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Anjok07 2023-09-25 17:18:58 -05:00 committed by GitHub
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commit f17e31bd51
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3 changed files with 72 additions and 44 deletions
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51
lib_v5/vr_network/model_param_init.py
View File

@ -1,36 +1,15 @@
import json
import pathlib
default_param = {}
default_param['bins'] = 768
default_param['unstable_bins'] = 9 # training only
default_param['reduction_bins'] = 762 # training only
default_param['bins'] = -1
default_param['unstable_bins'] = -1 # training only
default_param['stable_bins'] = -1 # training only
default_param['sr'] = 44100
default_param['pre_filter_start'] = 757
default_param['pre_filter_stop'] = 768
default_param['pre_filter_start'] = -1
default_param['pre_filter_stop'] = -1
default_param['band'] = {}
default_param['band'][1] = {
'sr': 11025,
'hl': 128,
'n_fft': 960,
'crop_start': 0,
'crop_stop': 245,
'lpf_start': 61, # inference only
'res_type': 'polyphase'
}
default_param['band'][2] = {
'sr': 44100,
'hl': 512,
'n_fft': 1536,
'crop_start': 24,
'crop_stop': 547,
'hpf_start': 81, # inference only
'res_type': 'sinc_best'
}
N_BINS = 'n_bins'
def int_keys(d):
r = {}
@ -40,20 +19,14 @@ def int_keys(d):
r[k] = v
return r
class ModelParameters(object):
def __init__(self, config_path=''):
if '.pth' == pathlib.Path(config_path).suffix:
import zipfile
with zipfile.ZipFile(config_path, 'r') as zip:
self.param = json.loads(zip.read('param.json'), object_pairs_hook=int_keys)
elif '.json' == pathlib.Path(config_path).suffix:
with open(config_path, 'r') as f:
with open(config_path, 'r') as f:
self.param = json.loads(f.read(), object_pairs_hook=int_keys)
else:
self.param = default_param
for k in ['mid_side', 'mid_side_b', 'mid_side_b2', 'stereo_w', 'stereo_n', 'reverse']:
if not k in self.param:
self.param[k] = False
self.param[k] = False
if N_BINS in self.param:
self.param['bins'] = self.param[N_BINS]

55
lib_v5/vr_network/modelparams/4band_v3_sn.json Normal file
View File

@ -0,0 +1,55 @@
{
"n_bins": 672,
"unstable_bins": 8,
"stable_bins": 530,
"band": {
"1": {
"sr": 7350,
"hl": 80,
"n_fft": 640,
"crop_start": 0,
"crop_stop": 85,
"lpf_start": 25,
"lpf_stop": 53,
"res_type": "polyphase"
},
"2": {
"sr": 7350,
"hl": 80,
"n_fft": 320,
"crop_start": 4,
"crop_stop": 87,
"hpf_start": 25,
"hpf_stop": 12,
"lpf_start": 31,
"lpf_stop": 62,
"res_type": "polyphase"
},
"3": {
"sr": 14700,
"hl": 160,
"n_fft": 512,
"crop_start": 17,
"crop_stop": 216,
"hpf_start": 48,
"hpf_stop": 24,
"lpf_start": 139,
"lpf_stop": 210,
"res_type": "polyphase"
},
"4": {
"sr": 44100,
"hl": 480,
"n_fft": 960,
"crop_start": 78,
"crop_stop": 383,
"hpf_start": 130,
"hpf_stop": 86,
"convert_channels": "stereo_n",
"res_type": "kaiser_fast"
}
},
"sr": 44100,
"pre_filter_start": 668,
"pre_filter_stop": 672
}

10
lib_v5/vr_network/nets_new.py
View File

@ -40,26 +40,26 @@ class BaseNet(nn.Module):
class CascadedNet(nn.Module):
def __init__(self, n_fft, nn_arch_size, nout=32, nout_lstm=128):
def __init__(self, n_fft, nn_arch_size=51000, nout=32, nout_lstm=128):
super(CascadedNet, self).__init__()
self.max_bin = n_fft // 2
self.output_bin = n_fft // 2 + 1
self.nin_lstm = self.max_bin // 2
self.offset = 64
nout = 64 if nn_arch_size == 218409 else nout
#print(nout, nout_lstm, n_fft)
self.stg1_low_band_net = nn.Sequential(
BaseNet(2, nout // 2, self.nin_lstm // 2, nout_lstm),
layers.Conv2DBNActiv(nout // 2, nout // 4, 1, 1, 0)
)
)
self.stg1_high_band_net = BaseNet(2, nout // 4, self.nin_lstm // 2, nout_lstm // 2)
self.stg2_low_band_net = nn.Sequential(
BaseNet(nout // 4 + 2, nout, self.nin_lstm // 2, nout_lstm),
layers.Conv2DBNActiv(nout, nout // 2, 1, 1, 0)
)
)
self.stg2_high_band_net = BaseNet(nout // 4 + 2, nout // 2, self.nin_lstm // 2, nout_lstm // 2)
self.stg3_full_band_net = BaseNet(3 * nout // 4 + 2, nout, self.nin_lstm, nout_lstm)