File size: 7,469 Bytes
16734a7 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 |
import soundfile as sf
import torch
import os
import librosa
import numpy as np
import onnxruntime as ort
from pathlib import Path
from argparse import ArgumentParser
from tqdm import tqdm
class ConvTDFNet:
def __init__(self, target_name, L, dim_f, dim_t, n_fft, hop=1024):
super(ConvTDFNet, self).__init__()
self.dim_c = 4
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)
self.target_name = target_name
out_c = self.dim_c * 4 if target_name == "*" else self.dim_c
self.freq_pad = torch.zeros([1, out_c, self.n_bins - self.dim_f, self.dim_t])
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, self.dim_c, self.n_bins, self.dim_t]
)
return x[:, :, : self.dim_f]
# Inversed Short-time Fourier transform (STFT).
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])
class Predictor:
def __init__(self, args):
self.args = args
self.model_ = ConvTDFNet(
target_name="vocals",
L=11,
dim_f=args["dim_f"],
dim_t=args["dim_t"],
n_fft=args["n_fft"]
)
if torch.cuda.is_available():
self.model = ort.InferenceSession(args['model_path'], providers=['CUDAExecutionProvider'])
else:
self.model = ort.InferenceSession(args['model_path'], providers=['CPUExecutionProvider'])
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)
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(np.array(mix_waves), dtype=torch.float32)
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)
progress_bar.close()
return _sources
def predict(self, file_path):
mix, rate = librosa.load(file_path, 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
return (mix - opt, opt, rate)
def main():
parser = ArgumentParser()
parser.add_argument("files", nargs="+", type=Path, default=[], help="Source audio path")
parser.add_argument("-o", "--output", type=Path, default=Path("separated"), help="Output folder")
parser.add_argument("-m", "--model_path", type=Path, help="MDX Net ONNX Model path")
parser.add_argument("-d", "--no-denoise", dest="denoise", action="store_false", default=True, help="Disable denoising")
parser.add_argument("-M", "--margin", type=int, default=44100, help="Margin")
parser.add_argument("-c", "--chunks", type=int, default=15, help="Chunk size")
parser.add_argument("-F", "--n_fft", type=int, default=6144)
parser.add_argument("-t", "--dim_t", type=int, default=8)
parser.add_argument("-f", "--dim_f", type=int, default=2048)
args = parser.parse_args()
dict_args = vars(args)
os.makedirs(args.output, exist_ok=True)
for file_path in args.files:
predictor = Predictor(args=dict_args)
vocals, no_vocals, sampling_rate = predictor.predict(file_path)
filename = os.path.splitext(os.path.split(file_path)[-1])[0]
sf.write(os.path.join(args.output, filename+"_no_vocals.wav"), no_vocals, sampling_rate)
sf.write(os.path.join(args.output, filename+"_vocals.wav"), vocals, sampling_rate)
if __name__ == "__main__":
main()
|