c8261b2ccc
* Reformat
* rewrite _get_name_params
* Add workflow for automatic formatting
* Revert "Add workflow for automatic formatting"
This reverts commit 9111c5dbc1.
* revert Retrieval_based_Voice_Conversion_WebUI.ipynb
---------
Co-authored-by: 源文雨 <41315874+fumiama@users.noreply.github.com>
668 lines
21 KiB
Python
668 lines
21 KiB
Python
import os, librosa
|
|
import numpy as np
|
|
import soundfile as sf
|
|
from tqdm import tqdm
|
|
import json, math, hashlib
|
|
|
|
|
|
def crop_center(h1, h2):
|
|
h1_shape = h1.size()
|
|
h2_shape = h2.size()
|
|
|
|
if h1_shape[3] == h2_shape[3]:
|
|
return h1
|
|
elif h1_shape[3] < h2_shape[3]:
|
|
raise ValueError("h1_shape[3] must be greater than h2_shape[3]")
|
|
|
|
# s_freq = (h2_shape[2] - h1_shape[2]) // 2
|
|
# e_freq = s_freq + h1_shape[2]
|
|
s_time = (h1_shape[3] - h2_shape[3]) // 2
|
|
e_time = s_time + h2_shape[3]
|
|
h1 = h1[:, :, :, s_time:e_time]
|
|
|
|
return h1
|
|
|
|
|
|
def wave_to_spectrogram(
|
|
wave, hop_length, n_fft, mid_side=False, mid_side_b2=False, reverse=False
|
|
):
|
|
if reverse:
|
|
wave_left = np.flip(np.asfortranarray(wave[0]))
|
|
wave_right = np.flip(np.asfortranarray(wave[1]))
|
|
elif mid_side:
|
|
wave_left = np.asfortranarray(np.add(wave[0], wave[1]) / 2)
|
|
wave_right = np.asfortranarray(np.subtract(wave[0], wave[1]))
|
|
elif mid_side_b2:
|
|
wave_left = np.asfortranarray(np.add(wave[1], wave[0] * 0.5))
|
|
wave_right = np.asfortranarray(np.subtract(wave[0], wave[1] * 0.5))
|
|
else:
|
|
wave_left = np.asfortranarray(wave[0])
|
|
wave_right = np.asfortranarray(wave[1])
|
|
|
|
spec_left = librosa.stft(wave_left, n_fft, hop_length=hop_length)
|
|
spec_right = librosa.stft(wave_right, n_fft, hop_length=hop_length)
|
|
|
|
spec = np.asfortranarray([spec_left, spec_right])
|
|
|
|
return spec
|
|
|
|
|
|
def wave_to_spectrogram_mt(
|
|
wave, hop_length, n_fft, mid_side=False, mid_side_b2=False, reverse=False
|
|
):
|
|
import threading
|
|
|
|
if reverse:
|
|
wave_left = np.flip(np.asfortranarray(wave[0]))
|
|
wave_right = np.flip(np.asfortranarray(wave[1]))
|
|
elif mid_side:
|
|
wave_left = np.asfortranarray(np.add(wave[0], wave[1]) / 2)
|
|
wave_right = np.asfortranarray(np.subtract(wave[0], wave[1]))
|
|
elif mid_side_b2:
|
|
wave_left = np.asfortranarray(np.add(wave[1], wave[0] * 0.5))
|
|
wave_right = np.asfortranarray(np.subtract(wave[0], wave[1] * 0.5))
|
|
else:
|
|
wave_left = np.asfortranarray(wave[0])
|
|
wave_right = np.asfortranarray(wave[1])
|
|
|
|
def run_thread(**kwargs):
|
|
global spec_left
|
|
spec_left = librosa.stft(**kwargs)
|
|
|
|
thread = threading.Thread(
|
|
target=run_thread,
|
|
kwargs={"y": wave_left, "n_fft": n_fft, "hop_length": hop_length},
|
|
)
|
|
thread.start()
|
|
spec_right = librosa.stft(wave_right, n_fft, hop_length=hop_length)
|
|
thread.join()
|
|
|
|
spec = np.asfortranarray([spec_left, spec_right])
|
|
|
|
return spec
|
|
|
|
|
|
def combine_spectrograms(specs, mp):
|
|
l = min([specs[i].shape[2] for i in specs])
|
|
spec_c = np.zeros(shape=(2, mp.param["bins"] + 1, l), dtype=np.complex64)
|
|
offset = 0
|
|
bands_n = len(mp.param["band"])
|
|
|
|
for d in range(1, bands_n + 1):
|
|
h = mp.param["band"][d]["crop_stop"] - mp.param["band"][d]["crop_start"]
|
|
spec_c[:, offset : offset + h, :l] = specs[d][
|
|
:, mp.param["band"][d]["crop_start"] : mp.param["band"][d]["crop_stop"], :l
|
|
]
|
|
offset += h
|
|
|
|
if offset > mp.param["bins"]:
|
|
raise ValueError("Too much bins")
|
|
|
|
# lowpass fiter
|
|
if (
|
|
mp.param["pre_filter_start"] > 0
|
|
): # and mp.param['band'][bands_n]['res_type'] in ['scipy', 'polyphase']:
|
|
if bands_n == 1:
|
|
spec_c = fft_lp_filter(
|
|
spec_c, mp.param["pre_filter_start"], mp.param["pre_filter_stop"]
|
|
)
|
|
else:
|
|
gp = 1
|
|
for b in range(
|
|
mp.param["pre_filter_start"] + 1, mp.param["pre_filter_stop"]
|
|
):
|
|
g = math.pow(
|
|
10, -(b - mp.param["pre_filter_start"]) * (3.5 - gp) / 20.0
|
|
)
|
|
gp = g
|
|
spec_c[:, b, :] *= g
|
|
|
|
return np.asfortranarray(spec_c)
|
|
|
|
|
|
def spectrogram_to_image(spec, mode="magnitude"):
|
|
if mode == "magnitude":
|
|
if np.iscomplexobj(spec):
|
|
y = np.abs(spec)
|
|
else:
|
|
y = spec
|
|
y = np.log10(y**2 + 1e-8)
|
|
elif mode == "phase":
|
|
if np.iscomplexobj(spec):
|
|
y = np.angle(spec)
|
|
else:
|
|
y = spec
|
|
|
|
y -= y.min()
|
|
y *= 255 / y.max()
|
|
img = np.uint8(y)
|
|
|
|
if y.ndim == 3:
|
|
img = img.transpose(1, 2, 0)
|
|
img = np.concatenate([np.max(img, axis=2, keepdims=True), img], axis=2)
|
|
|
|
return img
|
|
|
|
|
|
def reduce_vocal_aggressively(X, y, softmask):
|
|
v = X - y
|
|
y_mag_tmp = np.abs(y)
|
|
v_mag_tmp = np.abs(v)
|
|
|
|
v_mask = v_mag_tmp > y_mag_tmp
|
|
y_mag = np.clip(y_mag_tmp - v_mag_tmp * v_mask * softmask, 0, np.inf)
|
|
|
|
return y_mag * np.exp(1.0j * np.angle(y))
|
|
|
|
|
|
def mask_silence(mag, ref, thres=0.2, min_range=64, fade_size=32):
|
|
if min_range < fade_size * 2:
|
|
raise ValueError("min_range must be >= fade_area * 2")
|
|
|
|
mag = mag.copy()
|
|
|
|
idx = np.where(ref.mean(axis=(0, 1)) < thres)[0]
|
|
starts = np.insert(idx[np.where(np.diff(idx) != 1)[0] + 1], 0, idx[0])
|
|
ends = np.append(idx[np.where(np.diff(idx) != 1)[0]], idx[-1])
|
|
uninformative = np.where(ends - starts > min_range)[0]
|
|
if len(uninformative) > 0:
|
|
starts = starts[uninformative]
|
|
ends = ends[uninformative]
|
|
old_e = None
|
|
for s, e in zip(starts, ends):
|
|
if old_e is not None and s - old_e < fade_size:
|
|
s = old_e - fade_size * 2
|
|
|
|
if s != 0:
|
|
weight = np.linspace(0, 1, fade_size)
|
|
mag[:, :, s : s + fade_size] += weight * ref[:, :, s : s + fade_size]
|
|
else:
|
|
s -= fade_size
|
|
|
|
if e != mag.shape[2]:
|
|
weight = np.linspace(1, 0, fade_size)
|
|
mag[:, :, e - fade_size : e] += weight * ref[:, :, e - fade_size : e]
|
|
else:
|
|
e += fade_size
|
|
|
|
mag[:, :, s + fade_size : e - fade_size] += ref[
|
|
:, :, s + fade_size : e - fade_size
|
|
]
|
|
old_e = e
|
|
|
|
return mag
|
|
|
|
|
|
def align_wave_head_and_tail(a, b):
|
|
l = min([a[0].size, b[0].size])
|
|
|
|
return a[:l, :l], b[:l, :l]
|
|
|
|
|
|
def cache_or_load(mix_path, inst_path, mp):
|
|
mix_basename = os.path.splitext(os.path.basename(mix_path))[0]
|
|
inst_basename = os.path.splitext(os.path.basename(inst_path))[0]
|
|
|
|
cache_dir = "mph{}".format(
|
|
hashlib.sha1(json.dumps(mp.param, sort_keys=True).encode("utf-8")).hexdigest()
|
|
)
|
|
mix_cache_dir = os.path.join("cache", cache_dir)
|
|
inst_cache_dir = os.path.join("cache", cache_dir)
|
|
|
|
os.makedirs(mix_cache_dir, exist_ok=True)
|
|
os.makedirs(inst_cache_dir, exist_ok=True)
|
|
|
|
mix_cache_path = os.path.join(mix_cache_dir, mix_basename + ".npy")
|
|
inst_cache_path = os.path.join(inst_cache_dir, inst_basename + ".npy")
|
|
|
|
if os.path.exists(mix_cache_path) and os.path.exists(inst_cache_path):
|
|
X_spec_m = np.load(mix_cache_path)
|
|
y_spec_m = np.load(inst_cache_path)
|
|
else:
|
|
X_wave, y_wave, X_spec_s, y_spec_s = {}, {}, {}, {}
|
|
|
|
for d in range(len(mp.param["band"]), 0, -1):
|
|
bp = mp.param["band"][d]
|
|
|
|
if d == len(mp.param["band"]): # high-end band
|
|
X_wave[d], _ = librosa.load(
|
|
mix_path, bp["sr"], False, dtype=np.float32, res_type=bp["res_type"]
|
|
)
|
|
y_wave[d], _ = librosa.load(
|
|
inst_path,
|
|
bp["sr"],
|
|
False,
|
|
dtype=np.float32,
|
|
res_type=bp["res_type"],
|
|
)
|
|
else: # lower bands
|
|
X_wave[d] = librosa.resample(
|
|
X_wave[d + 1],
|
|
mp.param["band"][d + 1]["sr"],
|
|
bp["sr"],
|
|
res_type=bp["res_type"],
|
|
)
|
|
y_wave[d] = librosa.resample(
|
|
y_wave[d + 1],
|
|
mp.param["band"][d + 1]["sr"],
|
|
bp["sr"],
|
|
res_type=bp["res_type"],
|
|
)
|
|
|
|
X_wave[d], y_wave[d] = align_wave_head_and_tail(X_wave[d], y_wave[d])
|
|
|
|
X_spec_s[d] = wave_to_spectrogram(
|
|
X_wave[d],
|
|
bp["hl"],
|
|
bp["n_fft"],
|
|
mp.param["mid_side"],
|
|
mp.param["mid_side_b2"],
|
|
mp.param["reverse"],
|
|
)
|
|
y_spec_s[d] = wave_to_spectrogram(
|
|
y_wave[d],
|
|
bp["hl"],
|
|
bp["n_fft"],
|
|
mp.param["mid_side"],
|
|
mp.param["mid_side_b2"],
|
|
mp.param["reverse"],
|
|
)
|
|
|
|
del X_wave, y_wave
|
|
|
|
X_spec_m = combine_spectrograms(X_spec_s, mp)
|
|
y_spec_m = combine_spectrograms(y_spec_s, mp)
|
|
|
|
if X_spec_m.shape != y_spec_m.shape:
|
|
raise ValueError("The combined spectrograms are different: " + mix_path)
|
|
|
|
_, ext = os.path.splitext(mix_path)
|
|
|
|
np.save(mix_cache_path, X_spec_m)
|
|
np.save(inst_cache_path, y_spec_m)
|
|
|
|
return X_spec_m, y_spec_m
|
|
|
|
|
|
def spectrogram_to_wave(spec, hop_length, mid_side, mid_side_b2, reverse):
|
|
spec_left = np.asfortranarray(spec[0])
|
|
spec_right = np.asfortranarray(spec[1])
|
|
|
|
wave_left = librosa.istft(spec_left, hop_length=hop_length)
|
|
wave_right = librosa.istft(spec_right, hop_length=hop_length)
|
|
|
|
if reverse:
|
|
return np.asfortranarray([np.flip(wave_left), np.flip(wave_right)])
|
|
elif mid_side:
|
|
return np.asfortranarray(
|
|
[np.add(wave_left, wave_right / 2), np.subtract(wave_left, wave_right / 2)]
|
|
)
|
|
elif mid_side_b2:
|
|
return np.asfortranarray(
|
|
[
|
|
np.add(wave_right / 1.25, 0.4 * wave_left),
|
|
np.subtract(wave_left / 1.25, 0.4 * wave_right),
|
|
]
|
|
)
|
|
else:
|
|
return np.asfortranarray([wave_left, wave_right])
|
|
|
|
|
|
def spectrogram_to_wave_mt(spec, hop_length, mid_side, reverse, mid_side_b2):
|
|
import threading
|
|
|
|
spec_left = np.asfortranarray(spec[0])
|
|
spec_right = np.asfortranarray(spec[1])
|
|
|
|
def run_thread(**kwargs):
|
|
global wave_left
|
|
wave_left = librosa.istft(**kwargs)
|
|
|
|
thread = threading.Thread(
|
|
target=run_thread, kwargs={"stft_matrix": spec_left, "hop_length": hop_length}
|
|
)
|
|
thread.start()
|
|
wave_right = librosa.istft(spec_right, hop_length=hop_length)
|
|
thread.join()
|
|
|
|
if reverse:
|
|
return np.asfortranarray([np.flip(wave_left), np.flip(wave_right)])
|
|
elif mid_side:
|
|
return np.asfortranarray(
|
|
[np.add(wave_left, wave_right / 2), np.subtract(wave_left, wave_right / 2)]
|
|
)
|
|
elif mid_side_b2:
|
|
return np.asfortranarray(
|
|
[
|
|
np.add(wave_right / 1.25, 0.4 * wave_left),
|
|
np.subtract(wave_left / 1.25, 0.4 * wave_right),
|
|
]
|
|
)
|
|
else:
|
|
return np.asfortranarray([wave_left, wave_right])
|
|
|
|
|
|
def cmb_spectrogram_to_wave(spec_m, mp, extra_bins_h=None, extra_bins=None):
|
|
wave_band = {}
|
|
bands_n = len(mp.param["band"])
|
|
offset = 0
|
|
|
|
for d in range(1, bands_n + 1):
|
|
bp = mp.param["band"][d]
|
|
spec_s = np.ndarray(
|
|
shape=(2, bp["n_fft"] // 2 + 1, spec_m.shape[2]), dtype=complex
|
|
)
|
|
h = bp["crop_stop"] - bp["crop_start"]
|
|
spec_s[:, bp["crop_start"] : bp["crop_stop"], :] = spec_m[
|
|
:, offset : offset + h, :
|
|
]
|
|
|
|
offset += h
|
|
if d == bands_n: # higher
|
|
if extra_bins_h: # if --high_end_process bypass
|
|
max_bin = bp["n_fft"] // 2
|
|
spec_s[:, max_bin - extra_bins_h : max_bin, :] = extra_bins[
|
|
:, :extra_bins_h, :
|
|
]
|
|
if bp["hpf_start"] > 0:
|
|
spec_s = fft_hp_filter(spec_s, bp["hpf_start"], bp["hpf_stop"] - 1)
|
|
if bands_n == 1:
|
|
wave = spectrogram_to_wave(
|
|
spec_s,
|
|
bp["hl"],
|
|
mp.param["mid_side"],
|
|
mp.param["mid_side_b2"],
|
|
mp.param["reverse"],
|
|
)
|
|
else:
|
|
wave = np.add(
|
|
wave,
|
|
spectrogram_to_wave(
|
|
spec_s,
|
|
bp["hl"],
|
|
mp.param["mid_side"],
|
|
mp.param["mid_side_b2"],
|
|
mp.param["reverse"],
|
|
),
|
|
)
|
|
else:
|
|
sr = mp.param["band"][d + 1]["sr"]
|
|
if d == 1: # lower
|
|
spec_s = fft_lp_filter(spec_s, bp["lpf_start"], bp["lpf_stop"])
|
|
wave = librosa.resample(
|
|
spectrogram_to_wave(
|
|
spec_s,
|
|
bp["hl"],
|
|
mp.param["mid_side"],
|
|
mp.param["mid_side_b2"],
|
|
mp.param["reverse"],
|
|
),
|
|
bp["sr"],
|
|
sr,
|
|
res_type="sinc_fastest",
|
|
)
|
|
else: # mid
|
|
spec_s = fft_hp_filter(spec_s, bp["hpf_start"], bp["hpf_stop"] - 1)
|
|
spec_s = fft_lp_filter(spec_s, bp["lpf_start"], bp["lpf_stop"])
|
|
wave2 = np.add(
|
|
wave,
|
|
spectrogram_to_wave(
|
|
spec_s,
|
|
bp["hl"],
|
|
mp.param["mid_side"],
|
|
mp.param["mid_side_b2"],
|
|
mp.param["reverse"],
|
|
),
|
|
)
|
|
# wave = librosa.core.resample(wave2, bp['sr'], sr, res_type="sinc_fastest")
|
|
wave = librosa.core.resample(wave2, bp["sr"], sr, res_type="scipy")
|
|
|
|
return wave.T
|
|
|
|
|
|
def fft_lp_filter(spec, bin_start, bin_stop):
|
|
g = 1.0
|
|
for b in range(bin_start, bin_stop):
|
|
g -= 1 / (bin_stop - bin_start)
|
|
spec[:, b, :] = g * spec[:, b, :]
|
|
|
|
spec[:, bin_stop:, :] *= 0
|
|
|
|
return spec
|
|
|
|
|
|
def fft_hp_filter(spec, bin_start, bin_stop):
|
|
g = 1.0
|
|
for b in range(bin_start, bin_stop, -1):
|
|
g -= 1 / (bin_start - bin_stop)
|
|
spec[:, b, :] = g * spec[:, b, :]
|
|
|
|
spec[:, 0 : bin_stop + 1, :] *= 0
|
|
|
|
return spec
|
|
|
|
|
|
def mirroring(a, spec_m, input_high_end, mp):
|
|
if "mirroring" == a:
|
|
mirror = np.flip(
|
|
np.abs(
|
|
spec_m[
|
|
:,
|
|
mp.param["pre_filter_start"]
|
|
- 10
|
|
- input_high_end.shape[1] : mp.param["pre_filter_start"]
|
|
- 10,
|
|
:,
|
|
]
|
|
),
|
|
1,
|
|
)
|
|
mirror = mirror * np.exp(1.0j * np.angle(input_high_end))
|
|
|
|
return np.where(
|
|
np.abs(input_high_end) <= np.abs(mirror), input_high_end, mirror
|
|
)
|
|
|
|
if "mirroring2" == a:
|
|
mirror = np.flip(
|
|
np.abs(
|
|
spec_m[
|
|
:,
|
|
mp.param["pre_filter_start"]
|
|
- 10
|
|
- input_high_end.shape[1] : mp.param["pre_filter_start"]
|
|
- 10,
|
|
:,
|
|
]
|
|
),
|
|
1,
|
|
)
|
|
mi = np.multiply(mirror, input_high_end * 1.7)
|
|
|
|
return np.where(np.abs(input_high_end) <= np.abs(mi), input_high_end, mi)
|
|
|
|
|
|
def ensembling(a, specs):
|
|
for i in range(1, len(specs)):
|
|
if i == 1:
|
|
spec = specs[0]
|
|
|
|
ln = min([spec.shape[2], specs[i].shape[2]])
|
|
spec = spec[:, :, :ln]
|
|
specs[i] = specs[i][:, :, :ln]
|
|
|
|
if "min_mag" == a:
|
|
spec = np.where(np.abs(specs[i]) <= np.abs(spec), specs[i], spec)
|
|
if "max_mag" == a:
|
|
spec = np.where(np.abs(specs[i]) >= np.abs(spec), specs[i], spec)
|
|
|
|
return spec
|
|
|
|
|
|
def stft(wave, nfft, hl):
|
|
wave_left = np.asfortranarray(wave[0])
|
|
wave_right = np.asfortranarray(wave[1])
|
|
spec_left = librosa.stft(wave_left, nfft, hop_length=hl)
|
|
spec_right = librosa.stft(wave_right, nfft, hop_length=hl)
|
|
spec = np.asfortranarray([spec_left, spec_right])
|
|
|
|
return spec
|
|
|
|
|
|
def istft(spec, hl):
|
|
spec_left = np.asfortranarray(spec[0])
|
|
spec_right = np.asfortranarray(spec[1])
|
|
|
|
wave_left = librosa.istft(spec_left, hop_length=hl)
|
|
wave_right = librosa.istft(spec_right, hop_length=hl)
|
|
wave = np.asfortranarray([wave_left, wave_right])
|
|
|
|
|
|
if __name__ == "__main__":
|
|
import cv2
|
|
import sys
|
|
import time
|
|
import argparse
|
|
from model_param_init import ModelParameters
|
|
|
|
p = argparse.ArgumentParser()
|
|
p.add_argument(
|
|
"--algorithm",
|
|
"-a",
|
|
type=str,
|
|
choices=["invert", "invert_p", "min_mag", "max_mag", "deep", "align"],
|
|
default="min_mag",
|
|
)
|
|
p.add_argument(
|
|
"--model_params",
|
|
"-m",
|
|
type=str,
|
|
default=os.path.join("modelparams", "1band_sr44100_hl512.json"),
|
|
)
|
|
p.add_argument("--output_name", "-o", type=str, default="output")
|
|
p.add_argument("--vocals_only", "-v", action="store_true")
|
|
p.add_argument("input", nargs="+")
|
|
args = p.parse_args()
|
|
|
|
start_time = time.time()
|
|
|
|
if args.algorithm.startswith("invert") and len(args.input) != 2:
|
|
raise ValueError("There should be two input files.")
|
|
|
|
if not args.algorithm.startswith("invert") and len(args.input) < 2:
|
|
raise ValueError("There must be at least two input files.")
|
|
|
|
wave, specs = {}, {}
|
|
mp = ModelParameters(args.model_params)
|
|
|
|
for i in range(len(args.input)):
|
|
spec = {}
|
|
|
|
for d in range(len(mp.param["band"]), 0, -1):
|
|
bp = mp.param["band"][d]
|
|
|
|
if d == len(mp.param["band"]): # high-end band
|
|
wave[d], _ = librosa.load(
|
|
args.input[i],
|
|
bp["sr"],
|
|
False,
|
|
dtype=np.float32,
|
|
res_type=bp["res_type"],
|
|
)
|
|
|
|
if len(wave[d].shape) == 1: # mono to stereo
|
|
wave[d] = np.array([wave[d], wave[d]])
|
|
else: # lower bands
|
|
wave[d] = librosa.resample(
|
|
wave[d + 1],
|
|
mp.param["band"][d + 1]["sr"],
|
|
bp["sr"],
|
|
res_type=bp["res_type"],
|
|
)
|
|
|
|
spec[d] = wave_to_spectrogram(
|
|
wave[d],
|
|
bp["hl"],
|
|
bp["n_fft"],
|
|
mp.param["mid_side"],
|
|
mp.param["mid_side_b2"],
|
|
mp.param["reverse"],
|
|
)
|
|
|
|
specs[i] = combine_spectrograms(spec, mp)
|
|
|
|
del wave
|
|
|
|
if args.algorithm == "deep":
|
|
d_spec = np.where(np.abs(specs[0]) <= np.abs(spec[1]), specs[0], spec[1])
|
|
v_spec = d_spec - specs[1]
|
|
sf.write(
|
|
os.path.join("{}.wav".format(args.output_name)),
|
|
cmb_spectrogram_to_wave(v_spec, mp),
|
|
mp.param["sr"],
|
|
)
|
|
|
|
if args.algorithm.startswith("invert"):
|
|
ln = min([specs[0].shape[2], specs[1].shape[2]])
|
|
specs[0] = specs[0][:, :, :ln]
|
|
specs[1] = specs[1][:, :, :ln]
|
|
|
|
if "invert_p" == args.algorithm:
|
|
X_mag = np.abs(specs[0])
|
|
y_mag = np.abs(specs[1])
|
|
max_mag = np.where(X_mag >= y_mag, X_mag, y_mag)
|
|
v_spec = specs[1] - max_mag * np.exp(1.0j * np.angle(specs[0]))
|
|
else:
|
|
specs[1] = reduce_vocal_aggressively(specs[0], specs[1], 0.2)
|
|
v_spec = specs[0] - specs[1]
|
|
|
|
if not args.vocals_only:
|
|
X_mag = np.abs(specs[0])
|
|
y_mag = np.abs(specs[1])
|
|
v_mag = np.abs(v_spec)
|
|
|
|
X_image = spectrogram_to_image(X_mag)
|
|
y_image = spectrogram_to_image(y_mag)
|
|
v_image = spectrogram_to_image(v_mag)
|
|
|
|
cv2.imwrite("{}_X.png".format(args.output_name), X_image)
|
|
cv2.imwrite("{}_y.png".format(args.output_name), y_image)
|
|
cv2.imwrite("{}_v.png".format(args.output_name), v_image)
|
|
|
|
sf.write(
|
|
"{}_X.wav".format(args.output_name),
|
|
cmb_spectrogram_to_wave(specs[0], mp),
|
|
mp.param["sr"],
|
|
)
|
|
sf.write(
|
|
"{}_y.wav".format(args.output_name),
|
|
cmb_spectrogram_to_wave(specs[1], mp),
|
|
mp.param["sr"],
|
|
)
|
|
|
|
sf.write(
|
|
"{}_v.wav".format(args.output_name),
|
|
cmb_spectrogram_to_wave(v_spec, mp),
|
|
mp.param["sr"],
|
|
)
|
|
else:
|
|
if not args.algorithm == "deep":
|
|
sf.write(
|
|
os.path.join("ensembled", "{}.wav".format(args.output_name)),
|
|
cmb_spectrogram_to_wave(ensembling(args.algorithm, specs), mp),
|
|
mp.param["sr"],
|
|
)
|
|
|
|
if args.algorithm == "align":
|
|
trackalignment = [
|
|
{
|
|
"file1": '"{}"'.format(args.input[0]),
|
|
"file2": '"{}"'.format(args.input[1]),
|
|
}
|
|
]
|
|
|
|
for i, e in tqdm(enumerate(trackalignment), desc="Performing Alignment..."):
|
|
os.system(f"python lib/align_tracks.py {e['file1']} {e['file2']}")
|
|
|
|
# print('Total time: {0:.{1}f}s'.format(time.time() - start_time, 1))
|