[TTS]add starganv2 vc trainer (#3143)
* add starganv2 vc trainer * fix StarGANv2VCUpdater and losses * fix StarGANv2VCEvaluator * add some typehintpull/3155/head
TianYuan
2 years ago
committed by
GitHub
parent
54ef90fcec
commit
72aa19c32c
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# Copyright (c) 2023 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 paddle.optimizer import AdamW
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from paddle.optimizer.lr import OneCycleLR
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from yacs.config import CfgNode
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from paddlespeech.t2s.datasets.am_batch_fn import starganv2_vc_batch_fn
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from paddlespeech.t2s.datasets.data_table import DataTable
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from paddlespeech.t2s.models.starganv2_vc import ASRCNN
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from paddlespeech.t2s.models.starganv2_vc import Generator
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from paddlespeech.t2s.models.starganv2_vc import JDCNet
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from paddlespeech.t2s.models.starganv2_vc import MappingNetwork
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from paddlespeech.t2s.models.starganv2_vc import StarGANv2VCEvaluator
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from paddlespeech.t2s.models.starganv2_vc import StarGANv2VCUpdater
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from paddlespeech.t2s.models.starganv2_vc import StyleEncoder
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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.seeding import seed_everything
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from paddlespeech.t2s.training.trainer import Trainer
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from paddlespeech.utils.env import MODEL_HOME
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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()) 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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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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# to edit
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fields = ["speech", "speech_lengths"]
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converters = {"speech": np.load}
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collate_fn = starganv2_vc_batch_fn
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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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# load model
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model_version = '1.0'
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uncompress_path = download_and_decompress(StarGANv2VC_source[model_version],
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MODEL_HOME)
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generator = Generator(**config['generator_params'])
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mapping_network = MappingNetwork(**config['mapping_network_params'])
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style_encoder = StyleEncoder(**config['style_encoder_params'])
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# load pretrained model
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jdc_model_dir = os.path.join(uncompress_path, 'jdcnet.pdz')
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asr_model_dir = os.path.join(uncompress_path, 'asr.pdz')
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F0_model = JDCNet(num_class=1, seq_len=192)
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F0_model.set_state_dict(paddle.load(jdc_model_dir)['main_params'])
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F0_model.eval()
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asr_model = ASRCNN(**config['asr_params'])
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asr_model.set_state_dict(paddle.load(asr_model_dir)['main_params'])
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asr_model.eval()
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if world_size > 1:
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generator = DataParallel(generator)
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discriminator = DataParallel(discriminator)
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print("models done!")
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lr_schedule_g = OneCycleLR(**config["generator_scheduler_params"])
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optimizer_g = AdamW(
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learning_rate=lr_schedule_g,
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parameters=generator.parameters(),
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**config["generator_optimizer_params"])
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lr_schedule_s = OneCycleLR(**config["style_encoder_scheduler_params"])
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optimizer_s = AdamW(
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learning_rate=lr_schedule_s,
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parameters=style_encoder.parameters(),
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**config["style_encoder_optimizer_params"])
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lr_schedule_m = OneCycleLR(**config["mapping_network_scheduler_params"])
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optimizer_m = AdamW(
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learning_rate=lr_schedule_m,
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parameters=mapping_network.parameters(),
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**config["mapping_network_optimizer_params"])
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lr_schedule_d = OneCycleLR(**config["discriminator_scheduler_params"])
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optimizer_d = AdamW(
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learning_rate=lr_schedule_d,
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parameters=discriminator.parameters(),
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**config["discriminator_optimizer_params"])
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print("optimizers 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 = StarGANv2VCUpdater(
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models={
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"generator": generator,
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"style_encoder": style_encoder,
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"mapping_network": mapping_network,
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"discriminator": discriminator,
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"F0_model": F0_model,
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"asr_model": asr_model,
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},
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optimizers={
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"generator": optimizer_g,
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"style_encoder": optimizer_s,
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"mapping_network": optimizer_m,
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"discriminator": optimizer_d,
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},
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schedulers={
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"generator": lr_schedule_g,
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"style_encoder": lr_schedule_s,
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"mapping_network": lr_schedule_m,
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"discriminator": lr_schedule_d,
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},
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dataloader=train_dataloader,
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g_loss_params=config.loss_params.g_loss,
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d_loss_params=config.loss_params.d_loss,
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adv_cls_epoch=config.loss_params.adv_cls_epoch,
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con_reg_epoch=config.loss_params.con_reg_epoch,
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output_dir=output_dir)
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evaluator = StarGANv2VCEvaluator(
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models={
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"generator": generator,
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"style_encoder": style_encoder,
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"mapping_network": mapping_network,
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"discriminator": discriminator,
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"F0_model": F0_model,
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"asr_model": asr_model,
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},
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dataloader=dev_dataloader,
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g_loss_params=config.loss_params.g_loss,
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d_loss_params=config.loss_params.d_loss,
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adv_cls_epoch=config.loss_params.adv_cls_epoch,
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con_reg_epoch=config.loss_params.con_reg_epoch,
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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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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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print("Trainer Done!")
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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 HiFiGAN model.")
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parser.add_argument("--config", type=str, help="HiFiGAN 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.")
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args = parser.parse_args()
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with open(args.config, 'rt') 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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# Copyright (c) 2023 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 random
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import numpy as np
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import paddle
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import paddle.nn.functional as F
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from paddle import nn
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## 1. RandomTimeStrech
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class TimeStrech(nn.Layer):
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def __init__(self, scale):
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super().__init__()
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self.scale = scale
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def forward(self, x: paddle.Tensor):
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mel_size = x.shape[-1]
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x = F.interpolate(
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x,
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scale_factor=(1, self.scale),
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align_corners=False,
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mode='bilinear').squeeze()
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if x.shape[-1] < mel_size:
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noise_length = (mel_size - x.shape[-1])
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random_pos = random.randint(0, x.shape[-1]) - noise_length
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if random_pos < 0:
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random_pos = 0
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noise = x[..., random_pos:random_pos + noise_length]
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x = paddle.concat([x, noise], axis=-1)
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else:
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x = x[..., :mel_size]
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return x.unsqueeze(1)
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## 2. PitchShift
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class PitchShift(nn.Layer):
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def __init__(self, shift):
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super().__init__()
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self.shift = shift
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def forward(self, x: paddle.Tensor):
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if len(x.shape) == 2:
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x = x.unsqueeze(0)
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x = x.squeeze()
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mel_size = x.shape[1]
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shift_scale = (mel_size + self.shift) / mel_size
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x = F.interpolate(
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x.unsqueeze(1),
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scale_factor=(shift_scale, 1.),
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align_corners=False,
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mode='bilinear').squeeze(1)
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x = x[:, :mel_size]
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if x.shape[1] < mel_size:
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pad_size = mel_size - x.shape[1]
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x = paddle.cat(
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[x, paddle.zeros(x.shape[0], pad_size, x.shape[2])], axis=1)
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x = x.squeeze()
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return x.unsqueeze(1)
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## 3. ShiftBias
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class ShiftBias(nn.Layer):
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def __init__(self, bias):
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super().__init__()
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self.bias = bias
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def forward(self, x: paddle.Tensor):
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return x + self.bias
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## 4. Scaling
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class SpectScaling(nn.Layer):
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def __init__(self, scale):
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super().__init__()
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self.scale = scale
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def forward(self, x: paddle.Tensor):
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return x * self.scale
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## 5. Time Flip
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class TimeFlip(nn.Layer):
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def __init__(self, length):
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super().__init__()
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self.length = round(length)
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def forward(self, x: paddle.Tensor):
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if self.length > 1:
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start = np.random.randint(0, x.shape[-1] - self.length)
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x_ret = x.clone()
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x_ret[..., start:start + self.length] = paddle.flip(
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x[..., start:start + self.length], axis=[-1])
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x = x_ret
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return x
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class PhaseShuffle2D(nn.Layer):
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def __init__(self, n: int=2):
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super().__init__()
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self.n = n
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self.random = random.Random(1)
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||||||
|
|
||||||
|
def forward(self, x: paddle.Tensor, move=None):
|
||||||
|
# x.size = (B, C, M, L)
|
||||||
|
if move is None:
|
||||||
|
move = self.random.randint(-self.n, self.n)
|
||||||
|
|
||||||
|
if move == 0:
|
||||||
|
return x
|
||||||
|
else:
|
||||||
|
left = x[:, :, :, :move]
|
||||||
|
right = x[:, :, :, move:]
|
||||||
|
shuffled = paddle.concat([right, left], axis=3)
|
||||||
|
|
||||||
|
return shuffled
|
||||||
|
|
||||||
|
|
||||||
|
def build_transforms():
|
||||||
|
transforms = [
|
||||||
|
lambda M: TimeStrech(1 + (np.random.random() - 0.5) * M * 0.2),
|
||||||
|
lambda M: SpectScaling(1 + (np.random.random() - 1) * M * 0.1),
|
||||||
|
lambda M: PhaseShuffle2D(192),
|
||||||
|
]
|
||||||
|
N, M = len(transforms), np.random.random()
|
||||||
|
composed = nn.Sequential(
|
||||||
|
* [trans(M) for trans in np.random.choice(transforms, N)])
|
||||||
|
return composed
|
||||||
Loading…
Reference in new issue