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235 lines
7.8 KiB
235 lines
7.8 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 fastspeech2_multi_spk_batch_fn
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from paddlespeech.t2s.datasets.am_batch_fn import fastspeech2_single_spk_batch_fn
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from paddlespeech.t2s.datasets.data_table import DataTable
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from paddlespeech.t2s.models.fastspeech2 import FastSpeech2
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from paddlespeech.t2s.models.fastspeech2 import FastSpeech2Evaluator
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from paddlespeech.t2s.models.fastspeech2 import FastSpeech2Updater
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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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from paddlespeech.t2s.utils import str2bool
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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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if args.ngpu > 0 and paddle.is_compiled_with_cuda():
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paddle.set_device("gpu")
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elif args.nxpu > 0 and paddle.is_compiled_with_xpu():
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paddle.set_device("xpu")
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elif args.ngpu == 0 and args.nxpu == 0:
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paddle.set_device("cpu")
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else:
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raise ValueError(
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"Please make sure that the paddle you installed matches the device type you set, "
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"and that ngpu and nxpu cannot be negative at the same time.")
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world_size = paddle.distributed.get_world_size()
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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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fields = [
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"text", "text_lengths", "speech", "speech_lengths", "durations",
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"pitch", "energy"
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]
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converters = {"speech": np.load, "pitch": np.load, "energy": np.load}
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spk_num = None
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if args.speaker_dict is not None:
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print("multiple speaker fastspeech2!")
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collate_fn = fastspeech2_multi_spk_batch_fn
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with open(args.speaker_dict, 'rt', encoding='utf-8') as f:
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spk_id = [line.strip().split() for line in f.readlines()]
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spk_num = len(spk_id)
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fields += ["spk_id"]
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elif args.voice_cloning:
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print("Training voice cloning!")
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collate_fn = fastspeech2_multi_spk_batch_fn
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fields += ["spk_emb"]
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converters["spk_emb"] = np.load
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else:
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print("single speaker fastspeech2!")
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collate_fn = fastspeech2_single_spk_batch_fn
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print("spk_num:", spk_num)
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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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train_dataset = DataTable(
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data=train_metadata,
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fields=fields,
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converters=converters, )
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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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dev_dataset = DataTable(
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data=dev_metadata,
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fields=fields,
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converters=converters, )
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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=collate_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=collate_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, 'rt', encoding='utf-8') 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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odim = config.n_mels
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model = FastSpeech2(
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idim=vocab_size, odim=odim, spk_num=spk_num, **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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if "enable_speaker_classifier" in config.model:
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enable_spk_cls = config.model.enable_speaker_classifier
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else:
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enable_spk_cls = False
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updater = FastSpeech2Updater(
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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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enable_spk_cls=enable_spk_cls,
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**config["updater"], )
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trainer = Trainer(updater, (config.max_epoch, 'epoch'), output_dir)
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evaluator = FastSpeech2Evaluator(
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model,
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dev_dataloader,
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output_dir=output_dir,
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enable_spk_cls=enable_spk_cls,
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**config["updater"], )
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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(description="Train a FastSpeech2 model.")
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parser.add_argument("--config", type=str, help="fastspeech2 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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"--ngpu", type=int, default=1, help="if ngpu=0, use cpu or xpu.")
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parser.add_argument(
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"--nxpu",
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type=int,
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default=0,
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help="if ngpu=0 and nxpu > 0, use xpu. if ngpu=0 and nxpu=0, use cpu.")
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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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"--speaker-dict",
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type=str,
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default=None,
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help="speaker id map file for multiple speaker model.")
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parser.add_argument(
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"--voice-cloning",
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type=str2bool,
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default=False,
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help="whether training voice cloning model.")
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args = parser.parse_args()
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with open(args.config) as f:
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config = CfgNode(yaml.safe_load(f))
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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.ngpu > 1:
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dist.spawn(train_sp, (args, config), nprocs=args.ngpu)
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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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