# 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
from collections import defaultdict
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.tacotron2.config import get_cfg_defaults
from parakeet.exps.tacotron2.ljspeech import LJSpeech
from parakeet.exps.tacotron2.ljspeech import LJSpeechCollector
from parakeet.models.tacotron2 import Tacotron2
from parakeet.models.tacotron2 import Tacotron2Loss
from parakeet.training.cli import default_argument_parser
from parakeet.training.experiment import ExperimentBase
from parakeet.utils import display
from parakeet.utils import mp_tools
class Experiment(ExperimentBase):
def compute_losses(self, inputs, outputs):
texts, mel_targets, plens, slens = inputs
mel_outputs = outputs["mel_output"]
mel_outputs_postnet = outputs["mel_outputs_postnet"]
attention_weight = outputs["alignments"]
if self.config.model.use_stop_token:
stop_logits = outputs["stop_logits"]
else:
stop_logits = None
losses = self.criterion(mel_outputs, mel_outputs_postnet, mel_targets,
attention_weight, slens, plens, stop_logits)
return losses
def train_batch(self):
start = time.time()
batch = self.read_batch()
data_loader_time = time.time() - start
self.optimizer.clear_grad()
self.model.train()
texts, mels, text_lens, output_lens = batch
outputs = self.model(texts, text_lens, mels, output_lens)
losses = self.compute_losses(batch, outputs)
loss = losses["loss"]
loss.backward()
self.optimizer.step()
iteration_time = time.time() - start
losses_np = {k: float(v) for k, v in losses.items()}
# logging
msg = "Rank: {}, ".format(dist.get_rank())
msg += "step: {}, ".format(self.iteration)
msg += "time: {:>.3f}s/{:>.3f}s, ".format(data_loader_time,
iteration_time)
msg += ', '.join('{}: {:>.6f}'.format(k, v)
for k, v in losses_np.items())
self.logger.info(msg)
if dist.get_rank() == 0:
for k, v in losses_np.items():
self.visualizer.add_scalar(f"train_loss/{k}", v, self.iteration)
@mp_tools.rank_zero_only
@paddle.no_grad()
def valid(self):
valid_losses = defaultdict(list)
for i, batch in enumerate(self.valid_loader):
texts, mels, text_lens, output_lens = batch
outputs = self.model(texts, text_lens, mels, output_lens)
losses = self.compute_losses(batch, outputs)
for k, v in losses.items():
valid_losses[k].append(float(v))
attention_weights = outputs["alignments"]
self.visualizer.add_figure(
f"valid_sentence_{i}_alignments",
display.plot_alignment(attention_weights[0].numpy().T),
self.iteration)
self.visualizer.add_figure(
f"valid_sentence_{i}_target_spectrogram",
display.plot_spectrogram(mels[0].numpy().T), self.iteration)
self.visualizer.add_figure(
f"valid_sentence_{i}_predicted_spectrogram",
display.plot_spectrogram(outputs['mel_outputs_postnet'][0]
.numpy().T), self.iteration)
# write visual log
valid_losses = {k: np.mean(v) for k, v in valid_losses.items()}
# logging
msg = "Valid: "
msg += "step: {}, ".format(self.iteration)
msg += ', '.join('{}: {:>.6f}'.format(k, v)
for k, v in valid_losses.items())
self.logger.info(msg)
for k, v in valid_losses.items():
self.visualizer.add_scalar(f"valid/{k}", v, self.iteration)
def setup_model(self):
config = self.config
model = Tacotron2(
vocab_size=config.model.vocab_size,
d_mels=config.data.n_mels,
d_encoder=config.model.d_encoder,
encoder_conv_layers=config.model.encoder_conv_layers,
encoder_kernel_size=config.model.encoder_kernel_size,
d_prenet=config.model.d_prenet,
d_attention_rnn=config.model.d_attention_rnn,
d_decoder_rnn=config.model.d_decoder_rnn,
attention_filters=config.model.attention_filters,
attention_kernel_size=config.model.attention_kernel_size,
d_attention=config.model.d_attention,
d_postnet=config.model.d_postnet,
postnet_kernel_size=config.model.postnet_kernel_size,
postnet_conv_layers=config.model.postnet_conv_layers,
reduction_factor=config.model.reduction_factor,
p_encoder_dropout=config.model.p_encoder_dropout,
p_prenet_dropout=config.model.p_prenet_dropout,
p_attention_dropout=config.model.p_attention_dropout,
p_decoder_dropout=config.model.p_decoder_dropout,
p_postnet_dropout=config.model.p_postnet_dropout,
use_stop_token=config.model.use_stop_token)
if self.parallel:
model = paddle.DataParallel(model)
grad_clip = paddle.nn.ClipGradByGlobalNorm(
config.training.grad_clip_thresh)
optimizer = paddle.optimizer.Adam(
learning_rate=config.training.lr,
parameters=model.parameters(),
weight_decay=paddle.regularizer.L2Decay(
config.training.weight_decay),
grad_clip=grad_clip)
criterion = Tacotron2Loss(
use_stop_token_loss=config.model.use_stop_token,
use_guided_attention_loss=config.model.use_guided_attention_loss,
sigma=config.model.guided_attention_loss_sigma)
self.model = model
self.optimizer = optimizer
self.criterion = criterion
def setup_dataloader(self):
args = self.args
config = self.config
ljspeech_dataset = LJSpeech(args.data)
valid_set, train_set = dataset.split(ljspeech_dataset,
config.data.valid_size)
batch_fn = LJSpeechCollector(padding_idx=config.data.padding_idx)
if not self.parallel:
self.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,
shuffle=True,
drop_last=True)
self.train_loader = DataLoader(
train_set, batch_sampler=sampler, collate_fn=batch_fn)
self.valid_loader = DataLoader(
valid_set,
batch_size=config.data.batch_size,
shuffle=False,
drop_last=False,
collate_fn=batch_fn)
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)