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45 lines
1.8 KiB
Python
45 lines
1.8 KiB
Python
from models.synthesizer.hparams import hparams as _syn_hp
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# Audio settings------------------------------------------------------------------------
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# Match the values of the synthesizer
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sample_rate = _syn_hp.sample_rate
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n_fft = _syn_hp.n_fft
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num_mels = _syn_hp.num_mels
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hop_length = _syn_hp.hop_size
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win_length = _syn_hp.win_size
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fmin = _syn_hp.fmin
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min_level_db = _syn_hp.min_level_db
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ref_level_db = _syn_hp.ref_level_db
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mel_max_abs_value = _syn_hp.max_abs_value
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preemphasis = _syn_hp.preemphasis
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apply_preemphasis = _syn_hp.preemphasize
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bits = 9 # bit depth of signal
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mu_law = True # Recommended to suppress noise if using raw bits in hp.voc_mode
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# below
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# WAVERNN / VOCODER --------------------------------------------------------------------------------
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voc_mode = 'RAW' # either 'RAW' (softmax on raw bits) or 'MOL' (sample from
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# mixture of logistics)
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voc_upsample_factors = (5, 5, 8) # NB - this needs to correctly factorise hop_length
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voc_rnn_dims = 512
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voc_fc_dims = 512
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voc_compute_dims = 128
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voc_res_out_dims = 128
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voc_res_blocks = 10
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# Training
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voc_batch_size = 100
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voc_lr = 1e-4
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voc_gen_at_checkpoint = 5 # number of samples to generate at each checkpoint
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voc_pad = 2 # this will pad the input so that the resnet can 'see' wider
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# than input length
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voc_seq_len = hop_length * 5 # must be a multiple of hop_length
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# Generating / Synthesizing
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voc_gen_batched = True # very fast (realtime+) single utterance batched generation
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voc_target = 8000 # target number of samples to be generated in each batch entry
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voc_overlap = 400 # number of samples for crossfading between batches
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