Model training error

[YeekGames]

File "E:\Retrieval-based-Voice-Conversion-WebUI\infer\modules\train\train.py", line 256, in run net_g.module.load_state_dict( File "E:\Retrieval-based-Voice-Conversion-WebUI\venv\Lib\site-packages\torch\nn\modules\module.py", line 2593, in load_state_dict raise RuntimeError( RuntimeError: Error(s) in loading state_dict for SynthesizerTrnMs768NSFsid: Missing key(s) in state_dict: "enc_p.emb_pitch.weight", "dec.m_source.l_linear.weight", "dec.m_source.l_linear.bias", "dec.noise_convs.0.weight", "dec.noise_convs.0.bias", "dec.noise_convs.1.weight", "dec.noise_convs.1.bias", "dec.noise_convs.2.weight", "dec.noise_convs.2.bias", "dec.noise_convs.3.weight", "dec.noise_convs.3.bias".

Unable to train due to this error during One-click training.

OS: Win11
Python env: 3.11.9
torch: 2.0.1+cu117

[acely]

Same errors for me:

DEBUG:__main__:Write filelist done
INFO:__main__:Use gpus:
INFO:__main__:Execute: "python" infer/modules/train/train.py -e "arcpr" -sr 40k -f0 1 -bs 1 -te 20 -se 5 -pg "assets/pretrained_v2/G40k.pth" -pd "assets/pretrained_v2/D40k.pth" -l 0 -c 0 -sw 0 -v v2 -a ""
DEBUG:faiss.loader:Environment variable FAISS_OPT_LEVEL is not set, so let's pick the instruction set according to the current CPU
INFO:faiss.loader:Loading faiss.
INFO:faiss.loader:Successfully loaded faiss.
DEBUG:faiss.loader:Environment variable FAISS_OPT_LEVEL is not set, so let's pick the instruction set according to the current CPU
INFO:faiss.loader:Loading faiss.
INFO:faiss.loader:Successfully loaded faiss.
INFO:arcpr:{'data': {'filter_length': 2048, 'hop_length': 400, 'max_wav_value': 32768.0, 'mel_fmax': None, 'mel_fmin': 0.0, 'n_mel_channels': 125, 'sampling_rate': 40000, 'win_length': 2048, 'training_files': './logs/arcpr/filelist.txt'}, 'model': {'filter_channels': 768, 'gin_channels': 256, 'hidden_channels': 192, 'inter_channels': 192, 'kernel_size': 3, 'n_heads': 2, 'n_layers': 6, 'p_dropout': 0, 'resblock': '1', 'resblock_dilation_sizes': [[1, 3, 5], [1, 3, 5], [1, 3, 5]], 'resblock_kernel_sizes': [3, 7, 11], 'spk_embed_dim': 109, 'upsample_initial_channel': 512, 'upsample_kernel_sizes': [16, 16, 4, 4], 'upsample_rates': [10, 10, 2, 2], 'use_spectral_norm': False}, 'train': {'batch_size': 1, 'betas': [0.8, 0.99], 'c_kl': 1.0, 'c_mel': 45, 'epochs': 20000, 'eps': 1e-09, 'fp16_run': False, 'init_lr_ratio': 1, 'learning_rate': 0.0001, 'log_interval': 200, 'lr_decay': 0.999875, 'seed': 1234, 'segment_size': 12800, 'warmup_epochs': 0}, 'model_dir': './logs/arcpr', 'experiment_dir': './logs/arcpr', 'save_every_epoch': 5, 'name': 'arcpr', 'total_epoch': 20, 'pretrainG': 'assets/pretrained_v2/G40k.pth', 'pretrainD': 'assets/pretrained_v2/D40k.pth', 'version': 'v2', 'gpus': '0', 'sample_rate': '40k', 'if_f0': 1, 'if_latest': 0, 'save_every_weights': '0', 'if_cache_data_in_gpu': 0, 'author': ''}
/Users/ec2-user/Documents/torch311/lib/python3.11/site-packages/torch/nn/utils/weight_norm.py:144: FutureWarning: `torch.nn.utils.weight_norm` is deprecated in favor of `torch.nn.utils.parametrizations.weight_norm`.
  WeightNorm.apply(module, name, dim)
INFO:arcpr:loaded pretrained assets/pretrained_v2/G40k.pth
Process Process-1:
Traceback (most recent call last):
  File "/Users/ec2-user/Documents/Retrieval-based-Voice-Conversion-WebUI/infer/modules/train/train.py", line 235, in run
    utils.latest_checkpoint_path(hps.model_dir, "D_*.pth"), net_d, optim_d
    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/ec2-user/Documents/Retrieval-based-Voice-Conversion-WebUI/infer/lib/train/utils.py", line 120, in latest_checkpoint_path
    x = f_list[-1]
        ~~~~~~^^^^
IndexError: list index out of range

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/Users/ec2-user/Documents/torch311/lib/python3.11/multiprocessing/process.py", line 314, in _bootstrap
    self.run()
  File "/Users/ec2-user/Documents/torch311/lib/python3.11/multiprocessing/process.py", line 108, in run
    self._target(*self._args, **self._kwargs)
  File "/Users/ec2-user/Documents/Retrieval-based-Voice-Conversion-WebUI/infer/modules/train/train.py", line 255, in run
    net_g.module.load_state_dict(
  File "/Users/ec2-user/Documents/torch311/lib/python3.11/site-packages/torch/nn/modules/module.py", line 2624, in load_state_dict
    raise RuntimeError(
RuntimeError: Error(s) in loading state_dict for SynthesizerTrnMs768NSFsid:
	Missing key(s) in state_dict: "enc_p.emb_pitch.weight", "dec.m_source.l_linear.weight", "dec.m_source.l_linear.bias", "dec.noise_convs.0.weight", "dec.noise_convs.0.bias", "dec.noise_convs.1.weight", "dec.noise_convs.1.bias", "dec.noise_convs.2.weight", "dec.noise_convs.2.bias", "dec.noise_convs.3.weight", "dec.noise_convs.3.bias".
DEBUG:matplotlib.pyplot:Loaded backend macosx version unknown.

OS: macos 13 arm
python: 3.11

pretrained model downloaded from https://github.com/fumiama/RVC-Models-Downloader

Both 40k and 48k have this error.
Has the model structure changed?

[acely]

找到解决办法了，给infer/modules/train/train.py这个文件修改，load_state_dict函数加一个strict=False参数就行，加4个：

except:  # 如果首次不能加载，加载pretrain
      # traceback.print_exc()
      epoch_str = 1
      global_step = 0
      if hps.pretrainG != "":
          if rank == 0:
              logger.info("loaded pretrained %s" % (hps.pretrainG))
          if hasattr(net_g, "module"):
              logger.info(
                  net_g.module.load_state_dict(
                      torch.load(
                          hps.pretrainG, map_location="cpu", weights_only=True
                      )["model"], strict=False
                  )
              )  ##测试不加载优化器
          else:
              logger.info(
                  net_g.load_state_dict(
                      torch.load(
                          hps.pretrainG, map_location="cpu", weights_only=True
                      )["model"], strict=False
                  )
              )  ##测试不加载优化器
      if hps.pretrainD != "":
          if rank == 0:
              logger.info("loaded pretrained %s" % (hps.pretrainD))
          if hasattr(net_d, "module"):
              logger.info(
                  net_d.module.load_state_dict(
                      torch.load(
                          hps.pretrainD, map_location="cpu", weights_only=True
                      )["model"], strict=False
                  )
              )
          else:
              logger.info(
                  net_d.load_state_dict(
                      torch.load(
                          hps.pretrainD, map_location="cpu", weights_only=True
                      )["model"], strict=False
                  )
              )