56 lines
2.1 KiB
Python
56 lines
2.1 KiB
Python
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from yacs.config import CfgNode as CN
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_C = CN()
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_C.data = CN(
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dict(
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batch_size=16, # batch size
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valid_size=64, # the first N examples are reserved for validation
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sample_rate=22050, # Hz, sample rate
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n_fft=1024, # fft frame size
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win_length=1024, # window size
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hop_length=256, # hop size between ajacent frame
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f_max=8000, # Hz, max frequency when converting to mel
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d_mel=80, # mel bands
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padding_idx=0, # text embedding's padding index
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mel_start_value=0.5, # value for starting frame
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mel_end_value=-0.5, # # value for ending frame
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)
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)
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_C.model = CN(
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dict(
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d_encoder=512, # embedding & encoder's internal size
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d_decoder=256, # decoder's internal size
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n_heads=4, # actually it can differ at each layer
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d_ffn=1024, # encoder_d_ffn & decoder_d_ffn
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encoder_layers=4, # number of transformer encoder layer
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decoder_layers=4, # number of transformer decoder layer
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d_prenet=256, # decprenet's hidden size (d_mel=>d_prenet=>d_decoder)
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d_postnet=256, # decoder postnet(cnn)'s internal channel
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postnet_layers=5, # decoder postnet(cnn)'s layer
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postnet_kernel_size=5, # decoder postnet(cnn)'s kernel size
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max_reduction_factor=10, # max_reduction factor
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dropout=0.1, # global droput probability
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stop_loss_scale=8.0, # scaler for stop _loss
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decoder_prenet_dropout=0.5, # decoder prenet dropout probability
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)
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)
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_C.training = CN(
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dict(
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lr=1e-4, # learning rate
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drop_n_heads=[[0, 0], [15000, 1]],
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reduction_factor=[[0, 10], [80000, 4], [200000, 2]],
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plot_interval=1000, # plot attention and spectrogram
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valid_interval=1000, # validation
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save_interval=10000, # checkpoint
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max_iteration=900000, # max iteration to train
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)
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)
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def get_cfg_defaults():
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"""Get a yacs CfgNode object with default values for my_project."""
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# Return a clone so that the defaults will not be altered
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# This is for the "local variable" use pattern
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return _C.clone()
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