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Functions601 in github.com/AIFSH/NativeSpeaker

⨍ Functions 601◇ Types & classes 161

src/third_part/uvr5_pack/lib_v5/layers_537238KB.py:25

src/third_part/uvr5_pack/lib_v5/layers_537238KB.py:48

src/third_part/uvr5_pack/lib_v5/layers_537238KB.py:58

(self, x, skip=None)

src/third_part/uvr5_pack/lib_v5/layers_537238KB.py:73

src/third_part/facelib/detection/yolov5face/face_detector.py:140

Method__getattr__

src/third_part/wav2lip/hparams.py:22

Method__getitem__

src/third_part/uvr5_pack/lib_v5/dataset.py:19

(self,work_with_human=False)

src/translator.py:8

src/voice_clone.py:20

(self, version_name="v2.0.3")

src/voice_clone.py:57

(self,fidelity_weight=0.9)

src/upscale.py:24

(self,model_name)

src/lipsync.py:20

src/log_helper.py:35

(self, agg, is_half=False, tta=False)

src/audio_bgm_split.py:20

( self, model, vad, vad_params: dict, options : Na

src/third_part/whisperx/asr.py:96

(self, segments, lang, max_line_length = 45, min_char_length_splitter = 30, is_vtt = False)

src/third_part/whisperx/SubtitlesProcessor.py:35

(self, output_dir: str)

src/third_part/whisperx/utils.py:194

( self, segmentation: PipelineModel = "pyannote/segmentation", fscore: bool = False,

src/third_part/whisperx/vad.py:199

( self, model_name="pyannote/speaker-diarization-3.0", use_auth_token=None, de

src/third_part/whisperx/diarize.py:11

(self, start, end, speaker=None)

src/third_part/whisperx/diarize.py:71

src/third_part/uvr5_pack/lib_v5/nets_33966KB.py:40

(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU)

src/third_part/uvr5_pack/lib_v5/layers_123812KB .py:9

(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU)

src/third_part/uvr5_pack/lib_v5/layers_123812KB .py:30

( self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.ReLU, dropout=False )

src/third_part/uvr5_pack/lib_v5/layers_123812KB .py:66

(self, nin, nout, dilations=(4, 8, 16), activ=nn.ReLU)

src/third_part/uvr5_pack/lib_v5/layers_123812KB .py:87

(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU)

src/third_part/uvr5_pack/lib_v5/layers_33966KB.py:9

(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU)

src/third_part/uvr5_pack/lib_v5/layers_33966KB.py:30

( self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.ReLU, dropout=False )

src/third_part/uvr5_pack/lib_v5/layers_33966KB.py:66

(self, nin, nout, dilations=(4, 8, 16, 32, 64), activ=nn.ReLU)

src/third_part/uvr5_pack/lib_v5/layers_33966KB.py:87

src/third_part/uvr5_pack/lib_v5/nets_537227KB.py:41

(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU)

src/third_part/uvr5_pack/lib_v5/layers.py:9

(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU)

src/third_part/uvr5_pack/lib_v5/layers.py:30

( self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.ReLU, dropout=False )

src/third_part/uvr5_pack/lib_v5/layers.py:66

(self, nin, nout, dilations=(4, 8, 16), activ=nn.ReLU)

src/third_part/uvr5_pack/lib_v5/layers.py:87

(self, config_path="")

src/third_part/uvr5_pack/lib_v5/model_param_init.py:46

src/third_part/uvr5_pack/lib_v5/nets_537238KB.py:41

(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU)

src/third_part/uvr5_pack/lib_v5/layers_537227KB.py:9

(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU)

src/third_part/uvr5_pack/lib_v5/layers_537227KB.py:30

( self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.ReLU, dropout=False )

src/third_part/uvr5_pack/lib_v5/layers_537227KB.py:66

(self, nin, nout, dilations=(4, 8, 16, 32, 64), activ=nn.ReLU)

src/third_part/uvr5_pack/lib_v5/layers_537227KB.py:87

src/third_part/uvr5_pack/lib_v5/nets_123812KB.py:40

src/third_part/uvr5_pack/lib_v5/nets_61968KB.py:40

(self, patch_list)

src/third_part/uvr5_pack/lib_v5/dataset.py:13

(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU)

src/third_part/uvr5_pack/lib_v5/layers_123821KB.py:9

(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU)

src/third_part/uvr5_pack/lib_v5/layers_123821KB.py:30

( self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.ReLU, dropout=False )

src/third_part/uvr5_pack/lib_v5/layers_123821KB.py:66

(self, nin, nout, dilations=(4, 8, 16), activ=nn.ReLU)

src/third_part/uvr5_pack/lib_v5/layers_123821KB.py:87

src/third_part/uvr5_pack/lib_v5/nets.py:41

src/third_part/uvr5_pack/lib_v5/nets_123821KB.py:40

(self, n_fft, nout=32, nout_lstm=128)

src/third_part/uvr5_pack/lib_v5/nets_new.py:46

(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU)

src/third_part/uvr5_pack/lib_v5/layers_new.py:9

( self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.ReLU, dropout=False )

src/third_part/uvr5_pack/lib_v5/layers_new.py:43

(self, nin, nout, dilations=(4, 8, 12), activ=nn.ReLU, dropout=False)

src/third_part/uvr5_pack/lib_v5/layers_new.py:68

(self, nin_conv, nin_lstm, nout_lstm)

src/third_part/uvr5_pack/lib_v5/layers_new.py:106

(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU)

src/third_part/uvr5_pack/lib_v5/layers_537238KB.py:9

(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU)

src/third_part/uvr5_pack/lib_v5/layers_537238KB.py:30

( self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.ReLU, dropout=False )

src/third_part/uvr5_pack/lib_v5/layers_537238KB.py:66

(self, nin, nout, dilations=(4, 8, 16, 32, 64), activ=nn.ReLU)

src/third_part/uvr5_pack/lib_v5/layers_537238KB.py:87

(self, upscale_factor, face_size=512, crop_ratio=(1, 1),

src/third_part/facelib/utils/face_restoration_helper.py:56

(self, in_channels_list, out_channels)

src/third_part/facelib/detection/retinaface/retinaface_net.py:68

src/third_part/facelib/detection/retinaface/retinaface_net.py:102

(self, inchannels=512, num_anchors=3)

src/third_part/facelib/detection/retinaface/retinaface_net.py:140

(self, inchannels=512, num_anchors=3)

src/third_part/facelib/detection/retinaface/retinaface_net.py:154

(self, inchannels=512, num_anchors=3)

src/third_part/facelib/detection/retinaface/retinaface_net.py:167

(self, cfg, image_size=None, phase='train')

src/third_part/facelib/detection/retinaface/retinaface_utils.py:10

(self, network_name='resnet50', half=False, phase='test')

src/third_part/facelib/detection/retinaface/retinaface.py:76

config_name: name of .yaml config with network configuration from models/ folder. min_face : minimal face size in pixels. tar

src/third_part/facelib/detection/yolov5face/face_detector.py:28

(self, nc=80, anchors=(), ch=())

src/third_part/facelib/detection/yolov5face/models/yolo.py:33

(self, c1, c2, k=1, s=1, p=None, g=1, act=True)

src/third_part/facelib/detection/yolov5face/models/common.py:44

(self, c1, c2, k=3, s=2, p=None, g=1, act=True)

src/third_part/facelib/detection/yolov5face/models/common.py:58

(self, c1, c2, shortcut=True, g=1, e=0.5)

src/third_part/facelib/detection/yolov5face/models/common.py:76

(self, c1, c2, n=1, shortcut=True, g=1, e=0.5)

src/third_part/facelib/detection/yolov5face/models/common.py:89

(self, inp, oup, stride)

src/third_part/facelib/detection/yolov5face/models/common.py:121

(self, c1, c2, k=(5, 9, 13))

src/third_part/facelib/detection/yolov5face/models/common.py:175

(self, c1, c2, k=1, s=1, p=None, g=1, act=True)

src/third_part/facelib/detection/yolov5face/models/common.py:189

(self, dimension=1)

src/third_part/facelib/detection/yolov5face/models/common.py:199

src/third_part/facelib/detection/yolov5face/models/common.py:224

(self, imgs, pred, names=None)

src/third_part/facelib/detection/yolov5face/models/common.py:277

(self, c1, c2, k=(1, 3), s=1, equal_ch=True)

src/third_part/facelib/detection/yolov5face/models/experimental.py:26

(self, channels, normalize_shape=None, norm_type='bn')

src/third_part/facelib/parsing/parsenet.py:16

(self, channels, relu_type='relu')

src/third_part/facelib/parsing/parsenet.py:54

(self, in_channels, out_channels, kernel_size=3,

src/third_part/facelib/parsing/parsenet.py:76

(self, c_in, c_out, relu_type='prelu', norm_type='bn', scale='none')

src/third_part/facelib/parsing/parsenet.py:118

(self, in_chan, out_chan, ks=3, stride=1, padding=1)

src/third_part/facelib/parsing/bisenet.py:10

(self, in_chan, mid_chan, num_class)

src/third_part/facelib/parsing/bisenet.py:23

(self, in_chan, out_chan)

src/third_part/facelib/parsing/bisenet.py:36

src/third_part/facelib/parsing/bisenet.py:55

(self, in_chan, out_chan)

src/third_part/facelib/parsing/bisenet.py:89

(self, in_chan, out_chan, stride=1)

src/third_part/facelib/parsing/resnet.py:12

(self, **kwargs)

src/third_part/wav2lip/hparams.py:16

(self, landmarks_type, network_size=NetworkSize.LARGE, device='cuda', flip_input=False, face_

src/third_part/wav2lip/face_detection/api.py:48

(self, in_planes, out_planes)

src/third_part/wav2lip/face_detection/models.py:14

(self, inplanes, planes, stride=1, downsample=None)

src/third_part/wav2lip/face_detection/models.py:62

(self, num_modules, depth, num_features)

src/third_part/wav2lip/face_detection/models.py:99

(self, block=Bottleneck, layers=[3, 8, 36, 3], num_classes=68)

src/third_part/wav2lip/face_detection/models.py:206

(self, device, verbose)

src/third_part/wav2lip/face_detection/detection/core.py:18

(self, n_channels, scale=1.0)

src/third_part/wav2lip/face_detection/detection/sfd/net_s3fd.py:7

(self, device, path_to_detector=os.path.join(os.path.dirname(os.path.abspath(__file__)), 's3fd.pth'), verbose=

src/third_part/wav2lip/face_detection/detection/sfd/sfd_detector.py:17

src/third_part/wav2lip/models/syncnet.py:8

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