run_training_pipeline.py

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,
)
from TrainingPipelines.finetuning_shan import run as fine_tuning_shan

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,
    # finetuning shan
    "finetuning_shan": fine_tuning_shan,
}

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,
    )