dathudeptrai
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7a60755
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Parent(s):
4fbe2a9
🖤 Update config, checkpoint for mb_melgan thorsten german
Browse files- config.yml +107 -0
- model.h5 +3 -0
config.yml
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# This is the hyperparameter configuration file for Multi-Band MelGAN.
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# Please make sure this is adjusted for the Thorsten dataset. If you want to
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# apply to the other dataset, you might need to carefully change some parameters.
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# This configuration performs 1000k iters.
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###########################################################
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# FEATURE EXTRACTION SETTING #
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###########################################################
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sampling_rate: 22050
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hop_size: 256 # Hop size.
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format: "npy"
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###########################################################
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# GENERATOR NETWORK ARCHITECTURE SETTING #
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###########################################################
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model_type: "multiband_melgan_generator"
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multiband_melgan_generator_params:
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out_channels: 4 # Number of output channels (number of subbands).
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kernel_size: 7 # Kernel size of initial and final conv layers.
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filters: 384 # Initial number of channels for conv layers.
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upsample_scales: [8, 4, 2] # List of Upsampling scales.
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stack_kernel_size: 3 # Kernel size of dilated conv layers in residual stack.
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stacks: 4 # Number of stacks in a single residual stack module.
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is_weight_norm: false # Use weight-norm or not.
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###########################################################
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# DISCRIMINATOR NETWORK ARCHITECTURE SETTING #
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###########################################################
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multiband_melgan_discriminator_params:
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out_channels: 1 # Number of output channels.
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scales: 3 # Number of multi-scales.
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downsample_pooling: "AveragePooling1D" # Pooling type for the input downsampling.
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downsample_pooling_params: # Parameters of the above pooling function.
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pool_size: 4
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strides: 2
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kernel_sizes: [5, 3] # List of kernel size.
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filters: 16 # Number of channels of the initial conv layer.
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max_downsample_filters: 512 # Maximum number of channels of downsampling layers.
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downsample_scales: [4, 4, 4] # List of downsampling scales.
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nonlinear_activation: "LeakyReLU" # Nonlinear activation function.
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nonlinear_activation_params: # Parameters of nonlinear activation function.
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alpha: 0.2
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is_weight_norm: false # Use weight-norm or not.
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###########################################################
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# STFT LOSS SETTING #
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###########################################################
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stft_loss_params:
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fft_lengths: [1024, 2048, 512] # List of FFT size for STFT-based loss.
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frame_steps: [120, 240, 50] # List of hop size for STFT-based loss
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frame_lengths: [600, 1200, 240] # List of window length for STFT-based loss.
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subband_stft_loss_params:
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fft_lengths: [384, 683, 171] # List of FFT size for STFT-based loss.
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frame_steps: [30, 60, 10] # List of hop size for STFT-based loss
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frame_lengths: [150, 300, 60] # List of window length for STFT-based loss.
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###########################################################
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# ADVERSARIAL LOSS SETTING #
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###########################################################
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lambda_feat_match: 10.0 # Loss balancing coefficient for feature matching loss
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lambda_adv: 2.5 # Loss balancing coefficient for adversarial loss.
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###########################################################
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# DATA LOADER SETTING #
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###########################################################
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batch_size: 64 # Batch size for each GPU with assuming that gradient_accumulation_steps == 1.
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batch_max_steps: 8192 # Length of each audio in batch for training. Make sure dividable by hop_size.
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batch_max_steps_valid: 8192 # Length of each audio for validation. Make sure dividable by hope_size.
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remove_short_samples: true # Whether to remove samples the length of which are less than batch_max_steps.
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allow_cache: true # Whether to allow cache in dataset. If true, it requires cpu memory.
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is_shuffle: true # shuffle dataset after each epoch.
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###########################################################
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# OPTIMIZER & SCHEDULER SETTING #
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###########################################################
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generator_optimizer_params:
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lr_fn: "PiecewiseConstantDecay"
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lr_params:
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boundaries: [100000, 200000, 300000, 400000, 500000, 600000, 700000]
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values: [0.0005, 0.0005, 0.00025, 0.000125, 0.0000625, 0.00003125, 0.000015625, 0.000001]
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amsgrad: false
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discriminator_optimizer_params:
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lr_fn: "PiecewiseConstantDecay"
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lr_params:
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boundaries: [100000, 200000, 300000, 400000, 500000]
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values: [0.00025, 0.000125, 0.0000625, 0.00003125, 0.000015625, 0.000001]
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amsgrad: false
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gradient_accumulation_steps: 1
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###########################################################
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# INTERVAL SETTING #
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###########################################################
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discriminator_train_start_steps: 200000 # steps begin training discriminator
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train_max_steps: 4000000 # Number of training steps.
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save_interval_steps: 20000 # Interval steps to save checkpoint.
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eval_interval_steps: 5000 # Interval steps to evaluate the network.
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log_interval_steps: 200 # Interval steps to record the training log.
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###########################################################
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# OTHER SETTING #
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###########################################################
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num_save_intermediate_results: 1 # Number of batch to be saved as intermediate results.
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model.h5
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version https://git-lfs.github.com/spec/v1
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oid sha256:a8f6ca6f2e55b004a878e449028172c0c6a0b77f95e9b613bac43190734b27d6
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size 10232552
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