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.
258 lines
8.6 KiB
258 lines
8.6 KiB
# Copyright (c) 2023 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 AdamW
|
|
from paddle.optimizer.lr import StepDecay
|
|
from yacs.config import CfgNode
|
|
|
|
from paddlespeech.t2s.datasets.am_batch_fn import diffsinger_multi_spk_batch_fn
|
|
from paddlespeech.t2s.datasets.am_batch_fn import diffsinger_single_spk_batch_fn
|
|
from paddlespeech.t2s.datasets.data_table import DataTable
|
|
from paddlespeech.t2s.models.diffsinger import DiffSinger
|
|
from paddlespeech.t2s.models.diffsinger import DiffSingerEvaluator
|
|
from paddlespeech.t2s.models.diffsinger import DiffSingerUpdater
|
|
from paddlespeech.t2s.models.diffsinger import DiffusionLoss
|
|
from paddlespeech.t2s.models.diffsinger.fastspeech2midi import FastSpeech2MIDILoss
|
|
from paddlespeech.t2s.training.extensions.snapshot import Snapshot
|
|
from paddlespeech.t2s.training.extensions.visualizer import VisualDL
|
|
from paddlespeech.t2s.training.optimizer import build_optimizers
|
|
from paddlespeech.t2s.training.seeding import seed_everything
|
|
from paddlespeech.t2s.training.trainer import Trainer
|
|
|
|
|
|
def train_sp(args, config):
|
|
# decides device type and whether to run in parallel
|
|
# setup running environment correctly
|
|
if (not paddle.is_compiled_with_cuda()) or args.ngpu == 0:
|
|
paddle.set_device("cpu")
|
|
else:
|
|
paddle.set_device("gpu")
|
|
world_size = paddle.distributed.get_world_size()
|
|
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()}",
|
|
)
|
|
fields = [
|
|
"text", "text_lengths", "speech", "speech_lengths", "durations",
|
|
"pitch", "energy", "note", "note_dur", "is_slur"
|
|
]
|
|
converters = {"speech": np.load, "pitch": np.load, "energy": np.load}
|
|
spk_num = None
|
|
if args.speaker_dict is not None:
|
|
print("multiple speaker diffsinger!")
|
|
collate_fn = diffsinger_multi_spk_batch_fn
|
|
with open(args.speaker_dict, 'rt') as f:
|
|
spk_id = [line.strip().split() for line in f.readlines()]
|
|
spk_num = len(spk_id)
|
|
fields += ["spk_id"]
|
|
else:
|
|
collate_fn = diffsinger_single_spk_batch_fn
|
|
print("single speaker diffsinger!")
|
|
|
|
print("spk_num:", spk_num)
|
|
|
|
# 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=fields,
|
|
converters=converters, )
|
|
with jsonlines.open(args.dev_metadata, 'r') as reader:
|
|
dev_metadata = list(reader)
|
|
dev_dataset = DataTable(
|
|
data=dev_metadata,
|
|
fields=fields,
|
|
converters=converters, )
|
|
|
|
# collate function and dataloader
|
|
train_sampler = DistributedBatchSampler(
|
|
train_dataset,
|
|
batch_size=config.batch_size,
|
|
shuffle=True,
|
|
drop_last=True)
|
|
|
|
print("samplers done!")
|
|
|
|
train_dataloader = DataLoader(
|
|
train_dataset,
|
|
batch_sampler=train_sampler,
|
|
collate_fn=collate_fn,
|
|
num_workers=config.num_workers)
|
|
|
|
dev_dataloader = DataLoader(
|
|
dev_dataset,
|
|
shuffle=False,
|
|
drop_last=False,
|
|
batch_size=config.batch_size,
|
|
collate_fn=collate_fn,
|
|
num_workers=config.num_workers)
|
|
print("dataloaders done!")
|
|
|
|
with open(args.phones_dict, "r") as f:
|
|
phn_id = [line.strip().split() for line in f.readlines()]
|
|
vocab_size = len(phn_id)
|
|
print("vocab_size:", vocab_size)
|
|
|
|
with open(args.speech_stretchs, "r") as f:
|
|
spec_min = np.load(args.speech_stretchs)[0]
|
|
spec_max = np.load(args.speech_stretchs)[1]
|
|
spec_min = paddle.to_tensor(spec_min)
|
|
spec_max = paddle.to_tensor(spec_max)
|
|
print("min and max spec done!")
|
|
|
|
odim = config.n_mels
|
|
config["model"]["fastspeech2_params"]["spk_num"] = spk_num
|
|
model = DiffSinger(
|
|
spec_min=spec_min,
|
|
spec_max=spec_max,
|
|
idim=vocab_size,
|
|
odim=odim,
|
|
**config["model"], )
|
|
model_fs2 = model.fs2
|
|
model_ds = model.diffusion
|
|
if world_size > 1:
|
|
model = DataParallel(model)
|
|
model_fs2 = model._layers.fs2
|
|
model_ds = model._layers.diffusion
|
|
print("models done!")
|
|
|
|
criterion_fs2 = FastSpeech2MIDILoss(**config["fs2_updater"])
|
|
criterion_ds = DiffusionLoss(**config["ds_updater"])
|
|
print("criterions done!")
|
|
|
|
optimizer_fs2 = build_optimizers(model_fs2, **config["fs2_optimizer"])
|
|
lr_schedule_ds = StepDecay(**config["ds_scheduler_params"])
|
|
gradient_clip_ds = nn.ClipGradByGlobalNorm(config["ds_grad_norm"])
|
|
optimizer_ds = AdamW(
|
|
learning_rate=lr_schedule_ds,
|
|
grad_clip=gradient_clip_ds,
|
|
parameters=model_ds.parameters(),
|
|
**config["ds_optimizer_params"])
|
|
print("optimizer 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 = DiffSingerUpdater(
|
|
model=model,
|
|
optimizers={
|
|
"fs2": optimizer_fs2,
|
|
"ds": optimizer_ds,
|
|
},
|
|
criterions={
|
|
"fs2": criterion_fs2,
|
|
"ds": criterion_ds,
|
|
},
|
|
dataloader=train_dataloader,
|
|
ds_train_start_steps=config.ds_train_start_steps,
|
|
output_dir=output_dir,
|
|
only_train_diffusion=config["only_train_diffusion"])
|
|
|
|
evaluator = DiffSingerEvaluator(
|
|
model=model,
|
|
criterions={
|
|
"fs2": criterion_fs2,
|
|
"ds": criterion_ds,
|
|
},
|
|
dataloader=dev_dataloader,
|
|
output_dir=output_dir, )
|
|
|
|
trainer = Trainer(
|
|
updater,
|
|
stop_trigger=(config.train_max_steps, "iteration"),
|
|
out=output_dir, )
|
|
|
|
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 DiffSinger model.")
|
|
parser.add_argument("--config", type=str, help="diffsinger config file.")
|
|
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.")
|
|
parser.add_argument(
|
|
"--phones-dict", type=str, default=None, help="phone vocabulary file.")
|
|
parser.add_argument(
|
|
"--speaker-dict",
|
|
type=str,
|
|
default=None,
|
|
help="speaker id map file for multiple speaker model.")
|
|
parser.add_argument(
|
|
"--speech-stretchs",
|
|
type=str,
|
|
help="The min and max values of the mel spectrum.")
|
|
|
|
args = parser.parse_args()
|
|
|
|
with open(args.config) as f:
|
|
config = CfgNode(yaml.safe_load(f))
|
|
|
|
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()
|