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@ -95,7 +95,11 @@ class Predictor:
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)
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self.model = ort.InferenceSession(
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os.path.join(args.onnx, self.model_.target_name + ".onnx"),
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providers=["CUDAExecutionProvider", "DmlExecutionProvider", "CPUExecutionProvider"],
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providers=[
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"CUDAExecutionProvider",
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"DmlExecutionProvider",
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"CPUExecutionProvider",
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],
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)
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print("onnx load done")
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@ -27,7 +27,9 @@ def uvr(model_name, inp_root, save_root_vocal, paths, save_root_ins, agg, format
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func = AudioPre if "DeEcho" not in model_name else AudioPreDeEcho
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pre_fun = func(
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agg=int(agg),
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model_path=os.path.join(os.getenv("weight_uvr5_root"), model_name + ".pth"),
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model_path=os.path.join(
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os.getenv("weight_uvr5_root"), model_name + ".pth"
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),
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device=config.device,
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is_half=config.is_half,
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)
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@ -54,7 +56,10 @@ def uvr(model_name, inp_root, save_root_vocal, paths, save_root_ins, agg, format
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need_reformat = 1
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traceback.print_exc()
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if need_reformat == 1:
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tmp_path = "%s/%s.reformatted.wav" % (os.path.join("tmp"), os.path.basename(inp_path))
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tmp_path = "%s/%s.reformatted.wav" % (
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os.path.join("tmp"),
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os.path.basename(inp_path),
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)
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os.system(
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"ffmpeg -i %s -vn -acodec pcm_s16le -ac 2 -ar 44100 %s -y"
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% (inp_path, tmp_path)
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@ -89,4 +94,3 @@ def uvr(model_name, inp_root, save_root_vocal, paths, save_root_ins, agg, format
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if torch.cuda.is_available():
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torch.cuda.empty_cache()
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yield "\n".join(infos)
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