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Merge pull request #103 from kan-bayashi/pwg.v3
Add parallel_wavegan.v3 config and results
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| # This is the hyperparameter configuration file for Parallel WaveGAN. | ||
| # 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. | ||
| # The generator is PWG and the discriminator is MelGAN. This configuration | ||
| # requires ~9 GB GPU memory and takes ~21 days on TITAN V. | ||
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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_params: | ||
| in_channels: 1 # Number of input channels. | ||
| out_channels: 1 # Number of output channels. | ||
| kernel_size: 5 # Kernel size of dilated convolution. | ||
| layers: 30 # Number of residual block layers. | ||
| stacks: 3 # Number of stacks i.e., dilation cycles. | ||
| residual_channels: 64 # Number of channels in residual conv. | ||
| gate_channels: 128 # Number of channels in gated conv. | ||
| skip_channels: 64 # Number of channels in skip conv. | ||
| aux_channels: 80 # Number of channels for auxiliary feature conv. | ||
| # Must be the same as num_mels. | ||
| aux_context_window: 2 # Context window size for auxiliary feature. | ||
| # If set to 2, previous 2 and future 2 frames will be considered. | ||
| dropout: 0.0 # Dropout rate. 0.0 means no dropout applied. | ||
| use_weight_norm: true # Whether to use weight norm. | ||
| # If set to true, it will be applied to all of the conv layers. | ||
| upsample_net: "ConvInUpsampleNetwork" # Upsampling network architecture. | ||
| upsample_params: # Upsampling network parameters. | ||
| upsample_scales: [4, 4, 4, 4] # Upsampling scales. Prodcut of these must be the same as hop size. | ||
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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: 1024 # Maximum number of channels of downsampling layers. | ||
| downsample_scales: [4, 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 | ||
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| ########################################################### | ||
| # ADVERSARIAL LOSS SETTING # | ||
| ########################################################### | ||
| use_feat_match_loss: true # Whether to use feature matching loss. | ||
| lambda_feat_match: 25.0 # Loss balancing coefficient for feature matching loss. | ||
| lambda_adv: 4.0 # Loss balancing coefficient for adversarial loss. | ||
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| ########################################################### | ||
| # DATA LOADER SETTING # | ||
| ########################################################### | ||
| batch_size: 16 # Batch size. | ||
| batch_max_steps: 8196 # Length of each audio in batch. Make sure dividable by hop_size. | ||
| pin_memory: true # Whether to pin memory in Pytorch DataLoader. | ||
| num_workers: 2 # 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_params: | ||
| lr: 0.0001 # Generator's learning rate. | ||
| eps: 1.0e-6 # Generator's epsilon. | ||
| weight_decay: 0.0 # Generator's weight decay coefficient. | ||
| generator_scheduler_params: | ||
| step_size: 3000000 # Generator's scheduler step size. | ||
| gamma: 0.5 # Generator's scheduler gamma. | ||
| # At each step size, lr will be multiplied by this parameter. | ||
| generator_grad_norm: 10 # Generator's gradient norm. | ||
| discriminator_optimizer_params: | ||
| lr: 0.00005 # Discriminator's learning rate. | ||
| eps: 1.0e-6 # Discriminator's epsilon. | ||
| weight_decay: 0.0 # Discriminator's weight decay coefficient. | ||
| discriminator_scheduler_params: | ||
| step_size: 3000000 # Discriminator's scheduler step size. | ||
| gamma: 0.5 # Discriminator's scheduler gamma. | ||
| # At each step size, lr will be multiplied by this parameter. | ||
| discriminator_grad_norm: 1 # Discriminator's gradient norm. | ||
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| ########################################################### | ||
| # INTERVAL SETTING # | ||
| ########################################################### | ||
| discriminator_train_start_steps: 100000 # Number of steps to start to train discriminator. | ||
| train_max_steps: 3000000 # Number of training steps. | ||
| save_interval_steps: 5000 # Interval steps to save checkpoint. | ||
| eval_interval_steps: 1000 # Interval steps to evaluate the network. | ||
| log_interval_steps: 100 # 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. |