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8966d94 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 | from dataclasses import dataclass
from ...synthesizer.hparams import hparams as _syn_hp
@dataclass
class HParams:
# Audio settings------------------------------------------------------------------------
# Match the values of the synthesizer
sample_rate = _syn_hp.sample_rate
n_fft = _syn_hp.n_fft
num_mels = _syn_hp.num_mels
hop_length = _syn_hp.hop_size
win_length = _syn_hp.win_size
fmin = _syn_hp.fmin
min_level_db = _syn_hp.min_level_db
ref_level_db = _syn_hp.ref_level_db
mel_max_abs_value = _syn_hp.max_abs_value
preemphasis = _syn_hp.preemphasis
apply_preemphasis = _syn_hp.preemphasize
bits = 9 # bit depth of signal
mu_law = True # Recommended to suppress noise if using raw bits in hp.voc_mode
# below
# WAVERNN / VOCODER --------------------------------------------------------------------------------
voc_mode = "RAW" # either 'RAW' (softmax on raw bits) or 'MOL' (sample from
# mixture of logistics)
voc_upsample_factors = (
5,
5,
8,
) # NB - this needs to correctly factorise hop_length
voc_rnn_dims = 512
voc_fc_dims = 512
voc_compute_dims = 128
voc_res_out_dims = 128
voc_res_blocks = 10
# Training
voc_batch_size = 100
voc_lr = 1e-4
voc_gen_at_checkpoint = 5 # number of samples to generate at each checkpoint
voc_pad = 2 # this will pad the input so that the resnet can 'see' wider
# than input length
voc_seq_len = hop_length * 5 # must be a multiple of hop_length
# Generating / Synthesizing
voc_gen_batched = True # very fast (realtime+) single utterance batched generation
voc_target = 8000 # target number of samples to be generated in each batch entry
voc_overlap = 400 # number of samples for crossfading between batches
hparams = HParams()
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