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@ -447,12 +447,15 @@ class TransformerTTS(nn.Layer):
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# modifiy mod part of groundtruth
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# modifiy mod part of groundtruth
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if self.reduction_factor > 1:
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if self.reduction_factor > 1:
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olens = paddle.to_tensor(
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olens = olens - olens % self.reduction_factor
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[olen - olen % self.reduction_factor for olen in olens.numpy()])
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max_olen = max(olens)
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max_olen = max(olens)
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ys = ys[:, :max_olen]
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ys = ys[:, :max_olen]
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labels = labels[:, :max_olen]
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labels = labels[:, :max_olen]
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labels[:, -1] = 1.0 # make sure at least one frame has 1
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labels[:, -1] = 1.0 # make sure at least one frame has 1
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olens_in = olens // self.reduction_factor
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else:
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olens_in = olens
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need_dict = {}
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need_dict = {}
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need_dict['encoder'] = self.encoder
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need_dict['encoder'] = self.encoder
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need_dict['decoder'] = self.decoder
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need_dict['decoder'] = self.decoder
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@ -462,7 +465,7 @@ class TransformerTTS(nn.Layer):
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'num_layers_applied_guided_attn'] = self.num_layers_applied_guided_attn
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'num_layers_applied_guided_attn'] = self.num_layers_applied_guided_attn
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need_dict['use_scaled_pos_enc'] = self.use_scaled_pos_enc
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need_dict['use_scaled_pos_enc'] = self.use_scaled_pos_enc
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return after_outs, before_outs, logits, ys, labels, olens, ilens, need_dict
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return after_outs, before_outs, logits, ys, labels, olens, olens_in, need_dict
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def _forward(
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def _forward(
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self,
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self,
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@ -488,8 +491,7 @@ class TransformerTTS(nn.Layer):
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# thin out frames for reduction factor (B, Lmax, odim) -> (B, Lmax//r, odim)
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# thin out frames for reduction factor (B, Lmax, odim) -> (B, Lmax//r, odim)
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if self.reduction_factor > 1:
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if self.reduction_factor > 1:
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ys_in = ys[:, self.reduction_factor - 1::self.reduction_factor]
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ys_in = ys[:, self.reduction_factor - 1::self.reduction_factor]
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olens_in = olens.new(
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olens_in = olens // self.reduction_factor
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[olen // self.reduction_factor for olen in olens])
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else:
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else:
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ys_in, olens_in = ys, olens
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ys_in, olens_in = ys, olens
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@ -769,318 +771,3 @@ class TransformerTTSInference(nn.Layer):
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normalized_mel = self.acoustic_model.inference(text)[0]
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normalized_mel = self.acoustic_model.inference(text)[0]
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logmel = self.normalizer.inverse(normalized_mel)
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logmel = self.normalizer.inverse(normalized_mel)
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return logmel
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return logmel
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class TransformerTTSLoss(nn.Layer):
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"""Loss function module for Tacotron2."""
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def __init__(self,
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use_masking=True,
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use_weighted_masking=False,
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bce_pos_weight=5.0):
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"""Initialize Tactoron2 loss module.
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Parameters
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----------
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use_masking : bool
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Whether to apply masking for padded part in loss calculation.
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use_weighted_masking : bool
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Whether to apply weighted masking in loss calculation.
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bce_pos_weight : float
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Weight of positive sample of stop token.
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"""
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super().__init__()
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assert (use_masking != use_weighted_masking) or not use_masking
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self.use_masking = use_masking
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self.use_weighted_masking = use_weighted_masking
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# define criterions
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reduction = "none" if self.use_weighted_masking else "mean"
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self.l1_criterion = nn.L1Loss(reduction=reduction)
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self.mse_criterion = nn.MSELoss(reduction=reduction)
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self.bce_criterion = nn.BCEWithLogitsLoss(
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reduction=reduction, pos_weight=paddle.to_tensor(bce_pos_weight))
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def forward(self, after_outs, before_outs, logits, ys, labels, olens):
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"""Calculate forward propagation.
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Parameters
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----------
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after_outs : Tensor
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Batch of outputs after postnets (B, Lmax, odim).
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before_outs : Tensor
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Batch of outputs before postnets (B, Lmax, odim).
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logits : Tensor
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Batch of stop logits (B, Lmax).
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ys : Tensor
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Batch of padded target features (B, Lmax, odim).
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labels : LongTensor
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Batch of the sequences of stop token labels (B, Lmax).
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olens : LongTensor
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Batch of the lengths of each target (B,).
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Returns
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----------
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Tensor
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L1 loss value.
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Tensor
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Mean square error loss value.
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Tensor
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Binary cross entropy loss value.
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"""
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# make mask and apply it
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if self.use_masking:
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masks = make_non_pad_mask(olens).unsqueeze(-1)
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ys = ys.masked_select(masks.broadcast_to(ys.shape))
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after_outs = after_outs.masked_select(
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masks.broadcast_to(after_outs.shape))
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before_outs = before_outs.masked_select(
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masks.broadcast_to(before_outs.shape))
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# Operator slice does not have kernel for data_type[bool]
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tmp_masks = paddle.cast(masks, dtype='int64')
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tmp_masks = tmp_masks[:, :, 0]
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tmp_masks = paddle.cast(tmp_masks, dtype='bool')
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labels = labels.masked_select(tmp_masks.broadcast_to(labels.shape))
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logits = logits.masked_select(tmp_masks.broadcast_to(logits.shape))
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# calculate loss
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l1_loss = self.l1_criterion(after_outs, ys) + self.l1_criterion(
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before_outs, ys)
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mse_loss = self.mse_criterion(after_outs, ys) + self.mse_criterion(
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before_outs, ys)
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bce_loss = self.bce_criterion(logits, labels)
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# make weighted mask and apply it
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if self.use_weighted_masking:
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masks = make_non_pad_mask(olens).unsqueeze(-1)
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weights = masks.float() / masks.sum(dim=1, keepdim=True).float()
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out_weights = weights.div(ys.shape[0] * ys.shape[2])
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logit_weights = weights.div(ys.shape[0])
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# apply weight
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l1_loss = l1_loss.multiply(out_weights)
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l1_loss = l1_loss.masked_select(
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masks.broadcast_to(l1_loss.shape)).sum()
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mse_loss = mse_loss.multiply(out_weights)
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mse_loss = mse_loss.masked_select(
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masks.broadcast_to(mse_loss.shape)).sum()
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bce_loss = bce_loss.multiply(logit_weights.squeeze(-1))
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bce_loss = bce_loss.masked_select(
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masks.squeeze(-1).broadcast_to(bce_loss.shape)).sum()
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return l1_loss, mse_loss, bce_loss
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class GuidedAttentionLoss(nn.Layer):
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"""Guided attention loss function module.
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This module calculates the guided attention loss described
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in `Efficiently Trainable Text-to-Speech System Based
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on Deep Convolutional Networks with Guided Attention`_,
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which forces the attention to be diagonal.
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.. _`Efficiently Trainable Text-to-Speech System
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Based on Deep Convolutional Networks with Guided Attention`:
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https://arxiv.org/abs/1710.08969
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"""
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def __init__(self, sigma=0.4, alpha=1.0, reset_always=True):
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"""Initialize guided attention loss module.
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Parameters
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----------
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sigma : float, optional
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Standard deviation to control how close attention to a diagonal.
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alpha : float, optional
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Scaling coefficient (lambda).
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reset_always : bool, optional
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Whether to always reset masks.
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"""
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super(GuidedAttentionLoss, self).__init__()
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self.sigma = sigma
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self.alpha = alpha
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self.reset_always = reset_always
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self.guided_attn_masks = None
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self.masks = None
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def _reset_masks(self):
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self.guided_attn_masks = None
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self.masks = None
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def forward(self, att_ws, ilens, olens):
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"""Calculate forward propagation.
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Parameters
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----------
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att_ws : Tensor
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Batch of attention weights (B, T_max_out, T_max_in).
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ilens : LongTensor
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Batch of input lenghts (B,).
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olens : LongTensor
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Batch of output lenghts (B,).
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Returns
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----------
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Tensor
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Guided attention loss value.
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"""
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if self.guided_attn_masks is None:
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self.guided_attn_masks = self._make_guided_attention_masks(ilens,
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olens)
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if self.masks is None:
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self.masks = self._make_masks(ilens, olens)
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losses = self.guided_attn_masks * att_ws
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loss = paddle.mean(
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losses.masked_select(self.masks.broadcast_to(losses.shape)))
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if self.reset_always:
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self._reset_masks()
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return self.alpha * loss
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def _make_guided_attention_masks(self, ilens, olens):
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n_batches = len(ilens)
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max_ilen = max(ilens)
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max_olen = max(olens)
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guided_attn_masks = paddle.zeros((n_batches, max_olen, max_ilen))
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for idx, (ilen, olen) in enumerate(zip(ilens, olens)):
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ilen = int(ilen)
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olen = int(olen)
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guided_attn_masks[idx, :olen, :
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ilen] = self._make_guided_attention_mask(
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ilen, olen, self.sigma)
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return guided_attn_masks
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@staticmethod
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def _make_guided_attention_mask(ilen, olen, sigma):
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"""Make guided attention mask.
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Examples
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----------
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>>> guided_attn_mask =_make_guided_attention(5, 5, 0.4)
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>>> guided_attn_mask.shape
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[5, 5]
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>>> guided_attn_mask
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tensor([[0.0000, 0.1175, 0.3935, 0.6753, 0.8647],
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[0.1175, 0.0000, 0.1175, 0.3935, 0.6753],
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[0.3935, 0.1175, 0.0000, 0.1175, 0.3935],
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[0.6753, 0.3935, 0.1175, 0.0000, 0.1175],
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[0.8647, 0.6753, 0.3935, 0.1175, 0.0000]])
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>>> guided_attn_mask =_make_guided_attention(3, 6, 0.4)
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>>> guided_attn_mask.shape
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[6, 3]
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>>> guided_attn_mask
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tensor([[0.0000, 0.2934, 0.7506],
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[0.0831, 0.0831, 0.5422],
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[0.2934, 0.0000, 0.2934],
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[0.5422, 0.0831, 0.0831],
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[0.7506, 0.2934, 0.0000],
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[0.8858, 0.5422, 0.0831]])
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"""
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grid_x, grid_y = paddle.meshgrid(
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paddle.arange(olen), paddle.arange(ilen))
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grid_x = grid_x.cast(dtype=paddle.float32)
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grid_y = grid_y.cast(dtype=paddle.float32)
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return 1.0 - paddle.exp(-(
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(grid_y / ilen - grid_x / olen)**2) / (2 * (sigma**2)))
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@staticmethod
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def _make_masks(ilens, olens):
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"""Make masks indicating non-padded part.
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Parameters
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----------
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ilens (LongTensor or List): Batch of lengths (B,).
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olens (LongTensor or List): Batch of lengths (B,).
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Returns
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----------
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Tensor
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Mask tensor indicating non-padded part.
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Examples
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----------
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>>> ilens, olens = [5, 2], [8, 5]
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>>> _make_mask(ilens, olens)
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tensor([[[1, 1, 1, 1, 1],
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[1, 1, 1, 1, 1],
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[1, 1, 1, 1, 1],
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[1, 1, 1, 1, 1],
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[1, 1, 1, 1, 1],
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[1, 1, 1, 1, 1],
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[1, 1, 1, 1, 1],
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[1, 1, 1, 1, 1]],
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[[1, 1, 0, 0, 0],
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[1, 1, 0, 0, 0],
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[1, 1, 0, 0, 0],
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[1, 1, 0, 0, 0],
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[1, 1, 0, 0, 0],
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[0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0]]], dtype=paddle.uint8)
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"""
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# (B, T_in)
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in_masks = make_non_pad_mask(ilens)
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# (B, T_out)
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out_masks = make_non_pad_mask(olens)
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# (B, T_out, T_in)
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return paddle.logical_and(
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out_masks.unsqueeze(-1), in_masks.unsqueeze(-2))
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class GuidedMultiHeadAttentionLoss(GuidedAttentionLoss):
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"""Guided attention loss function module for multi head attention.
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Parameters
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----------
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sigma : float, optional
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Standard deviation to controlGuidedAttentionLoss
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how close attention to a diagonal.
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alpha : float, optional
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Scaling coefficient (lambda).
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reset_always : bool, optional
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Whether to always reset masks.
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"""
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def forward(self, att_ws, ilens, olens):
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"""Calculate forward propagation.
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Parameters
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----------
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att_ws : Tensor
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Batch of multi head attention weights (B, H, T_max_out, T_max_in).
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ilens : Tensor
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Batch of input lenghts (B,).
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olens : Tensor
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Batch of output lenghts (B,).
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Returns
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----------
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Tensor
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Guided attention loss value.
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"""
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if self.guided_attn_masks is None:
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self.guided_attn_masks = (
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self._make_guided_attention_masks(ilens, olens).unsqueeze(1))
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if self.masks is None:
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self.masks = self._make_masks(ilens, olens).unsqueeze(1)
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losses = self.guided_attn_masks * att_ws
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loss = paddle.mean(
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losses.masked_select(self.masks.broadcast_to(losses.shape)))
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if self.reset_always:
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self._reset_masks()
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return self.alpha * loss
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