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from utils.argutils import print_args
from vocoder.wavernn.train import train
from vocoder.hifigan.train import train as train_hifigan
from vocoder.fregan.train import train as train_fregan
from utils.util import AttrDict
from pathlib import Path
import argparse
import json
import torch
import torch.multiprocessing as mp
if __name__ == "__main__":
parser = argparse.ArgumentParser(
description="Trains the vocoder from the synthesizer audios and the GTA synthesized mels, "
"or ground truth mels.",
formatter_class=argparse.ArgumentDefaultsHelpFormatter
)
parser.add_argument("run_id", type=str, help= \
"Name for this model instance. If a model state from the same run ID was previously "
"saved, the training will restart from there. Pass -f to overwrite saved states and "
"restart from scratch.")
parser.add_argument("datasets_root", type=str, help= \
"Path to the directory containing your SV2TTS directory. Specifying --syn_dir or --voc_dir "
"will take priority over this argument.")
parser.add_argument("vocoder_type", type=str, default="wavernn", help= \
"Choose the vocoder type for train. Defaults to wavernn"
"Now, Support <hifigan> and <wavernn> for choose")
parser.add_argument("--syn_dir", type=str, default=argparse.SUPPRESS, help= \
"Path to the synthesizer directory that contains the ground truth mel spectrograms, "
"the wavs and the embeds. Defaults to <datasets_root>/SV2TTS/synthesizer/.")
parser.add_argument("--voc_dir", type=str, default=argparse.SUPPRESS, help= \
"Path to the vocoder directory that contains the GTA synthesized mel spectrograms. "
"Defaults to <datasets_root>/SV2TTS/vocoder/. Unused if --ground_truth is passed.")
parser.add_argument("-m", "--models_dir", type=str, default="vocoder/saved_models/", help=\
"Path to the directory that will contain the saved model weights, as well as backups "
"of those weights and wavs generated during training.")
parser.add_argument("-g", "--ground_truth", action="store_true", help= \
"Train on ground truth spectrograms (<datasets_root>/SV2TTS/synthesizer/mels).")
parser.add_argument("-s", "--save_every", type=int, default=1000, help= \
"Number of steps between updates of the model on the disk. Set to 0 to never save the "
"model.")
parser.add_argument("-b", "--backup_every", type=int, default=25000, help= \
"Number of steps between backups of the model. Set to 0 to never make backups of the "
"model.")
parser.add_argument("-f", "--force_restart", action="store_true", help= \
"Do not load any saved model and restart from scratch.")
parser.add_argument("--config", type=str, default="vocoder/hifigan/config_16k_.json")
args = parser.parse_args()
if not hasattr(args, "syn_dir"):
args.syn_dir = Path(args.datasets_root, "SV2TTS", "synthesizer")
args.syn_dir = Path(args.syn_dir)
if not hasattr(args, "voc_dir"):
args.voc_dir = Path(args.datasets_root, "SV2TTS", "vocoder")
args.voc_dir = Path(args.voc_dir)
del args.datasets_root
args.models_dir = Path(args.models_dir)
args.models_dir.mkdir(exist_ok=True)
print_args(args, parser)
# Process the arguments
if args.vocoder_type == "wavernn":
# Run the training wavernn
delattr(args, 'vocoder_type')
delattr(args, 'config')
train(**vars(args))
elif args.vocoder_type == "hifigan":
with open(args.config) as f:
json_config = json.load(f)
h = AttrDict(json_config)
if h.num_gpus > 1:
h.num_gpus = torch.cuda.device_count()
h.batch_size = int(h.batch_size / h.num_gpus)
print('Batch size per GPU :', h.batch_size)
mp.spawn(train_hifigan, nprocs=h.num_gpus, args=(args, h,))
else:
train_hifigan(0, args, h)
elif args.vocoder_type == "fregan":
with open('vocoder/fregan/config.json') as f:
json_config = json.load(f)
h = AttrDict(json_config)
if h.num_gpus > 1:
h.num_gpus = torch.cuda.device_count()
h.batch_size = int(h.batch_size / h.num_gpus)
print('Batch size per GPU :', h.batch_size)
mp.spawn(train_fregan, nprocs=h.num_gpus, args=(args, h,))
else:
train_fregan(0, args, h)
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