Format code (#366)
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207
MDXNet.py
207
MDXNet.py
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@ -1,5 +1,5 @@
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import soundfile as sf
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import torch,pdb,time,argparse,os,warnings,sys,librosa
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import torch, pdb, time, argparse, os, warnings, sys, librosa
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import numpy as np
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import onnxruntime as ort
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from scipy.io.wavfile import write
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@ -8,96 +8,133 @@ import torch
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import torch.nn as nn
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dim_c = 4
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class Conv_TDF_net_trim():
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def __init__(self, device, model_name, target_name,
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L, dim_f, dim_t, n_fft, hop=1024):
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class Conv_TDF_net_trim:
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def __init__(
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self, device, model_name, target_name, L, dim_f, dim_t, n_fft, hop=1024
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):
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super(Conv_TDF_net_trim, self).__init__()
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self.dim_f = dim_f
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self.dim_t = 2 ** dim_t
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self.dim_t = 2**dim_t
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self.n_fft = n_fft
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self.hop = hop
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self.n_bins = self.n_fft // 2 + 1
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self.chunk_size = hop * (self.dim_t - 1)
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self.window = torch.hann_window(window_length=self.n_fft, periodic=True).to(device)
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self.window = torch.hann_window(window_length=self.n_fft, periodic=True).to(
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device
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)
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self.target_name = target_name
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self.blender = 'blender' in model_name
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self.blender = "blender" in model_name
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out_c = dim_c * 4 if target_name == '*' else dim_c
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self.freq_pad = torch.zeros([1, out_c, self.n_bins - self.dim_f, self.dim_t]).to(device)
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out_c = dim_c * 4 if target_name == "*" else dim_c
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self.freq_pad = torch.zeros(
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[1, out_c, self.n_bins - self.dim_f, self.dim_t]
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).to(device)
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self.n = L // 2
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def stft(self, x):
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x = x.reshape([-1, self.chunk_size])
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x = torch.stft(x, n_fft=self.n_fft, hop_length=self.hop, window=self.window, center=True, return_complex=True)
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x = torch.stft(
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x,
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n_fft=self.n_fft,
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hop_length=self.hop,
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window=self.window,
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center=True,
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return_complex=True,
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)
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x = torch.view_as_real(x)
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x = x.permute([0, 3, 1, 2])
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x = x.reshape([-1, 2, 2, self.n_bins, self.dim_t]).reshape([-1, dim_c, self.n_bins, self.dim_t])
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return x[:, :, :self.dim_f]
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x = x.reshape([-1, 2, 2, self.n_bins, self.dim_t]).reshape(
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[-1, dim_c, self.n_bins, self.dim_t]
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)
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return x[:, :, : self.dim_f]
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def istft(self, x, freq_pad=None):
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freq_pad = self.freq_pad.repeat([x.shape[0], 1, 1, 1]) if freq_pad is None else freq_pad
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freq_pad = (
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self.freq_pad.repeat([x.shape[0], 1, 1, 1])
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if freq_pad is None
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else freq_pad
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)
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x = torch.cat([x, freq_pad], -2)
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c = 4 * 2 if self.target_name == '*' else 2
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x = x.reshape([-1, c, 2, self.n_bins, self.dim_t]).reshape([-1, 2, self.n_bins, self.dim_t])
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c = 4 * 2 if self.target_name == "*" else 2
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x = x.reshape([-1, c, 2, self.n_bins, self.dim_t]).reshape(
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[-1, 2, self.n_bins, self.dim_t]
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)
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x = x.permute([0, 2, 3, 1])
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x = x.contiguous()
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x = torch.view_as_complex(x)
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x = torch.istft(x, n_fft=self.n_fft, hop_length=self.hop, window=self.window, center=True)
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x = torch.istft(
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x, n_fft=self.n_fft, hop_length=self.hop, window=self.window, center=True
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)
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return x.reshape([-1, c, self.chunk_size])
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def get_models(device, dim_f, dim_t, n_fft):
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return Conv_TDF_net_trim(
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device=device,
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model_name='Conv-TDF', target_name='vocals',
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model_name="Conv-TDF",
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target_name="vocals",
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L=11,
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dim_f=dim_f, dim_t=dim_t,
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n_fft=n_fft
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dim_f=dim_f,
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dim_t=dim_t,
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n_fft=n_fft,
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)
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warnings.filterwarnings("ignore")
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cpu = torch.device('cpu')
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device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
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cpu = torch.device("cpu")
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device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
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class Predictor:
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def __init__(self,args):
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self.args=args
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self.model_ = get_models(device=cpu, dim_f=args.dim_f, dim_t=args.dim_t, n_fft=args.n_fft)
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self.model = ort.InferenceSession(os.path.join(args.onnx,self.model_.target_name+'.onnx'), providers=['CUDAExecutionProvider', 'CPUExecutionProvider'])
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print('onnx load done')
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def __init__(self, args):
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self.args = args
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self.model_ = get_models(
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device=cpu, dim_f=args.dim_f, dim_t=args.dim_t, n_fft=args.n_fft
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)
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self.model = ort.InferenceSession(
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os.path.join(args.onnx, self.model_.target_name + ".onnx"),
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providers=["CUDAExecutionProvider", "CPUExecutionProvider"],
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)
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print("onnx load done")
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def demix(self, mix):
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samples = mix.shape[-1]
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margin = self.args.margin
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chunk_size = self.args.chunks*44100
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assert not margin == 0, 'margin cannot be zero!'
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chunk_size = self.args.chunks * 44100
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assert not margin == 0, "margin cannot be zero!"
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if margin > chunk_size:
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margin = chunk_size
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segmented_mix = {}
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if self.args.chunks == 0 or samples < chunk_size:
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chunk_size = samples
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counter = -1
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for skip in range(0, samples, chunk_size):
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counter+=1
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counter += 1
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s_margin = 0 if counter == 0 else margin
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end = min(skip+chunk_size+margin, samples)
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end = min(skip + chunk_size + margin, samples)
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start = skip-s_margin
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start = skip - s_margin
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segmented_mix[skip] = mix[:,start:end].copy()
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segmented_mix[skip] = mix[:, start:end].copy()
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if end == samples:
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break
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sources = self.demix_base(segmented_mix, margin_size=margin)
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'''
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"""
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mix:(2,big_sample)
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segmented_mix:offset->(2,small_sample)
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sources:(1,2,big_sample)
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'''
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"""
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return sources
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def demix_base(self, mixes, margin_size):
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chunked_sources = []
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progress_bar = tqdm(total=len(mixes))
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@ -106,15 +143,17 @@ class Predictor:
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cmix = mixes[mix]
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sources = []
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n_sample = cmix.shape[1]
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model=self.model_
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trim = model.n_fft//2
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gen_size = model.chunk_size-2*trim
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pad = gen_size - n_sample%gen_size
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mix_p = np.concatenate((np.zeros((2,trim)), cmix, np.zeros((2,pad)), np.zeros((2,trim))), 1)
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model = self.model_
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trim = model.n_fft // 2
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gen_size = model.chunk_size - 2 * trim
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pad = gen_size - n_sample % gen_size
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mix_p = np.concatenate(
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(np.zeros((2, trim)), cmix, np.zeros((2, pad)), np.zeros((2, trim))), 1
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)
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mix_waves = []
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i = 0
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while i < n_sample + pad:
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waves = np.array(mix_p[:, i:i+model.chunk_size])
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waves = np.array(mix_p[:, i : i + model.chunk_size])
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mix_waves.append(waves)
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i += gen_size
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mix_waves = torch.tensor(mix_waves, dtype=torch.float32).to(cpu)
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@ -122,68 +161,84 @@ class Predictor:
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_ort = self.model
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spek = model.stft(mix_waves)
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if self.args.denoise:
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spec_pred = -_ort.run(None, {'input': -spek.cpu().numpy()})[0]*0.5+_ort.run(None, {'input': spek.cpu().numpy()})[0]*0.5
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spec_pred = (
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-_ort.run(None, {"input": -spek.cpu().numpy()})[0] * 0.5
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+ _ort.run(None, {"input": spek.cpu().numpy()})[0] * 0.5
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)
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tar_waves = model.istft(torch.tensor(spec_pred))
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else:
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tar_waves = model.istft(torch.tensor(_ort.run(None, {'input': spek.cpu().numpy()})[0]))
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tar_signal = tar_waves[:,:,trim:-trim].transpose(0,1).reshape(2, -1).numpy()[:, :-pad]
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tar_waves = model.istft(
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torch.tensor(_ort.run(None, {"input": spek.cpu().numpy()})[0])
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)
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tar_signal = (
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tar_waves[:, :, trim:-trim]
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.transpose(0, 1)
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.reshape(2, -1)
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.numpy()[:, :-pad]
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)
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start = 0 if mix == 0 else margin_size
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end = None if mix == list(mixes.keys())[::-1][0] else -margin_size
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if margin_size == 0:
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end = None
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sources.append(tar_signal[:,start:end])
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sources.append(tar_signal[:, start:end])
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progress_bar.update(1)
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chunked_sources.append(sources)
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_sources = np.concatenate(chunked_sources, axis=-1)
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# del self.model
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progress_bar.close()
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return _sources
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def prediction(self, m,vocal_root,others_root,format):
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os.makedirs(vocal_root,exist_ok=True)
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os.makedirs(others_root,exist_ok=True)
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def prediction(self, m, vocal_root, others_root, format):
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os.makedirs(vocal_root, exist_ok=True)
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os.makedirs(others_root, exist_ok=True)
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basename = os.path.basename(m)
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mix, rate = librosa.load(m, mono=False, sr=44100)
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if mix.ndim == 1:
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mix = np.asfortranarray([mix,mix])
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mix = np.asfortranarray([mix, mix])
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mix = mix.T
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sources = self.demix(mix.T)
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opt=sources[0].T
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sf.write("%s/%s_main_vocal.%s"%(vocal_root,basename,format), mix-opt, rate)
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sf.write("%s/%s_others.%s"%(others_root,basename,format), opt , rate)
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opt = sources[0].T
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sf.write(
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"%s/%s_main_vocal.%s" % (vocal_root, basename, format), mix - opt, rate
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)
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sf.write("%s/%s_others.%s" % (others_root, basename, format), opt, rate)
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class MDXNetDereverb():
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def __init__(self,chunks):
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self.onnx="uvr5_weights/onnx_dereverb_By_FoxJoy"
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self.shifts=10#'Predict with randomised equivariant stabilisation'
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self.mixing="min_mag"#['default','min_mag','max_mag']
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self.chunks=chunks
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self.margin=44100
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self.dim_t=9
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self.dim_f=3072
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self.n_fft=6144
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self.denoise=True
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self.pred=Predictor(self)
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def _path_audio_(self,input,vocal_root,others_root,format):
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self.pred.prediction(input,vocal_root,others_root,format)
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class MDXNetDereverb:
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def __init__(self, chunks):
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self.onnx = "uvr5_weights/onnx_dereverb_By_FoxJoy"
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self.shifts = 10 #'Predict with randomised equivariant stabilisation'
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self.mixing = "min_mag" # ['default','min_mag','max_mag']
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self.chunks = chunks
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self.margin = 44100
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self.dim_t = 9
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self.dim_f = 3072
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self.n_fft = 6144
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self.denoise = True
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self.pred = Predictor(self)
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if __name__ == '__main__':
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dereverb=MDXNetDereverb(15)
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def _path_audio_(self, input, vocal_root, others_root, format):
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self.pred.prediction(input, vocal_root, others_root, format)
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if __name__ == "__main__":
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dereverb = MDXNetDereverb(15)
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from time import time as ttime
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t0=ttime()
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t0 = ttime()
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dereverb._path_audio_(
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"雪雪伴奏对消HP5.wav",
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"vocal",
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"others",
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)
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t1=ttime()
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print(t1-t0)
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t1 = ttime()
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print(t1 - t0)
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'''
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"""
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runtime\python.exe MDXNet.py
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@ -195,4 +250,4 @@ runtime\python.exe MDXNet.py
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half15:0.7G->6.6G,22.69s
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fp32-15:0.7G->6.6G,20.85s
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'''
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"""
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82
infer-web.py
82
infer-web.py
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@ -83,7 +83,7 @@ import gradio as gr
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import logging
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from vc_infer_pipeline import VC
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from config import Config
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from infer_uvr5 import _audio_pre_,_audio_pre_new
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from infer_uvr5 import _audio_pre_, _audio_pre_new
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from my_utils import load_audio
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from train.process_ckpt import show_info, change_info, merge, extract_small_model
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@ -134,7 +134,7 @@ for root, dirs, files in os.walk(index_root, topdown=False):
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index_paths.append("%s/%s" % (root, name))
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uvr5_names = []
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for name in os.listdir(weight_uvr5_root):
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if name.endswith(".pth")or "onnx"in name:
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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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@ -151,7 +151,7 @@ def vc_single(
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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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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
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if input_audio_path is None:
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@ -236,7 +236,7 @@ def vc_multi(
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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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format1,
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):
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try:
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dir_path = (
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@ -267,13 +267,15 @@ def vc_multi(
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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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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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sf.write(
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"%s/%s.%s" % (opt_root, os.path.basename(path),format1), audio_opt,tgt_sr
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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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except:
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info += traceback.format_exc()
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@ -284,7 +286,7 @@ def vc_multi(
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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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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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@ -294,10 +296,10 @@ def uvr(model_name, inp_root, save_root_vocal, paths, save_root_ins, agg,format0
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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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pre_fun=MDXNetDereverb(15)
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if model_name == "onnx_dereverb_By_FoxJoy":
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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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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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@ -319,7 +321,9 @@ def uvr(model_name, inp_root, save_root_vocal, paths, save_root_ins, agg,format0
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and info["streams"][0]["sample_rate"] == "44100"
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):
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need_reformat = 0
|
||||
pre_fun._path_audio_(inp_path, save_root_ins, save_root_vocal,format0)
|
||||
pre_fun._path_audio_(
|
||||
inp_path, save_root_ins, save_root_vocal, format0
|
||||
)
|
||||
done = 1
|
||||
except:
|
||||
need_reformat = 1
|
||||
|
|
@ -333,7 +337,9 @@ def uvr(model_name, inp_root, save_root_vocal, paths, save_root_ins, agg,format0
|
|||
inp_path = tmp_path
|
||||
try:
|
||||
if done == 0:
|
||||
pre_fun._path_audio_(inp_path, save_root_ins, save_root_vocal,format0)
|
||||
pre_fun._path_audio_(
|
||||
inp_path, save_root_ins, save_root_vocal, format0
|
||||
)
|
||||
infos.append("%s->Success" % (os.path.basename(inp_path)))
|
||||
yield "\n".join(infos)
|
||||
except:
|
||||
|
|
@ -346,7 +352,7 @@ def uvr(model_name, inp_root, save_root_vocal, paths, save_root_ins, agg,format0
|
|||
yield "\n".join(infos)
|
||||
finally:
|
||||
try:
|
||||
if (model_name == "onnx_dereverb_By_FoxJoy"):
|
||||
if model_name == "onnx_dereverb_By_FoxJoy":
|
||||
del pre_fun.pred.model
|
||||
del pre_fun.pred.model_
|
||||
else:
|
||||
|
|
@ -804,7 +810,7 @@ def train_index(exp_dir1, version19):
|
|||
faiss.write_index(
|
||||
index,
|
||||
"%s/trained_IVF%s_Flat_nprobe_%s_%s_%s.index"
|
||||
% (exp_dir, n_ivf, index_ivf.nprobe,exp_dir1, version19),
|
||||
% (exp_dir, n_ivf, index_ivf.nprobe, exp_dir1, version19),
|
||||
)
|
||||
# faiss.write_index(index, '%s/trained_IVF%s_Flat_FastScan_%s.index'%(exp_dir,n_ivf,version19))
|
||||
infos.append("adding")
|
||||
|
|
@ -815,11 +821,11 @@ def train_index(exp_dir1, version19):
|
|||
faiss.write_index(
|
||||
index,
|
||||
"%s/added_IVF%s_Flat_nprobe_%s_%s_%s.index"
|
||||
% (exp_dir, n_ivf, index_ivf.nprobe,exp_dir1, version19),
|
||||
% (exp_dir, n_ivf, index_ivf.nprobe, exp_dir1, version19),
|
||||
)
|
||||
infos.append(
|
||||
"成功构建索引，added_IVF%s_Flat_nprobe_%s_%s_%s.index"
|
||||
% (n_ivf, index_ivf.nprobe,exp_dir1, version19)
|
||||
% (n_ivf, index_ivf.nprobe, exp_dir1, version19)
|
||||
)
|
||||
# faiss.write_index(index, '%s/added_IVF%s_Flat_FastScan_%s.index'%(exp_dir,n_ivf,version19))
|
||||
# infos.append("成功构建索引，added_IVF%s_Flat_FastScan_%s.index"%(n_ivf,version19))
|
||||
|
|
@ -1044,7 +1050,7 @@ def train1key(
|
|||
faiss.write_index(
|
||||
index,
|
||||
"%s/trained_IVF%s_Flat_nprobe_%s_%s_%s.index"
|
||||
% (model_log_dir, n_ivf, index_ivf.nprobe,exp_dir1, version19),
|
||||
% (model_log_dir, n_ivf, index_ivf.nprobe, exp_dir1, version19),
|
||||
)
|
||||
yield get_info_str("adding index")
|
||||
batch_size_add = 8192
|
||||
|
|
@ -1053,11 +1059,11 @@ def train1key(
|
|||
faiss.write_index(
|
||||
index,
|
||||
"%s/added_IVF%s_Flat_nprobe_%s_%s_%s.index"
|
||||
% (model_log_dir, n_ivf, index_ivf.nprobe,exp_dir1, version19),
|
||||
% (model_log_dir, n_ivf, index_ivf.nprobe, exp_dir1, version19),
|
||||
)
|
||||
yield get_info_str(
|
||||
"成功构建索引, added_IVF%s_Flat_nprobe_%s_%s_%s.index"
|
||||
% (n_ivf, index_ivf.nprobe, exp_dir1,version19)
|
||||
% (n_ivf, index_ivf.nprobe, exp_dir1, version19)
|
||||
)
|
||||
yield get_info_str(i18n("全流程结束！"))
|
||||
|
||||
|
|
@ -1175,8 +1181,10 @@ with gr.Blocks() as app:
|
|||
value="E:\\codes\\py39\\test-20230416b\\todo-songs\\冬之花clip1.wav",
|
||||
)
|
||||
f0method0 = gr.Radio(
|
||||
label=i18n("选择音高提取算法,输入歌声可用pm提速,harvest低音好但巨慢无比,crepe效果好但吃GPU"),
|
||||
choices=["pm", "harvest","crepe"],
|
||||
label=i18n(
|
||||
"选择音高提取算法,输入歌声可用pm提速,harvest低音好但巨慢无比,crepe效果好但吃GPU"
|
||||
),
|
||||
choices=["pm", "harvest", "crepe"],
|
||||
value="pm",
|
||||
interactive=True,
|
||||
)
|
||||
|
|
@ -1233,7 +1241,9 @@ with gr.Blocks() as app:
|
|||
protect0 = gr.Slider(
|
||||
minimum=0,
|
||||
maximum=0.5,
|
||||
label=i18n("保护清辅音和呼吸声，防止电音撕裂等artifact，拉满0.5不开启，调低加大保护力度但可能降低索引效果"),
|
||||
label=i18n(
|
||||
"保护清辅音和呼吸声，防止电音撕裂等artifact，拉满0.5不开启，调低加大保护力度但可能降低索引效果"
|
||||
),
|
||||
value=0.33,
|
||||
step=0.01,
|
||||
interactive=True,
|
||||
|
|
@ -1258,7 +1268,7 @@ with gr.Blocks() as app:
|
|||
filter_radius0,
|
||||
resample_sr0,
|
||||
rms_mix_rate0,
|
||||
protect0
|
||||
protect0,
|
||||
],
|
||||
[vc_output1, vc_output2],
|
||||
)
|
||||
|
|
@ -1273,8 +1283,10 @@ with gr.Blocks() as app:
|
|||
)
|
||||
opt_input = gr.Textbox(label=i18n("指定输出文件夹"), value="opt")
|
||||
f0method1 = gr.Radio(
|
||||
label=i18n("选择音高提取算法,输入歌声可用pm提速,harvest低音好但巨慢无比,crepe效果好但吃GPU"),
|
||||
choices=["pm", "harvest","crepe"],
|
||||
label=i18n(
|
||||
"选择音高提取算法,输入歌声可用pm提速,harvest低音好但巨慢无比,crepe效果好但吃GPU"
|
||||
),
|
||||
choices=["pm", "harvest", "crepe"],
|
||||
value="pm",
|
||||
interactive=True,
|
||||
)
|
||||
|
|
@ -1328,7 +1340,9 @@ with gr.Blocks() as app:
|
|||
protect1 = gr.Slider(
|
||||
minimum=0,
|
||||
maximum=0.5,
|
||||
label=i18n("保护清辅音和呼吸声，防止电音撕裂等artifact，拉满0.5不开启，调低加大保护力度但可能降低索引效果"),
|
||||
label=i18n(
|
||||
"保护清辅音和呼吸声，防止电音撕裂等artifact，拉满0.5不开启，调低加大保护力度但可能降低索引效果"
|
||||
),
|
||||
value=0.33,
|
||||
step=0.01,
|
||||
interactive=True,
|
||||
|
|
@ -1342,9 +1356,9 @@ with gr.Blocks() as app:
|
|||
file_count="multiple", label=i18n("也可批量输入音频文件, 二选一, 优先读文件夹")
|
||||
)
|
||||
with gr.Row():
|
||||
format1= gr.Radio(
|
||||
format1 = gr.Radio(
|
||||
label=i18n("导出文件格式"),
|
||||
choices=["wav", "flac","mp3","m4a"],
|
||||
choices=["wav", "flac", "mp3", "m4a"],
|
||||
value="flac",
|
||||
interactive=True,
|
||||
)
|
||||
|
|
@ -1367,7 +1381,7 @@ with gr.Blocks() as app:
|
|||
resample_sr1,
|
||||
rms_mix_rate1,
|
||||
protect1,
|
||||
format1
|
||||
format1,
|
||||
],
|
||||
[vc_output3],
|
||||
)
|
||||
|
|
@ -1412,10 +1426,12 @@ with gr.Blocks() as app:
|
|||
opt_vocal_root = gr.Textbox(
|
||||
label=i18n("指定输出主人声文件夹"), value="opt"
|
||||
)
|
||||
opt_ins_root = gr.Textbox(label=i18n("指定输出非主人声文件夹"), value="opt")
|
||||
format0= gr.Radio(
|
||||
opt_ins_root = gr.Textbox(
|
||||
label=i18n("指定输出非主人声文件夹"), value="opt"
|
||||
)
|
||||
format0 = gr.Radio(
|
||||
label=i18n("导出文件格式"),
|
||||
choices=["wav", "flac","mp3","m4a"],
|
||||
choices=["wav", "flac", "mp3", "m4a"],
|
||||
value="flac",
|
||||
interactive=True,
|
||||
)
|
||||
|
|
@ -1430,7 +1446,7 @@ with gr.Blocks() as app:
|
|||
wav_inputs,
|
||||
opt_ins_root,
|
||||
agg,
|
||||
format0
|
||||
format0,
|
||||
],
|
||||
[vc_output4],
|
||||
)
|
||||
|
|
|
|||
|
|
@ -1,7 +1,9 @@
|
|||
import os, sys, torch, warnings, pdb
|
||||
|
||||
now_dir = os.getcwd()
|
||||
sys.path.append(now_dir)
|
||||
from json import load as ll
|
||||
|
||||
warnings.filterwarnings("ignore")
|
||||
import librosa
|
||||
import importlib
|
||||
|
|
@ -15,6 +17,7 @@ import soundfile as sf
|
|||
from uvr5_pack.lib_v5.nets_new import CascadedNet
|
||||
from uvr5_pack.lib_v5 import nets_61968KB as nets
|
||||
|
||||
|
||||
class _audio_pre_:
|
||||
def __init__(self, agg, model_path, device, is_half):
|
||||
self.model_path = model_path
|
||||
|
|
@ -41,7 +44,7 @@ class _audio_pre_:
|
|||
self.mp = mp
|
||||
self.model = model
|
||||
|
||||
def _path_audio_(self, music_file, ins_root=None, vocal_root=None,format="flac"):
|
||||
def _path_audio_(self, music_file, ins_root=None, vocal_root=None, format="flac"):
|
||||
if ins_root is None and vocal_root is None:
|
||||
return "No save root."
|
||||
name = os.path.basename(music_file)
|
||||
|
|
@ -122,9 +125,11 @@ class _audio_pre_:
|
|||
print("%s instruments done" % name)
|
||||
sf.write(
|
||||
os.path.join(
|
||||
ins_root, "instrument_{}_{}.{}".format(name, self.data["agg"],format)
|
||||
ins_root,
|
||||
"instrument_{}_{}.{}".format(name, self.data["agg"], format),
|
||||
),
|
||||
(np.array(wav_instrument) * 32768).astype("int16"), self.mp.param["sr"],
|
||||
(np.array(wav_instrument) * 32768).astype("int16"),
|
||||
self.mp.param["sr"],
|
||||
) #
|
||||
if vocal_root is not None:
|
||||
if self.data["high_end_process"].startswith("mirroring"):
|
||||
|
|
@ -139,11 +144,13 @@ class _audio_pre_:
|
|||
print("%s vocals done" % name)
|
||||
sf.write(
|
||||
os.path.join(
|
||||
vocal_root, "vocal_{}_{}.{}".format(name, self.data["agg"],format)
|
||||
vocal_root, "vocal_{}_{}.{}".format(name, self.data["agg"], format)
|
||||
),
|
||||
(np.array(wav_vocals) * 32768).astype("int16"), self.mp.param["sr"],
|
||||
(np.array(wav_vocals) * 32768).astype("int16"),
|
||||
self.mp.param["sr"],
|
||||
)
|
||||
|
||||
|
||||
class _audio_pre_new:
|
||||
def __init__(self, agg, model_path, device, is_half):
|
||||
self.model_path = model_path
|
||||
|
|
@ -157,9 +164,9 @@ class _audio_pre_new:
|
|||
"agg": agg,
|
||||
"high_end_process": "mirroring",
|
||||
}
|
||||
mp=ModelParameters("uvr5_pack/lib_v5/modelparams/4band_v3.json")
|
||||
nout=64 if "DeReverb"in model_path else 48
|
||||
model = CascadedNet(mp.param["bins"] * 2,nout)
|
||||
mp = ModelParameters("uvr5_pack/lib_v5/modelparams/4band_v3.json")
|
||||
nout = 64 if "DeReverb" in model_path else 48
|
||||
model = CascadedNet(mp.param["bins"] * 2, nout)
|
||||
cpk = torch.load(model_path, map_location="cpu")
|
||||
model.load_state_dict(cpk)
|
||||
model.eval()
|
||||
|
|
@ -171,7 +178,9 @@ class _audio_pre_new:
|
|||
self.mp = mp
|
||||
self.model = model
|
||||
|
||||
def _path_audio_(self, music_file, vocal_root=None, ins_root=None,format="flac"):#3个VR模型vocal和ins是反的
|
||||
def _path_audio_(
|
||||
self, music_file, vocal_root=None, ins_root=None, format="flac"
|
||||
): # 3个VR模型vocal和ins是反的
|
||||
if ins_root is None and vocal_root is None:
|
||||
return "No save root."
|
||||
name = os.path.basename(music_file)
|
||||
|
|
@ -252,9 +261,11 @@ class _audio_pre_new:
|
|||
print("%s instruments done" % name)
|
||||
sf.write(
|
||||
os.path.join(
|
||||
ins_root, "main_vocal_{}_{}.{}".format(name, self.data["agg"],format)
|
||||
ins_root,
|
||||
"main_vocal_{}_{}.{}".format(name, self.data["agg"], format),
|
||||
),
|
||||
(np.array(wav_instrument) * 32768).astype("int16"),self.mp.param["sr"],
|
||||
(np.array(wav_instrument) * 32768).astype("int16"),
|
||||
self.mp.param["sr"],
|
||||
) #
|
||||
if vocal_root is not None:
|
||||
if self.data["high_end_process"].startswith("mirroring"):
|
||||
|
|
@ -269,9 +280,10 @@ class _audio_pre_new:
|
|||
print("%s vocals done" % name)
|
||||
sf.write(
|
||||
os.path.join(
|
||||
vocal_root, "others_{}_{}.{}".format(name, self.data["agg"],format)
|
||||
vocal_root, "others_{}_{}.{}".format(name, self.data["agg"], format)
|
||||
),
|
||||
(np.array(wav_vocals) * 32768).astype("int16"),self.mp.param["sr"],
|
||||
(np.array(wav_vocals) * 32768).astype("int16"),
|
||||
self.mp.param["sr"],
|
||||
)
|
||||
|
||||
|
||||
|
|
@ -283,7 +295,7 @@ if __name__ == "__main__":
|
|||
# model_path = "uvr5_weights/VR-DeEchoNormal.pth"
|
||||
model_path = "uvr5_weights/DeEchoNormal.pth"
|
||||
# pre_fun = _audio_pre_(model_path=model_path, device=device, is_half=True,agg=10)
|
||||
pre_fun = _audio_pre_new(model_path=model_path, device=device, is_half=True,agg=10)
|
||||
pre_fun = _audio_pre_new(model_path=model_path, device=device, is_half=True, agg=10)
|
||||
audio_path = "雪雪伴奏对消HP5.wav"
|
||||
save_path = "opt"
|
||||
pre_fun._path_audio_(audio_path, save_path, save_path)
|
||||
|
|
|
|||
|
|
@ -4,27 +4,29 @@ import torch.nn.functional as F
|
|||
|
||||
from uvr5_pack.lib_v5 import spec_utils
|
||||
|
||||
class Conv2DBNActiv(nn.Module):
|
||||
|
||||
class Conv2DBNActiv(nn.Module):
|
||||
def __init__(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU):
|
||||
super(Conv2DBNActiv, self).__init__()
|
||||
self.conv = nn.Sequential(
|
||||
nn.Conv2d(
|
||||
nin, nout,
|
||||
nin,
|
||||
nout,
|
||||
kernel_size=ksize,
|
||||
stride=stride,
|
||||
padding=pad,
|
||||
dilation=dilation,
|
||||
bias=False),
|
||||
bias=False,
|
||||
),
|
||||
nn.BatchNorm2d(nout),
|
||||
activ()
|
||||
activ(),
|
||||
)
|
||||
|
||||
def __call__(self, x):
|
||||
return self.conv(x)
|
||||
|
||||
class Encoder(nn.Module):
|
||||
|
||||
class Encoder(nn.Module):
|
||||
def __init__(self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.LeakyReLU):
|
||||
super(Encoder, self).__init__()
|
||||
self.conv1 = Conv2DBNActiv(nin, nout, ksize, stride, pad, activ=activ)
|
||||
|
|
@ -38,15 +40,16 @@ class Encoder(nn.Module):
|
|||
|
||||
|
||||
class Decoder(nn.Module):
|
||||
|
||||
def __init__(self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.ReLU, dropout=False):
|
||||
def __init__(
|
||||
self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.ReLU, dropout=False
|
||||
):
|
||||
super(Decoder, self).__init__()
|
||||
self.conv1 = Conv2DBNActiv(nin, nout, ksize, 1, pad, activ=activ)
|
||||
# self.conv2 = Conv2DBNActiv(nout, nout, ksize, 1, pad, activ=activ)
|
||||
self.dropout = nn.Dropout2d(0.1) if dropout else None
|
||||
|
||||
def __call__(self, x, skip=None):
|
||||
x = F.interpolate(x, scale_factor=2, mode='bilinear', align_corners=True)
|
||||
x = F.interpolate(x, scale_factor=2, mode="bilinear", align_corners=True)
|
||||
|
||||
if skip is not None:
|
||||
skip = spec_utils.crop_center(skip, x)
|
||||
|
|
@ -62,12 +65,11 @@ class Decoder(nn.Module):
|
|||
|
||||
|
||||
class ASPPModule(nn.Module):
|
||||
|
||||
def __init__(self, nin, nout, dilations=(4, 8, 12), activ=nn.ReLU, dropout=False):
|
||||
super(ASPPModule, self).__init__()
|
||||
self.conv1 = nn.Sequential(
|
||||
nn.AdaptiveAvgPool2d((1, None)),
|
||||
Conv2DBNActiv(nin, nout, 1, 1, 0, activ=activ)
|
||||
Conv2DBNActiv(nin, nout, 1, 1, 0, activ=activ),
|
||||
)
|
||||
self.conv2 = Conv2DBNActiv(nin, nout, 1, 1, 0, activ=activ)
|
||||
self.conv3 = Conv2DBNActiv(
|
||||
|
|
@ -84,7 +86,9 @@ class ASPPModule(nn.Module):
|
|||
|
||||
def forward(self, x):
|
||||
_, _, h, w = x.size()
|
||||
feat1 = F.interpolate(self.conv1(x), size=(h, w), mode='bilinear', align_corners=True)
|
||||
feat1 = F.interpolate(
|
||||
self.conv1(x), size=(h, w), mode="bilinear", align_corners=True
|
||||
)
|
||||
feat2 = self.conv2(x)
|
||||
feat3 = self.conv3(x)
|
||||
feat4 = self.conv4(x)
|
||||
|
|
@ -99,19 +103,14 @@ class ASPPModule(nn.Module):
|
|||
|
||||
|
||||
class LSTMModule(nn.Module):
|
||||
|
||||
def __init__(self, nin_conv, nin_lstm, nout_lstm):
|
||||
super(LSTMModule, self).__init__()
|
||||
self.conv = Conv2DBNActiv(nin_conv, 1, 1, 1, 0)
|
||||
self.lstm = nn.LSTM(
|
||||
input_size=nin_lstm,
|
||||
hidden_size=nout_lstm // 2,
|
||||
bidirectional=True
|
||||
input_size=nin_lstm, hidden_size=nout_lstm // 2, bidirectional=True
|
||||
)
|
||||
self.dense = nn.Sequential(
|
||||
nn.Linear(nout_lstm, nin_lstm),
|
||||
nn.BatchNorm1d(nin_lstm),
|
||||
nn.ReLU()
|
||||
nn.Linear(nout_lstm, nin_lstm), nn.BatchNorm1d(nin_lstm), nn.ReLU()
|
||||
)
|
||||
|
||||
def forward(self, x):
|
||||
|
|
|
|||
|
|
@ -3,9 +3,11 @@ from torch import nn
|
|||
import torch.nn.functional as F
|
||||
from uvr5_pack.lib_v5 import layers_new as layers
|
||||
|
||||
class BaseNet(nn.Module):
|
||||
|
||||
def __init__(self, nin, nout, nin_lstm, nout_lstm, dilations=((4, 2), (8, 4), (12, 6))):
|
||||
class BaseNet(nn.Module):
|
||||
def __init__(
|
||||
self, nin, nout, nin_lstm, nout_lstm, dilations=((4, 2), (8, 4), (12, 6))
|
||||
):
|
||||
super(BaseNet, self).__init__()
|
||||
self.enc1 = layers.Conv2DBNActiv(nin, nout, 3, 1, 1)
|
||||
self.enc2 = layers.Encoder(nout, nout * 2, 3, 2, 1)
|
||||
|
|
@ -38,8 +40,8 @@ class BaseNet(nn.Module):
|
|||
|
||||
return h
|
||||
|
||||
class CascadedNet(nn.Module):
|
||||
|
||||
class CascadedNet(nn.Module):
|
||||
def __init__(self, n_fft, nout=32, nout_lstm=128):
|
||||
super(CascadedNet, self).__init__()
|
||||
|
||||
|
|
@ -50,24 +52,30 @@ class CascadedNet(nn.Module):
|
|||
|
||||
self.stg1_low_band_net = nn.Sequential(
|
||||
BaseNet(2, nout // 2, self.nin_lstm // 2, nout_lstm),
|
||||
layers.Conv2DBNActiv(nout // 2, nout // 4, 1, 1, 0)
|
||||
)
|
||||
|
||||
self.stg1_high_band_net = BaseNet(2, nout // 4, self.nin_lstm // 2, nout_lstm // 2)
|
||||
layers.Conv2DBNActiv(nout // 2, nout // 4, 1, 1, 0),
|
||||
)
|
||||
|
||||
self.stg1_high_band_net = BaseNet(
|
||||
2, nout // 4, self.nin_lstm // 2, nout_lstm // 2
|
||||
)
|
||||
|
||||
self.stg2_low_band_net = nn.Sequential(
|
||||
BaseNet(nout // 4 + 2, nout, self.nin_lstm // 2, nout_lstm),
|
||||
layers.Conv2DBNActiv(nout, nout // 2, 1, 1, 0)
|
||||
)
|
||||
self.stg2_high_band_net = BaseNet(nout // 4 + 2, nout // 2, self.nin_lstm // 2, nout_lstm // 2)
|
||||
layers.Conv2DBNActiv(nout, nout // 2, 1, 1, 0),
|
||||
)
|
||||
self.stg2_high_band_net = BaseNet(
|
||||
nout // 4 + 2, nout // 2, self.nin_lstm // 2, nout_lstm // 2
|
||||
)
|
||||
|
||||
self.stg3_full_band_net = BaseNet(3 * nout // 4 + 2, nout, self.nin_lstm, nout_lstm)
|
||||
self.stg3_full_band_net = BaseNet(
|
||||
3 * nout // 4 + 2, nout, self.nin_lstm, nout_lstm
|
||||
)
|
||||
|
||||
self.out = nn.Conv2d(nout, 2, 1, bias=False)
|
||||
self.aux_out = nn.Conv2d(3 * nout // 4, 2, 1, bias=False)
|
||||
|
||||
def forward(self, x):
|
||||
x = x[:, :, :self.max_bin]
|
||||
x = x[:, :, : self.max_bin]
|
||||
|
||||
bandw = x.size()[2] // 2
|
||||
l1_in = x[:, :, :bandw]
|
||||
|
|
@ -89,7 +97,7 @@ class CascadedNet(nn.Module):
|
|||
mask = F.pad(
|
||||
input=mask,
|
||||
pad=(0, 0, 0, self.output_bin - mask.size()[2]),
|
||||
mode='replicate'
|
||||
mode="replicate",
|
||||
)
|
||||
|
||||
if self.training:
|
||||
|
|
@ -98,7 +106,7 @@ class CascadedNet(nn.Module):
|
|||
aux = F.pad(
|
||||
input=aux,
|
||||
pad=(0, 0, 0, self.output_bin - aux.size()[2]),
|
||||
mode='replicate'
|
||||
mode="replicate",
|
||||
)
|
||||
return mask, aux
|
||||
else:
|
||||
|
|
@ -108,17 +116,17 @@ class CascadedNet(nn.Module):
|
|||
mask = self.forward(x)
|
||||
|
||||
if self.offset > 0:
|
||||
mask = mask[:, :, :, self.offset:-self.offset]
|
||||
mask = mask[:, :, :, self.offset : -self.offset]
|
||||
assert mask.size()[3] > 0
|
||||
|
||||
return mask
|
||||
|
||||
def predict(self, x,aggressiveness=None):
|
||||
def predict(self, x, aggressiveness=None):
|
||||
mask = self.forward(x)
|
||||
pred_mag = x * mask
|
||||
|
||||
if self.offset > 0:
|
||||
pred_mag = pred_mag[:, :, :, self.offset:-self.offset]
|
||||
pred_mag = pred_mag[:, :, :, self.offset : -self.offset]
|
||||
assert pred_mag.size()[3] > 0
|
||||
|
||||
return pred_mag
|
||||
|
|
|
|||
|
|
@ -2,7 +2,7 @@ import numpy as np, parselmouth, torch, pdb
|
|||
from time import time as ttime
|
||||
import torch.nn.functional as F
|
||||
import scipy.signal as signal
|
||||
import pyworld, os, traceback, faiss, librosa,torchcrepe
|
||||
import pyworld, os, traceback, faiss, librosa, torchcrepe
|
||||
from scipy import signal
|
||||
from functools import lru_cache
|
||||
|
||||
|
|
@ -162,7 +162,7 @@ class VC(object):
|
|||
big_npy,
|
||||
index_rate,
|
||||
version,
|
||||
protect
|
||||
protect,
|
||||
): # ,file_index,file_big_npy
|
||||
feats = torch.from_numpy(audio0)
|
||||
if self.is_half:
|
||||
|
|
@ -184,8 +184,8 @@ class VC(object):
|
|||
with torch.no_grad():
|
||||
logits = model.extract_features(**inputs)
|
||||
feats = model.final_proj(logits[0]) if version == "v1" else logits[0]
|
||||
if(protect<0.5):
|
||||
feats0=feats.clone()
|
||||
if protect < 0.5:
|
||||
feats0 = feats.clone()
|
||||
if (
|
||||
isinstance(index, type(None)) == False
|
||||
and isinstance(big_npy, type(None)) == False
|
||||
|
|
@ -211,8 +211,10 @@ class VC(object):
|
|||
)
|
||||
|
||||
feats = F.interpolate(feats.permute(0, 2, 1), scale_factor=2).permute(0, 2, 1)
|
||||
if(protect<0.5):
|
||||
feats0 = F.interpolate(feats0.permute(0, 2, 1), scale_factor=2).permute(0, 2, 1)
|
||||
if protect < 0.5:
|
||||
feats0 = F.interpolate(feats0.permute(0, 2, 1), scale_factor=2).permute(
|
||||
0, 2, 1
|
||||
)
|
||||
t1 = ttime()
|
||||
p_len = audio0.shape[0] // self.window
|
||||
if feats.shape[1] < p_len:
|
||||
|
|
@ -221,13 +223,13 @@ class VC(object):
|
|||
pitch = pitch[:, :p_len]
|
||||
pitchf = pitchf[:, :p_len]
|
||||
|
||||
if(protect<0.5):
|
||||
if protect < 0.5:
|
||||
pitchff = pitchf.clone()
|
||||
pitchff[pitchf > 0] = 1
|
||||
pitchff[pitchf < 1] = protect
|
||||
pitchff = pitchff.unsqueeze(-1)
|
||||
feats = feats * pitchff + feats0 * (1 - pitchff)
|
||||
feats=feats.to(feats0.dtype)
|
||||
feats = feats.to(feats0.dtype)
|
||||
p_len = torch.tensor([p_len], device=self.device).long()
|
||||
with torch.no_grad():
|
||||
if pitch != None and pitchf != None:
|
||||
|
|
@ -356,7 +358,7 @@ class VC(object):
|
|||
big_npy,
|
||||
index_rate,
|
||||
version,
|
||||
protect
|
||||
protect,
|
||||
)[self.t_pad_tgt : -self.t_pad_tgt]
|
||||
)
|
||||
else:
|
||||
|
|
@ -373,7 +375,7 @@ class VC(object):
|
|||
big_npy,
|
||||
index_rate,
|
||||
version,
|
||||
protect
|
||||
protect,
|
||||
)[self.t_pad_tgt : -self.t_pad_tgt]
|
||||
)
|
||||
s = t
|
||||
|
|
@ -391,7 +393,7 @@ class VC(object):
|
|||
big_npy,
|
||||
index_rate,
|
||||
version,
|
||||
protect
|
||||
protect,
|
||||
)[self.t_pad_tgt : -self.t_pad_tgt]
|
||||
)
|
||||
else:
|
||||
|
|
@ -408,7 +410,7 @@ class VC(object):
|
|||
big_npy,
|
||||
index_rate,
|
||||
version,
|
||||
protect
|
||||
protect,
|
||||
)[self.t_pad_tgt : -self.t_pad_tgt]
|
||||
)
|
||||
audio_opt = np.concatenate(audio_opt)
|
||||
|
|
|
|||
Loading…
Reference in New Issue