def main():
parser = argparse.ArgumentParser()
parser.add_argument('--checkpoint', required=True, help='Path to model checkpoint')
parser.add_argument('--hparams', default='',
help='Hyperparameter overrides as a comma-separated list of name=value pairs')
args = parser.parse_args()
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
hparams.parse(args.hparams)
run_eval(args)
def main():
args = docopt(__doc__)
print("Command line args:\n", args)
checkpoint_dir = args["--checkpoint-dir"]
data_root = args["--data-root"]
dataset_name = args["--dataset"]
assert dataset_name in ["jsut"]
dataset = importlib.import_module("data." + dataset_name)
dataset_instance = dataset.instantiate(in_dir="", out_dir=data_root)
hparams.parse(args["--hparams"])
print(hparams_debug_string())
tf.logging.set_verbosity(tf.logging.INFO)
train(hparams, checkpoint_dir, dataset_instance.source_files,
dataset_instance.target_files)
def main():
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('--model', default='Tacotron')
parser.add_argument('--dataset', default='LJSpeech-1.1')
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', type=bool, default=False)
parser.add_argument('--book', default='northandsouth')
parser.add_argument('--n_jobs', type=int, default=cpu_count())
args = parser.parse_args()
accepted_models = ['Tacotron', 'WaveRNN']
if args.model not in accepted_models:
raise ValueError(
'please enter a valid model to train: {}'.format(accepted_models))
modified_hp = hparams.parse(args.hparams)
if args.model == 'Tacotron':
preprocess(args, modified_hp)
else:
wavernn_preprocess(args, modified_hp)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--input_dir',
default='tacotron_log',
help='folder to contain inputs sentences/targets')
parser.add_argument(
'--hparams',
default='',
help=
'Hyperparameter overrides as a comma-separated list of name=value pairs'
)
parser.add_argument(
'--text_list',
default='',
help=
'Text file contains list of texts to be synthesized. Valid if mode=eval'
)
parser.add_argument('--output_dir',
default='taco_output/',
help='folder to contain synthesized mel spectrograms')
args = parser.parse_args()
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
modified_hp = hparams.parse(args.hparams)
sentences = _get_sentences(args)
#try:
# checkpoint_path = tf.train.get_checkpoint_state(os.path.join(args.input_dir,'taco_pretrained')).model_checkpoint_path
# log('loaded model at {}'.format(checkpoint_path))
#except:
# raise RuntimeError('Failed to load checkpoint at {}'.format(args.checkpoint))
checkpoint_path = "tacotron_log/taco_pretrained/tacotron_model.ckpt-7500"
_run_eval(args, checkpoint_path, args.output_dir, modified_hp, sentences)
def prepare_run(args, weight):
modified_hp = hparams.parse(args.hparams)
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
run_name = args.name or args.tacotron_name or args.model
taco_checkpoint = os.path.join('Tacotron_VAE/logs-' + run_name + weight , 'taco_' + args.checkpoint)
return taco_checkpoint, modified_hp
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--base_dir', default=os.path.expanduser('~/crnn'))
parser.add_argument('--input', default='training/train.txt')
parser.add_argument('--model', default='crnn')
parser.add_argument('--restore_step',
type=int,
help='Global step to restore from checkpoint.')
parser.add_argument('--summary_interval', type=int, default=100)
parser.add_argument('--checkpoint_interval', type=int, default=1000)
args = parser.parse_args()
run_name = args.name or args.model
log_dir = os.path.join(args.base_dir, 'logs-%s' % run_name)
os.makedirs(log_dir, exist_ok=True)
hparams.parse(args.hparams)
train(log_dir, args)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--checkpoint',
required=True,
help='Path to model checkpoint')
parser.add_argument(
'--hparams',
default='',
help=
'Hyperparameter overrides as a comma-separated list of name=value pairs'
)
args = parser.parse_args()
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
hparams.max_iters = 100
hparams.parse(args.hparams)
run_eval(args)
def main():
print('initializing preprocessing..')
parser = argparse.ArgumentParser()
parser.add_argument('--input_dir', default='')
parser.add_argument('--output_dir', default='')
parser.add_argument(
'--hparams',
default='',
help=
'Hyperparameter overrides as a comma-separated list of name=value pairs'
)
parser.add_argument('--dataset', default='LJSpeech-1.1')
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='training_data_dual_channels')
#parser.add_argument('--n_jobs', type=int, default=cpu_count())
parser.add_argument('--n_jobs', type=int, default=1)
args = parser.parse_args()
modified_hp = hparams.parse(args.hparams)
assert args.merge_books in ('False', 'True')
run_preprocess(args, modified_hp)
print('Warning: preprocessed format is audio [T], mel [T, C] linear[T, C]')
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='emt4')
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='training_data')
parser.add_argument('--n_jobs', type=int, default=cpu_count())
parser.add_argument('--folder_wav_dir', default='../../data/')
parser.add_argument('--TEST', default=False, action='store_true')
parser.add_argument('--philly', default=False, action='store_true')
parser.add_argument('--db', type=int, default=hparams.trim_top_db)
args = parser.parse_args()
modified_hp = hparams.parse(args.hparams)
modified_hp.trim_top_db = args.db
assert args.merge_books in ('False', 'True')
run_preprocess(args, modified_hp)
def prepare_run(args):
modified_hp = hparams.parse(args.hparams)
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
run_name = args.name or args.name or args.model
cent_checkpoint = os.path.join('logs-' + run_name,
'cent_' + args.checkpoint)
return cent_checkpoint, modified_hp
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--checkpoint',
default='logs-tacotron',
help='Path to model checkpoint')
parser.add_argument(
'--hparams',
default='',
help=
'Hyperparameter overrides as a comma-separated list of name=value pairs'
)
args = parser.parse_args()
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
os.environ['CUDA_VISIBLE_DEVICES'] = '1'
hparams.parse(args.hparams)
run_eval(args.checkpoint)
def prepare_run(args):
modified_hp = hparams.parse(args.hparams)
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
run_name = args.name or args.tacotron_name or args.model
taco_checkpoint = args.output_model_path
return taco_checkpoint, modified_hp
def prepare_run(args):
modified_hp = hparams.parse(args.hparams)
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
taco_checkpoint = args.taco_checkpoint
wave_checkpoint = args.wave_checkpoint
return taco_checkpoint, wave_checkpoint, modified_hp
def init_tacotron2(args):
# t2
print('\n#####################################')
if args.model == 'Tacotron':
print('\nInitialising Tacotron Model...\n')
t2_hparams = hparams.parse(args.hparams)
try:
checkpoint_path = tf.train.get_checkpoint_state(
args.taco_checkpoint).model_checkpoint_path
log('loaded model at {}'.format(checkpoint_path))
except:
raise RuntimeError('Failed to load checkpoint at {}'.format(
args.taco_checkpoint))
output_dir = 'tacotron_' + args.output_dir
eval_dir = os.path.join(output_dir, 'eval')
log_dir = os.path.join(output_dir, 'logs-eval')
print('eval_dir:', eval_dir)
print('args.mels_dir:', args.mels_dir)
# Create output path if it doesn't exist
os.makedirs(eval_dir, exist_ok=True)
os.makedirs(log_dir, exist_ok=True)
os.makedirs(os.path.join(log_dir, 'wavs'), exist_ok=True)
os.makedirs(os.path.join(log_dir, 'plots'), exist_ok=True)
log(hparams_debug_string())
synth = Synthesizer()
synth.load(checkpoint_path, t2_hparams)
return synth, eval_dir, log_dir
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='SIWIS')
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='training_data')
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():
parser = argparse.ArgumentParser()
parser.add_argument('--checkpoint',
required=True,
help='Path to model checkpoint')
parser.add_argument(
'--hparams',
default='',
help=
'Hyperparameter overrides as a comma-separated list of name=value pairs'
)
parser.add_argument('--text', default='黑熊闯进王明辉家后院觅食~铁砂掌爱好者张辉表演劈砖')
args = parser.parse_args()
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
hparams.parse(args.hparams)
run_eval(args)
def prepare_run(args):
modified_hp = hparams.parse(args.hparams)
print(hparams_debug_string())
os.environ['TF_CPP_MIN_LOG_LEVEL'] = str(args.tf_log_level)
run_name = args.name or args.model
log_dir = os.path.join(args.base_dir, 'logs-{}'.format(run_name))
os.makedirs(log_dir, exist_ok=True)
return log_dir, modified_hp
def tacotron_synthesize(args):
hparams.parse(args.hparams)
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
output_dir = 'tacotron_' + args.output_dir
if args.text_list != '':
with open(args.text_list, 'rb') as f:
sentences = list(map(lambda l:l.decode("utf-8")[:-1], f.readlines()))
else:
sentences = hparams.sentences
try:
checkpoint_path = tf.train.get_checkpoint_state(args.checkpoint).model_checkpoint_path
print('loaded model at {}'.format(checkpoint_path))
except:
raise AssertionError('Cannot restore checkpoint: {}, did you train a model?'.format(args.checkpoint))
run_eval(args, checkpoint_path, output_dir, sentences)
def prepare_run(args):
modified_hp = hparams.parse(args.hparams)
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
taco_checkpoint = os.path.join('logs-' + args.model,
'taco_' + args.checkpoint)
return taco_checkpoint, modified_hp
def main():
args = docopt(__doc__)
print("Command line args:\n", args)
checkpoint_dir = args["--checkpoint-dir"]
source_data_root = args["--source-data-root"]
target_data_root = args["--target-data-root"]
selected_list_dir = args["--selected-list-dir"]
use_multi_gpu = args["--multi-gpus"]
if args["--hparam-json-file"]:
with open(args["--hparam-json-file"]) as f:
json = "".join(f.readlines())
hparams.parse_json(json)
hparams.parse(args["--hparams"])
training_list = list(load_key_list("train.csv", selected_list_dir))
validation_list = list(load_key_list("validation.csv", selected_list_dir))
training_source_files = [os.path.join(source_data_root, f"{key}.{hparams.source_file_extension}") for key in
training_list]
training_target_files = [os.path.join(target_data_root, f"{key}.{hparams.target_file_extension}") for key in
training_list]
validation_source_files = [os.path.join(source_data_root, f"{key}.{hparams.source_file_extension}") for key in
validation_list]
validation_target_files = [os.path.join(target_data_root, f"{key}.{hparams.target_file_extension}") for key in
validation_list]
log = logging.getLogger("tensorflow")
log.setLevel(logging.INFO)
formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
fh = logging.FileHandler(hparams.logfile)
fh.setLevel(logging.INFO)
fh.setFormatter(formatter)
log.addHandler(fh)
tf.logging.set_verbosity(tf.logging.INFO)
tf.logging.info(hparams_debug_string())
train_and_evaluate(hparams,
checkpoint_dir,
training_source_files,
training_target_files,
validation_source_files,
validation_target_files,
use_multi_gpu)
def main():
args = docopt(__doc__)
print("Command line args:\n", args)
checkpoint_dir = args["--checkpoint-dir"]
data_root = args["--data-root"]
dataset_name = args["--dataset"]
assert dataset_name in ["blizzard2012", "ljspeech"]
corpus = importlib.import_module("datasets." + dataset_name)
corpus_instance = corpus.instantiate(in_dir="", out_dir=data_root)
hparams.parse(args["--hparams"])
print(hparams_debug_string())
tf.logging.set_verbosity(tf.logging.INFO)
train_and_evaluate(hparams, checkpoint_dir,
corpus_instance.training_target_files,
corpus_instance.validation_target_files)
def gst_synthesize(args, checkpoint, sentences=None, reference_mel=None):
output_dir = "gst_" + args.output_dir
checkpoint_path = tf.train.get_checkpoint_state(
checkpoint).model_checkpoint_path
log('loaded model at {}'.format(checkpoint_path))
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
hparams.parse(args.hparams)
if args.mode == 'eval':
return run_eval(args, checkpoint_path, output_dir, sentences,
reference_mel)
elif args.mode == 'synthesis':
return run_synthesis(args, checkpoint_path, output_dir)
else:
run_live(args, checkpoint_path)
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 or args.model
log_dir = os.path.join(args.base_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 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 or args.model
log_dir = os.path.join(args.base_dir, 'logs-{}'.format(run_name))
os.makedirs(log_dir, exist_ok=True)
infolog.init(os.path.join(log_dir, 'Terminal_train_log'), run_name)
return log_dir, modified_hp
def main():
parser = argparse.ArgumentParser()
parser.add_argument(
'--base_dir',
default=os.path.expanduser(
'C:\\Users\\blcdec\\project\\gst-tacotron\\tacotron_imu'))
parser.add_argument('--input', default='training/train.txt')
parser.add_argument('--model', default='tacotron')
parser.add_argument(
'--name',
help='Name of the run. Used for logging. Defaults to model name.')
parser.add_argument(
'--hparams',
default='',
help=
'Hyperparameter overrides as a comma-separated list of name=value pairs'
)
parser.add_argument('--restore_step',
type=int,
help='Global step to restore from checkpoint.')
parser.add_argument('--summary_interval',
type=int,
default=100,
help='Steps between running summary ops.')
parser.add_argument('--checkpoint_interval',
type=int,
default=1000,
help='Steps between writing checkpoints.')
parser.add_argument('--slack_url',
help='Slack webhook URL to get periodic reports.')
parser.add_argument('--tf_log_level',
type=int,
default=1,
help='Tensorflow C++ log level.')
parser.add_argument('--git',
action='store_true',
help='If set, verify that the client is clean.')
args = parser.parse_args()
os.environ['TF_CPP_MIN_LOG_LEVEL'] = str(args.tf_log_level)
run_name = args.name or args.model
log_dir = os.path.join(args.base_dir, 'logs-%s' % run_name)
os.makedirs(log_dir, exist_ok=True)
infolog.init(os.path.join(log_dir, 'train.log'), run_name, args.slack_url)
hparams.parse(args.hparams)
train(log_dir, args)
def prepare_run(args):
modified_hp = hparams.parse(args.hparams)
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '1'
taco_checkpoint = os.path.join(args.base_dir, 'logs-' + args.model, 'taco_pretrained')
wave_checkpoint = os.path.join(args.base_dir, 'logs-' + args.model, 'wavernn_pretrained', 'wavernn_model.pyt')
return taco_checkpoint, wave_checkpoint, modified_hp
def tacotron_synthesize(args):
hparams.parse(args.hparams)
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
output_dir = 'tacotron_' + args.output_dir
try:
checkpoint_path = tf.train.get_checkpoint_state(
args.checkpoint).model_checkpoint_path
print('loaded model at {}'.format(checkpoint_path))
except:
raise AssertionError(
'Cannot restore checkpoint: {}, did you train a model?'.format(
args.checkpoint))
if args.mode == 'eval':
run_eval(args, checkpoint_path, output_dir)
else:
run_synthesis(args, checkpoint_path, output_dir)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--checkpoint',
required=True,
help='Path to model checkpoint')
parser.add_argument(
'--hparams',
default='',
help=
'Hyperparameter overrides as a comma-separated list of name=value pairs'
)
parser.add_argument('--gpu', default='1')
args = parser.parse_args()
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu
hparams.parse(args.hparams)
run_eval(args)
def prepare_run(args):
modified_hp = hparams.parse(args.hparams)
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
run_name = args.name or args.gst_name
gst_checkpoint = os.path.join('logs-' + run_name, 'gst_' + args.checkpoint)
run_name = args.name or args.wavenet_name
wave_checkpoint = os.path.join('logs-' + run_name, 'wave_' + args.checkpoint)
return gst_checkpoint, wave_checkpoint, modified_hp
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--base_dir', default=os.getcwd())
#### read both positive metadata and negative metadata
parser.add_argument('--input_pos', default='training/train-pos.txt')
parser.add_argument('--input_neg', default='training/train-neg.txt')
parser.add_argument('--model', default='ttsGAN')
parser.add_argument(
'--name',
help='Name of the run. Used for logging. Defaults to model name.')
parser.add_argument(
'--hparams',
default='',
help=
'Hyperparameter overrides as a comma-separated list of name=value pairs'
)
parser.add_argument('--restore_step',
type=int,
help='Global step to restore from checkpoint.')
parser.add_argument('--summary_interval',
type=int,
default=100,
help='Steps between running summary ops.')
parser.add_argument('--checkpoint_interval',
type=int,
default=1000,
help='Steps between writing checkpoints.')
parser.add_argument('--tf_log_level',
type=int,
default=1,
help='Tensorflow C++ log level.')
parser.add_argument('--slack_url', default=None)
parser.add_argument('--git', default=False)
args = parser.parse_args()
os.environ['TF_CPP_MIN_LOG_LEVEL'] = str(args.tf_log_level)
run_name = args.name or args.model
log_dir = os.path.join(args.base_dir, 'logs-%s' % run_name)
os.makedirs(log_dir, exist_ok=True)
infolog.init(os.path.join(log_dir, 'train.log'), run_name, args.slack_url)
hparams.parse(args.hparams)
train(log_dir, args)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--name', default='test', help='Name of the run. Used for logging. Defaults to model name.')
parser.add_argument('--hp', default='',
help='Hyperparameter overrides as a comma-separated list of name=value pairs')
parser.add_argument('--model', default='SED_MDD')
parser.add_argument('--restore_step', type=int, help='Global step to restore from checkpoint.')
parser.add_argument('--summary_interval', type=int, default=100, help='Steps between running summary ops.')
parser.add_argument('--checkpoint_interval', type=int, default=1000, help='Steps between writing checkpoints.')
parser.add_argument('--tf_log_level', type=int, default=1, help='Tensorflow C++ log level.')
args = parser.parse_args()
os.environ['TF_CPP_MIN_LOG_LEVEL'] = str(args.tf_log_level)
#os.environ["CUDA_VISIBLE_DEVICES"] = '0'
run_name = args.name
log_dir = hp.logdir
os.makedirs(log_dir, exist_ok=True)
infolog.init(os.path.join(log_dir, 'train.log'), run_name)
hp.parse(args.hp)
train(log_dir, args)
def main():
args = get_args()
if args.preset is not None:
with open(args.preset) as f:
hparams.parse_json(f.read())
modified_hp = hparams.parse(args.hparams)
print(hparams_debug_string())
synthesis(args.checkpoint_path, args.local_path, args.global_id,
args.output_dir, modified_hp)
def prepare_run(args):
modified_hp = hparams.parse(args.hparams)
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
run_name = args.name or args.tacotron_name or args.model
taco_checkpoint = os.path.join('logs-' + run_name, 'taco_' + args.checkpoint)
run_name = args.name or args.wavenet_name or args.model
wave_checkpoint = os.path.join('logs-' + run_name, 'wave_' + args.checkpoint)
return taco_checkpoint, wave_checkpoint, modified_hp
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--base_dir', default=os.path.expanduser('~/tacotron'))
parser.add_argument('--input', default='training/train.txt')
parser.add_argument('--model', default='tacotron')
parser.add_argument('--name', help='Name of the run. Used for logging. Defaults to model name.')
parser.add_argument('--hparams', default='',
help='Hyperparameter overrides as a comma-separated list of name=value pairs')
parser.add_argument('--restore_step', type=int, help='Global step to restore from checkpoint.')
parser.add_argument('--summary_interval', type=int, default=100,
help='Steps between running summary ops.')
parser.add_argument('--checkpoint_interval', type=int, default=1000,
help='Steps between writing checkpoints.')
parser.add_argument('--slack_url', help='Slack webhook URL to get periodic reports.')
parser.add_argument('--tf_log_level', type=int, default=1, help='Tensorflow C++ log level.')
parser.add_argument('--git', action='store_true', help='If set, verify that the client is clean.')
args = parser.parse_args()
os.environ['TF_CPP_MIN_LOG_LEVEL'] = str(args.tf_log_level)
run_name = args.name or args.model
log_dir = os.path.join(args.base_dir, 'logs-%s' % run_name)
os.makedirs(log_dir, exist_ok=True)
infolog.init(os.path.join(log_dir, 'train.log'), run_name, args.slack_url)
hparams.parse(args.hparams)
train(log_dir, 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='LJSpeech-1.1')
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='training_data')
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)
class SynthesisResource:
def on_get(self, req, res):
if not req.params.get('text'):
raise falcon.HTTPBadRequest()
res.data = synthesizer.synthesize(req.params.get('text'))
res.content_type = 'audio/wav'
synthesizer = Synthesizer()
api = falcon.API()
api.add_route('/synthesize', SynthesisResource())
api.add_route('/', UIResource())
if __name__ == '__main__':
from wsgiref import simple_server
parser = argparse.ArgumentParser()
parser.add_argument('--checkpoint', required=True, help='Full path to model checkpoint')
parser.add_argument('--port', type=int, default=9000)
parser.add_argument('--hparams', default='',
help='Hyperparameter overrides as a comma-separated list of name=value pairs')
args = parser.parse_args()
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
hparams.parse(args.hparams)
print(hparams_debug_string())
synthesizer.load(args.checkpoint)
print('Serving on port %d' % args.port)
simple_server.make_server('0.0.0.0', args.port, api).serve_forever()
else:
synthesizer.load(os.environ['CHECKPOINT'])