RuntimeError: Given groups=1, weight of size [48, 2, 1, 1], expected input[1, 4, 256, 7501] to have 2 channels, but got 4 channels instead

I am trying to run 12-48 / aero-nfft=512-hl=256 Although I have no idea what is 512 and 256

installed all requirements

run my command like this and got error

python predict.py dset=4-16 experiment=aero_4-16_512_256 +filename="D:\86 se courses youtube kanali\aero\5dk.mp3" +output="D:\86 se courses youtube kanali\aero\5_v2dk.mp3" checkpoint_file="D:\86 se courses youtube kanali\aero\checkpoint.th"

I want to improve quality of this audio 5 min sound : https://sndup.net/stjs/

(env) D:\86 se courses youtube kanali\aero>python predict.py dset=4-16 experiment=aero_4-16_512_256 +filename="D:\86 se courses youtube kanali\aero\5dk.mp3" +output="D:\86 se courses youtube kanali\aero\5_v2dk.mp3" checkpoint_file="D:\86 se courses youtube kanali\aero\checkpoint.th"
D:\86 se courses youtube kanali\aero\env\lib\site-packages\hydra\_internal\defaults_list.py:251: UserWarning: In 'main_config': Defaults list is missing `_self_`. See https://hydra.cc/docs/upgrades/1.0_to_1.1/default_composition_order for more information
  warnings.warn(msg, UserWarning)
{'experiment': {'name': 'aero-nfft=${experiment.nfft}-hl=${experiment.hop_length}', 'lr_sr': 4000, 'hr_sr': 16000, 'segment': 2, 'stride': 2, 'pad': True, 'upsample': False, 'batch_size': 16, 'nfft': 512, 'hop_length': 256, 'model': 'aero', 'aero': {'in_channels': 1, 'out_channels': 1, 'channels': 48, 'growth': 2, 'nfft': '${experiment.nfft}', 'hop_length': '${experiment.hop_length}', 'end_iters': 0, 'cac': True, 'rewrite': True, 'hybrid': False, 'hybrid_old': False, 'freq_emb': 0.2, 'emb_scale': 10, 'emb_smooth': True, 'kernel_size': 8, 'strides': [4, 4, 2, 2], 'context': 1, 'context_enc': 0, 'freq_ends': 4, 'enc_freq_attn': 0, 'norm_starts': 2, 'norm_groups': 4, 'dconv_mode': 1, 'dconv_depth': 2, 'dconv_comp': 4, 'dconv_time_attn': 2, 'dconv_lstm': 2, 'dconv_init': 0.001, 'rescale': 0.1, 'lr_sr': '${experiment.lr_sr}', 'hr_sr': '${experiment.hr_sr}', 'spec_upsample': True, 'act_func': 'snake', 'debug': False}, 'adversarial': True, 'features_loss_lambda': 100, 'only_features_loss': False, 'only_adversarial_loss': False, 'discriminator_models': ['msd_melgan'], 'melgan_discriminator': {'n_layers': 4, 'num_D': 3, 'downsampling_factor': 4, 'ndf': 16}}, 'dset': {'name': '4-16', 'train': 'egs/vctk/4-16/tr', 'valid': None, 'test': 'egs/vctk/4-16/val'}, 'num_prints': 5, 'device': 'cuda', 'num_workers': 2, 'verbose': 0, 'show': 0, 'log_results': True, 'checkpoint': True, 'continue_from': '', 'continue_best': False, 'restart': False, 'checkpoint_file': 'D:\\86 se courses youtube kanali\\aero\\checkpoint.th', 'best_file': 'best.th', 'history_file': 'history.json', 'test_results_file': 'test_results.json', 'samples_dir': 'samples', 'keep_history': True, 'seed': 2036, 'dummy': '', 'visqol': True, 'visqol_path': None, 'eval_every': 25, 'enhance_samples_limit': -1, 'valid_equals_test': None, 'cross_valid': False, 'cross_valid_every': 5, 'joint_evaluate_and_enhance': True, 'evaluate_on_best': False, 'wandb': {'project_name': 'Spectral Bandwidth Extension', 'entity': None, 'mode': 'online', 'log': 'all', 'log_freq': 5, 'n_files_to_log': 10, 'n_files_to_log_to_table': 10, 'tags': [], 'resume': False}, 'optim': 'adam', 'lr': 0.0003, 'beta1': 0.8, 'beta2': 0.999, 'losses': ['stft'], 'stft_sc_factor': 0.5, 'stft_mag_factor': 0.5, 'epochs': 125, 'ddp': False, 'ddp_backend': 'nccl', 'rendezvous_file': './rendezvous', 'rank': None, 'world_size': None, 'filename': 'D:\\86 se courses youtube kanali\\aero\\5dk.mp3', 'output': 'D:\\86 se courses youtube kanali\\aero\\5_v2dk.mp3'}
[2023-02-09 14:36:36,703][__main__][INFO] - Loading model aero from last state.
[2023-02-09 14:36:38,679][__main__][INFO] - lr wav shape: torch.Size([2, 14400000])
[2023-02-09 14:36:38,680][__main__][INFO] - number of chunks: 30
Error executing job with overrides: ['dset=4-16', 'experiment=aero_4-16_512_256', '+filename=D:\\86 se courses youtube kanali\\aero\\5dk.mp3', '+output=D:\\86 se courses youtube kanali\\aero\\5_v2dk.mp3', 'checkpoint_file=D:\\86 se courses youtube kanali\\aero\\checkpoint.th']
Traceback (most recent call last):
  File "D:\86 se courses youtube kanali\aero\predict.py", line 77, in main
    pr_chunk = model(lr_chunk.unsqueeze(0).to(device)).squeeze(0)
  File "D:\86 se courses youtube kanali\aero\env\lib\site-packages\torch\nn\modules\module.py", line 1130, in _call_impl
    return forward_call(*input, **kwargs)
  File "D:\86 se courses youtube kanali\aero\src\models\aero.py", line 472, in forward
    x = encode(x, inject)
  File "D:\86 se courses youtube kanali\aero\env\lib\site-packages\torch\nn\modules\module.py", line 1130, in _call_impl
    return forward_call(*input, **kwargs)
  File "D:\86 se courses youtube kanali\aero\src\models\aero.py", line 120, in forward
    x = self.pre_conv(x)
  File "D:\86 se courses youtube kanali\aero\env\lib\site-packages\torch\nn\modules\module.py", line 1130, in _call_impl
    return forward_call(*input, **kwargs)
  File "D:\86 se courses youtube kanali\aero\env\lib\site-packages\torch\nn\modules\conv.py", line 457, in forward
    return self._conv_forward(input, self.weight, self.bias)
  File "D:\86 se courses youtube kanali\aero\env\lib\site-packages\torch\nn\modules\conv.py", line 453, in _conv_forward
    return F.conv2d(input, weight, bias, self.stride,
RuntimeError: Given groups=1, weight of size [48, 2, 1, 1], expected input[1, 4, 256, 7501] to have 2 channels, but got 4 channels instead

Set the environment variable HYDRA_FULL_ERROR=1 for a complete stack trace.

slp-rl / aero

RuntimeError: Given groups=1, weight of size [48, 2, 1, 1], expected input[1, 4, 256, 7501] to have 2 channels, but got 4 channels instead #3