Source code for paddlespeech.t2s.models.transformer_tts.transformer_tts_updater
# Copyright (c) 2021 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 logging
from pathlib import Path
from typing import Sequence
import paddle
from paddle import distributed as dist
from paddle.io import DataLoader
from paddle.nn import Layer
from paddle.optimizer import Optimizer
from paddlespeech.t2s.modules.losses import GuidedMultiHeadAttentionLoss
from paddlespeech.t2s.modules.losses import Tacotron2Loss as TransformerTTSLoss
from paddlespeech.t2s.training.extensions.evaluator import StandardEvaluator
from paddlespeech.t2s.training.reporter import report
from paddlespeech.t2s.training.updaters.standard_updater import StandardUpdater
logging.basicConfig(
format='%(asctime)s [%(levelname)s] [%(filename)s:%(lineno)d] %(message)s',
datefmt='[%Y-%m-%d %H:%M:%S]')
logger = logging.getLogger(__name__)
logger.setLevel(logging.INFO)
[docs]class TransformerTTSUpdater(StandardUpdater):
def __init__(
self,
model: Layer,
optimizer: Optimizer,
dataloader: DataLoader,
init_state=None,
use_masking: bool=False,
use_weighted_masking: bool=False,
output_dir: Path=None,
bce_pos_weight: float=5.0,
loss_type: str="L1",
use_guided_attn_loss: bool=True,
modules_applied_guided_attn: Sequence[str]=("encoder-decoder"),
guided_attn_loss_sigma: float=0.4,
guided_attn_loss_lambda: float=1.0, ):
super().__init__(model, optimizer, dataloader, init_state=None)
self.loss_type = loss_type
self.use_guided_attn_loss = use_guided_attn_loss
self.modules_applied_guided_attn = modules_applied_guided_attn
self.criterion = TransformerTTSLoss(
use_masking=use_masking,
use_weighted_masking=use_weighted_masking,
bce_pos_weight=bce_pos_weight)
if self.use_guided_attn_loss:
self.attn_criterion = GuidedMultiHeadAttentionLoss(
sigma=guided_attn_loss_sigma,
alpha=guided_attn_loss_lambda, )
log_file = output_dir / 'worker_{}.log'.format(dist.get_rank())
self.filehandler = logging.FileHandler(str(log_file))
logger.addHandler(self.filehandler)
self.logger = logger
self.msg = ""
[docs] def update_core(self, batch):
self.msg = "Rank: {}, ".format(dist.get_rank())
losses_dict = {}
after_outs, before_outs, logits, ys, stop_labels, olens, olens_in, need_dict = self.model(
text=batch["text"],
text_lengths=batch["text_lengths"],
speech=batch["speech"],
speech_lengths=batch["speech_lengths"], )
l1_loss, l2_loss, bce_loss = self.criterion(
after_outs=after_outs,
before_outs=before_outs,
logits=logits,
ys=ys,
stop_labels=stop_labels,
olens=olens)
report("train/bce_loss", float(bce_loss))
report("train/l1_loss", float(l1_loss))
report("train/l2_loss", float(l2_loss))
losses_dict["bce_loss"] = float(bce_loss)
losses_dict["l1_loss"] = float(l1_loss)
losses_dict["l2_loss"] = float(l2_loss)
# caluculate loss values
if self.loss_type == "L1":
loss = l1_loss + bce_loss
elif self.loss_type == "L2":
loss = l2_loss + bce_loss
elif self.loss_type == "L1+L2":
loss = l1_loss + l2_loss + bce_loss
else:
raise ValueError("unknown --loss-type " + self.loss_type)
# calculate guided attention loss
if self.use_guided_attn_loss:
# calculate for encoder
if "encoder" in self.modules_applied_guided_attn:
att_ws = []
for idx, layer_idx in enumerate(
reversed(range(len(need_dict['encoder'].encoders)))):
att_ws += [
need_dict['encoder'].encoders[layer_idx].self_attn.
attn[:, :need_dict['num_heads_applied_guided_attn']]
]
if idx + 1 == need_dict['num_layers_applied_guided_attn']:
break
# (B, H*L, T_in, T_in)
att_ws = paddle.concat(att_ws, axis=1)
enc_attn_loss = self.attn_criterion(
att_ws=att_ws,
ilens=batch["text_lengths"] + 1,
olens=batch["text_lengths"] + 1)
loss = loss + enc_attn_loss
report("train/enc_attn_loss", float(enc_attn_loss))
losses_dict["enc_attn_loss"] = float(enc_attn_loss)
# calculate for decoder
if "decoder" in self.modules_applied_guided_attn:
att_ws = []
for idx, layer_idx in enumerate(
reversed(range(len(need_dict['decoder'].decoders)))):
att_ws += [
need_dict['decoder'].decoders[layer_idx].self_attn.
attn[:, :need_dict['num_heads_applied_guided_attn']]
]
if idx + 1 == need_dict['num_layers_applied_guided_attn']:
break
# (B, H*L, T_out, T_out)
att_ws = paddle.concat(att_ws, axis=1)
dec_attn_loss = self.attn_criterion(
att_ws=att_ws, ilens=olens_in, olens=olens_in)
report("train/dec_attn_loss", float(dec_attn_loss))
losses_dict["dec_attn_loss"] = float(dec_attn_loss)
loss = loss + dec_attn_loss
# calculate for encoder-decoder
if "encoder-decoder" in self.modules_applied_guided_attn:
att_ws = []
for idx, layer_idx in enumerate(
reversed(range(len(need_dict['decoder'].decoders)))):
att_ws += [
need_dict['decoder'].decoders[layer_idx].src_attn.
attn[:, :need_dict['num_heads_applied_guided_attn']]
]
if idx + 1 == need_dict['num_layers_applied_guided_attn']:
break
# (B, H*L, T_out, T_in)
att_ws = paddle.concat(att_ws, axis=1)
enc_dec_attn_loss = self.attn_criterion(
att_ws=att_ws,
ilens=batch["text_lengths"] + 1,
olens=olens_in)
report("train/enc_dec_attn_loss", float(enc_dec_attn_loss))
losses_dict["enc_dec_attn_loss"] = float(enc_dec_attn_loss)
loss = loss + enc_dec_attn_loss
if need_dict['use_scaled_pos_enc']:
report("train/encoder_alpha",
float(need_dict['encoder'].embed[-1].alpha))
report("train/decoder_alpha",
float(need_dict['decoder'].embed[-1].alpha))
losses_dict["encoder_alpha"] = float(
need_dict['encoder'].embed[-1].alpha)
losses_dict["decoder_alpha"] = float(
need_dict['decoder'].embed[-1].alpha)
optimizer = self.optimizer
optimizer.clear_grad()
loss.backward()
optimizer.step()
report("train/loss", float(loss))
losses_dict["loss"] = float(loss)
self.msg += ', '.join('{}: {:>.6f}'.format(k, v)
for k, v in losses_dict.items())
[docs]class TransformerTTSEvaluator(StandardEvaluator):
def __init__(
self,
model: Layer,
dataloader: DataLoader,
init_state=None,
use_masking: bool=False,
use_weighted_masking: bool=False,
output_dir: Path=None,
bce_pos_weight: float=5.0,
loss_type: str="L1",
use_guided_attn_loss: bool=True,
modules_applied_guided_attn: Sequence[str]=("encoder-decoder"),
guided_attn_loss_sigma: float=0.4,
guided_attn_loss_lambda: float=1.0, ):
super().__init__(model, dataloader)
self.loss_type = loss_type
self.use_guided_attn_loss = use_guided_attn_loss
self.modules_applied_guided_attn = modules_applied_guided_attn
self.criterion = TransformerTTSLoss(
use_masking=use_masking,
use_weighted_masking=use_weighted_masking,
bce_pos_weight=bce_pos_weight)
if self.use_guided_attn_loss:
self.attn_criterion = GuidedMultiHeadAttentionLoss(
sigma=guided_attn_loss_sigma,
alpha=guided_attn_loss_lambda, )
log_file = output_dir / 'worker_{}.log'.format(dist.get_rank())
self.filehandler = logging.FileHandler(str(log_file))
logger.addHandler(self.filehandler)
self.logger = logger
self.msg = ""
[docs] def evaluate_core(self, batch):
self.msg = "Evaluate: "
losses_dict = {}
after_outs, before_outs, logits, ys, stop_labels, olens, olens_in, need_dict = self.model(
text=batch["text"],
text_lengths=batch["text_lengths"],
speech=batch["speech"],
speech_lengths=batch["speech_lengths"])
l1_loss, l2_loss, bce_loss = self.criterion(
after_outs=after_outs,
before_outs=before_outs,
logits=logits,
ys=ys,
stop_labels=stop_labels,
olens=olens)
report("eval/bce_loss", float(bce_loss))
report("eval/l1_loss", float(l1_loss))
report("eval/l2_loss", float(l2_loss))
losses_dict["bce_loss"] = float(bce_loss)
losses_dict["l1_loss"] = float(l1_loss)
losses_dict["l2_loss"] = float(l2_loss)
# caluculate loss values
if self.loss_type == "L1":
loss = l1_loss + bce_loss
elif self.loss_type == "L2":
loss = l2_loss + bce_loss
elif self.loss_type == "L1+L2":
loss = l1_loss + l2_loss + bce_loss
else:
raise ValueError("unknown --loss-type " + self.loss_type)
# calculate guided attention loss
if self.use_guided_attn_loss:
# calculate for encoder
if "encoder" in self.modules_applied_guided_attn:
att_ws = []
for idx, layer_idx in enumerate(
reversed(range(len(need_dict['encoder'].encoders)))):
att_ws += [
need_dict['encoder'].encoders[layer_idx].self_attn.
attn[:, :need_dict['num_heads_applied_guided_attn']]
]
if idx + 1 == need_dict['num_layers_applied_guided_attn']:
break
# (B, H*L, T_in, T_in)
att_ws = paddle.concat(att_ws, axis=1)
enc_attn_loss = self.attn_criterion(
att_ws=att_ws,
ilens=batch["text_lengths"] + 1,
olens=batch["text_lengths"] + 1)
loss = loss + enc_attn_loss
report("train/enc_attn_loss", float(enc_attn_loss))
losses_dict["enc_attn_loss"] = float(enc_attn_loss)
# calculate for decoder
if "decoder" in self.modules_applied_guided_attn:
att_ws = []
for idx, layer_idx in enumerate(
reversed(range(len(need_dict['decoder'].decoders)))):
att_ws += [
need_dict['decoder'].decoders[layer_idx].self_attn.
attn[:, :need_dict['num_heads_applied_guided_attn']]
]
if idx + 1 == need_dict['num_layers_applied_guided_attn']:
break
# (B, H*L, T_out, T_out)
att_ws = paddle.concat(att_ws, axis=1)
dec_attn_loss = self.attn_criterion(
att_ws=att_ws, ilens=olens_in, olens=olens_in)
report("eval/dec_attn_loss", float(dec_attn_loss))
losses_dict["dec_attn_loss"] = float(dec_attn_loss)
loss = loss + dec_attn_loss
# calculate for encoder-decoder
if "encoder-decoder" in self.modules_applied_guided_attn:
att_ws = []
for idx, layer_idx in enumerate(
reversed(range(len(need_dict['decoder'].decoders)))):
att_ws += [
need_dict['decoder'].decoders[layer_idx].src_attn.
attn[:, :need_dict['num_heads_applied_guided_attn']]
]
if idx + 1 == need_dict['num_layers_applied_guided_attn']:
break
# (B, H*L, T_out, T_in)
att_ws = paddle.concat(att_ws, axis=1)
enc_dec_attn_loss = self.attn_criterion(
att_ws=att_ws,
ilens=batch["text_lengths"] + 1,
olens=olens_in)
report("eval/enc_dec_attn_loss", float(enc_dec_attn_loss))
losses_dict["enc_dec_attn_loss"] = float(enc_dec_attn_loss)
loss = loss + enc_dec_attn_loss
if need_dict['use_scaled_pos_enc']:
report("eval/encoder_alpha",
float(need_dict['encoder'].embed[-1].alpha))
report("eval/decoder_alpha",
float(need_dict['decoder'].embed[-1].alpha))
losses_dict["encoder_alpha"] = float(
need_dict['encoder'].embed[-1].alpha)
losses_dict["decoder_alpha"] = float(
need_dict['decoder'].embed[-1].alpha)
report("eval/loss", float(loss))
losses_dict["loss"] = float(loss)
self.msg += ', '.join('{}: {:>.6f}'.format(k, v)
for k, v in losses_dict.items())
self.logger.info(self.msg)