def preprocess_acoustic_model(exp: experiment.Experiment, args):
""" Preprocesses data given in args using the experiment
stored in exp. """
#_get_raw(exp, args)
logger.info("Loading feature model wrapper %s for preprocessing" %
(exp.config["acoustic_model"]))
wrappers.load(exp.config["acoustic_model"]).preprocess(exp, vars(args))
logger.info("Preprocessing done")
def synthesize(exp: experiment.Experiment, args) -> None:
""" Synthesizes inside an experiment folder. """
sentences = _get_sentences(args)
logger.info("Loading acoustic feature model wrapper %s for synthesis" %
(exp.config["acoustic_model"]))
acoustic_module_wrapper = wrappers.load(exp.config["acoustic_model"])
acoustic_module_wrapper.generate(exp,
sentences,
generate_features=args.use_wavegen,
generate_waveforms=(not args.use_wavegen))
logger.info("Synthesis from acoustic feature model done")
if args.use_wavegen:
wavegen_module_wrapper = wrappers.load(exp.config["wavegen_model"])
wavegen_module_wrapper.generate(exp)
def train(exp: experiment.Experiment, args) -> None:
""" Trains acoustic feature prediction and waveform generation models. """
# TODO: pause/resume training
if args.model in ("acoustic", "both"):
logger.info("Loading acoustic feature model wrapper %s for training" %
(exp.config["acoustic_model"]))
module_wrapper = wrappers.load(exp.config["acoustic_model"])
module_wrapper.train(exp, vars(args))
logger.info("Training acoustic feature model done")
# TODO: generate intermediate features if successful
if args.model in ("wavegen", "both") and exp.config["wavegen_model"]:
# TODO: check if intermediate features exist
logger.info("Loading waveform generation model wrapper %s for training" %
(exp.config["wavegen_model"]))
module_wrapper = wrappers.load(exp.config["wavegen_model"])
module_wrapper.train(exp, vars(args))
def create_wavegen_model(exp: experiment.Experiment, args):
""" Loads and creates the wavegen model. """
module_wrapper = wrappers.load(exp.config["wavegen_model"])
module_wrapper.create(exp, vars(args))
if args.download_wavegen_model:
logger.info("Downloading wavegen model %s for model type %s" %
(args.download_wavegen_model, args.wavegen_model))
module_wrapper.download_pretrained(
exp, consts.PRETRAINED_WAVEGEN_MODELS[args.download_wavegen_model][
args.wavegen_model])
def create_acoustic_model(exp: experiment.Experiment, args):
""" Loads and creates the acoustic model. """
module_wrapper = wrappers.load(exp.config["acoustic_model"])
module_wrapper.create(exp, vars(args))
if args.download_acoustic_model:
logger.info("Downloading acoustic model %s for model type %s" %
(args.download_acoustic_model, args.acoustic_model))
module_wrapper.download_pretrained(
exp, consts.PRETRAINED_ACOUSTIC_MODELS[
args.download_acoustic_model][args.acoustic_model])
def main():
""" main function for creating a new experiment directory. """
parser = argparse.ArgumentParser()
parser.add_argument('experiment_dir',
help='Directory for the experiment to create.')
parser.add_argument('--acoustic_model', default="tacotron2",
help='Model to use for acoustic feature prediction (tacotron2).')
parser.add_argument('--download_acoustic_model', default=None,
choices=consts.PRETRAINED_FEATURE_MODELS.keys(),
help=('Name of a pretrained feature model to download (%s)'
% (" ".join(consts.PRETRAINED_FEATURE_MODELS.keys()))))
parser.add_argument('--wavegen_model', default="none",
help='Model to use for waveform generation (wavernn or none). Default: none')
parser.add_argument('--force', action='store_const', const=True,
help='Forces creation of this experiment, deleting an existing experiment if necessary')
args = parser.parse_args()
if os.path.exists(args.experiment_dir):
if args.force:
logger.info("Deleting existing experiment at %s" %
(args.experiment_dir))
shutil.rmtree(args.experiment_dir)
else:
print("Experiment already exists at %s, stopping" %
(args.experiment_dir))
return -1
logger.info("Creating experiment at %s" % (args.experiment_dir))
exp = experiment.create(args.experiment_dir, args)
try:
module_wrapper = wrappers.load(exp.config["acoustic_model"])
module_wrapper.create(exp, vars(args))
if args.download_acoustic_model:
logger.info("Downloading feature model %s" %
(args.download_acoustic_model))
module_wrapper.download_pretrained(
exp, _get_download_url(consts.PRETRAINED_FEATURE_MODELS, args))
experiment.save(exp)
except ModuleNotFoundError as mnfe:
print(mnfe)
print("Module for %s not found, did you run install.sh?" %
(args.acoustic_model))
return 0