Spaces:
Running
on
Zero
Running
on
Zero
File size: 7,743 Bytes
06c138d |
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 |
import os,sys
parent_directory = os.path.dirname(os.path.abspath(__file__))
import logging,pdb
logger = logging.getLogger(__name__)
import librosa
import numpy as np
import soundfile as sf
import torch
from uvr5.lib.lib_v5 import nets_61968KB as Nets
from uvr5.lib.lib_v5 import spec_utils
from uvr5.lib.lib_v5.model_param_init import ModelParameters
from uvr5.lib.utils import inference
class AudioPre:
def __init__(self, agg, model_path, device, is_half, tta=False):
self.model_path = model_path
self.device = device
self.data = {
# Processing Options
"postprocess": False,
"tta": tta,
# Constants
"window_size": 512,
"agg": agg,
"high_end_process": "mirroring",
}
mp = ModelParameters("%s/lib/lib_v5/modelparams/4band_v2.json"%parent_directory)
model = Nets.CascadedASPPNet(mp.param["bins"] * 2)
cpk = torch.load(model_path, map_location="cpu")
model.load_state_dict(cpk)
model.eval()
if is_half:
model = model.half().to(device)
else:
model = model.to(device)
self.mp = mp
self.model = model
def _path_audio_(
self, music_file, ins_root=None, vocal_root=None, format="flac", is_hp3=False
):
if ins_root is None and vocal_root is None:
return "No save root."
name = os.path.basename(music_file)
if ins_root is not None:
os.makedirs(ins_root, exist_ok=True)
if vocal_root is not None:
os.makedirs(vocal_root, exist_ok=True)
X_wave, y_wave, X_spec_s, y_spec_s = {}, {}, {}, {}
bands_n = len(self.mp.param["band"])
# print(bands_n)
for d in range(bands_n, 0, -1):
bp = self.mp.param["band"][d]
if d == bands_n: # high-end band
(
X_wave[d],
_,
) = librosa.core.load( # 理论上librosa读取可能对某些音频有bug，应该上ffmpeg读取，但是太麻烦了弃坑
music_file,
bp["sr"],
False,
dtype=np.float32,
res_type=bp["res_type"],
)
if X_wave[d].ndim == 1:
X_wave[d] = np.asfortranarray([X_wave[d], X_wave[d]])
else: # lower bands
X_wave[d] = librosa.core.resample(
X_wave[d + 1],
self.mp.param["band"][d + 1]["sr"],
bp["sr"],
res_type=bp["res_type"],
)
# Stft of wave source
X_spec_s[d] = spec_utils.wave_to_spectrogram_mt(
X_wave[d],
bp["hl"],
bp["n_fft"],
self.mp.param["mid_side"],
self.mp.param["mid_side_b2"],
self.mp.param["reverse"],
)
# pdb.set_trace()
if d == bands_n and self.data["high_end_process"] != "none":
input_high_end_h = (bp["n_fft"] // 2 - bp["crop_stop"]) + (
self.mp.param["pre_filter_stop"] - self.mp.param["pre_filter_start"]
)
input_high_end = X_spec_s[d][
:, bp["n_fft"] // 2 - input_high_end_h : bp["n_fft"] // 2, :
]
X_spec_m = spec_utils.combine_spectrograms(X_spec_s, self.mp)
aggresive_set = float(self.data["agg"] / 100)
aggressiveness = {
"value": aggresive_set,
"split_bin": self.mp.param["band"][1]["crop_stop"],
}
with torch.no_grad():
pred, X_mag, X_phase = inference(
X_spec_m, self.device, self.model, aggressiveness, self.data
)
# Postprocess
if self.data["postprocess"]:
pred_inv = np.clip(X_mag - pred, 0, np.inf)
pred = spec_utils.mask_silence(pred, pred_inv)
y_spec_m = pred * X_phase
v_spec_m = X_spec_m - y_spec_m
if is_hp3 == True:
ins_root,vocal_root = vocal_root,ins_root
if ins_root is not None:
if self.data["high_end_process"].startswith("mirroring"):
input_high_end_ = spec_utils.mirroring(
self.data["high_end_process"], y_spec_m, input_high_end, self.mp
)
wav_instrument = spec_utils.cmb_spectrogram_to_wave(
y_spec_m, self.mp, input_high_end_h, input_high_end_
)
else:
wav_instrument = spec_utils.cmb_spectrogram_to_wave(y_spec_m, self.mp)
logger.info("%s instruments done" % name)
if is_hp3 == True:
head = "vocal_"
else:
head = "instrument_"
if format in ["wav", "flac"]:
sf.write(
os.path.join(
ins_root,
head + "{}_{}.{}".format(name, self.data["agg"], format),
),
(np.array(wav_instrument)).astype("float32"),
self.mp.param["sr"],
) #
else:
path = os.path.join(
ins_root, head + "{}_{}.wav".format(name, self.data["agg"])
)
sf.write(
path,
(np.array(wav_instrument)).astype("float32"),
self.mp.param["sr"],
)
if os.path.exists(path):
opt_format_path = path[:-4] + ".%s" % format
os.system("ffmpeg -i %s -vn %s -q:a 2 -y" % (path, opt_format_path))
if os.path.exists(opt_format_path):
try:
os.remove(path)
except:
pass
if vocal_root is not None:
if is_hp3 == True:
head = "instrument_"
else:
head = "vocal_"
if self.data["high_end_process"].startswith("mirroring"):
input_high_end_ = spec_utils.mirroring(
self.data["high_end_process"], v_spec_m, input_high_end, self.mp
)
wav_vocals = spec_utils.cmb_spectrogram_to_wave(
v_spec_m, self.mp, input_high_end_h, input_high_end_
)
else:
wav_vocals = spec_utils.cmb_spectrogram_to_wave(v_spec_m, self.mp)
logger.info("%s vocals done" % name)
if format in ["wav", "flac"]:
sf.write(
os.path.join(
vocal_root,
head + "{}_{}.{}".format(name, self.data["agg"], format),
),
(np.array(wav_vocals)).astype("float32"),
self.mp.param["sr"],
)
else:
path = os.path.join(
vocal_root, head + "{}_{}.wav".format(name, self.data["agg"])
)
sf.write(
path,
(np.array(wav_vocals)).astype("float32"),
self.mp.param["sr"],
)
if os.path.exists(path):
opt_format_path = path[:-4] + ".%s" % format
os.system("ffmpeg -i %s -vn %s -q:a 2 -y" % (path, opt_format_path))
if os.path.exists(opt_format_path):
try:
os.remove(path)
except:
pass
|