mirror of
https://github.com/Anjok07/ultimatevocalremovergui.git
synced 2024-11-13 18:40:48 +01:00
108 lines
3.1 KiB
Python
108 lines
3.1 KiB
Python
# Copyright (c) Facebook, Inc. and its affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the license found in the
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# LICENSE file in the root directory of this source tree.
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# author: adefossez
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import logging
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from diffq import DiffQuantizer
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import torch.hub
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from .model import Demucs
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from .tasnet import ConvTasNet
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from .utils import set_state
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logger = logging.getLogger(__name__)
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ROOT = "https://dl.fbaipublicfiles.com/demucs/v3.0/"
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PRETRAINED_MODELS = {
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'demucs': 'e07c671f',
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'demucs48_hq': '28a1282c',
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'demucs_extra': '3646af93',
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'demucs_quantized': '07afea75',
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'tasnet': 'beb46fac',
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'tasnet_extra': 'df3777b2',
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'demucs_unittest': '09ebc15f',
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}
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SOURCES = ["drums", "bass", "other", "vocals"]
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def get_url(name):
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sig = PRETRAINED_MODELS[name]
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return ROOT + name + "-" + sig[:8] + ".th"
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def is_pretrained(name):
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return name in PRETRAINED_MODELS
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def load_pretrained(name):
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if name == "demucs":
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return demucs(pretrained=True)
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elif name == "demucs48_hq":
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return demucs(pretrained=True, hq=True, channels=48)
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elif name == "demucs_extra":
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return demucs(pretrained=True, extra=True)
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elif name == "demucs_quantized":
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return demucs(pretrained=True, quantized=True)
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elif name == "demucs_unittest":
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return demucs_unittest(pretrained=True)
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elif name == "tasnet":
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return tasnet(pretrained=True)
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elif name == "tasnet_extra":
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return tasnet(pretrained=True, extra=True)
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else:
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raise ValueError(f"Invalid pretrained name {name}")
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def _load_state(name, model, quantizer=None):
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url = get_url(name)
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state = torch.hub.load_state_dict_from_url(url, map_location='cpu', check_hash=True)
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set_state(model, quantizer, state)
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if quantizer:
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quantizer.detach()
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def demucs_unittest(pretrained=True):
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model = Demucs(channels=4, sources=SOURCES)
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if pretrained:
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_load_state('demucs_unittest', model)
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return model
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def demucs(pretrained=True, extra=False, quantized=False, hq=False, channels=64):
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if not pretrained and (extra or quantized or hq):
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raise ValueError("if extra or quantized is True, pretrained must be True.")
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model = Demucs(sources=SOURCES, channels=channels)
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if pretrained:
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name = 'demucs'
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if channels != 64:
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name += str(channels)
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quantizer = None
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if sum([extra, quantized, hq]) > 1:
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raise ValueError("Only one of extra, quantized, hq, can be True.")
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if quantized:
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quantizer = DiffQuantizer(model, group_size=8, min_size=1)
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name += '_quantized'
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if extra:
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name += '_extra'
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if hq:
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name += '_hq'
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_load_state(name, model, quantizer)
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return model
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def tasnet(pretrained=True, extra=False):
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if not pretrained and extra:
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raise ValueError("if extra is True, pretrained must be True.")
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model = ConvTasNet(X=10, sources=SOURCES)
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if pretrained:
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name = 'tasnet'
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if extra:
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name = 'tasnet_extra'
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_load_state(name, model)
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return model
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