remove moved func
This commit is contained in:
Ftps
parent
a8854a71c4
commit
c054138f0f
465
infer-web.py
465
infer-web.py
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@ -4,7 +4,7 @@ import sys
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now_dir = os.getcwd()
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sys.path.append(now_dir)
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import traceback, pdb
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import traceback
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import warnings
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import numpy as np
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@ -19,25 +19,19 @@ from subprocess import Popen
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from time import sleep
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import faiss
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import ffmpeg
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import gradio as gr
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import soundfile as sf
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from config import Config
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from configs.config import Config
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import fairseq
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from i18n import I18nAuto
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from 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 lib.infer_pack.models_onnx import SynthesizerTrnMsNSFsidM
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from infer_uvr5 import _audio_pre_, _audio_pre_new
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from lib.audio import load_audio
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from lib.train.process_ckpt import change_info, extract_small_model, merge, show_info
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from vc_infer_pipeline import VC
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from sklearn.cluster import MiniBatchKMeans
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from dotenv import load_dotenv
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from infer.modules.vc.modules import VC
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from infer.modules.uvr5.modules import uvr
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from infer.modules.onnx.export import export_onnx
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logging.getLogger("numba").setLevel(logging.WARNING)
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now_dir = os.getcwd()
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@ -48,12 +42,13 @@ shutil.rmtree("%s/runtime/Lib/site-packages/uvr5_pack" % (now_dir), ignore_error
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os.makedirs(tmp, exist_ok=True)
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os.makedirs(os.path.join(now_dir, "logs"), exist_ok=True)
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os.makedirs(os.path.join(now_dir, "weights"), exist_ok=True)
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os.environ["TEMP"] = tmp
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warnings.filterwarnings("ignore")
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torch.manual_seed(114514)
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load_dotenv()
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config = Config()
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vc = VC(config)
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if config.dml == True:
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def forward_dml(ctx, x, scale):
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@ -127,27 +122,10 @@ class ToolButton(gr.Button, gr.components.FormComponent):
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return "button"
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hubert_model = None
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def load_hubert():
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global hubert_model
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models, _, _ = fairseq.checkpoint_utils.load_model_ensemble_and_task(
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["hubert_base.pt"],
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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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weight_uvr5_root = "uvr5_weights"
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weight_root = os.getenv("weight_root")
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weight_uvr5_root = os.getenv("weight_uvr5_root")
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index_root = "logs"
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names = []
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for name in os.listdir(weight_root):
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if name.endswith(".pth"):
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@ -162,365 +140,6 @@ for name in os.listdir(weight_uvr5_root):
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if name.endswith(".pth") or "onnx" in name:
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uvr5_names.append(name.replace(".pth", ""))
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cpt = None
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def vc_single(
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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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# 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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): # spk_item, input_audio0, vc_transform0,f0_file,f0method0
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global tgt_sr, net_g, vc, hubert_model, version, cpt
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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 not hubert_model:
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load_hubert()
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if_f0 = cpt.get("f0", 1)
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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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# file_big_npy = (
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# file_big_npy.strip(" ").strip('"').strip("\n").strip('"').strip(" ")
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# )
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audio_opt = vc.pipeline(
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hubert_model,
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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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# file_big_npy,
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index_rate,
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if_f0,
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filter_radius,
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tgt_sr,
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resample_sr,
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rms_mix_rate,
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version,
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protect,
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f0_file=f0_file,
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)
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index_info = (
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"Using index:%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 "Success.\n %s\nTime:\n npy:%ss, f0:%ss, infer:%ss" % (
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index_info,
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times[0],
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times[1],
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times[2],
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), (
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resample_sr if resample_sr >= 16000 and tgt_sr != resample_sr else tgt_sr,
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audio_opt,
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)
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except:
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info = traceback.format_exc()
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print(info)
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return info, (None, None)
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def vc_multi(
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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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# 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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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 = [os.path.join(dir_path, name) for name in os.listdir(dir_path)]
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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 = 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" % (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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def uvr(model_name, inp_root, save_root_vocal, paths, save_root_ins, agg, format0):
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infos = []
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try:
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inp_root = inp_root.strip(" ").strip('"').strip("\n").strip('"').strip(" ")
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save_root_vocal = (
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save_root_vocal.strip(" ").strip('"').strip("\n").strip('"').strip(" ")
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)
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save_root_ins = (
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save_root_ins.strip(" ").strip('"').strip("\n").strip('"').strip(" ")
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)
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if model_name == "onnx_dereverb_By_FoxJoy":
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from MDXNet import MDXNetDereverb
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pre_fun = MDXNetDereverb(15)
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else:
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func = _audio_pre_ if "DeEcho" not in model_name else _audio_pre_new
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pre_fun = func(
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agg=int(agg),
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model_path=os.path.join(weight_uvr5_root, model_name + ".pth"),
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device=config.device,
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is_half=config.is_half,
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)
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if inp_root != "":
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paths = [os.path.join(inp_root, name) for name in os.listdir(inp_root)]
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else:
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paths = [path.name for path in paths]
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for path in paths:
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inp_path = os.path.join(inp_root, path)
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need_reformat = 1
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done = 0
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try:
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info = ffmpeg.probe(inp_path, cmd="ffprobe")
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if (
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info["streams"][0]["channels"] == 2
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and info["streams"][0]["sample_rate"] == "44100"
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):
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need_reformat = 0
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pre_fun._path_audio_(
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inp_path, save_root_ins, save_root_vocal, format0
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)
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done = 1
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except:
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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" % (tmp, os.path.basename(inp_path))
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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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)
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inp_path = tmp_path
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try:
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if done == 0:
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pre_fun._path_audio_(
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inp_path, save_root_ins, save_root_vocal, format0
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)
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infos.append("%s->Success" % (os.path.basename(inp_path)))
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yield "\n".join(infos)
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except:
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infos.append(
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"%s->%s" % (os.path.basename(inp_path), traceback.format_exc())
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)
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yield "\n".join(infos)
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except:
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infos.append(traceback.format_exc())
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yield "\n".join(infos)
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finally:
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try:
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if model_name == "onnx_dereverb_By_FoxJoy":
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del pre_fun.pred.model
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del pre_fun.pred.model_
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else:
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del pre_fun.model
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del pre_fun
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except:
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traceback.print_exc()
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print("clean_empty_cache")
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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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def get_index_path_from_model(sid):
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sel_index_path = ""
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name = os.path.join("logs", sid.split(".")[0], "")
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# print(name)
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for f in index_paths:
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if name in f:
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# print("selected index path:", f)
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sel_index_path = f
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break
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return sel_index_path
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# 一个选项卡全局只能有一个音色
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def get_vc(sid, to_return_protect0, to_return_protect1):
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global n_spk, tgt_sr, net_g, vc, cpt, version
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if sid == "" or sid == []:
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global hubert_model
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if 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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print("clean_empty_cache")
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del net_g, n_spk, vc, hubert_model, tgt_sr # ,cpt
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hubert_model = net_g = n_spk = vc = hubert_model = 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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if_f0 = cpt.get("f0", 1)
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version = cpt.get("version", "v1")
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if version == "v1":
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if if_f0 == 1:
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net_g = SynthesizerTrnMs256NSFsid(
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*cpt["config"], is_half=config.is_half
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)
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else:
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net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
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elif version == "v2":
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if if_f0 == 1:
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net_g = SynthesizerTrnMs768NSFsid(
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*cpt["config"], is_half=config.is_half
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)
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else:
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net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"])
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del net_g, 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 = "%s/%s" % (weight_root, sid)
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print("loading %s" % person)
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cpt = torch.load(person, map_location="cpu")
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tgt_sr = cpt["config"][-1]
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cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0] # n_spk
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if_f0 = cpt.get("f0", 1)
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if if_f0 == 0:
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to_return_protect0 = to_return_protect1 = {
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"visible": False,
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"value": 0.33,
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"__type__": "update",
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}
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else:
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to_return_protect0 = {
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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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to_return_protect1 = {
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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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version = cpt.get("version", "v1")
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if version == "v1":
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if if_f0 == 1:
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net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=config.is_half)
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else:
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net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
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elif version == "v2":
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if if_f0 == 1:
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net_g = SynthesizerTrnMs768NSFsid(*cpt["config"], is_half=config.is_half)
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else:
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net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"])
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del net_g.enc_q
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print(net_g.load_state_dict(cpt["weight"], strict=False))
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net_g.eval().to(config.device)
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if config.is_half:
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net_g = net_g.half()
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else:
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net_g = net_g.float()
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vc = VC(tgt_sr, config)
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n_spk = cpt["config"][-3]
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index = {"value": get_index_path_from_model(sid), "__type__": "update"}
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return (
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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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def change_choices():
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names = []
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for name in os.listdir(weight_root):
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@ -1385,54 +1004,6 @@ def change_f0_method(f0method8):
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return {"visible": visible, "__type__": "update"}
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def export_onnx(ModelPath, ExportedPath):
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global cpt
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cpt = torch.load(ModelPath, map_location="cpu")
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cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0]
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vec_channels = 256 if cpt.get("version", "v1") == "v1" else 768
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test_phone = torch.rand(1, 200, vec_channels) # hidden unit
|
||||
test_phone_lengths = torch.tensor([200]).long() # hidden unit 长度（貌似没啥用）
|
||||
test_pitch = torch.randint(size=(1, 200), low=5, high=255) # 基频（单位赫兹）
|
||||
test_pitchf = torch.rand(1, 200) # nsf基频
|
||||
test_ds = torch.LongTensor([0]) # 说话人ID
|
||||
test_rnd = torch.rand(1, 192, 200) # 噪声（加入随机因子）
|
||||
|
||||
device = "cpu" # 导出时设备（不影响使用模型）
|
||||
|
||||
net_g = SynthesizerTrnMsNSFsidM(
|
||||
*cpt["config"], is_half=False, version=cpt.get("version", "v1")
|
||||
) # fp32导出（C++要支持fp16必须手动将内存重新排列所以暂时不用fp16）
|
||||
net_g.load_state_dict(cpt["weight"], strict=False)
|
||||
input_names = ["phone", "phone_lengths", "pitch", "pitchf", "ds", "rnd"]
|
||||
output_names = [
|
||||
"audio",
|
||||
]
|
||||
# net_g.construct_spkmixmap(n_speaker) 多角色混合轨道导出
|
||||
torch.onnx.export(
|
||||
net_g,
|
||||
(
|
||||
test_phone.to(device),
|
||||
test_phone_lengths.to(device),
|
||||
test_pitch.to(device),
|
||||
test_pitchf.to(device),
|
||||
test_ds.to(device),
|
||||
test_rnd.to(device),
|
||||
),
|
||||
ExportedPath,
|
||||
dynamic_axes={
|
||||
"phone": [1],
|
||||
"pitch": [1],
|
||||
"pitchf": [1],
|
||||
"rnd": [2],
|
||||
},
|
||||
do_constant_folding=False,
|
||||
opset_version=13,
|
||||
verbose=False,
|
||||
input_names=input_names,
|
||||
output_names=output_names,
|
||||
)
|
||||
return "Finished"
|
||||
|
||||
|
||||
with gr.Blocks(title="RVC WebUI") as app:
|
||||
|
|
@ -1551,7 +1122,7 @@ with gr.Blocks(title="RVC WebUI") as app:
|
|||
vc_output1 = gr.Textbox(label=i18n("输出信息"))
|
||||
vc_output2 = gr.Audio(label=i18n("输出音频(右下角三个点,点了可以下载)"))
|
||||
but0.click(
|
||||
vc_single,
|
||||
vc.vc_single,
|
||||
[
|
||||
spk_item,
|
||||
input_audio0,
|
||||
|
|
@ -1671,7 +1242,7 @@ with gr.Blocks(title="RVC WebUI") as app:
|
|||
but1 = gr.Button(i18n("转换"), variant="primary")
|
||||
vc_output3 = gr.Textbox(label=i18n("输出信息"))
|
||||
but1.click(
|
||||
vc_multi,
|
||||
vc.vc_multi,
|
||||
[
|
||||
spk_item,
|
||||
dir_input,
|
||||
|
|
@ -1693,7 +1264,7 @@ with gr.Blocks(title="RVC WebUI") as app:
|
|||
api_name="infer_convert_batch",
|
||||
)
|
||||
sid0.change(
|
||||
fn=get_vc,
|
||||
fn=vc.get_vc,
|
||||
inputs=[sid0, protect0, protect1],
|
||||
outputs=[spk_item, protect0, protect1, file_index2, file_index4],
|
||||
)
|
||||
|
|
@ -1967,7 +1538,7 @@ with gr.Blocks(title="RVC WebUI") as app:
|
|||
info3,
|
||||
api_name="train_start",
|
||||
)
|
||||
but4.click(train_index, [exp_dir1, version19], info3)
|
||||
but4.click(train_index, [exp_dir1, version19, config.n_cpu], info3)
|
||||
but5.click(
|
||||
train1key,
|
||||
[
|
||||
|
|
|
|||
Loading…
Reference in New Issue