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Update config.py

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class CONFIG:
    gpus = "0,1"  # List of gpu devices

    class TRAIN:
        batch_size = 90  # number of audio files per batch
        lr = 1e-4  # learning rate
        epochs = 150  # max training epochs
        workers = 12  # number of dataloader workers
        val_split = 0.1  # validation set proportion
        clipping_val = 1.0  # gradient clipping value
        patience = 3  # learning rate scheduler's patience
        factor = 0.5  # learning rate reduction factor

    # Model config
    class MODEL:
        enc_layers = 4  # number of MLP blocks in the encoder
        enc_in_dim = 384  # dimension of the input projection layer in the encoder
        enc_dim = 768  # dimension of the MLP blocks
        pred_dim = 512  # dimension of the LSTM in the predictor
        pred_layers = 1  # number of LSTM layers in the predictor

    # Dataset config
    class DATA:
        dataset = 'vctk'  # dataset to use
        '''
        Dictionary that specifies paths to root directories and train/test text files of each datasets.
        'root' is the path to the dataset and each line of the train.txt/test.txt files should contains the path to an
        audio file from 'root'. 
        '''
        data_dir = {'vctk': {'root': 'data/vctk/wav48',
                             'train': "data/vctk/train.txt",
                             'test': "data/vctk/test.txt"},
                    }

        assert dataset in data_dir.keys(), 'Unknown dataset.'
        sr = 48000  # audio sampling rate
        audio_chunk_len = 122880  # size of chunk taken in each audio files
        window_size = 960  # window size of the STFT operation, equivalent to packet size
        stride = 480  # stride of the STFT operation

        class TRAIN:
            packet_sizes = [256, 512, 768, 960, 1024,
                            1536]  # packet sizes for training. All sizes should be divisible by 'audio_chunk_len'
            transition_probs = ((0.9, 0.1), (0.5, 0.1), (0.5, 0.5))  # list of trainsition probs for Markow Chain

        class EVAL:
            packet_size = 960  # 20ms
            transition_probs = [(0.9, 0.1)]  # (0.9, 0.1) ~ 10%; (0.8, 0.2) ~ 20%; (0.6, 0.4) ~ 40%
            masking = 'gen'  # whether using simulation or real traces from Microsoft to generate masks
            assert masking in ['gen', 'real']
            trace_path = 'test_samples/blind/lossy_singals'  # must be clarified if masking = 'real'

    class LOG:
        log_dir = 'lightning_logs'  # checkpoint and log directory
        sample_path = 'audio_samples'  # path to save generated audio samples in evaluation.

    class TEST:
        in_dir = 'test_samples/blind/lossy_signals'  # path to test audio inputs
        out_dir = 'test_samples/blind/lossy_signals_out'  # path to generated outputs

	class CONFIG:
	gpus = "0,1" # List of gpu devices

	class TRAIN:
	batch_size = 90 # number of audio files per batch
	lr = 1e-4 # learning rate
	epochs = 150 # max training epochs
	workers = 12 # number of dataloader workers
	val_split = 0.1 # validation set proportion
	clipping_val = 1.0 # gradient clipping value
	patience = 3 # learning rate scheduler's patience
	factor = 0.5 # learning rate reduction factor

	# Model config
	class MODEL:
	enc_layers = 4 # number of MLP blocks in the encoder
	enc_in_dim = 384 # dimension of the input projection layer in the encoder
	enc_dim = 768 # dimension of the MLP blocks
	pred_dim = 512 # dimension of the LSTM in the predictor
	pred_layers = 1 # number of LSTM layers in the predictor

	# Dataset config
	class DATA:
	dataset = 'vctk' # dataset to use
	'''
	Dictionary that specifies paths to root directories and train/test text files of each datasets.
	'root' is the path to the dataset and each line of the train.txt/test.txt files should contains the path to an
	audio file from 'root'.
	'''
	data_dir = {'vctk': {'root': 'data/vctk/wav48',
	'train': "data/vctk/train.txt",
	'test': "data/vctk/test.txt"},
	}

	assert dataset in data_dir.keys(), 'Unknown dataset.'
	sr = 48000 # audio sampling rate
	audio_chunk_len = 122880 # size of chunk taken in each audio files
	window_size = 960 # window size of the STFT operation, equivalent to packet size
	stride = 480 # stride of the STFT operation

	class TRAIN:
	packet_sizes = [256, 512, 768, 960, 1024,
	1536] # packet sizes for training. All sizes should be divisible by 'audio_chunk_len'
	transition_probs = ((0.9, 0.1), (0.5, 0.1), (0.5, 0.5)) # list of trainsition probs for Markow Chain

	class EVAL:
	packet_size = 960 # 20ms
	transition_probs = [(0.9, 0.1)] # (0.9, 0.1) ~ 10%; (0.8, 0.2) ~ 20%; (0.6, 0.4) ~ 40%
	masking = 'gen' # whether using simulation or real traces from Microsoft to generate masks
	assert masking in ['gen', 'real']
	trace_path = 'test_samples/blind/lossy_singals' # must be clarified if masking = 'real'

	class LOG:
	log_dir = 'lightning_logs' # checkpoint and log directory
	sample_path = 'audio_samples' # path to save generated audio samples in evaluation.

	class TEST:
	in_dir = 'test_samples/blind/lossy_signals' # path to test audio inputs
	out_dir = 'test_samples/blind/lossy_signals_out' # path to generated outputs