Cool_Tools/Retrieval-based-Voice-Conversion-WebUI
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feat(gui): trained_xxxx.index hint (fix #1070)

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源文雨 2024-01-25 21:53:29 +09:00
parent 918ab7b62f
commit 3b2a1d081c
2 changed files with 12 additions and 10 deletions
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2
gui_v1.py
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@ -530,7 +530,7 @@ if __name__ == "__main__":
self.gui_config.sg_hostapi = self.hostapis[0] self.gui_config.sg_hostapi = self.hostapis[0]
self.window["sg_hostapi"].Update(values=self.hostapis) self.window["sg_hostapi"].Update(values=self.hostapis)
self.window["sg_hostapi"].Update(value=self.gui_config.sg_hostapi) self.window["sg_hostapi"].Update(value=self.gui_config.sg_hostapi)
if self.gui_config.sg_input_device not in self.input_devices: if self.gui_config.sg_input_device not in self.input_devices and len(self.input_devices) > 0:
self.gui_config.sg_input_device = self.input_devices[0] self.gui_config.sg_input_device = self.input_devices[0]
self.window["sg_input_device"].Update(values=self.input_devices) self.window["sg_input_device"].Update(values=self.input_devices)
self.window["sg_input_device"].Update( self.window["sg_input_device"].Update(

20
tools/rvc_for_realtime.py
View File

@ -372,15 +372,17 @@ class RVC:
if hasattr(self, "index") and self.index_rate != 0: if hasattr(self, "index") and self.index_rate != 0:
npy = feats[0][skip_head // 2 :].cpu().numpy().astype("float32") npy = feats[0][skip_head // 2 :].cpu().numpy().astype("float32")
score, ix = self.index.search(npy, k=8) score, ix = self.index.search(npy, k=8)
weight = np.square(1 / score) if (ix >= 0).all():
weight /= weight.sum(axis=1, keepdims=True) weight = np.square(1 / score)
npy = np.sum(self.big_npy[ix] * np.expand_dims(weight, axis=2), axis=1) weight /= weight.sum(axis=1, keepdims=True)
if self.config.is_half: npy = np.sum(self.big_npy[ix] * np.expand_dims(weight, axis=2), axis=1)
npy = npy.astype("float16") if self.config.is_half:
feats[0][skip_head // 2 :] = ( npy = npy.astype("float16")
torch.from_numpy(npy).unsqueeze(0).to(self.device) * self.index_rate feats[0][skip_head // 2 :] = (
+ (1 - self.index_rate) * feats[0][skip_head // 2 :] torch.from_numpy(npy).unsqueeze(0).to(self.device) * self.index_rate
) + (1 - self.index_rate) * feats[0][skip_head // 2 :]
)
else: printt("Invalid index. You MUST use added_xxxx.index but not trained_xxxx.index!")
else: else:
printt("Index search FAILED or disabled") printt("Index search FAILED or disabled")
except: except: