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.
334 lines
14 KiB
334 lines
14 KiB
# 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)
|
|
|
|
|
|
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 = ""
|
|
|
|
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())
|
|
|
|
|
|
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 = ""
|
|
|
|
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)
|