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Functions74 in github.com/Liu-Tianchi/Nes2Net

⨍ Functions 74◇ Types & classes 20
↓ 8 callersFunctionISD_additive_noise
(x, P, g_sd)
RawBoost.py:69
↓ 8 callersFunctionLnL_convolutive_noise
(x,N_f,nBands,minF,maxF,minBW,maxBW,minCoeff,maxCoeff,minG,maxG,minBiasLinNonLin,maxBiasLinNonLin,fs)
RawBoost.py:55
↓ 6 callersFunctionnormWav
(x,always)
RawBoost.py:15
↓ 6 callersFunctionrandRange
(x1, x2, integer)
RawBoost.py:9
↓ 5 callersFunctionSSI_additive_noise
(x,SNRmin,SNRmax,nBands,minF,maxF,minBW,maxBW,minCoeff,maxCoeff,minG,maxG,fs)
RawBoost.py:85
↓ 5 callersMethod__init__
(self, in_dim, bottleneck_dim=128, global_context_att=False)
models/WavLM_Nes2Net_X_SeLU.py:14
↓ 5 callersMethod__init__
(self, in_dim, bottleneck_dim=128, global_context_att=False)
models/WavLM_Nes2Net.py:168
↓ 5 callersMethod__init__
(self, in_dim, bottleneck_dim=128, global_context_att=False)
models/WavLM_Nes2Net_X.py:16
↓ 4 callersFunctioncalculate_eer
(scores, labels)
EER_minDCF.py:13
↓ 4 callersFunctioncalculate_mindcf
(scores, labels, prior=0.5, cost_fn=1.0)
EER_minDCF.py:19
↓ 2 callersFunctioncompute_eer
(target_scores, nontarget_scores)
utils.py:45
↓ 2 callersFunctionfilterFIR
(x,b)
RawBoost.py:47
↓ 2 callersFunctiongenNotchCoeffs
(nBands,minF,maxF,minBW,maxBW,minCoeff,maxCoeff,minG,maxG,fs)
RawBoost.py:24
↓ 1 callersMethod_Att_merge
(self, x)
models/WavLM_Nes2Net_X_SeLU.py:108
↓ 1 callersMethod_Att_merge
(self, x)
models/WavLM_Nes2Net.py:61
↓ 1 callersMethod_Att_merge
(self, x)
models/WavLM_Nes2Net_X.py:111
↓ 1 callersMethod_SE_merge
(self, x)
models/WavLM_Nes2Net_X_SeLU.py:98
↓ 1 callersMethod_SE_merge
(self, x)
models/WavLM_Nes2Net.py:51
↓ 1 callersMethod_SE_merge
(self, x)
models/WavLM_Nes2Net_X.py:101
↓ 1 callersMethod_weighted_sum
(self, x)
models/WavLM_Nes2Net_X_SeLU.py:87
↓ 1 callersMethod_weighted_sum
(self, x)
models/WavLM_Nes2Net.py:40
↓ 1 callersMethod_weighted_sum
(self, x)
models/WavLM_Nes2Net_X.py:90
↓ 1 callersFunctioncheck_condition
(i, m)
train.py:31
↓ 1 callersFunctioncompute_det_curve
(target_scores, nontarget_scores)
utils.py:26
↓ 1 callersFunctionmain
(args)
eval.py:17
↓ 1 callersFunctionmain
(scores_file, labels_file)
EER_minDCF.py:30
↓ 1 callersFunctionmain
(args)
train.py:36
↓ 1 callersFunctionmain
(args)
easy_inference_demo.py:17
↓ 1 callersFunctionpad
(x, max_len)
eval.py:8
↓ 1 callersFunctionpad
(x, max_len)
easy_inference_demo.py:8
↓ 1 callersFunctionpad_random
(x: np.ndarray, max_len: int = 64000)
datasetsrawboost_TC.py:11
↓ 1 callersFunctionprocess_Rawboost_feature
(feature, sr,args,algo)
datasetsrawboost_TC.py:90
↓ 1 callersFunctionread_labels
(file_path)
EER_minDCF.py:9
↓ 1 callersFunctionread_scores
(file_path)
EER_minDCF.py:5
↓ 1 callersFunctionset_seed
set initial seed for reproduction
utils.py:14
Method__getitem__
(self, index)
datasetsrawboost_TC.py:51
Method__init__
(self, gamma=2.0, alpha=0.25, use_logits=True)
train.py:16
Method__init__
(self, base_dir, partition="train", max_len=64000, args=None, algo=5)
datasetsrawboost_TC.py:25
Method__init__
(self, device, args)
models/WavLM_Nes2Net_X_SeLU.py:59
Method__init__
(self, channels, SE_ratio=8)
models/WavLM_Nes2Net_X_SeLU.py:144
Method__init__
(self, inplanes, planes, kernel_size=None, dilation=None, scale=8, SE_ratio=8)
models/WavLM_Nes2Net_X_SeLU.py:164
Method__init__
(self, Nes_ratio=[8, 8], input_channel=1024, dilation=2, pool_func='mean', SE_ratio=8)
models/WavLM_Nes2Net_X_SeLU.py:218
Method__init__
(self, args, device)
models/WavLM_Nes2Net_X_SeLU.py:267
Method__init__
(self, device, args)
models/WavLM_Nes2Net.py:12
Method__init__
(self, channels, SE_ratio=8)
models/WavLM_Nes2Net.py:98
Method__init__
(self, inplanes, planes, kernel_size=None, dilation=None, scale=8, SE_ratio=8)
models/WavLM_Nes2Net.py:115
Method__init__
(self, Nes_ratio=[8, 8], input_channel=1024, dilation=2, pool_func='mean', SE_ratio=[8])
models/WavLM_Nes2Net.py:215
Method__init__
(self, args, device)
models/WavLM_Nes2Net.py:264
Method__init__
(self, device, args)
models/WavLM_Nes2Net_X.py:62
Method__init__
(self, channels, SE_ratio=8)
models/WavLM_Nes2Net_X.py:148
Method__init__
(self, inplanes, planes, kernel_size=None, dilation=None, scale=8, SE_ratio=8)
models/WavLM_Nes2Net_X.py:165
Method__init__
(self, Nes_ratio=[8, 8], input_channel=1024, dilation=2, pool_func='mean', SE_ratio=[8])
models/WavLM_Nes2Net_X.py:220
Method__init__
(self, args, device)
models/WavLM_Nes2Net_X.py:269
Method__len__
(self)
datasetsrawboost_TC.py:48
Methodforward
(self, logits, targets)
train.py:22
Methodforward
x: a 3-dimensional tensor in tdnn-based architecture (B,F,T) or a 4-dimensional tensor in resnet architecture (B,C,F,T)
models/WavLM_Nes2Net_X_SeLU.py:31
Methodforward
(self, input_data)
models/WavLM_Nes2Net_X_SeLU.py:122
Methodforward
(self, input)
models/WavLM_Nes2Net_X_SeLU.py:157
Methodforward
(self, x)
models/WavLM_Nes2Net_X_SeLU.py:188
Methodforward
(self, x)
models/WavLM_Nes2Net_X_SeLU.py:242
Methodforward
(self, x, SSL_freeze=False)
models/WavLM_Nes2Net_X_SeLU.py:283
Methodforward
(self, input_data)
models/WavLM_Nes2Net.py:75
Methodforward
(self, input)
models/WavLM_Nes2Net.py:108
Methodforward
(self, x)
models/WavLM_Nes2Net.py:135
Methodforward
x: a 3-dimensional tensor in tdnn-based architecture (B,F,T) or a 4-dimensional tensor in resnet architecture (B,C,F,T)
models/WavLM_Nes2Net.py:185
Methodforward
(self, x)
models/WavLM_Nes2Net.py:238
Methodforward
(self, x, SSL_freeze=False)
models/WavLM_Nes2Net.py:274
Methodforward
x: a 3-dimensional tensor in tdnn-based architecture (B,F,T) or a 4-dimensional tensor in resnet architecture (B,C,F,T)
models/WavLM_Nes2Net_X.py:33
Methodforward
(self, input_data)
models/WavLM_Nes2Net_X.py:125
Methodforward
(self, input)
models/WavLM_Nes2Net_X.py:158
Methodforward
(self, x)
models/WavLM_Nes2Net_X.py:190
Methodforward
(self, x)
models/WavLM_Nes2Net_X.py:243
Methodforward
(self, x, SSL_freeze=False)
models/WavLM_Nes2Net_X.py:279
Functionseed_worker
Used in generating seed for the worker of torch.utils.data.Dataloader
utils.py:5