You can not select more than 25 topics Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
PaddleSpeech/parakeet/exps/waveflow/train.py

161 lines
5.5 KiB

# Copyright (c) 2020 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 time
import numpy as np
import paddle
from paddle import distributed as dist
from paddle.io import DataLoader
from paddle.io import DistributedBatchSampler
from parakeet.data import dataset
from parakeet.exps.waveflow.config import get_cfg_defaults
from parakeet.exps.waveflow.ljspeech import LJSpeech
from parakeet.exps.waveflow.ljspeech import LJSpeechClipCollector
from parakeet.exps.waveflow.ljspeech import LJSpeechCollector
from parakeet.models.waveflow import ConditionalWaveFlow
from parakeet.models.waveflow import WaveFlowLoss
from parakeet.training.cli import default_argument_parser
from parakeet.training.experiment import ExperimentBase
from parakeet.utils import mp_tools
class Experiment(ExperimentBase):
def setup_model(self):
config = self.config
model = ConditionalWaveFlow(
upsample_factors=config.model.upsample_factors,
n_flows=config.model.n_flows,
n_layers=config.model.n_layers,
n_group=config.model.n_group,
channels=config.model.channels,
n_mels=config.data.n_mels,
kernel_size=config.model.kernel_size)
if self.parallel:
model = paddle.DataParallel(model)
optimizer = paddle.optimizer.Adam(
config.training.lr, parameters=model.parameters())
criterion = WaveFlowLoss(sigma=config.model.sigma)
self.model = model
self.optimizer = optimizer
self.criterion = criterion
def setup_dataloader(self):
config = self.config
args = self.args
ljspeech_dataset = LJSpeech(args.data)
valid_set, train_set = dataset.split(ljspeech_dataset,
config.data.valid_size)
batch_fn = LJSpeechClipCollector(config.data.clip_frames,
config.data.hop_length)
if not self.parallel:
train_loader = DataLoader(
train_set,
batch_size=config.data.batch_size,
shuffle=True,
drop_last=True,
collate_fn=batch_fn)
else:
sampler = DistributedBatchSampler(
train_set,
batch_size=config.data.batch_size,
num_replicas=dist.get_world_size(),
rank=dist.get_rank(),
shuffle=True,
drop_last=True)
train_loader = DataLoader(
train_set, batch_sampler=sampler, collate_fn=batch_fn)
valid_batch_fn = LJSpeechCollector()
valid_loader = DataLoader(
valid_set, batch_size=1, collate_fn=valid_batch_fn)
self.train_loader = train_loader
self.valid_loader = valid_loader
def compute_outputs(self, mel, wav):
# model_core = model._layers if isinstance(model, paddle.DataParallel) else model
z, log_det_jocobian = self.model(wav, mel)
return z, log_det_jocobian
def train_batch(self):
start = time.time()
batch = self.read_batch()
data_loader_time = time.time() - start
self.model.train()
self.optimizer.clear_grad()
mel, wav = batch
z, log_det_jocobian = self.compute_outputs(mel, wav)
loss = self.criterion(z, log_det_jocobian)
loss.backward()
self.optimizer.step()
iteration_time = time.time() - start
loss_value = float(loss)
msg = "Rank: {}, ".format(dist.get_rank())
msg += "step: {}, ".format(self.iteration)
msg += "time: {:>.3f}s/{:>.3f}s, ".format(data_loader_time,
iteration_time)
msg += "loss: {:>.6f}".format(loss_value)
self.logger.info(msg)
if dist.get_rank() == 0:
self.visualizer.add_scalar("train/loss", loss_value, self.iteration)
@mp_tools.rank_zero_only
@paddle.no_grad()
def valid(self):
valid_iterator = iter(self.valid_loader)
valid_losses = []
mel, wav = next(valid_iterator)
z, log_det_jocobian = self.compute_outputs(mel, wav)
loss = self.criterion(z, log_det_jocobian)
valid_losses.append(float(loss))
valid_loss = np.mean(valid_losses)
self.visualizer.add_scalar("valid/loss", valid_loss, self.iteration)
def main_sp(config, args):
exp = Experiment(config, args)
exp.setup()
exp.resume_or_load()
exp.run()
def main(config, args):
if args.nprocs > 1 and args.device == "gpu":
dist.spawn(main_sp, args=(config, args), nprocs=args.nprocs)
else:
main_sp(config, args)
if __name__ == "__main__":
config = get_cfg_defaults()
parser = default_argument_parser()
args = parser.parse_args()
if args.config:
config.merge_from_file(args.config)
if args.opts:
config.merge_from_list(args.opts)
config.freeze()
print(config)
print(args)
main(config, args)