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# Copyright (c) 2017-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the LICENSE file in
# the root directory of this source tree. An additional grant of patent rights
# can be found in the PATENTS file in the same directory.
import logging
from dataclasses import dataclass, field
from functools import lru_cache
from typing import Any, Dict, List
import torch
import torch.nn.functional as F
from omegaconf import II
from fairseq import utils
from fairseq.logging import metrics
from fairseq.criterions import FairseqCriterion, register_criterion
from fairseq.data.data_utils import lengths_to_mask
from fairseq.dataclass import FairseqDataclass
logger = logging.getLogger(__name__)
@dataclass
class Tacotron2CriterionConfig(FairseqDataclass):
bce_pos_weight: float = field(
default=1.0,
metadata={"help": "weight of positive examples for BCE loss"},
)
use_guided_attention_loss: bool = field(
default=False,
metadata={"help": "use guided attention loss"},
)
guided_attention_loss_sigma: float = field(
default=0.4,
metadata={"help": "weight of positive examples for BCE loss"},
)
ctc_weight: float = field(default=0.0, metadata={"help": "weight for CTC loss"})
sentence_avg: bool = II("optimization.sentence_avg")
class GuidedAttentionLoss(torch.nn.Module):
"""
Efficiently Trainable Text-to-Speech System Based on Deep Convolutional
Networks with Guided Attention (https://arxiv.org/abs/1710.08969)
"""
def __init__(self, sigma):
super().__init__()
self.sigma = sigma
@staticmethod
@lru_cache(maxsize=8)
def _get_weight(s_len, t_len, sigma):
grid_x, grid_y = torch.meshgrid(torch.arange(t_len), torch.arange(s_len))
grid_x = grid_x.to(s_len.device)
grid_y = grid_y.to(s_len.device)
w = (grid_y.float() / s_len - grid_x.float() / t_len) ** 2
return 1.0 - torch.exp(-w / (2 * (sigma**2)))
def _get_weights(self, src_lens, tgt_lens):
bsz, max_s_len, max_t_len = len(src_lens), max(src_lens), max(tgt_lens)
weights = torch.zeros((bsz, max_t_len, max_s_len))
for i, (s_len, t_len) in enumerate(zip(src_lens, tgt_lens)):
weights[i, :t_len, :s_len] = self._get_weight(s_len, t_len, self.sigma)
return weights
@staticmethod
def _get_masks(src_lens, tgt_lens):
in_masks = lengths_to_mask(src_lens)
out_masks = lengths_to_mask(tgt_lens)
return out_masks.unsqueeze(2) & in_masks.unsqueeze(1)
def forward(self, attn, src_lens, tgt_lens, reduction="mean"):
weights = self._get_weights(src_lens, tgt_lens).to(attn.device)
masks = self._get_masks(src_lens, tgt_lens).to(attn.device)
loss = (weights * attn.transpose(1, 2)).masked_select(masks)
loss = torch.sum(loss) if reduction == "sum" else torch.mean(loss)
return loss
@register_criterion("tacotron2", dataclass=Tacotron2CriterionConfig)
class Tacotron2Criterion(FairseqCriterion):
def __init__(
self,
task,
sentence_avg,
use_guided_attention_loss,
guided_attention_loss_sigma,
bce_pos_weight,
ctc_weight,
):
super().__init__(task)
self.sentence_avg = sentence_avg
self.bce_pos_weight = bce_pos_weight
self.guided_attn = None
if use_guided_attention_loss:
self.guided_attn = GuidedAttentionLoss(guided_attention_loss_sigma)
self.ctc_weight = ctc_weight
def forward(self, model, sample, reduction="mean"):
bsz, max_len, _ = sample["target"].size()
feat_tgt = sample["target"]
feat_len = sample["target_lengths"].view(bsz, 1).expand(-1, max_len)
eos_tgt = torch.arange(max_len).to(sample["target"].device)
eos_tgt = eos_tgt.view(1, max_len).expand(bsz, -1)
eos_tgt = (eos_tgt == (feat_len - 1)).float()
src_tokens = sample["net_input"]["src_tokens"]
src_lens = sample["net_input"]["src_lengths"]
tgt_lens = sample["target_lengths"]
feat_out, eos_out, extra = model(
src_tokens=src_tokens,
src_lengths=src_lens,
prev_output_tokens=sample["net_input"]["prev_output_tokens"],
incremental_state=None,
target_lengths=tgt_lens,
speaker=sample["speaker"],
)
l1_loss, mse_loss, eos_loss = self.compute_loss(
extra["feature_out"],
feat_out,
eos_out,
feat_tgt,
eos_tgt,
tgt_lens,
reduction,
)
attn_loss = torch.tensor(0.0).type_as(l1_loss)
if self.guided_attn is not None:
attn_loss = self.guided_attn(extra["attn"], src_lens, tgt_lens, reduction)
ctc_loss = torch.tensor(0.0).type_as(l1_loss)
if self.ctc_weight > 0.0:
net_output = (feat_out, eos_out, extra)
lprobs = model.get_normalized_probs(net_output, log_probs=True)
lprobs = lprobs.transpose(0, 1) # T x B x C
src_mask = lengths_to_mask(src_lens)
src_tokens_flat = src_tokens.masked_select(src_mask)
ctc_loss = (
F.ctc_loss(
lprobs,
src_tokens_flat,
tgt_lens,
src_lens,
reduction=reduction,
zero_infinity=True,
)
* self.ctc_weight
)
loss = l1_loss + mse_loss + eos_loss + attn_loss + ctc_loss
sample_size = sample["nsentences"] if self.sentence_avg else sample["ntokens"]
logging_output = {
"loss": utils.item(loss.data),
"ntokens": sample["ntokens"],
"nsentences": sample["nsentences"],
"sample_size": sample_size,
"l1_loss": utils.item(l1_loss.data),
"mse_loss": utils.item(mse_loss.data),
"eos_loss": utils.item(eos_loss.data),
"attn_loss": utils.item(attn_loss.data),
"ctc_loss": utils.item(ctc_loss.data),
}
return loss, sample_size, logging_output
def compute_loss(
self,
feat_out,
feat_out_post,
eos_out,
feat_tgt,
eos_tgt,
tgt_lens,
reduction="mean",
):
mask = lengths_to_mask(tgt_lens)
_eos_out = eos_out[mask].squeeze()
_eos_tgt = eos_tgt[mask]
_feat_tgt = feat_tgt[mask]
_feat_out = feat_out[mask]
_feat_out_post = feat_out_post[mask]
l1_loss = F.l1_loss(_feat_out, _feat_tgt, reduction=reduction) + F.l1_loss(
_feat_out_post, _feat_tgt, reduction=reduction
)
mse_loss = F.mse_loss(_feat_out, _feat_tgt, reduction=reduction) + F.mse_loss(
_feat_out_post, _feat_tgt, reduction=reduction
)
eos_loss = F.binary_cross_entropy_with_logits(
_eos_out,
_eos_tgt,
pos_weight=torch.tensor(self.bce_pos_weight),
reduction=reduction,
)
return l1_loss, mse_loss, eos_loss
@classmethod
def reduce_metrics(cls, logging_outputs: List[Dict[str, Any]]) -> None:
ns = [log.get("sample_size", 0) for log in logging_outputs]
ntot = sum(ns)
ws = [n / (ntot + 1e-8) for n in ns]
for key in ["loss", "l1_loss", "mse_loss", "eos_loss", "attn_loss", "ctc_loss"]:
vals = [log.get(key, 0) for log in logging_outputs]
val = sum(val * w for val, w in zip(vals, ws))
metrics.log_scalar(key, val, ntot, round=3)
metrics.log_scalar("sample_size", ntot, len(logging_outputs))
# inference metrics
if "targ_frames" not in logging_outputs[0]:
return
n = sum(log.get("targ_frames", 0) for log in logging_outputs)
for key, new_key in [
("mcd_loss", "mcd_loss"),
("pred_frames", "pred_ratio"),
("nins", "ins_rate"),
("ndel", "del_rate"),
]:
val = sum(log.get(key, 0) for log in logging_outputs)
metrics.log_scalar(new_key, val / n, n, round=3)
@staticmethod
def logging_outputs_can_be_summed() -> bool:
return False
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