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| # This is the hyperparameter configuration file for MelGAN. | ||
| # Please make sure this is adjusted for the LJSpeech dataset. If you want to | ||
| # apply to the other dataset, you might need to carefully change some parameters. | ||
| # This configuration requires ~ 8GB memory and takes around 1 week on Titan V. | ||
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| # This configuration is based on multi-band MelGAN but the hop size and sampling | ||
| # rate is different from the paper (16kHz vs 22.05kHz). The number of iteraions | ||
| # is now shown in the paper so currently we train 1M iterations (not sure enough | ||
| # to converge). Compared to multi_band_melgan.v1 config, Adam optimizer without | ||
| # gradient norm is used, which is based on @dathudeptrai advice. | ||
| # https://github.com/kan-bayashi/ParallelWaveGAN/issues/143#issuecomment-632539906 | ||
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| # We found that the use of small batch_max_steps (e.g. 8192) has no bad effect for | ||
| # the quality, so if you want to accelerate the training, please reduce it. | ||
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| ########################################################### | ||
| # FEATURE EXTRACTION SETTING # | ||
| ########################################################### | ||
| sampling_rate: 22050 # Sampling rate. | ||
| fft_size: 1024 # FFT size. | ||
| hop_size: 256 # Hop size. | ||
| win_length: null # Window length. | ||
| # If set to null, it will be the same as fft_size. | ||
| window: "hann" # Window function. | ||
| num_mels: 80 # Number of mel basis. | ||
| fmin: 80 # Minimum freq in mel basis calculation. | ||
| fmax: 7600 # Maximum frequency in mel basis calculation. | ||
| global_gain_scale: 1.0 # Will be multiplied to all of waveform. | ||
| trim_silence: true # Whether to trim the start and end of silence. | ||
| trim_threshold_in_db: 60 # Need to tune carefully if the recording is not good. | ||
| trim_frame_size: 2048 # Frame size in trimming. | ||
| trim_hop_size: 512 # Hop size in trimming. | ||
| format: "hdf5" # Feature file format. "npy" or "hdf5" is supported. | ||
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| ########################################################### | ||
| # GENERATOR NETWORK ARCHITECTURE SETTING # | ||
| ########################################################### | ||
| generator_type: "MelGANGenerator" # Generator type. | ||
| generator_params: | ||
| in_channels: 80 # Number of input channels. | ||
| out_channels: 4 # Number of output channels. | ||
| kernel_size: 7 # Kernel size of initial and final conv layers. | ||
| channels: 384 # Initial number of channels for conv layers. | ||
| upsample_scales: [8, 4, 2] # List of Upsampling scales. | ||
| stack_kernel_size: 3 # Kernel size of dilated conv layers in residual stack. | ||
| stacks: 4 # Number of stacks in a single residual stack module. | ||
| use_weight_norm: True # Whether to use weight normalization. | ||
| use_causal_conv: False # Whether to use causal convolution. | ||
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| ########################################################### | ||
| # DISCRIMINATOR NETWORK ARCHITECTURE SETTING # | ||
| ########################################################### | ||
| discriminator_type: "MelGANMultiScaleDiscriminator" # Discriminator type. | ||
| discriminator_params: | ||
| in_channels: 1 # Number of input channels. | ||
| out_channels: 1 # Number of output channels. | ||
| scales: 3 # Number of multi-scales. | ||
| downsample_pooling: "AvgPool1d" # Pooling type for the input downsampling. | ||
| downsample_pooling_params: # Parameters of the above pooling function. | ||
| kernel_size: 4 | ||
| stride: 2 | ||
| padding: 1 | ||
| count_include_pad: False | ||
| kernel_sizes: [5, 3] # List of kernel size. | ||
| channels: 16 # Number of channels of the initial conv layer. | ||
| max_downsample_channels: 512 # Maximum number of channels of downsampling layers. | ||
| downsample_scales: [4, 4, 4] # List of downsampling scales. | ||
| nonlinear_activation: "LeakyReLU" # Nonlinear activation function. | ||
| nonlinear_activation_params: # Parameters of nonlinear activation function. | ||
| negative_slope: 0.2 | ||
| use_weight_norm: True # Whether to use weight norm. | ||
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| ########################################################### | ||
| # STFT LOSS SETTING # | ||
| ########################################################### | ||
| stft_loss_params: | ||
| fft_sizes: [1024, 2048, 512] # List of FFT size for STFT-based loss. | ||
| hop_sizes: [120, 240, 50] # List of hop size for STFT-based loss | ||
| win_lengths: [600, 1200, 240] # List of window length for STFT-based loss. | ||
| window: "hann_window" # Window function for STFT-based loss | ||
| use_subband_stft_loss: true | ||
| subband_stft_loss_params: | ||
| fft_sizes: [384, 683, 171] # List of FFT size for STFT-based loss. | ||
| hop_sizes: [30, 60, 10] # List of hop size for STFT-based loss | ||
| win_lengths: [150, 300, 60] # List of window length for STFT-based loss. | ||
| window: "hann_window" # Window function for STFT-based loss | ||
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| ########################################################### | ||
| # ADVERSARIAL LOSS SETTING # | ||
| ########################################################### | ||
| use_feat_match_loss: false # Whether to use feature matching loss. | ||
| lambda_adv: 2.5 # Loss balancing coefficient for adversarial loss. | ||
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| ########################################################### | ||
| # DATA LOADER SETTING # | ||
| ########################################################### | ||
| batch_size: 64 # Batch size. | ||
| batch_max_steps: 16384 # Length of each audio in batch. Make sure dividable by hop_size. | ||
| pin_memory: true # Whether to pin memory in Pytorch DataLoader. | ||
| num_workers: 4 # Number of workers in Pytorch DataLoader. | ||
| remove_short_samples: true # Whether to remove samples the length of which are less than batch_max_steps. | ||
| allow_cache: true # Whether to allow cache in dataset. If true, it requires cpu memory. | ||
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| ########################################################### | ||
| # OPTIMIZER & SCHEDULER SETTING # | ||
| ########################################################### | ||
| generator_optimizer_type: "Adam" # Generator's optimizer type. | ||
| generator_optimizer_params: | ||
| lr: 1.0e-3 # Generator's learning rate. | ||
| eps: 1.0e-7 # Generator's epsilon. | ||
| weight_decay: 0.0 # Generator's weight decay coefficient. | ||
| amsgrad: true | ||
| generator_grad_norm: -1 # Generator's gradient norm. | ||
| generator_scheduler_type: "MultiStepLR" # Generator's scheduler type. | ||
| generator_scheduler_params: | ||
| gamma: 0.5 # Generator's scheduler gamma. | ||
| milestones: # At each milestone, lr will be multiplied by gamma. | ||
| - 100000 | ||
| - 200000 | ||
| - 300000 | ||
| - 400000 | ||
| - 500000 | ||
| - 600000 | ||
| discriminator_optimizer_type: "Adam" # Discriminator's optimizer type. | ||
| discriminator_optimizer_params: | ||
| lr: 1.0e-3 # Discriminator's learning rate. | ||
| eps: 1.0e-7 # Discriminator's epsilon. | ||
| weight_decay: 0.0 # Discriminator's weight decay coefficient. | ||
| amsgrad: true | ||
| discriminator_grad_norm: -1 # Discriminator's gradient norm. | ||
| discriminator_scheduler_type: "MultiStepLR" # Discriminator's scheduler type. | ||
| discriminator_scheduler_params: | ||
| gamma: 0.5 # Discriminator's scheduler gamma. | ||
| milestones: # At each milestone, lr will be multiplied by gamma. | ||
| - 100000 | ||
| - 200000 | ||
| - 300000 | ||
| - 400000 | ||
| - 500000 | ||
| - 600000 | ||
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| ########################################################### | ||
| # INTERVAL SETTING # | ||
| ########################################################### | ||
| discriminator_train_start_steps: 200000 # Number of steps to start to train discriminator. | ||
| train_max_steps: 1000000 # Number of training steps. | ||
| save_interval_steps: 50000 # Interval steps to save checkpoint. | ||
| eval_interval_steps: 1000 # Interval steps to evaluate the network. | ||
| log_interval_steps: 1000 # Interval steps to record the training log. | ||
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| ########################################################### | ||
| # OTHER SETTING # | ||
| ########################################################### | ||
| num_save_intermediate_results: 4 # Number of results to be saved as intermediate results. |