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[Ben10lightningx]

When I enter the command python train.py -c config.json -p train_config.output_directory=outdir data_config.use_attn_prior=1 like it says to do in the readME I get this output:

train_config.output_directory:outdir was not parsed output_directory=outdir output_directory:outdir was not parsed data_config.use_attn_prior=1 use_attn_prior=1 {'train_config': {'output_directory': 'outdir', 'epochs': 1000, 'optim_algo': 'RAdam', 'learning_rate': 0.001, 'weight_decay': 1e-06, 'grad_clip_val': 1, 'sigma': 1.0, 'iters_per_checkpoint': 1000, 'batch_size': 6, 'seed': 1234, 'check point_path': '', 'ignore_layers': [], 'finetune_layers': [], 'include_layers': ['speaker', 'encoder', 'embedding'], 'warmstart_checkpoint_path': '', 'with_tensorboard': True, 'fp16_run': True, 'gate_loss': True, 'use_ctc_loss': True, ' ctc_loss_weight': 0.01, 'blank_logprob': -8, 'ctc_loss_start_iter': 10000}, 'data_config': {'training_files': 'C:\Scripts\Voice\flowtron\filelists\list.txt', 'validation_files': 'C:\Scripts\Voice\flowtron\filelists \list.txt', 'text_cleaners': ['flowtron_cleaners'], 'p_arpabet': 0.5, 'cmudict_path': 'data/cmudict_dictionary', 'sampling_rate': 22050, 'filter_length': 1024, 'hop_length': 256, 'win_length': 1024, 'mel_fmin': 0.0, 'mel_fmax': 8000.0 , 'max_wav_value': 32768.0, 'use_attn_prior': 1, 'attn_prior_threshold': 0.0, 'prior_cache_path': '/attention_prior_cache', 'betab_scaling_factor': 1.0, 'keep_ambiguous': False}, 'dist_config': {'dist_backend': 'nccl', 'dist_url': 'tcp ://localhost:54321'}, 'model_config': {'n_speakers': 1, 'n_speaker_dim': 128, 'n_text': 185, 'n_text_dim': 512, 'n_flows': 2, 'n_mel_channels': 80, 'n_attn_channels': 640, 'n_hidden': 1024, 'n_lstm_layers': 2, 'mel_encoder_n_hidden': 5 12, 'n_components': 0, 'mean_scale': 0.0, 'fixed_gaussian': True, 'dummy_speaker_embedding': False, 'use_gate_layer': True, 'use_cumm_attention': False}}

got rank 0 and world size 1 ... Initializing RAdam optimizer Flowtron( (speaker_embedding): Embedding(1, 128) (embedding): Embedding(185, 512) (flows): ModuleList( (0): AR_Step( (conv): Conv1d(1024, 160, kernel_size=(1,), stride=(1,)) (lstm): LSTM(1664, 1024, num_layers=2) (attention_lstm): LSTM(80, 1024) (attention_layer): Attention( (softmax): Softmax(dim=2) (query): LinearNorm( (linear_layer): Linear(in_features=1024, out_features=640, bias=False) ) (key): LinearNorm( (linear_layer): Linear(in_features=640, out_features=640, bias=False) ) (value): LinearNorm( (linear_layer): Linear(in_features=640, out_features=640, bias=False) ) (v): LinearNorm( (linear_layer): Linear(in_features=640, out_features=1, bias=False) ) ) (dense_layer): DenseLayer( (layers): ModuleList( (0): LinearNorm( (linear_layer): Linear(in_features=1024, out_features=1024, bias=True) ) (1): LinearNorm( (linear_layer): Linear(in_features=1024, out_features=1024, bias=True) ) ) ) ) (1): AR_Back_Step( (ar_step): AR_Step( (conv): Conv1d(1024, 160, kernel_size=(1,), stride=(1,)) (lstm): LSTM(1664, 1024, num_layers=2) (attention_lstm): LSTM(80, 1024) (attention_layer): Attention( (softmax): Softmax(dim=2) (query): LinearNorm( (linear_layer): Linear(in_features=1024, out_features=640, bias=False) ) (key): LinearNorm( (linear_layer): Linear(in_features=640, out_features=640, bias=False) ) (value): LinearNorm( (linear_layer): Linear(in_features=640, out_features=640, bias=False) ) (v): LinearNorm( (linear_layer): Linear(in_features=640, out_features=1, bias=False) ) ) (dense_layer): DenseLayer( (layers): ModuleList( (0): LinearNorm( (linear_layer): Linear(in_features=1024, out_features=1024, bias=True) ) (1): LinearNorm( (linear_layer): Linear(in_features=1024, out_features=1024, bias=True) ) ) ) (gate_layer): LinearNorm( (linear_layer): Linear(in_features=1664, out_features=1, bias=True) ) ) ) ) (encoder): Encoder( (convolutions): ModuleList( (0): Sequential( (0): ConvNorm( (conv): Conv1d(512, 512, kernel_size=(5,), stride=(1,), padding=(2,)) ) (1): InstanceNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False) ) (1): Sequential( (0): ConvNorm( (conv): Conv1d(512, 512, kernel_size=(5,), stride=(1,), padding=(2,)) ) (1): InstanceNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False) ) (2): Sequential( (0): ConvNorm( (conv): Conv1d(512, 512, kernel_size=(5,), stride=(1,), padding=(2,)) ) (1): InstanceNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False) ) ) (lstm): LSTM(512, 256, batch_first=True, bidirectional=True) ) ) Number of speakers : 1 Number of speakers : 1 Setting up Tensorboard log in outdir\logs Epoch: 0 C:\Scripts\Voice\flowtron\data.py:55: WavFileWarning: Chunk (non-data) not understood, skipping it. sampling_rate, data = read(full_path) C:\Scripts\Voice\flowtron\data.py:56: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array usin g the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at ..\torch\csrc\utils \tensor_numpy.cpp:180.) return torch.from_numpy(data).float(), sampling_rate Traceback (most recent call last): File "C:\Scripts\Voice\flowtron\train.py", line 415, in train(n_gpus, rank, **train_config) File "C:\Scripts\Voice\flowtron\train.py", line 281, in train for batch in train_loader: File "C:\Users\user.conda\envs\tensor\lib\site-packages\torch\utils\data\dataloader.py", line 521, in next data = self._next_data() File "C:\Users\user.conda\envs\tensor\lib\site-packages\torch\utils\data\dataloader.py", line 1203, in _next_data return self._process_data(data) File "C:\Users\user.conda\envs\tensor\lib\site-packages\torch\utils\data\dataloader.py", line 1229, in _process_data data.reraise() File "C:\Users\user.conda\envs\tensor\lib\site-packages\torch_utils.py", line 425, in reraise raise self.exc_type(msg) IndexError: Caught IndexError in DataLoader worker process 0. Original Traceback (most recent call last): File "C:\Users\user.conda\envs\tensor\lib\site-packages\torch\utils\data_utils\worker.py", line 287, in _worker_loop data = fetcher.fetch(index) File "C:\Users\user.conda\envs\tensor\lib\site-packages\torch\utils\data_utils\fetch.py", line 44, in fetch data = [self.dataset[idx] for idx in possibly_batched_index] File "C:\Users\user.conda\envs\tensor\lib\site-packages\torch\utils\data_utils\fetch.py", line 44, in data = [self.dataset[idx] for idx in possibly_batched_index] File "C:\Scripts\Voice\flowtron\data.py", line 182, in getitem attn_prior = self.compute_attention_prior( File "C:\Scripts\Voice\flowtron\data.py", line 112, in compute_attention_prior folder_path = audiopath.split('/')[-2] IndexError: list index out of range