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.
248 lines
8.2 KiB
248 lines
8.2 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.io import DataLoader
|
|
from paddle.io import DistributedBatchSampler
|
|
from yacs.config import CfgNode
|
|
|
|
from paddlespeech.t2s.datasets.am_batch_fn import speedyspeech_multi_spk_batch_fn
|
|
from paddlespeech.t2s.datasets.am_batch_fn import speedyspeech_single_spk_batch_fn
|
|
from paddlespeech.t2s.datasets.data_table import DataTable
|
|
from paddlespeech.t2s.models.speedyspeech import SpeedySpeech
|
|
from paddlespeech.t2s.models.speedyspeech import SpeedySpeechEvaluator
|
|
from paddlespeech.t2s.models.speedyspeech import SpeedySpeechUpdater
|
|
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
|
|
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:
|
|
if (not paddle.is_compiled_with_xpu()) or args.nxpu == 0:
|
|
paddle.set_device("cpu")
|
|
else:
|
|
paddle.set_device("xpu")
|
|
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()}",
|
|
)
|
|
|
|
fields = [
|
|
"phones", "tones", "num_phones", "num_frames", "feats", "durations"
|
|
]
|
|
|
|
spk_num = None
|
|
if args.speaker_dict is not None:
|
|
print("multiple speaker speedyspeech!")
|
|
collate_fn = speedyspeech_multi_spk_batch_fn
|
|
with open(args.speaker_dict, 'rt', encoding='utf-8') as f:
|
|
spk_id = [line.strip().split() for line in f.readlines()]
|
|
spk_num = len(spk_id)
|
|
fields += ["spk_id"]
|
|
else:
|
|
print("single speaker speedyspeech!")
|
|
collate_fn = speedyspeech_single_spk_batch_fn
|
|
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)
|
|
if args.use_relative_path:
|
|
# if use_relative_path in preprocess, covert it to absolute path here
|
|
metadata_dir = Path(args.train_metadata).parent
|
|
for item in train_metadata:
|
|
item["feats"] = str(metadata_dir / item["feats"])
|
|
|
|
train_dataset = DataTable(
|
|
data=train_metadata,
|
|
fields=fields,
|
|
converters={
|
|
"feats": np.load,
|
|
}, )
|
|
with jsonlines.open(args.dev_metadata, 'r') as reader:
|
|
dev_metadata = list(reader)
|
|
if args.use_relative_path:
|
|
# if use_relative_path in preprocess, covert it to absolute path here
|
|
metadata_dir = Path(args.dev_metadata).parent
|
|
for item in dev_metadata:
|
|
item["feats"] = str(metadata_dir / item["feats"])
|
|
|
|
dev_dataset = DataTable(
|
|
data=dev_metadata,
|
|
fields=fields,
|
|
converters={
|
|
"feats": np.load,
|
|
}, )
|
|
|
|
# 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, 'rt', encoding='utf-8') 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.tones_dict, 'rt', encoding='utf-8') as f:
|
|
tone_id = [line.strip().split() for line in f.readlines()]
|
|
tone_size = len(tone_id)
|
|
print("tone_size:", tone_size)
|
|
|
|
model = SpeedySpeech(
|
|
vocab_size=vocab_size,
|
|
tone_size=tone_size,
|
|
spk_num=spk_num,
|
|
**config["model"])
|
|
if world_size > 1:
|
|
model = DataParallel(model)
|
|
print("model done!")
|
|
optimizer = build_optimizers(model, **config["optimizer"])
|
|
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 = SpeedySpeechUpdater(
|
|
model=model,
|
|
optimizer=optimizer,
|
|
dataloader=train_dataloader,
|
|
output_dir=output_dir)
|
|
|
|
trainer = Trainer(updater, (config.max_epoch, 'epoch'), output_dir)
|
|
|
|
evaluator = SpeedySpeechEvaluator(
|
|
model, dev_dataloader, output_dir=output_dir)
|
|
|
|
if dist.get_rank() == 0:
|
|
trainer.extend(evaluator, trigger=(1, "epoch"))
|
|
trainer.extend(VisualDL(output_dir), trigger=(1, "iteration"))
|
|
trainer.extend(
|
|
Snapshot(max_size=config.num_snapshots), trigger=(1, 'epoch'))
|
|
trainer.run()
|
|
|
|
|
|
def main():
|
|
# parse args and config and redirect to train_sp
|
|
parser = argparse.ArgumentParser(
|
|
description="Train a Speedyspeech model with a single speaker dataset.")
|
|
parser.add_argument("--config", type=str, help="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(
|
|
"--nxpu",
|
|
type=int,
|
|
default=0,
|
|
help="if nxpu == 0 and ngpu == 0, use cpu.")
|
|
parser.add_argument(
|
|
"--ngpu", type=int, default=1, help="if ngpu == 0, use cpu or xpu")
|
|
|
|
parser.add_argument(
|
|
"--use-relative-path",
|
|
type=str2bool,
|
|
default=False,
|
|
help="whether use relative path in metadata")
|
|
|
|
parser.add_argument(
|
|
"--phones-dict", type=str, default=None, help="phone vocabulary file.")
|
|
|
|
parser.add_argument(
|
|
"--tones-dict", type=str, default=None, help="tone vocabulary file.")
|
|
|
|
parser.add_argument(
|
|
"--speaker-dict",
|
|
type=str,
|
|
default=None,
|
|
help="speaker id map file for multiple speaker model.")
|
|
|
|
# 这里可以多传入 max_epoch 等
|
|
args, rest = parser.parse_known_args()
|
|
|
|
with open(args.config) as f:
|
|
config = CfgNode(yaml.safe_load(f))
|
|
|
|
if rest:
|
|
extra = []
|
|
# to support key=value format
|
|
for item in rest:
|
|
# remove "--"
|
|
item = item[2:]
|
|
extra.extend(item.split("=", maxsplit=1))
|
|
config.merge_from_list(extra)
|
|
|
|
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()
|