286 lines
8.9 KiB
Python
286 lines
8.9 KiB
Python
import soundfile as sf
|
|
import torch, pdb, os, warnings, librosa
|
|
import numpy as np
|
|
from tqdm import tqdm
|
|
import torch
|
|
|
|
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")
|
|
import sys
|
|
|
|
now_dir = os.getcwd()
|
|
sys.path.append(now_dir)
|
|
from config import Config
|
|
|
|
cpu = torch.device("cpu")
|
|
device = Config().device
|
|
# 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
|
|
)
|
|
import onnxruntime as ort
|
|
|
|
print(ort.get_available_providers())
|
|
self.model = ort.InferenceSession(
|
|
os.path.join(args.onnx, self.model_.target_name + ".onnx"),
|
|
providers=[
|
|
"CUDAExecutionProvider",
|
|
"DmlExecutionProvider",
|
|
"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
|
|
|
|
"""
|