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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
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