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import argparse
import os
import random
import sys
import torch
from TrainingPipelines.AlignerPipeline import run as aligner
from TrainingPipelines.HiFiGAN_combined import run as HiFiGAN
from TrainingPipelines.StochasticToucanTTS_Nancy import run as nancystoch
from TrainingPipelines.ToucanTTS_IntegrationTest import run as tt_integration_test
from TrainingPipelines.ToucanTTS_MLS_English import run as mls
from TrainingPipelines.ToucanTTS_Massive_stage1 import run as stage1
from TrainingPipelines.ToucanTTS_Massive_stage2 import run as stage2
from TrainingPipelines.ToucanTTS_Massive_stage3 import run as stage3
from TrainingPipelines.ToucanTTS_MetaCheckpoint import run as meta
from TrainingPipelines.ToucanTTS_Nancy import run as nancy
from TrainingPipelines.finetuning_example_multilingual import run as fine_tuning_example_multilingual
from TrainingPipelines.finetuning_example_simple import run as fine_tuning_example_simple
pipeline_dict = {
# the finetuning example
"finetuning_example_simple" : fine_tuning_example_simple,
"finetuning_example_multilingual": fine_tuning_example_multilingual,
# integration tests
"tt_it" : tt_integration_test,
# regular ToucanTTS pipelines
"nancy" : nancy,
"mls" : mls,
"nancystoch" : nancystoch,
"meta" : meta,
"stage1" : stage1,
"stage2" : stage2,
"stage3" : stage3,
# training the aligner from scratch (not recommended, best to use provided checkpoint)
"aligner" : aligner,
# vocoder training (not recommended, best to use provided checkpoint)
"hifigan" : HiFiGAN
}
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Training with the IMS Toucan Speech Synthesis Toolkit')
parser.add_argument('pipeline',
choices=list(pipeline_dict.keys()),
help="Select pipeline to train.")
parser.add_argument('--gpu_id',
type=str,
help="Which GPU(s) to run on. If not specified runs on CPU, but other than for integration tests that doesn't make much sense.",
default="cpu")
parser.add_argument('--resume_checkpoint',
type=str,
help="Path to checkpoint to resume from.",
default=None)
parser.add_argument('--resume',
action="store_true",
help="Automatically load the highest checkpoint and continue from there.",
default=False)
parser.add_argument('--finetune',
action="store_true",
help="Whether to fine-tune from the specified checkpoint.",
default=False)
parser.add_argument('--model_save_dir',
type=str,
help="Directory where the checkpoints should be saved to.",
default=None)
parser.add_argument('--wandb',
action="store_true",
help="Whether to use weights and biases to track training runs. Requires you to run wandb login and place your auth key before.",
default=False)
parser.add_argument('--wandb_resume_id',
type=str,
help="ID of a stopped wandb run to continue tracking",
default=None)
args = parser.parse_args()
if args.finetune and args.resume_checkpoint is None and not args.resume:
print("Need to provide path to checkpoint to fine-tune from!")
sys.exit()
if args.gpu_id == "cpu":
os.environ["CUDA_VISIBLE_DEVICES"] = ""
device = torch.device("cpu")
print(f"No GPU specified, using CPU. Training will likely not work without GPU.")
gpu_count = 1 # for technical reasons this is set to one, indicating it's not gpu_count training, even though there is no GPU in this case
else:
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = f"{args.gpu_id}"
device = torch.device("cuda")
print(f"Making GPU {os.environ['CUDA_VISIBLE_DEVICES']} the only visible device(s).")
gpu_count = len(args.gpu_id.replace(",", " ").split())
# example call for gpu_count training:
# torchrun --standalone --nproc_per_node=4 --nnodes=1 run_training_pipeline.py nancy --gpu_id "1,2,3"
torch.manual_seed(9665)
random.seed(9665)
torch.random.manual_seed(9665)
torch.multiprocessing.set_sharing_strategy('file_system')
pipeline_dict[args.pipeline](gpu_id=args.gpu_id,
resume_checkpoint=args.resume_checkpoint,
resume=args.resume,
finetune=args.finetune,
model_dir=args.model_save_dir,
use_wandb=args.wandb,
wandb_resume_id=args.wandb_resume_id,
gpu_count=gpu_count) |