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@ -55,10 +55,11 @@ class Config:
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if (
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("16" in self.gpu_name and "V100" not in self.gpu_name.upper())
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or "P40" in self.gpu_name.upper()
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or "1060" in self.gpu_name
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or "1070" in self.gpu_name
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or "1080" in self.gpu_name
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):
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print("16系显卡强制单精度")
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print("16系/10系显卡和P40强制单精度")
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self.is_half = False
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for config_file in ["32k.json", "40k.json", "48k.json"]:
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with open(f"configs/{config_file}", "r") as f:
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18
gui.py
18
gui.py
@ -38,7 +38,8 @@ class RVC:
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self.window = 160
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if index_rate != 0:
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self.index = faiss.read_index(index_path)
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self.big_npy = np.load(npy_path)
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# self.big_npy = np.load(npy_path)
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self.big_npy = index.reconstruct_n(0, self.index.ntotal)
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print("index search enabled")
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self.index_rate = index_rate
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model_path = hubert_path
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@ -125,8 +126,15 @@ class RVC:
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####索引优化
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if hasattr(self, "index") and hasattr(self, "big_npy") and self.index_rate != 0:
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npy = feats[0].cpu().numpy().astype("float32")
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_, I = self.index.search(npy, 1)
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npy = self.big_npy[I.squeeze()].astype("float16")
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# _, I = self.index.search(npy, 1)
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# npy = self.big_npy[I.squeeze()].astype("float16")
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score, ix = index.search(npy, k=8)
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weight = np.square(1 / score)
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weight /= weight.sum(axis=1, keepdims=True)
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npy = np.sum(big_npy[ix] * np.expand_dims(weight, axis=2), axis=1).astype("float16")
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feats = (
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torch.from_numpy(npy).unsqueeze(0).to(device) * self.index_rate
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+ (1 - self.index_rate) * feats
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@ -204,7 +212,7 @@ class GUI:
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layout=[
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[
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sg.Input(
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default_text="TEMP\\hubert_base.pt", key="hubert_path"
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default_text="hubert_base.pt", key="hubert_path"
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),
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sg.FileBrowse(i18n("Hubert模型")),
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],
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@ -221,7 +229,7 @@ class GUI:
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],
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[
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sg.Input(
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default_text="TEMP\\big_src_feature_atri.npy",
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default_text="你不需要填写这个You don't need write this.",
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key="npy_path",
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),
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sg.FileBrowse(i18n("选择.npy文件")),
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@ -1312,7 +1312,7 @@ with gr.Blocks() as app:
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interactive=True,
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)
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batch_size12 = gr.Slider(
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minimum=0,
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minimum=1,
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maximum=40,
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step=1,
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label=i18n("每张显卡的batch_size"),
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@ -1540,4 +1540,4 @@ with gr.Blocks() as app:
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inbrowser=not config.noautoopen,
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server_port=config.listen_port,
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quiet=True,
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)
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)
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