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