, "in_dir"):
args.in_dir = os.path.join(args.datasets_root, "SV2TTS", "synthesizer")
if not hasattr(args, "out_dir"):
args.out_dir = os.path.join(args.datasets_root, "SV2TTS", "vocoder")
run_synthesis(args.in_dir, args.out_dir, args.model_dir, modified_hp)
"and writes them to the disk. Audio files are also saved, to be used by the "
"vocoder for training.",
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument("datasets_root", type=Path, help=\
"Path to the directory containing your LibriSpeech/TTS datasets.")
parser.add_argument("-o", "--out_dir", type=Path, default=argparse.SUPPRESS, help=\
"Path to the output directory that will contain the mel spectrograms, the audios and the "
"embeds. Defaults to <datasets_root>/SV2TTS/synthesizer/")
parser.add_argument("-n", "--n_processes", type=int, default=None, help=\
"Number of processes in parallel.")
parser.add_argument("-s", "--skip_existing", action="store_true", help=\
"Whether to overwrite existing files with the same name. Useful if the preprocessing was "
"interrupted.")
parser.add_argument("--hparams", type=str, default="", help=\
"Hyperparameter overrides as a comma-separated list of name-value pairs")
args = parser.parse_args()
# Process the arguments
if not hasattr(args, "out_dir"):
args.out_dir = args.datasets_root.joinpath("SV2TTS", "synthesizer")
# Create directories
assert args.datasets_root.exists()
args.out_dir.mkdir(exist_ok=True, parents=True)
# Preprocess the dataset
print_args(args, parser)
args.hparams = hparams.parse(args.hparams)
# preprocess_librispeech(**vars(args))
preprocess_KSponSpeech(**vars(args))
description=
"Creates ground-truth aligned (GTA) spectrograms from the vocoder.",
formatter_class=MyFormatter)
parser.add_argument("datasets_root", type=str, help=\
"Path to the directory containing your SV2TTS directory. If you specify both --in_dir and "
"--out_dir, this argument won't be used.")
parser.add_argument("--model_dir", type=str,
default="synthesizer/saved_models/logs-pretrained/", help=\
"Path to the pretrained model directory.")
parser.add_argument("-i", "--in_dir", type=str, default=argparse.SUPPRESS, help= \
"Path to the synthesizer directory that contains the mel spectrograms, the wavs and the "
"embeds. Defaults to <datasets_root>/SV2TTS/synthesizer/.")
parser.add_argument("-o", "--out_dir", type=str, default=argparse.SUPPRESS, help= \
"Path to the output vocoder directory that will contain the ground truth aligned mel "
"spectrograms. Defaults to <datasets_root>/SV2TTS/vocoder/.")
parser.add_argument(
"--hparams",
default="",
help="Hyperparameter overrides as a comma-separated list of name=value "
"pairs")
args = parser.parse_args()
print_args(args, parser)
modified_hp = hparams.parse(args.hparams)
if not hasattr(args, "in_dir"):
args.in_dir = os.path.join(args.datasets_root, "SV2TTS", "synthesizer")
if not hasattr(args, "out_dir"):
args.out_dir = os.path.join(args.datasets_root, "SV2TTS", "vocoder")
run_synthesis(args.in_dir, args.out_dir, args.model_dir, modified_hp)
