305 lines
10 KiB
Python
305 lines
10 KiB
Python
import traceback
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import logging
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logger = logging.getLogger(__name__)
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import numpy as np
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import soundfile as sf
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import torch
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from infer.lib.audio import load_audio
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from infer.lib.infer_pack.models import (
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SynthesizerTrnMs256NSFsid,
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SynthesizerTrnMs256NSFsid_nono,
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SynthesizerTrnMs768NSFsid,
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SynthesizerTrnMs768NSFsid_nono,
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)
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from infer.modules.vc.pipeline import Pipeline
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from infer.modules.vc.utils import *
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class VC:
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def __init__(self, config):
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self.n_spk = None
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self.tgt_sr = None
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self.net_g = None
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self.pipeline = None
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self.cpt = None
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self.version = None
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self.if_f0 = None
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self.version = None
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self.hubert_model = None
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self.config = config
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def get_vc(self, sid, *to_return_protect):
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logger.info("Get sid: " + sid)
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to_return_protect0 = {
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"visible": self.if_f0 != 0,
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"value": to_return_protect[0]
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if self.if_f0 != 0 and to_return_protect
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else 0.5,
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"__type__": "update",
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}
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to_return_protect1 = {
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"visible": self.if_f0 != 0,
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"value": to_return_protect[1]
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if self.if_f0 != 0 and to_return_protect
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else 0.33,
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"__type__": "update",
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}
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if not sid:
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if self.hubert_model is not None: # 考虑到轮询, 需要加个判断看是否 sid 是由有模型切换到无模型的
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logger.info("Clean model cache")
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del (
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self.net_g,
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self.n_spk,
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self.vc,
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self.hubert_model,
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self.tgt_sr,
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) # ,cpt
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self.hubert_model = (
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self.net_g
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) = self.n_spk = self.vc = self.hubert_model = self.tgt_sr = None
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if torch.cuda.is_available():
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torch.cuda.empty_cache()
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###楼下不这么折腾清理不干净
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self.if_f0 = self.cpt.get("f0", 1)
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self.version = self.cpt.get("version", "v1")
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if self.version == "v1":
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if self.if_f0 == 1:
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self.net_g = SynthesizerTrnMs256NSFsid(
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*self.cpt["config"], is_half=self.config.is_half
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)
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else:
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self.net_g = SynthesizerTrnMs256NSFsid_nono(*self.cpt["config"])
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elif self.version == "v2":
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if self.if_f0 == 1:
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self.net_g = SynthesizerTrnMs768NSFsid(
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*self.cpt["config"], is_half=self.config.is_half
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)
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else:
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self.net_g = SynthesizerTrnMs768NSFsid_nono(*self.cpt["config"])
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del self.net_g, self.cpt
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if torch.cuda.is_available():
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torch.cuda.empty_cache()
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return (
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{"visible": False, "__type__": "update"},
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{
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"visible": True,
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"value": to_return_protect0,
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"__type__": "update",
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},
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{
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"visible": True,
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"value": to_return_protect1,
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"__type__": "update",
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},
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"",
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"",
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)
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person = f'{os.getenv("weight_root")}/{sid}'
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logger.info(f"Loading: {person}")
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self.cpt = torch.load(person, map_location="cpu")
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self.tgt_sr = self.cpt["config"][-1]
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self.cpt["config"][-3] = self.cpt["weight"]["emb_g.weight"].shape[0] # n_spk
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self.if_f0 = self.cpt.get("f0", 1)
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self.version = self.cpt.get("version", "v1")
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synthesizer_class = {
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("v1", 1): SynthesizerTrnMs256NSFsid,
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("v1", 0): SynthesizerTrnMs256NSFsid_nono,
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("v2", 1): SynthesizerTrnMs768NSFsid,
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("v2", 0): SynthesizerTrnMs768NSFsid_nono,
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}
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self.net_g = synthesizer_class.get(
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(self.version, self.if_f0), SynthesizerTrnMs256NSFsid
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)(*self.cpt["config"], is_half=self.config.is_half)
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del self.net_g.enc_q
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self.net_g.load_state_dict(self.cpt["weight"], strict=False)
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self.net_g.eval().to(self.config.device)
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if self.config.is_half:
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self.net_g = self.net_g.half()
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else:
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self.net_g = self.net_g.float()
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self.pipeline = Pipeline(self.tgt_sr, self.config)
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n_spk = self.cpt["config"][-3]
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index = {"value": get_index_path_from_model(sid), "__type__": "update"}
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logger.info("Select index: " + index["value"])
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return (
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(
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{"visible": True, "maximum": n_spk, "__type__": "update"},
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to_return_protect0,
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to_return_protect1,
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index,
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index,
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)
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if to_return_protect
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else {"visible": True, "maximum": n_spk, "__type__": "update"}
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)
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def vc_single(
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self,
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sid,
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input_audio_path,
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f0_up_key,
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f0_file,
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f0_method,
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file_index,
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file_index2,
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index_rate,
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filter_radius,
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resample_sr,
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rms_mix_rate,
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protect,
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):
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if input_audio_path is None:
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return "You need to upload an audio", None
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f0_up_key = int(f0_up_key)
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try:
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audio = load_audio(input_audio_path, 16000)
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audio_max = np.abs(audio).max() / 0.95
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if audio_max > 1:
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audio /= audio_max
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times = [0, 0, 0]
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if self.hubert_model is None:
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self.hubert_model = load_hubert(self.config)
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file_index = (
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(
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file_index.strip(" ")
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.strip('"')
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.strip("\n")
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.strip('"')
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.strip(" ")
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.replace("trained", "added")
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)
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if file_index != ""
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else file_index2
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) # 防止小白写错，自动帮他替换掉
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audio_opt = self.pipeline.pipeline(
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self.hubert_model,
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self.net_g,
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sid,
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audio,
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input_audio_path,
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times,
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f0_up_key,
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f0_method,
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file_index,
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index_rate,
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self.if_f0,
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filter_radius,
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self.tgt_sr,
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resample_sr,
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rms_mix_rate,
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self.version,
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protect,
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f0_file,
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)
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if self.tgt_sr != resample_sr >= 16000:
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self.tgt_sr = resample_sr
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index_info = (
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"Index:\n%s." % file_index
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if os.path.exists(file_index)
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else "Index not used."
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)
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return (
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"Success.\n%s\nTime:\nnpy: %.2fs, f0: %.2fs, infer: %.2fs." % (index_info, *times),
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(self.tgt_sr, audio_opt),
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)
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except:
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info = traceback.format_exc()
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logger.warn(info)
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return info, (None, None)
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def vc_multi(
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self,
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sid,
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dir_path,
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opt_root,
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paths,
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f0_up_key,
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f0_method,
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file_index,
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file_index2,
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index_rate,
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filter_radius,
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resample_sr,
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rms_mix_rate,
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protect,
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format1,
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):
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try:
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dir_path = (
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dir_path.strip(" ").strip('"').strip("\n").strip('"').strip(" ")
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) # 防止小白拷路径头尾带了空格和"和回车
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opt_root = opt_root.strip(" ").strip('"').strip("\n").strip('"').strip(" ")
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os.makedirs(opt_root, exist_ok=True)
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try:
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if dir_path != "":
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paths = [
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os.path.join(dir_path, name) for name in os.listdir(dir_path)
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]
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else:
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paths = [path.name for path in paths]
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except:
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traceback.print_exc()
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paths = [path.name for path in paths]
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infos = []
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for path in paths:
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info, opt = self.vc_single(
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sid,
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path,
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f0_up_key,
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None,
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f0_method,
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file_index,
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file_index2,
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# file_big_npy,
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index_rate,
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filter_radius,
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resample_sr,
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rms_mix_rate,
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protect,
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)
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if "Success" in info:
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try:
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tgt_sr, audio_opt = opt
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if format1 in ["wav", "flac"]:
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sf.write(
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"%s/%s.%s"
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% (opt_root, os.path.basename(path), format1),
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audio_opt,
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tgt_sr,
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)
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else:
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path = "%s/%s.wav" % (opt_root, os.path.basename(path))
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sf.write(
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path,
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audio_opt,
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tgt_sr,
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)
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if os.path.exists(path):
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os.system(
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"ffmpeg -i %s -vn %s -q:a 2 -y"
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% (path, path[:-4] + ".%s" % format1)
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)
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except:
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info += traceback.format_exc()
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infos.append("%s->%s" % (os.path.basename(path), info))
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yield "\n".join(infos)
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yield "\n".join(infos)
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except:
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yield traceback.format_exc()
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