batch_size报错，

File "E:\python_learning\AI算法\MockingBird-main\synthesizer\models\tacotron.py", line
547, in load
self.load_state_dict(checkpoint["model_state"], strict=False)
File "E:\Anaconda\envs\python_learning\lib\site-packages\torch\nn\modules\module.py",
line 1044, in load_state_dict
raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format(
RuntimeError: Error(s) in loading state_dict for Tacotron:
size mismatch for encoder_proj.weight: copying a param with shape torch.Size([12
8, 512]) from checkpoint, the shape in current model is torch.Size([128, 1024]).
size mismatch for decoder.attn_rnn.weight_ih: copying a param with shape torch.Size([384, 768]) from checkpoint, the shape in current model is torch.Size([384, 1280]).
size mismatch for decoder.rnn_input.weight: copying a param with shape torch.Size([1024, 640]) from checkpoint, the shape in current model is torch.Size([1024, 1152]).
size mismatch for decoder.stop_proj.weight: copying a param with shape torch.Size([1, 1536]) from checkpoint, the shape in current model is torch.Size([1, 2048]).

Start in synthesizer/models/tacotron.py at load, then inspect the checkpoint and current model configuration to compare the reported tensor dimensions. Confirm which configuration produced the checkpoint; done means the intended checkpoint loads without state_dict size-mismatch errors.

Written by the indexing model from the issue text.