Instructions to use Ritori/Yue_tacotron2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Ritori/Yue_tacotron2 with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Ritori/Yue_tacotron2", dtype="auto") - Notebooks
- Google Colab
- Kaggle
File size: 673 Bytes
a722365 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | from torch import nn
class Tacotron2Loss(nn.Module):
def __init__(self):
super(Tacotron2Loss, self).__init__()
def forward(self, model_output, targets):
mel_target, gate_target = targets[0], targets[1]
mel_target.requires_grad = False
gate_target.requires_grad = False
gate_target = gate_target.view(-1, 1)
mel_out, mel_out_postnet, gate_out, _ = model_output
gate_out = gate_out.view(-1, 1)
mel_loss = nn.MSELoss()(mel_out, mel_target) + \
nn.MSELoss()(mel_out_postnet, mel_target)
gate_loss = nn.BCEWithLogitsLoss()(gate_out, gate_target)
return mel_loss + gate_loss
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