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223 lines
7.6 KiB
223 lines
7.6 KiB
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import argparse
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import logging
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import os
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import shutil
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from pathlib import Path
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import jsonlines
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import numpy as np
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import paddle
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import yaml
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from paddle import DataParallel
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from paddle import distributed as dist
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from paddle.io import DataLoader
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from paddle.io import DistributedBatchSampler
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from yacs.config import CfgNode
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from paddlespeech.t2s.datasets.am_batch_fn import speedyspeech_batch_fn
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from paddlespeech.t2s.datasets.data_table import DataTable
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from paddlespeech.t2s.models.speedyspeech import SpeedySpeech
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from paddlespeech.t2s.models.speedyspeech import SpeedySpeechEvaluator
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from paddlespeech.t2s.models.speedyspeech import SpeedySpeechUpdater
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from paddlespeech.t2s.training.extensions.snapshot import Snapshot
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from paddlespeech.t2s.training.extensions.visualizer import VisualDL
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from paddlespeech.t2s.training.optimizer import build_optimizers
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from paddlespeech.t2s.training.seeding import seed_everything
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from paddlespeech.t2s.training.trainer import Trainer
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def train_sp(args, config):
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# decides device type and whether to run in parallel
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# setup running environment correctly
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world_size = paddle.distributed.get_world_size()
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if not paddle.is_compiled_with_cuda():
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paddle.set_device("cpu")
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else:
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paddle.set_device("gpu")
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if world_size > 1:
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paddle.distributed.init_parallel_env()
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# set the random seed, it is a must for multiprocess training
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seed_everything(config.seed)
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print(
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f"rank: {dist.get_rank()}, pid: {os.getpid()}, parent_pid: {os.getppid()}",
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)
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# dataloader has been too verbose
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logging.getLogger("DataLoader").disabled = True
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# construct dataset for training and validation
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with jsonlines.open(args.train_metadata, 'r') as reader:
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train_metadata = list(reader)
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if args.use_relative_path:
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# if use_relative_path in preprocess, covert it to absolute path here
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metadata_dir = Path(args.train_metadata).parent
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for item in train_metadata:
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item["feats"] = str(metadata_dir / item["feats"])
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train_dataset = DataTable(
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data=train_metadata,
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fields=[
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"phones", "tones", "num_phones", "num_frames", "feats", "durations"
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],
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converters={
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"feats": np.load,
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}, )
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with jsonlines.open(args.dev_metadata, 'r') as reader:
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dev_metadata = list(reader)
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if args.use_relative_path:
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# if use_relative_path in preprocess, covert it to absolute path here
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metadata_dir = Path(args.dev_metadata).parent
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for item in dev_metadata:
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item["feats"] = str(metadata_dir / item["feats"])
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dev_dataset = DataTable(
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data=dev_metadata,
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fields=[
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"phones", "tones", "num_phones", "num_frames", "feats", "durations"
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],
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converters={
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"feats": np.load,
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}, )
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# collate function and dataloader
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train_sampler = DistributedBatchSampler(
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train_dataset,
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batch_size=config.batch_size,
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shuffle=True,
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drop_last=True)
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print("samplers done!")
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train_dataloader = DataLoader(
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train_dataset,
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batch_sampler=train_sampler,
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collate_fn=speedyspeech_batch_fn,
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num_workers=config.num_workers)
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dev_dataloader = DataLoader(
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dev_dataset,
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shuffle=False,
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drop_last=False,
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batch_size=config.batch_size,
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collate_fn=speedyspeech_batch_fn,
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num_workers=config.num_workers)
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print("dataloaders done!")
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with open(args.phones_dict, "r") as f:
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phn_id = [line.strip().split() for line in f.readlines()]
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vocab_size = len(phn_id)
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print("vocab_size:", vocab_size)
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with open(args.tones_dict, "r") as f:
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tone_id = [line.strip().split() for line in f.readlines()]
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tone_size = len(tone_id)
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print("tone_size:", tone_size)
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model = SpeedySpeech(
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vocab_size=vocab_size, tone_size=tone_size, **config["model"])
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if world_size > 1:
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model = DataParallel(model)
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print("model done!")
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optimizer = build_optimizers(model, **config["optimizer"])
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print("optimizer done!")
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output_dir = Path(args.output_dir)
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output_dir.mkdir(parents=True, exist_ok=True)
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if dist.get_rank() == 0:
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config_name = args.config.split("/")[-1]
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# copy conf to output_dir
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shutil.copyfile(args.config, output_dir / config_name)
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updater = SpeedySpeechUpdater(
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model=model,
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optimizer=optimizer,
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dataloader=train_dataloader,
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output_dir=output_dir)
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trainer = Trainer(updater, (config.max_epoch, 'epoch'), output_dir)
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evaluator = SpeedySpeechEvaluator(
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model, dev_dataloader, output_dir=output_dir)
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if dist.get_rank() == 0:
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trainer.extend(evaluator, trigger=(1, "epoch"))
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trainer.extend(VisualDL(output_dir), trigger=(1, "iteration"))
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trainer.extend(
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Snapshot(max_size=config.num_snapshots), trigger=(1, 'epoch'))
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trainer.run()
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def main():
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# parse args and config and redirect to train_sp
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parser = argparse.ArgumentParser(
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description="Train a Speedyspeech model with sigle speaker dataset.")
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parser.add_argument("--config", type=str, help="config file.")
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parser.add_argument("--train-metadata", type=str, help="training data.")
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parser.add_argument("--dev-metadata", type=str, help="dev data.")
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parser.add_argument("--output-dir", type=str, help="output dir.")
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parser.add_argument(
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"--device", type=str, default="gpu", help="device type to use.")
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parser.add_argument(
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"--nprocs", type=int, default=1, help="number of processes.")
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parser.add_argument("--verbose", type=int, default=1, help="verbose.")
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def str2bool(str):
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return True if str.lower() == 'true' else False
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parser.add_argument(
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"--use-relative-path",
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type=str2bool,
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default=False,
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help="whether use relative path in metadata")
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parser.add_argument(
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"--phones-dict", type=str, default=None, help="phone vocabulary file.")
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parser.add_argument(
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"--tones-dict", type=str, default=None, help="tone vocabulary file.")
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# 这里可以多传入 max_epoch 等
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args, rest = parser.parse_known_args()
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if args.device == "cpu" and args.nprocs > 1:
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raise RuntimeError("Multiprocess training on CPU is not supported.")
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with open(args.config) as f:
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config = CfgNode(yaml.safe_load(f))
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if rest:
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extra = []
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# to support key=value format
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for item in rest:
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# remove "--"
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item = item[2:]
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extra.extend(item.split("=", maxsplit=1))
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config.merge_from_list(extra)
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print("========Args========")
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print(yaml.safe_dump(vars(args)))
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print("========Config========")
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print(config)
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print(
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f"master see the word size: {dist.get_world_size()}, from pid: {os.getpid()}"
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)
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# dispatch
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if args.nprocs > 1:
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dist.spawn(train_sp, (args, config), nprocs=args.nprocs)
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else:
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train_sp(args, config)
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if __name__ == "__main__":
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main()
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