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Running
on
Zero
| """ | |
| Taken from ESPNet | |
| Adapted by Flux | |
| """ | |
| import torch | |
| from Architectures.GeneralLayers.DurationPredictor import DurationPredictorLoss | |
| from Utility.utils import make_non_pad_mask | |
| class ToucanTTSLoss(torch.nn.Module): | |
| def __init__(self): | |
| super().__init__() | |
| self.l1_criterion = torch.nn.L1Loss(reduction="none") | |
| self.l2_criterion = torch.nn.MSELoss(reduction="none") | |
| self.duration_criterion = DurationPredictorLoss(reduction="none") | |
| def forward(self, predicted_features, gold_features, features_lengths, text_lengths, gold_durations, predicted_durations, predicted_pitch, predicted_energy, gold_pitch, gold_energy): | |
| """ | |
| Args: | |
| predicted_features (Tensor): Batch of outputs before postnets (B, Lmax, odim). | |
| gold_features (Tensor): Batch of target features (B, Lmax, odim). | |
| features_lengths (LongTensor): Batch of the lengths of each target (B,). | |
| gold_durations (LongTensor): Batch of durations (B, Tmax). | |
| gold_pitch (LongTensor): Batch of pitch (B, Tmax). | |
| gold_energy (LongTensor): Batch of energy (B, Tmax). | |
| predicted_durations (LongTensor): Batch of outputs of duration predictor (B, Tmax). | |
| predicted_pitch (LongTensor): Batch of outputs of pitch predictor (B, Tmax). | |
| predicted_energy (LongTensor): Batch of outputs of energy predictor (B, Tmax). | |
| text_lengths (LongTensor): Batch of the lengths of each input (B,). | |
| Returns: | |
| Tensor: L1 loss value. | |
| Tensor: Duration loss value | |
| """ | |
| # calculate losses | |
| distance_loss = self.l1_criterion(predicted_features, gold_features) | |
| duration_loss = self.duration_criterion(predicted_durations, gold_durations) | |
| pitch_loss = self.l2_criterion(predicted_pitch, gold_pitch) | |
| energy_loss = self.l2_criterion(predicted_energy, gold_energy) | |
| # make weighted masks to ensure that long samples and short samples are all equally important | |
| out_masks = make_non_pad_mask(features_lengths).unsqueeze(-1).to(gold_features.device) | |
| out_weights = out_masks.float() / out_masks.sum(dim=1, keepdim=True).float() | |
| out_weights /= gold_features.size(0) * gold_features.size(-1) | |
| duration_masks = make_non_pad_mask(text_lengths).to(gold_features.device) | |
| duration_weights = (duration_masks.float() / duration_masks.sum(dim=1, keepdim=True).float()) | |
| variance_masks = duration_masks.unsqueeze(-1) | |
| variance_weights = duration_weights.unsqueeze(-1) | |
| # apply weighted masks | |
| distance_loss = distance_loss.mul(out_weights).masked_select(out_masks).sum() | |
| duration_loss = (duration_loss.mul(duration_weights).masked_select(duration_masks).sum()) | |
| pitch_loss = pitch_loss.mul(variance_weights).masked_select(variance_masks).sum() | |
| energy_loss = (energy_loss.mul(variance_weights).masked_select(variance_masks).sum()) | |
| return distance_loss, duration_loss, pitch_loss, energy_loss | |