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import soundfile as sf
import torch, pdb, time, argparse, os, warnings, sys, librosa
import numpy as np
import onnxruntime as ort
from scipy.io.wavfile import write
from tqdm import tqdm
import torch
import torch.nn as nn
dim_c = 4
class Conv_TDF_net_trim:
def __init__(
self, device, model_name, target_name, L, dim_f, dim_t, n_fft, hop=1024
):
super(Conv_TDF_net_trim, self).__init__()
self.dim_f = dim_f
self.dim_t = 2**dim_t
self.n_fft = n_fft
self.hop = hop
self.n_bins = self.n_fft // 2 + 1
self.chunk_size = hop * (self.dim_t - 1)
self.window = torch.hann_window(window_length=self.n_fft, periodic=True).to(
device
)
self.target_name = target_name
self.blender = "blender" in model_name
out_c = dim_c * 4 if target_name == "*" else dim_c
self.freq_pad = torch.zeros(
[1, out_c, self.n_bins - self.dim_f, self.dim_t]
).to(device)
self.n = L // 2
def stft(self, x):
x = x.reshape([-1, self.chunk_size])
x = torch.stft(
x,
n_fft=self.n_fft,
hop_length=self.hop,
window=self.window,
center=True,
return_complex=True,
)
x = torch.view_as_real(x)
x = x.permute([0, 3, 1, 2])
x = x.reshape([-1, 2, 2, self.n_bins, self.dim_t]).reshape(
[-1, dim_c, self.n_bins, self.dim_t]
)
return x[:, :, : self.dim_f]
def istft(self, x, freq_pad=None):
freq_pad = (
self.freq_pad.repeat([x.shape[0], 1, 1, 1])
if freq_pad is None
else freq_pad
)
x = torch.cat([x, freq_pad], -2)
c = 4 * 2 if self.target_name == "*" else 2
x = x.reshape([-1, c, 2, self.n_bins, self.dim_t]).reshape(
[-1, 2, self.n_bins, self.dim_t]
)
x = x.permute([0, 2, 3, 1])
x = x.contiguous()
x = torch.view_as_complex(x)
x = torch.istft(
x, n_fft=self.n_fft, hop_length=self.hop, window=self.window, center=True
)
return x.reshape([-1, c, self.chunk_size])
def get_models(device, dim_f, dim_t, n_fft):
return Conv_TDF_net_trim(
device=device,
model_name="Conv-TDF",
target_name="vocals",
L=11,
dim_f=dim_f,
dim_t=dim_t,
n_fft=n_fft,
)
warnings.filterwarnings("ignore")
cpu = torch.device("cpu")
if torch.cuda.is_available():
device = torch.device("cuda:0")
elif torch.backends.mps.is_available():
device = torch.device("mps")
else:
device = torch.device("cpu")
class Predictor:
def __init__(self, args):
self.args = args
self.model_ = get_models(
device=cpu, dim_f=args.dim_f, dim_t=args.dim_t, n_fft=args.n_fft
)
self.model = ort.InferenceSession(
os.path.join(args.onnx, self.model_.target_name + ".onnx"),
providers=["CUDAExecutionProvider", "CPUExecutionProvider"],
)
print("onnx load done")
def demix(self, mix):
samples = mix.shape[-1]
margin = self.args.margin
chunk_size = self.args.chunks * 44100
assert not margin == 0, "margin cannot be zero!"
if margin > chunk_size:
margin = chunk_size
segmented_mix = {}
if self.args.chunks == 0 or samples < chunk_size:
chunk_size = samples
counter = -1
for skip in range(0, samples, chunk_size):
counter += 1
s_margin = 0 if counter == 0 else margin
end = min(skip + chunk_size + margin, samples)
start = skip - s_margin
segmented_mix[skip] = mix[:, start:end].copy()
if end == samples:
break
sources = self.demix_base(segmented_mix, margin_size=margin)
"""
mix:(2,big_sample)
segmented_mix:offset->(2,small_sample)
sources:(1,2,big_sample)
"""
return sources
def demix_base(self, mixes, margin_size):
chunked_sources = []
progress_bar = tqdm(total=len(mixes))
progress_bar.set_description("Processing")
for mix in mixes:
cmix = mixes[mix]
sources = []
n_sample = cmix.shape[1]
model = self.model_
trim = model.n_fft // 2
gen_size = model.chunk_size - 2 * trim
pad = gen_size - n_sample % gen_size
mix_p = np.concatenate(
(np.zeros((2, trim)), cmix, np.zeros((2, pad)), np.zeros((2, trim))), 1
)
mix_waves = []
i = 0
while i < n_sample + pad:
waves = np.array(mix_p[:, i : i + model.chunk_size])
mix_waves.append(waves)
i += gen_size
mix_waves = torch.tensor(mix_waves, dtype=torch.float32).to(cpu)
with torch.no_grad():
_ort = self.model
spek = model.stft(mix_waves)
if self.args.denoise:
spec_pred = (
-_ort.run(None, {"input": -spek.cpu().numpy()})[0] * 0.5
+ _ort.run(None, {"input": spek.cpu().numpy()})[0] * 0.5
)
tar_waves = model.istft(torch.tensor(spec_pred))
else:
tar_waves = model.istft(
torch.tensor(_ort.run(None, {"input": spek.cpu().numpy()})[0])
)
tar_signal = (
tar_waves[:, :, trim:-trim]
.transpose(0, 1)
.reshape(2, -1)
.numpy()[:, :-pad]
)
start = 0 if mix == 0 else margin_size
end = None if mix == list(mixes.keys())[::-1][0] else -margin_size
if margin_size == 0:
end = None
sources.append(tar_signal[:, start:end])
progress_bar.update(1)
chunked_sources.append(sources)
_sources = np.concatenate(chunked_sources, axis=-1)
# del self.model
progress_bar.close()
return _sources
def prediction(self, m, vocal_root, others_root, format):
os.makedirs(vocal_root, exist_ok=True)
os.makedirs(others_root, exist_ok=True)
basename = os.path.basename(m)
mix, rate = librosa.load(m, mono=False, sr=44100)
if mix.ndim == 1:
mix = np.asfortranarray([mix, mix])
mix = mix.T
sources = self.demix(mix.T)
opt = sources[0].T
if format in ["wav", "flac"]:
sf.write(
"%s/%s_main_vocal.%s" % (vocal_root, basename, format), mix - opt, rate
)
sf.write("%s/%s_others.%s" % (others_root, basename, format), opt, rate)
else:
path_vocal = "%s/%s_main_vocal.wav" % (vocal_root, basename)
path_other = "%s/%s_others.wav" % (others_root, basename)
sf.write(path_vocal, mix - opt, rate)
sf.write(path_other, opt, rate)
if os.path.exists(path_vocal):
os.system(
"ffmpeg -i %s -vn %s -q:a 2 -y"
% (path_vocal, path_vocal[:-4] + ".%s" % format)
)
if os.path.exists(path_other):
os.system(
"ffmpeg -i %s -vn %s -q:a 2 -y"
% (path_other, path_other[:-4] + ".%s" % format)
)
class MDXNetDereverb:
def __init__(self, chunks):
self.onnx = "uvr5_weights/onnx_dereverb_By_FoxJoy"
self.shifts = 10 #'Predict with randomised equivariant stabilisation'
self.mixing = "min_mag" # ['default','min_mag','max_mag']
self.chunks = chunks
self.margin = 44100
self.dim_t = 9
self.dim_f = 3072
self.n_fft = 6144
self.denoise = True
self.pred = Predictor(self)
def _path_audio_(self, input, vocal_root, others_root, format):
self.pred.prediction(input, vocal_root, others_root, format)
if __name__ == "__main__":
dereverb = MDXNetDereverb(15)
from time import time as ttime
t0 = ttime()
dereverb._path_audio_(
"雪雪伴奏对消HP5.wav",
"vocal",
"others",
)
t1 = ttime()
print(t1 - t0)
"""
runtime\python.exe MDXNet.py
6G:
15/9:0.8G->6.8G
14:0.8G->6.5G
25:炸
half15:0.7G->6.6G,22.69s
fp32-15:0.7G->6.6G,20.85s
"""
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