You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
265 lines
8.8 KiB
265 lines
8.8 KiB
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
import argparse
|
|
import logging
|
|
import os
|
|
import shutil
|
|
from pathlib import Path
|
|
|
|
import jsonlines
|
|
import numpy as np
|
|
import paddle
|
|
import yaml
|
|
from paddle import DataParallel
|
|
from paddle import distributed as dist
|
|
from paddle import nn
|
|
from paddle.io import DataLoader
|
|
from paddle.io import DistributedBatchSampler
|
|
from paddle.optimizer import Adam # No RAdaom
|
|
from paddle.optimizer.lr import StepDecay
|
|
from yacs.config import CfgNode
|
|
|
|
from paddlespeech.t2s.datasets.data_table import DataTable
|
|
from paddlespeech.t2s.datasets.vocoder_batch_fn import Clip
|
|
from paddlespeech.t2s.models.parallel_wavegan import PWGDiscriminator
|
|
from paddlespeech.t2s.models.parallel_wavegan import PWGEvaluator
|
|
from paddlespeech.t2s.models.parallel_wavegan import PWGGenerator
|
|
from paddlespeech.t2s.models.parallel_wavegan import PWGUpdater
|
|
from paddlespeech.t2s.modules.losses import MultiResolutionSTFTLoss
|
|
from paddlespeech.t2s.training.extensions.snapshot import Snapshot
|
|
from paddlespeech.t2s.training.extensions.visualizer import VisualDL
|
|
from paddlespeech.t2s.training.seeding import seed_everything
|
|
from paddlespeech.t2s.training.trainer import Trainer
|
|
from paddlespeech.t2s.utils import str2bool
|
|
|
|
|
|
def train_sp(args, config):
|
|
# decides device type and whether to run in parallel
|
|
# setup running environment correctly
|
|
world_size = paddle.distributed.get_world_size()
|
|
if (not paddle.is_compiled_with_cuda()) or args.ngpu == 0:
|
|
paddle.set_device("cpu")
|
|
else:
|
|
paddle.set_device("gpu")
|
|
if world_size > 1:
|
|
paddle.distributed.init_parallel_env()
|
|
|
|
# set the random seed, it is a must for multiprocess training
|
|
seed_everything(config.seed)
|
|
|
|
print(
|
|
f"rank: {dist.get_rank()}, pid: {os.getpid()}, parent_pid: {os.getppid()}",
|
|
)
|
|
|
|
# dataloader has been too verbose
|
|
logging.getLogger("DataLoader").disabled = True
|
|
|
|
# construct dataset for training and validation
|
|
with jsonlines.open(args.train_metadata, 'r') as reader:
|
|
train_metadata = list(reader)
|
|
train_dataset = DataTable(
|
|
data=train_metadata,
|
|
fields=["wave", "feats"],
|
|
converters={
|
|
"wave": np.load,
|
|
"feats": np.load,
|
|
}, )
|
|
with jsonlines.open(args.dev_metadata, 'r') as reader:
|
|
dev_metadata = list(reader)
|
|
dev_dataset = DataTable(
|
|
data=dev_metadata,
|
|
fields=["wave", "feats"],
|
|
converters={
|
|
"wave": np.load,
|
|
"feats": np.load,
|
|
}, )
|
|
|
|
# collate function and dataloader
|
|
train_sampler = DistributedBatchSampler(
|
|
train_dataset,
|
|
batch_size=config.batch_size,
|
|
shuffle=True,
|
|
drop_last=True)
|
|
dev_sampler = DistributedBatchSampler(
|
|
dev_dataset,
|
|
batch_size=config.batch_size,
|
|
shuffle=False,
|
|
drop_last=False)
|
|
print("samplers done!")
|
|
|
|
train_batch_fn = Clip(
|
|
batch_max_steps=config.batch_max_steps,
|
|
hop_size=config.n_shift,
|
|
aux_context_window=config.generator_params.aux_context_window)
|
|
|
|
train_dataloader = DataLoader(
|
|
train_dataset,
|
|
batch_sampler=train_sampler,
|
|
collate_fn=train_batch_fn,
|
|
num_workers=config.num_workers)
|
|
|
|
dev_dataloader = DataLoader(
|
|
dev_dataset,
|
|
batch_sampler=dev_sampler,
|
|
collate_fn=train_batch_fn,
|
|
num_workers=config.num_workers)
|
|
print("dataloaders done!")
|
|
|
|
generator = PWGGenerator(**config["generator_params"])
|
|
discriminator = PWGDiscriminator(**config["discriminator_params"])
|
|
if world_size > 1:
|
|
generator = DataParallel(generator)
|
|
discriminator = DataParallel(discriminator)
|
|
print("models done!")
|
|
|
|
criterion_stft = MultiResolutionSTFTLoss(**config["stft_loss_params"])
|
|
criterion_mse = nn.MSELoss()
|
|
print("criterions done!")
|
|
|
|
lr_schedule_g = StepDecay(**config["generator_scheduler_params"])
|
|
gradient_clip_g = nn.ClipGradByGlobalNorm(config["generator_grad_norm"])
|
|
optimizer_g = Adam(
|
|
learning_rate=lr_schedule_g,
|
|
grad_clip=gradient_clip_g,
|
|
parameters=generator.parameters(),
|
|
**config["generator_optimizer_params"])
|
|
lr_schedule_d = StepDecay(**config["discriminator_scheduler_params"])
|
|
gradient_clip_d = nn.ClipGradByGlobalNorm(config["discriminator_grad_norm"])
|
|
optimizer_d = Adam(
|
|
learning_rate=lr_schedule_d,
|
|
grad_clip=gradient_clip_d,
|
|
parameters=discriminator.parameters(),
|
|
**config["discriminator_optimizer_params"])
|
|
print("optimizers done!")
|
|
|
|
output_dir = Path(args.output_dir)
|
|
output_dir.mkdir(parents=True, exist_ok=True)
|
|
if dist.get_rank() == 0:
|
|
config_name = args.config.split("/")[-1]
|
|
# copy conf to output_dir
|
|
shutil.copyfile(args.config, output_dir / config_name)
|
|
|
|
updater = PWGUpdater(
|
|
models={
|
|
"generator": generator,
|
|
"discriminator": discriminator,
|
|
},
|
|
optimizers={
|
|
"generator": optimizer_g,
|
|
"discriminator": optimizer_d,
|
|
},
|
|
criterions={
|
|
"stft": criterion_stft,
|
|
"mse": criterion_mse,
|
|
},
|
|
schedulers={
|
|
"generator": lr_schedule_g,
|
|
"discriminator": lr_schedule_d,
|
|
},
|
|
dataloader=train_dataloader,
|
|
discriminator_train_start_steps=config.discriminator_train_start_steps,
|
|
lambda_adv=config.lambda_adv,
|
|
output_dir=output_dir)
|
|
|
|
evaluator = PWGEvaluator(
|
|
models={
|
|
"generator": generator,
|
|
"discriminator": discriminator,
|
|
},
|
|
criterions={
|
|
"stft": criterion_stft,
|
|
"mse": criterion_mse,
|
|
},
|
|
dataloader=dev_dataloader,
|
|
lambda_adv=config.lambda_adv,
|
|
output_dir=output_dir)
|
|
trainer = Trainer(
|
|
updater,
|
|
stop_trigger=(config.train_max_steps, "iteration"),
|
|
out=output_dir,
|
|
profiler_options=args.profiler_options)
|
|
|
|
if dist.get_rank() == 0:
|
|
trainer.extend(
|
|
evaluator, trigger=(config.eval_interval_steps, 'iteration'))
|
|
trainer.extend(VisualDL(output_dir), trigger=(1, 'iteration'))
|
|
trainer.extend(
|
|
Snapshot(max_size=config.num_snapshots),
|
|
trigger=(config.save_interval_steps, 'iteration'))
|
|
|
|
print("Trainer Done!")
|
|
trainer.run()
|
|
|
|
|
|
def main():
|
|
# parse args and config and redirect to train_sp
|
|
|
|
parser = argparse.ArgumentParser(
|
|
description="Train a ParallelWaveGAN model.")
|
|
parser.add_argument(
|
|
"--config", type=str, help="config file to overwrite default config.")
|
|
parser.add_argument("--train-metadata", type=str, help="training data.")
|
|
parser.add_argument("--dev-metadata", type=str, help="dev data.")
|
|
parser.add_argument("--output-dir", type=str, help="output dir.")
|
|
parser.add_argument(
|
|
"--ngpu", type=int, default=1, help="if ngpu == 0, use cpu.")
|
|
|
|
benchmark_group = parser.add_argument_group(
|
|
'benchmark', 'arguments related to benchmark.')
|
|
benchmark_group.add_argument(
|
|
"--batch-size", type=int, default=8, help="batch size.")
|
|
benchmark_group.add_argument(
|
|
"--max-iter", type=int, default=400000, help="train max steps.")
|
|
|
|
benchmark_group.add_argument(
|
|
"--run-benchmark",
|
|
type=str2bool,
|
|
default=False,
|
|
help="runing benchmark or not, if True, use the --batch-size and --max-iter."
|
|
)
|
|
benchmark_group.add_argument(
|
|
"--profiler_options",
|
|
type=str,
|
|
default=None,
|
|
help="The option of profiler, which should be in format \"key1=value1;key2=value2;key3=value3\"."
|
|
)
|
|
|
|
args = parser.parse_args()
|
|
|
|
with open(args.config, 'rt') as f:
|
|
config = CfgNode(yaml.safe_load(f))
|
|
|
|
# 增加 --batch_size --max_iter 用于 benchmark 调用
|
|
if args.run_benchmark:
|
|
config.batch_size = args.batch_size
|
|
config.train_max_steps = args.max_iter
|
|
|
|
print("========Args========")
|
|
print(yaml.safe_dump(vars(args)))
|
|
print("========Config========")
|
|
print(config)
|
|
print(
|
|
f"master see the word size: {dist.get_world_size()}, from pid: {os.getpid()}"
|
|
)
|
|
|
|
# dispatch
|
|
if args.ngpu > 1:
|
|
dist.spawn(train_sp, (args, config), nprocs=args.ngpu)
|
|
else:
|
|
train_sp(args, config)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|