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108 lines
5.9 KiB
108 lines
5.9 KiB
###########################################################
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# FEATURE EXTRACTION SETTING #
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###########################################################
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fs: 24000 # sr
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n_fft: 2048 # FFT size (samples).
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n_shift: 300 # Hop size (samples). 12.5ms
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win_length: 1200 # Window length (samples). 50ms
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# If set to null, it will be the same as fft_size.
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window: "hann" # Window function.
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# Only used for feats_type != raw
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fmin: 80 # Minimum frequency of Mel basis.
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fmax: 7600 # Maximum frequency of Mel basis.
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n_mels: 80 # The number of mel basis.
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# Only used for the model using pitch features (e.g. FastSpeech2)
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# The canton datasets we use are different from others like Databaker or LJSpeech,
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# we set it to 110 to avoid too many zero-pitch problem.
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# Reference: https://github.com/JeremyCCHsu/Python-Wrapper-for-World-Vocoder/issues/38
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f0min: 110 # Minimum f0 for pitch extraction.
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f0max: 400 # Maximum f0 for pitch extraction.
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###########################################################
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# DATA SETTING #
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###########################################################
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batch_size: 32
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num_workers: 2
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###########################################################
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# MODEL SETTING #
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###########################################################
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model:
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adim: 384 # attention dimension
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aheads: 2 # number of attention heads
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elayers: 4 # number of encoder layers
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eunits: 1536 # number of encoder ff units
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dlayers: 4 # number of decoder layers
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dunits: 1536 # number of decoder ff units
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positionwise_layer_type: conv1d # type of position-wise layer
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positionwise_conv_kernel_size: 3 # kernel size of position wise conv layer
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duration_predictor_layers: 2 # number of layers of duration predictor
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duration_predictor_chans: 256 # number of channels of duration predictor
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duration_predictor_kernel_size: 3 # filter size of duration predictor
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postnet_layers: 5 # number of layers of postnset
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postnet_filts: 5 # filter size of conv layers in postnet
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postnet_chans: 256 # number of channels of conv layers in postnet
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use_scaled_pos_enc: True # whether to use scaled positional encoding
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encoder_normalize_before: True # whether to perform layer normalization before the input
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decoder_normalize_before: True # whether to perform layer normalization before the input
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reduction_factor: 1 # reduction factor
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init_type: xavier_uniform # initialization type
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init_enc_alpha: 1.0 # initial value of alpha of encoder scaled position encoding
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init_dec_alpha: 1.0 # initial value of alpha of decoder scaled position encoding
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transformer_enc_dropout_rate: 0.2 # dropout rate for transformer encoder layer
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transformer_enc_positional_dropout_rate: 0.2 # dropout rate for transformer encoder positional encoding
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transformer_enc_attn_dropout_rate: 0.2 # dropout rate for transformer encoder attention layer
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transformer_dec_dropout_rate: 0.2 # dropout rate for transformer decoder layer
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transformer_dec_positional_dropout_rate: 0.2 # dropout rate for transformer decoder positional encoding
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transformer_dec_attn_dropout_rate: 0.2 # dropout rate for transformer decoder attention layer
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pitch_predictor_layers: 5 # number of conv layers in pitch predictor
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pitch_predictor_chans: 256 # number of channels of conv layers in pitch predictor
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pitch_predictor_kernel_size: 5 # kernel size of conv leyers in pitch predictor
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pitch_predictor_dropout: 0.5 # dropout rate in pitch predictor
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pitch_embed_kernel_size: 1 # kernel size of conv embedding layer for pitch
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pitch_embed_dropout: 0.0 # dropout rate after conv embedding layer for pitch
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stop_gradient_from_pitch_predictor: True # whether to stop the gradient from pitch predictor to encoder
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energy_predictor_layers: 2 # number of conv layers in energy predictor
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energy_predictor_chans: 256 # number of channels of conv layers in energy predictor
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energy_predictor_kernel_size: 3 # kernel size of conv leyers in energy predictor
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energy_predictor_dropout: 0.5 # dropout rate in energy predictor
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energy_embed_kernel_size: 1 # kernel size of conv embedding layer for energy
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energy_embed_dropout: 0.0 # dropout rate after conv embedding layer for energy
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stop_gradient_from_energy_predictor: False # whether to stop the gradient from energy predictor to encoder
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spk_embed_dim: 256 # speaker embedding dimension
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spk_embed_integration_type: concat # speaker embedding integration type
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###########################################################
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# UPDATER SETTING #
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###########################################################
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updater:
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use_masking: True # whether to apply masking for padded part in loss calculation
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###########################################################
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# OPTIMIZER SETTING #
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###########################################################
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optimizer:
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optim: adam # optimizer type
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learning_rate: 0.001 # learning rate
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###########################################################
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# TRAINING SETTING #
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###########################################################
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max_epoch: 1000
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num_snapshots: 5
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###########################################################
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# OTHER SETTING #
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###########################################################
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seed: 10086
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