text: Title: 训练时出错：RuntimeError: Error(s) in loading state_dict for Tacotron:
  Body: Arguments:
      run_id:          mandarin
      syn_dir:         k:/mockingbird/datame/SV2TTS/synthesizer
      models_dir:      synthesizer/saved_models/
      save_every:      1000
      backup_every:    25000
      log_every:       200
      force_restart:   False
      hparams:
  
  Checkpoint path: synthesizer\saved_models\mandarin\mandarin.pt
  Loading training data from: k:\mockingbird\datame\SV2TTS\synthesizer\train.txt
  Using model: Tacotron
  Using device: cpu
  
  Initialising Tacotron Model...
  
  Trainable Parameters: 32.866M
  
  Loading weights at synthesizer\saved_models\mandarin\mandarin.pt
  Traceback (most recent call last):
    File "synthesizer_train.py", line 37, in <module>
      train(**vars(args))
    File "K:\MockingBird\synthesizer\train.py", line 114, in train
      model.load(weights_fpath, optimizer)
    File "K:\MockingBird\synthesizer\models\tacotron.py", line 536, in load
      self.load_state_dict(checkpoint["model_state"], strict=False)
    File "f:\anaconda3\envs\mockingbird\lib\site-packages\torch\nn\modules\module.py", line 1482, 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([128, 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]).
  
  我已经把symbol里的那行字符改成旧版的那个了，还是报这个错。我这里用的是自己的数据，模仿aishell3的结构放了，已经做了 pre.py 的预处理，在开始训练这一步的时候就出了这个错