reconstruct uvr
· ffmpeg processing moved to soundfile, pydub · Optimization of for statement · Change in file path acquisition method ·ffmpegの処理をsoundfile, pydubに移行 ·for文の最適化 ·pathのからの取得方法の変更
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Ftps
@@ -1,100 +1,93 @@
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import logging
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import os
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import traceback
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from glob import glob
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from pathlib import Path
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import ffmpeg
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import soundfile as sf
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import torch
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from pydub import AudioSegment
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from rvc.configs.config import Config
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from rvc.modules.uvr5.mdxnet import MDXNetDereverb
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from rvc.modules.uvr5.vr import AudioPre, AudioPreDeEcho
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logger: logging.Logger = logging.getLogger(__name__)
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config = Config()
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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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class UVR:
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def __init__(self):
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self.need_reformat: bool = True
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self.config: Config = Config()
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def uvr_wrapper(
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self,
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audio_path: Path,
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save_vocal_path: Path | None = None,
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save_ins_path: Path | None = None,
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agg: int = 10,
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export_format: str = "flac",
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model_name: str | None = None,
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temp_path: Path | None = None,
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):
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infos = []
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save_vocal_path = (
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os.getenv("save_uvr_path") if not save_vocal_path else save_vocal_path
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)
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save_ins_path = (
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os.getenv("save_uvr_path") if not save_ins_path else save_ins_path
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)
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if model_name is None:
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model_name = os.path.basename(glob(f"{os.getenv('weight_uvr5_root')}/*")[0])
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is_hp3 = "HP3" in model_name
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if model_name == "onnx_dereverb_By_FoxJoy":
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pre_fun = MDXNetDereverb(15, config.device)
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pre_fun = MDXNetDereverb(15, self.config.device)
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else:
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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(
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os.getenv("weight_uvr5_root"), model_name + ".pth"
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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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device=self.config.device,
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is_half=self.config.is_half,
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)
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is_hp3 = "HP3" in model_name
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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, is_hp3=is_hp3
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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" % (
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os.path.join(os.environ["TEMP"]),
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os.path.basename(inp_path),
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process_paths = (
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[
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_
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for _ in glob(f"{audio_path}/*")
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if os.path.splitext(_)[-1][1:].upper() in sf.available_formats()
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]
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if os.path.isdir(audio_path)
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else audio_path
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)
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for process_path in [process_paths]:
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print(f"path: {process_path}")
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info = sf.info(process_path)
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if not (info.channels == 2 and info.samplerate == "44100"):
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tmp_path = os.path.join(
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temp_path or os.environ.get("TEMP"), os.path.basename(process_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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AudioSegment.from_file(process_path).export(
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tmp_path,
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format="wav",
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codec="pcm_s16le",
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bitrate="16k",
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parameters=["-ar", "44100"],
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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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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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pre_fun._path_audio_(
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process_path,
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save_vocal_path,
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save_ins_path,
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export_format,
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is_hp3=is_hp3,
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)
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infos.append(f"{os.path.basename(process_path)}->Success" )
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yield "\n".join(infos)
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if torch.cuda.is_available():
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torch.cuda.empty_cache()
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logger.info("Executed torch.cuda.empty_cache()")
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yield "\n".join(infos)
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