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Update app.py
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app.py
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@@ -4,7 +4,6 @@ os.environ["no_proxy"] = "localhost, 127.0.0.1, ::1"
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import threading
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from time import sleep
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from subprocess import Popen
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from typing import Any
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import faiss
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import spaces
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from random import shuffle
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@@ -195,7 +194,6 @@ else:
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or "M4" in gpu_name.upper()
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or "T4" in gpu_name.upper()
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or "TITAN" in gpu_name.upper()
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or "ZERO" in gpu_name.upper()
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): # A10#A100#V100#A40#P40#M40#K80#A4500
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if_gpu_ok = True # 至少有一张能用的N卡
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gpu_infos.append("%s\t%s" % (i, gpu_name))
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@@ -222,31 +220,30 @@ from lib.infer_pack.models import (
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SynthesizerTrnMs768NSFsid_nono,
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)
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import soundfile as sf
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from fairseq import checkpoint_utils
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import gradio as gr
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import logging
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from vc_infer_pipeline import VC
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from config import Config
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config = Config()
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# from trainset_preprocess_pipeline import PreProcess
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logging.getLogger("numba").setLevel(logging.WARNING)
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def load_hubert():
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suffix="",
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)
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hubert_model = models[0]
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hubert_model = hubert_model.to(config.device)
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if config.is_half:
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hubert_model = hubert_model.half()
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else:
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hubert_model = hubert_model.float()
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hubert_model.eval()
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weight_root = "weights"
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@@ -262,7 +259,7 @@ for root, dirs, files in os.walk(index_root, topdown=False):
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index_paths.append("%s/%s" % (root, name))
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def vc_single(
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sid,
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input_audio_path,
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import threading
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from time import sleep
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from subprocess import Popen
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import faiss
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import spaces
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from random import shuffle
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or "M4" in gpu_name.upper()
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or "T4" in gpu_name.upper()
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or "TITAN" in gpu_name.upper()
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): # A10#A100#V100#A40#P40#M40#K80#A4500
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if_gpu_ok = True # 至少有一张能用的N卡
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gpu_infos.append("%s\t%s" % (i, gpu_name))
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SynthesizerTrnMs768NSFsid_nono,
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)
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import soundfile as sf
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import gradio as gr
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import logging
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from vc_infer_pipeline import VC
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from config import Config
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import torch.nn as nn
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import numpy as np
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config = Config()
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# from trainset_preprocess_pipeline import PreProcess
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logging.getLogger("numba").setLevel(logging.WARNING)
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class HuBERT(nn.Module):
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def __init__(self, model_path):
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super(HuBERT, self).__init__()
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self.model = torch.hub.load('pytorch/fairseq', 'hubert_base') # should load without using hubert_base.pt, and without fairseq.
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def extract_features(self, waveform):
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return self.model.extract_features(waveform)
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def load_hubert():
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model_path = "hubert_base.pt" # Your model path
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hubert_model = HuBERT(model_path)
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return hubert_model
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weight_root = "weights"
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index_paths.append("%s/%s" % (root, name))
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def vc_single(
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sid,
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input_audio_path,
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