86 lines
3.1 KiB
Python
86 lines
3.1 KiB
Python
import os
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from ..lib.audio import audio_to_bytes, bytes_to_audio, save_input_audio
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import folder_paths
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from ..lib.utils import get_filenames, get_hash, get_optimal_torch_device
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from ..lib import BASE_CACHE_DIR, BASE_MODELS_DIR, karafan
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from ..uvr5_cli import Separator
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from .settings.downloader import KARAFAN_MODELS, MDX_MODELS, RVC_DOWNLOAD_LINK, VR_MODELS, download_file
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temp_path = folder_paths.get_temp_directory()
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cache_dir = os.path.join(BASE_CACHE_DIR,"uvr")
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device = get_optimal_torch_device()
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is_half = True
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class UVR5Node:
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def __init__(self):
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pass
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@classmethod
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def INPUT_TYPES(cls):
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model_list = MDX_MODELS + VR_MODELS + KARAFAN_MODELS + get_filenames(root=BASE_MODELS_DIR,format_func=os.path.basename)
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return {
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"required": {
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"audio": ("VHS_AUDIO",),
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"model": (model_list,{
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"default": "UVR/HP5-vocals+instrumentals.pth"
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}),
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"agg":("INT",{
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"default": 10,
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"min": 0, #Minimum value
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"max": 20, #Maximum value
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"step": 1, #Slider's step
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"display": "slider"
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}),
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"format":(["wav", "flac", "mp3"],{
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"default": "flac"
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}),
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},
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"optional": {
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"use_cache": ("BOOLEAN",{"default": True})
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}
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}
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RETURN_TYPES = ("VHS_AUDIO","VHS_AUDIO","VHS_AUDIO")
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RETURN_NAMES = ("primary_stem","secondary_stem","audio_passthrough")
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FUNCTION = "split"
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CATEGORY = "🌺RVC-Studio/uvr"
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def split(self, audio, model, **kwargs):
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filename = os.path.basename(model)
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subfolder = os.path.dirname(model)
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model_path = os.path.join(BASE_MODELS_DIR,subfolder,filename)
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if not os.path.isfile(model_path):
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download_link = f"{RVC_DOWNLOAD_LINK}{model}"
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params = model_path, download_link
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if download_file(params): print(f"successfully downloaded: {model_path}")
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input_audio = bytes_to_audio(audio())
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audio_path = os.path.join(temp_path,"uvr",f"{get_hash(audio(),model, *kwargs.items())}.wav")
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if not os.path.isfile(audio_path):
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os.makedirs(os.path.dirname(audio_path),exist_ok=True)
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print(save_input_audio(audio_path,input_audio))
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if "karafan" in model_path:
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vocals, instrumental, input_audio = karafan.inference.Process(audio_path,cache_dir=cache_dir,**kwargs)
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else:
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model = Separator(
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model_path=model_path,
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device=device,
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is_half="cuda" in str(device),
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cache_dir=cache_dir,
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**kwargs
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)
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vocals, instrumental, input_audio = model.run_inference(audio_path,format=kwargs.get("format"))
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return (lambda:audio_to_bytes(*vocals), lambda:audio_to_bytes(*instrumental), audio)
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@classmethod
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def IS_CHANGED(cls, *args, **kwargs):
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print(f"{args=} {kwargs=}")
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return get_hash(*args, *kwargs.items()) |