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Running
on
Zero
Running
on
Zero
File size: 2,499 Bytes
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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
import torch.nn as nn
import numpy as np
import torch.nn.functional as F
def log_dur_loss(dur_pred_log, dur_target, mask, loss_type="l1"):
# dur_pred_log: (B, N)
# dur_target: (B, N)
# mask: (B, N) mask is 0
dur_target_log = torch.log(1 + dur_target)
if loss_type == "l1":
loss = F.l1_loss(
dur_pred_log, dur_target_log, reduction="none"
).float() * mask.to(dur_target.dtype)
elif loss_type == "l2":
loss = F.mse_loss(
dur_pred_log, dur_target_log, reduction="none"
).float() * mask.to(dur_target.dtype)
else:
raise NotImplementedError()
loss = loss.sum() / (mask.to(dur_target.dtype).sum())
return loss
def log_pitch_loss(pitch_pred_log, pitch_target, mask, loss_type="l1"):
pitch_target_log = torch.log(pitch_target)
if loss_type == "l1":
loss = F.l1_loss(
pitch_pred_log, pitch_target_log, reduction="none"
).float() * mask.to(pitch_target.dtype)
elif loss_type == "l2":
loss = F.mse_loss(
pitch_pred_log, pitch_target_log, reduction="none"
).float() * mask.to(pitch_target.dtype)
else:
raise NotImplementedError()
loss = loss.sum() / (mask.to(pitch_target.dtype).sum() + 1e-8)
return loss
def diff_loss(pred, target, mask, loss_type="l1"):
# pred: (B, d, T)
# target: (B, d, T)
# mask: (B, T)
if loss_type == "l1":
loss = F.l1_loss(pred, target, reduction="none").float() * (
mask.to(pred.dtype).unsqueeze(1)
)
elif loss_type == "l2":
loss = F.mse_loss(pred, target, reduction="none").float() * (
mask.to(pred.dtype).unsqueeze(1)
)
else:
raise NotImplementedError()
loss = (torch.mean(loss, dim=1)).sum() / (mask.to(pred.dtype).sum())
return loss
def diff_ce_loss(pred_dist, gt_indices, mask):
# pred_dist: (nq, B, T, 1024)
# gt_indices: (nq, B, T)
pred_dist = pred_dist.permute(1, 3, 0, 2) # (B, 1024, nq, T)
gt_indices = gt_indices.permute(1, 0, 2).long() # (B, nq, T)
loss = F.cross_entropy(
pred_dist, gt_indices, reduction="none"
).float() # (B, nq, T)
loss = loss * mask.to(loss.dtype).unsqueeze(1)
loss = (torch.mean(loss, dim=1)).sum() / (mask.to(loss.dtype).sum())
return loss
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