train.py

# 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

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 parakeet.datasets.data_table import DataTable
from parakeet.datasets.am_batch_fn import fastspeech2_single_spk_batch_fn
from parakeet.datasets.am_batch_fn import fastspeech2_multi_spk_batch_fn
from parakeet.models.fastspeech2 import FastSpeech2
from parakeet.models.fastspeech2 import FastSpeech2Evaluator
from parakeet.models.fastspeech2 import FastSpeech2Updater
from parakeet.training.extensions.snapshot import Snapshot
from parakeet.training.extensions.visualizer import VisualDL
from parakeet.training.optimizer import build_optimizers
from parakeet.training.seeding import seed_everything
from parakeet.training.trainer import Trainer
from pathlib import Path
from visualdl import LogWriter
from yacs.config import CfgNode


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():
        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"
    ]
    if args.speaker_dict is not None:
        print("multiple speaker fastspeech2!")
        collate_fn = fastspeech2_multi_spk_batch_fn
        with open(args.speaker_dict, 'rt') as f:
            spk_id = [line.strip().split() for line in f.readlines()]
        num_speakers = len(spk_id)
        fields += ["spk_id"]
    else:
        print("single speaker fastspeech2!")
        collate_fn = fastspeech2_single_spk_batch_fn
        num_speakers = None
    print("num_speakers:", num_speakers)

    # 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={"speech": np.load,
                    "pitch": np.load,
                    "energy": np.load}, )
    with jsonlines.open(args.dev_metadata, 'r') as reader:
        dev_metadata = list(reader)
    dev_dataset = DataTable(
        data=dev_metadata,
        fields=fields,
        converters={"speech": np.load,
                    "pitch": np.load,
                    "energy": 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, "r") as f:
        phn_id = [line.strip().split() for line in f.readlines()]
    vocab_size = len(phn_id)
    print("vocab_size:", vocab_size)

    odim = config.n_mels
    model = FastSpeech2(
        idim=vocab_size,
        odim=odim,
        num_speakers=num_speakers,
        **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 = FastSpeech2Updater(
        model=model,
        optimizer=optimizer,
        dataloader=train_dataloader,
        output_dir=output_dir,
        **config["updater"])

    trainer = Trainer(updater, (config.max_epoch, 'epoch'), output_dir)

    evaluator = FastSpeech2Evaluator(
        model, dev_dataloader, output_dir=output_dir, **config["updater"])

    if dist.get_rank() == 0:
        trainer.extend(evaluator, trigger=(1, "epoch"))
        writer = LogWriter(str(output_dir))
        trainer.extend(VisualDL(writer), trigger=(1, "iteration"))
        trainer.extend(
            Snapshot(max_size=config.num_snapshots), trigger=(1, 'epoch'))
    # print(trainer.extensions)
    trainer.run()


def main():
    # parse args and config and redirect to train_sp
    parser = argparse.ArgumentParser(description="Train a FastSpeech2 model.")
    parser.add_argument("--config", type=str, help="fastspeech2 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(
        "--device", type=str, default="gpu", help="device type to use.")
    parser.add_argument(
        "--nprocs", type=int, default=1, help="number of processes.")
    parser.add_argument("--verbose", type=int, default=1, help="verbose.")
    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.")

    args = parser.parse_args()
    if args.device == "cpu" and args.nprocs > 1:
        raise RuntimeError("Multiprocess training on CPU is not supported.")

    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.nprocs > 1:
        dist.spawn(train_sp, (args, config), nprocs=args.nprocs)
    else:
        train_sp(args, config)


if __name__ == "__main__":
    main()