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3 years ago
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###########################################################
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# PARAS SETTING #
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###########################################################
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# Set to -1 to indicate that the parameter is the same as the pretrained model configuration
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batch_size: -1
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learning_rate: 0.0001 # learning rate
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num_snapshots: -1
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# frozen_layers should be a list
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# if you don't need to freeze, set frozen_layers to []
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frozen_layers: ["encoder", "duration_predictor"]
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# 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 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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from typing import List
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import jsonlines
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import numpy as np
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import paddle
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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 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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def freeze_layer(model, layers: List[str]):
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"""freeze layers
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Args:
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layers (List[str]): frozen layers
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"""
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for layer in layers:
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for param in eval("model." + layer + ".parameters()"):
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param.trainable = False
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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 (not paddle.is_compiled_with_cuda()) or args.ngpu == 0:
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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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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') 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, "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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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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# freeze layer
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if args.frozen_layers != []:
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freeze_layer(model, args.frozen_layers)
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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 = 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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**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, dev_dataloader, output_dir=output_dir, **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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