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# Copyright (c) 2023 Amphion.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
from models.svc.base import SVCTrainer
from modules.encoder.condition_encoder import ConditionEncoder
from models.svc.transformer.transformer import Transformer
from models.svc.transformer.conformer import Conformer
from utils.ssim import SSIM
class TransformerTrainer(SVCTrainer):
def __init__(self, args, cfg):
SVCTrainer.__init__(self, args, cfg)
self.ssim_loss = SSIM()
def _build_model(self):
self.cfg.model.condition_encoder.f0_min = self.cfg.preprocess.f0_min
self.cfg.model.condition_encoder.f0_max = self.cfg.preprocess.f0_max
self.condition_encoder = ConditionEncoder(self.cfg.model.condition_encoder)
if self.cfg.model.transformer.type == "transformer":
self.acoustic_mapper = Transformer(self.cfg.model.transformer)
elif self.cfg.model.transformer.type == "conformer":
self.acoustic_mapper = Conformer(self.cfg.model.transformer)
else:
raise NotImplementedError
model = torch.nn.ModuleList([self.condition_encoder, self.acoustic_mapper])
return model
def _forward_step(self, batch):
total_loss = 0
device = self.accelerator.device
mel = batch["mel"]
mask = batch["mask"]
condition = self.condition_encoder(batch)
mel_pred = self.acoustic_mapper(condition, mask)
l1_loss = torch.sum(torch.abs(mel_pred - mel) * batch["mask"]) / torch.sum(
batch["mask"]
)
self._check_nan(l1_loss, mel_pred, mel)
total_loss += l1_loss
ssim_loss = self.ssim_loss(mel_pred, mel)
ssim_loss = torch.sum(ssim_loss * batch["mask"]) / torch.sum(batch["mask"])
self._check_nan(ssim_loss, mel_pred, mel)
total_loss += ssim_loss
return total_loss
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