nohup: ignoring input
INFO:ljs_base:{'train': {'log_interval': 200, 'eval_interval': 1000, 'seed': 1234, 'epochs': 20000, 'learning_rate': 0.0002, 'betas': [0.8, 0.99], 'eps': 1e-09, 'batch_size': 64, 'fp16_run': False, 'lr_decay': 0.999875, 'segment_size': 8192, 'init_lr_ratio': 1, 'warmup_epochs': 0, 'c_mel': 45, 'c_kl': 1.0}, 'data': {'training_files': 'filelists/ljs_audio_text_train_filelist.txt.cleaned', 'validation_files': 'filelists/ljs_audio_text_val_filelist.txt.cleaned', 'text_cleaners': ['english_cleaners2'], 'max_wav_value': 32768.0, 'sampling_rate': 22050, 'filter_length': 1024, 'hop_length': 256, 'win_length': 1024, 'n_mel_channels': 80, 'mel_fmin': 0.0, 'mel_fmax': None, 'add_blank': True, 'n_speakers': 0, 'cleaned_text': True}, 'model': {'inter_channels': 192, 'hidden_channels': 192, 'filter_channels': 768, 'n_heads': 2, 'n_layers': 6, 'kernel_size': 3, 'p_dropout': 0.1, 'resblock': '1', 'resblock_kernel_sizes': [3, 7, 11], 'resblock_dilation_sizes': [[1, 3, 5], [1, 3, 5], [1, 3, 5]], 'upsample_rates': [8, 8, 2, 2], 'upsample_initial_channel': 512, 'upsample_kernel_sizes': [16, 16, 4, 4], 'n_layers_q': 3, 'use_spectral_norm': False}, 'model_dir': './logs/ljs_base'}
WARNING: Logging before InitGoogleLogging() is written to STDERR
I0829 14:14:48.131174 68339 ProcessGroupNCCL.cpp:686] [Rank 1] ProcessGroupNCCL initialization options:NCCL_ASYNC_ERROR_HANDLING: 1, NCCL_DESYNC_DEBUG: 0, NCCL_ENABLE_TIMING: 0, NCCL_BLOCKING_WAIT: 0, TIMEOUT(ms): 1800000, USE_HIGH_PRIORITY_STREAM: 0, TORCH_DISTRIBUTED_DEBUG: OFF, NCCL_DEBUG: OFF, ID=94222121121904
WARNING: Logging before InitGoogleLogging() is written to STDERR
I0829 14:14:48.142663 68338 ProcessGroupNCCL.cpp:686] [Rank 0] ProcessGroupNCCL initialization options:NCCL_ASYNC_ERROR_HANDLING: 1, NCCL_DESYNC_DEBUG: 0, NCCL_ENABLE_TIMING: 0, NCCL_BLOCKING_WAIT: 0, TIMEOUT(ms): 1800000, USE_HIGH_PRIORITY_STREAM: 0, TORCH_DISTRIBUTED_DEBUG: OFF, NCCL_DEBUG: OFF, ID=94043438289712
/usr/local/lib/python3.10/site-packages/torch/nn/utils/weight_norm.py:30: UserWarning: torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.
  warnings.warn("torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.")
/usr/local/lib/python3.10/site-packages/torch/nn/utils/weight_norm.py:30: UserWarning: torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.
  warnings.warn("torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.")
I0829 14:14:50.271715 68338 ProcessGroupNCCL.cpp:1340] NCCL_DEBUG: N/A
SynthesizerTrn(
  (enc_p): TextEncoder(
    (emb): Embedding(178, 192)
    (encoder): Encoder(
      (drop): Dropout(p=0.1, inplace=False)
      (attn_layers): ModuleList(
        (0-5): 6 x MultiHeadAttention(
          (conv_q): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
          (conv_k): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
          (conv_v): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
          (conv_o): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
          (drop): Dropout(p=0.1, inplace=False)
        )
      )
      (norm_layers_1): ModuleList(
        (0-5): 6 x LayerNorm()
      )
      (ffn_layers): ModuleList(
        (0-5): 6 x FFN(
          (conv_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,))
          (conv_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,))
          (drop): Dropout(p=0.1, inplace=False)
        )
      )
      (norm_layers_2): ModuleList(
        (0-5): 6 x LayerNorm()
      )
    )
    (proj): Conv1d(192, 384, kernel_size=(1,), stride=(1,))
  )
  (dec): Generator(
    (conv_pre): Conv1d(192, 512, kernel_size=(7,), stride=(1,), padding=(3,))
    (ups): ModuleList(
      (0): ConvTranspose1d(512, 256, kernel_size=(16,), stride=(8,), padding=(4,))
      (1): ConvTranspose1d(256, 128, kernel_size=(16,), stride=(8,), padding=(4,))
      (2): ConvTranspose1d(128, 64, kernel_size=(4,), stride=(2,), padding=(1,))
      (3): ConvTranspose1d(64, 32, kernel_size=(4,), stride=(2,), padding=(1,))
    )
    (resblocks): ModuleList(
      (0): ResBlock1(
        (convs1): ModuleList(
          (0): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(1,))
          (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,))
          (2): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,))
        )
        (convs2): ModuleList(
          (0-2): 3 x Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(1,))
        )
      )
      (1): ResBlock1(
        (convs1): ModuleList(
          (0): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(3,))
          (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,))
          (2): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,))
        )
        (convs2): ModuleList(
          (0-2): 3 x Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(3,))
        )
      )
      (2): ResBlock1(
        (convs1): ModuleList(
          (0): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(5,))
          (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,))
          (2): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,))
        )
        (convs2): ModuleList(
          (0-2): 3 x Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(5,))
        )
      )
      (3): ResBlock1(
        (convs1): ModuleList(
          (0): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(1,))
          (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,))
          (2): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,))
        )
        (convs2): ModuleList(
          (0-2): 3 x Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(1,))
        )
      )
      (4): ResBlock1(
        (convs1): ModuleList(
          (0): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(3,))
          (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,))
          (2): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,))
        )
        (convs2): ModuleList(
          (0-2): 3 x Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(3,))
        )
      )
      (5): ResBlock1(
        (convs1): ModuleList(
          (0): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(5,))
          (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,))
          (2): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,))
        )
        (convs2): ModuleList(
          (0-2): 3 x Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(5,))
        )
      )
      (6): ResBlock1(
        (convs1): ModuleList(
          (0): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(1,))
          (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,))
          (2): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,))
        )
        (convs2): ModuleList(
          (0-2): 3 x Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(1,))
        )
      )
      (7): ResBlock1(
        (convs1): ModuleList(
          (0): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(3,))
          (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,))
          (2): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,))
        )
        (convs2): ModuleList(
          (0-2): 3 x Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(3,))
        )
      )
      (8): ResBlock1(
        (convs1): ModuleList(
          (0): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(5,))
          (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,))
          (2): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,))
        )
        (convs2): ModuleList(
          (0-2): 3 x Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(5,))
        )
      )
      (9): ResBlock1(
        (convs1): ModuleList(
          (0): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(1,))
          (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,))
          (2): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,))
        )
        (convs2): ModuleList(
          (0-2): 3 x Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(1,))
        )
      )
      (10): ResBlock1(
        (convs1): ModuleList(
          (0): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(3,))
          (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,))
          (2): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,))
        )
        (convs2): ModuleList(
          (0-2): 3 x Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(3,))
        )
      )
      (11): ResBlock1(
        (convs1): ModuleList(
          (0): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(5,))
          (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,))
          (2): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,))
        )
        (convs2): ModuleList(
          (0-2): 3 x Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(5,))
        )
      )
    )
    (conv_post): Conv1d(32, 1, kernel_size=(7,), stride=(1,), padding=(3,), bias=False)
  )
  (enc_q): PosteriorEncoder(
    (pre): Conv1d(513, 192, kernel_size=(1,), stride=(1,))
    (enc): WN(
      (in_layers): ModuleList(
        (0-15): 16 x Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,))
      )
      (res_skip_layers): ModuleList(
        (0-14): 15 x Conv1d(192, 384, kernel_size=(1,), stride=(1,))
        (15): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
      )
      (drop): Dropout(p=0, inplace=False)
    )
    (proj): Conv1d(192, 384, kernel_size=(1,), stride=(1,))
  )
  (flow): ResidualCouplingBlock(
    (flows): ModuleList(
      (0): ResidualCouplingLayer(
        (pre): Conv1d(96, 192, kernel_size=(1,), stride=(1,))
        (enc): WN(
          (in_layers): ModuleList(
            (0-3): 4 x Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,))
          )
          (res_skip_layers): ModuleList(
            (0-2): 3 x Conv1d(192, 384, kernel_size=(1,), stride=(1,))
            (3): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
          )
          (drop): Dropout(p=0, inplace=False)
        )
        (post): Conv1d(192, 96, kernel_size=(1,), stride=(1,))
      )
      (1): Flip()
      (2): ResidualCouplingLayer(
        (pre): Conv1d(96, 192, kernel_size=(1,), stride=(1,))
        (enc): WN(
          (in_layers): ModuleList(
            (0-3): 4 x Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,))
          )
          (res_skip_layers): ModuleList(
            (0-2): 3 x Conv1d(192, 384, kernel_size=(1,), stride=(1,))
            (3): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
          )
          (drop): Dropout(p=0, inplace=False)
        )
        (post): Conv1d(192, 96, kernel_size=(1,), stride=(1,))
      )
      (3): Flip()
      (4): ResidualCouplingLayer(
        (pre): Conv1d(96, 192, kernel_size=(1,), stride=(1,))
        (enc): WN(
          (in_layers): ModuleList(
            (0-3): 4 x Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,))
          )
          (res_skip_layers): ModuleList(
            (0-2): 3 x Conv1d(192, 384, kernel_size=(1,), stride=(1,))
            (3): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
          )
          (drop): Dropout(p=0, inplace=False)
        )
        (post): Conv1d(192, 96, kernel_size=(1,), stride=(1,))
      )
      (5): Flip()
      (6): ResidualCouplingLayer(
        (pre): Conv1d(96, 192, kernel_size=(1,), stride=(1,))
        (enc): WN(
          (in_layers): ModuleList(
            (0-3): 4 x Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,))
          )
          (res_skip_layers): ModuleList(
            (0-2): 3 x Conv1d(192, 384, kernel_size=(1,), stride=(1,))
            (3): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
          )
          (drop): Dropout(p=0, inplace=False)
        )
        (post): Conv1d(192, 96, kernel_size=(1,), stride=(1,))
      )
      (7): Flip()
    )
  )
  (dp): StochasticDurationPredictor(
    (log_flow): Log()
    (flows): ModuleList(
      (0): ElementwiseAffine()
      (1): ConvFlow(
        (pre): Conv1d(1, 192, kernel_size=(1,), stride=(1,))
        (convs): DDSConv(
          (drop): Dropout(p=0.0, inplace=False)
          (convs_sep): ModuleList(
            (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192)
            (1): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192)
            (2): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192)
          )
          (convs_1x1): ModuleList(
            (0-2): 3 x Conv1d(192, 192, kernel_size=(1,), stride=(1,))
          )
          (norms_1): ModuleList(
            (0-2): 3 x LayerNorm()
          )
          (norms_2): ModuleList(
            (0-2): 3 x LayerNorm()
          )
        )
        (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,))
      )
      (2): Flip()
      (3): ConvFlow(
        (pre): Conv1d(1, 192, kernel_size=(1,), stride=(1,))
        (convs): DDSConv(
          (drop): Dropout(p=0.0, inplace=False)
          (convs_sep): ModuleList(
            (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192)
            (1): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192)
            (2): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192)
          )
          (convs_1x1): ModuleList(
            (0-2): 3 x Conv1d(192, 192, kernel_size=(1,), stride=(1,))
          )
          (norms_1): ModuleList(
            (0-2): 3 x LayerNorm()
          )
          (norms_2): ModuleList(
            (0-2): 3 x LayerNorm()
          )
        )
        (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,))
      )
      (4): Flip()
      (5): ConvFlow(
        (pre): Conv1d(1, 192, kernel_size=(1,), stride=(1,))
        (convs): DDSConv(
          (drop): Dropout(p=0.0, inplace=False)
          (convs_sep): ModuleList(
            (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192)
            (1): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192)
            (2): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192)
          )
          (convs_1x1): ModuleList(
            (0-2): 3 x Conv1d(192, 192, kernel_size=(1,), stride=(1,))
          )
          (norms_1): ModuleList(
            (0-2): 3 x LayerNorm()
          )
          (norms_2): ModuleList(
            (0-2): 3 x LayerNorm()
          )
        )
        (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,))
      )
      (6): Flip()
      (7): ConvFlow(
        (pre): Conv1d(1, 192, kernel_size=(1,), stride=(1,))
        (convs): DDSConv(
          (drop): Dropout(p=0.0, inplace=False)
          (convs_sep): ModuleList(
            (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192)
            (1): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192)
            (2): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192)
          )
          (convs_1x1): ModuleList(
            (0-2): 3 x Conv1d(192, 192, kernel_size=(1,), stride=(1,))
          )
          (norms_1): ModuleList(
            (0-2): 3 x LayerNorm()
          )
          (norms_2): ModuleList(
            (0-2): 3 x LayerNorm()
          )
        )
        (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,))
      )
      (8): Flip()
    )
    (post_pre): Conv1d(1, 192, kernel_size=(1,), stride=(1,))
    (post_proj): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
    (post_convs): DDSConv(
      (drop): Dropout(p=0.5, inplace=False)
      (convs_sep): ModuleList(
        (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192)
        (1): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192)
        (2): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192)
      )
      (convs_1x1): ModuleList(
        (0-2): 3 x Conv1d(192, 192, kernel_size=(1,), stride=(1,))
      )
      (norms_1): ModuleList(
        (0-2): 3 x LayerNorm()
      )
      (norms_2): ModuleList(
        (0-2): 3 x LayerNorm()
      )
    )
    (post_flows): ModuleList(
      (0): ElementwiseAffine()
      (1): ConvFlow(
        (pre): Conv1d(1, 192, kernel_size=(1,), stride=(1,))
        (convs): DDSConv(
          (drop): Dropout(p=0.0, inplace=False)
          (convs_sep): ModuleList(
            (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192)
            (1): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192)
            (2): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192)
          )
          (convs_1x1): ModuleList(
            (0-2): 3 x Conv1d(192, 192, kernel_size=(1,), stride=(1,))
          )
          (norms_1): ModuleList(
            (0-2): 3 x LayerNorm()
          )
          (norms_2): ModuleList(
            (0-2): 3 x LayerNorm()
          )
        )
        (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,))
      )
      (2): Flip()
      (3): ConvFlow(
        (pre): Conv1d(1, 192, kernel_size=(1,), stride=(1,))
        (convs): DDSConv(
          (drop): Dropout(p=0.0, inplace=False)
          (convs_sep): ModuleList(
            (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192)
            (1): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192)
            (2): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192)
          )
          (convs_1x1): ModuleList(
            (0-2): 3 x Conv1d(192, 192, kernel_size=(1,), stride=(1,))
          )
          (norms_1): ModuleList(
            (0-2): 3 x LayerNorm()
          )
          (norms_2): ModuleList(
            (0-2): 3 x LayerNorm()
          )
        )
        (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,))
      )
      (4): Flip()
      (5): ConvFlow(
        (pre): Conv1d(1, 192, kernel_size=(1,), stride=(1,))
        (convs): DDSConv(
          (drop): Dropout(p=0.0, inplace=False)
          (convs_sep): ModuleList(
            (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192)
            (1): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192)
            (2): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192)
          )
          (convs_1x1): ModuleList(
            (0-2): 3 x Conv1d(192, 192, kernel_size=(1,), stride=(1,))
          )
          (norms_1): ModuleList(
            (0-2): 3 x LayerNorm()
          )
          (norms_2): ModuleList(
            (0-2): 3 x LayerNorm()
          )
        )
        (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,))
      )
      (6): Flip()
      (7): ConvFlow(
        (pre): Conv1d(1, 192, kernel_size=(1,), stride=(1,))
        (convs): DDSConv(
          (drop): Dropout(p=0.0, inplace=False)
          (convs_sep): ModuleList(
            (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192)
            (1): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192)
            (2): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192)
          )
          (convs_1x1): ModuleList(
            (0-2): 3 x Conv1d(192, 192, kernel_size=(1,), stride=(1,))
          )
          (norms_1): ModuleList(
            (0-2): 3 x LayerNorm()
          )
          (norms_2): ModuleList(
            (0-2): 3 x LayerNorm()
          )
        )
        (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,))
      )
      (8): Flip()
    )
    (pre): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
    (proj): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
    (convs): DDSConv(
      (drop): Dropout(p=0.5, inplace=False)
      (convs_sep): ModuleList(
        (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192)
        (1): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192)
        (2): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192)
      )
      (convs_1x1): ModuleList(
        (0-2): 3 x Conv1d(192, 192, kernel_size=(1,), stride=(1,))
      )
      (norms_1): ModuleList(
        (0-2): 3 x LayerNorm()
      )
      (norms_2): ModuleList(
        (0-2): 3 x LayerNorm()
      )
    )
  )
)
MultiPeriodDiscriminator(
  (discriminators): ModuleList(
    (0): DiscriminatorS(
      (convs): ModuleList(
        (0): Conv1d(1, 16, kernel_size=(15,), stride=(1,), padding=(7,))
        (1): Conv1d(16, 64, kernel_size=(41,), stride=(4,), padding=(20,), groups=4)
        (2): Conv1d(64, 256, kernel_size=(41,), stride=(4,), padding=(20,), groups=16)
        (3): Conv1d(256, 1024, kernel_size=(41,), stride=(4,), padding=(20,), groups=64)
        (4): Conv1d(1024, 1024, kernel_size=(41,), stride=(4,), padding=(20,), groups=256)
        (5): Conv1d(1024, 1024, kernel_size=(5,), stride=(1,), padding=(2,))
      )
      (conv_post): Conv1d(1024, 1, kernel_size=(3,), stride=(1,), padding=(1,))
    )
    (1-5): 5 x DiscriminatorP(
      (convs): ModuleList(
        (0): Conv2d(1, 32, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0))
        (1): Conv2d(32, 128, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0))
        (2): Conv2d(128, 512, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0))
        (3): Conv2d(512, 1024, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0))
        (4): Conv2d(1024, 1024, kernel_size=(5, 1), stride=(1, 1), padding=(2, 0))
      )
      (conv_post): Conv2d(1024, 1, kernel_size=(3, 1), stride=(1, 1), padding=(1, 0))
    )
  )
)
./logs/ljs_base/G_0.pth
INFO:ljs_base:Loaded checkpoint './logs/ljs_base/G_0.pth' (iteration 1)
./logs/ljs_base/G_0.pth
INFO:root:Loaded checkpoint './logs/ljs_base/G_0.pth' (iteration 1)
./logs/ljs_base/D_0.pth
INFO:ljs_base:Loaded checkpoint './logs/ljs_base/D_0.pth' (iteration 1)
INFO:ljs_base:====> Epoch: 1 | Total Samples: 12492
./logs/ljs_base/D_0.pth
INFO:root:Loaded checkpoint './logs/ljs_base/D_0.pth' (iteration 1)
/usr/local/lib/python3.10/site-packages/torch/autograd/__init__.py:251: UserWarning: Grad strides do not match bucket view strides. This may indicate grad was not created according to the gradient layout contract, or that the param's strides changed since DDP was constructed.  This is not an error, but may impair performance.
grad.sizes() = [1, 9, 96], strides() = [46944, 96, 1]
bucket_view.sizes() = [1, 9, 96], strides() = [864, 96, 1] (Triggered internally at /data/jenkins_workspace/workspace/pytorch@4/torch/csrc/distributed/c10d/reducer.cpp:320.)
  Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
/usr/local/lib/python3.10/site-packages/torch/autograd/__init__.py:251: UserWarning: Grad strides do not match bucket view strides. This may indicate grad was not created according to the gradient layout contract, or that the param's strides changed since DDP was constructed.  This is not an error, but may impair performance.
grad.sizes() = [1, 9, 96], strides() = [48096, 96, 1]
bucket_view.sizes() = [1, 9, 96], strides() = [864, 96, 1] (Triggered internally at /data/jenkins_workspace/workspace/pytorch@4/torch/csrc/distributed/c10d/reducer.cpp:320.)
  Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
INFO:ljs_base:Train Epoch: 1 [0%]
INFO:ljs_base:[4.670844554901123, 2.8319339752197266, 0.3068731725215912, 73.78945922851562, 1.4018142223358154, 91.56964874267578, 0, 0.0002]
DEBUG:matplotlib:matplotlib data path: /usr/local/lib/python3.10/site-packages/matplotlib/mpl-data
DEBUG:matplotlib:CONFIGDIR=/root/.config/matplotlib
DEBUG:matplotlib:interactive is False
DEBUG:matplotlib:platform is linux
INFO:ljs_base:Saving model and optimizer state at iteration 1 to ./logs/ljs_base/G_0.pth
INFO:ljs_base:Saving model and optimizer state at iteration 1 to ./logs/ljs_base/D_0.pth
INFO:ljs_base:====> Epoch: 1
INFO:ljs_base:====> FPS: 25.03245151912063
INFO:ljs_base:====> Epoch: 2 | Total Samples: 12492
run.sh: line 13: 68143 Killed                  python train.py -c configs/ljs_base.json -m ljs_base