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mirror of synced 2024-12-21 03:45:56 +01:00
Retrieval-based-Voice-Conve.../infer/lib/uvr5_pack/lib_v5/spec_utils.py
tkyaji 330bdd9692 fix: Updated librosa to version 0.10.2
There is a bug in librosa 0.9.1.
https://github.com/librosa/librosa/pull/1594

As a result, an error occurs when executing the "Vocals/Accompaniment Separation & Reverberation Removal" function.

To address this issue, librosa has been upgraded to version 0.10.2.
Additionally, torchcrepe has been upgraded due to its dependency on librosa.
2024-06-26 21:59:55 +09:00

677 lines
21 KiB
Python

import hashlib
import json
import math
import os
import librosa
import numpy as np
import soundfile as sf
from tqdm import tqdm
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=n_fft, hop_length=hop_length)
spec_right = librosa.stft(wave_right, n_fft=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=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,
sr=bp["sr"],
mono=False,
dtype=np.float32,
res_type=bp["res_type"]
)
y_wave[d], _ = librosa.load(
inst_path,
sr=bp["sr"],
mono=False,
dtype=np.float32,
res_type=bp["res_type"],
)
else: # lower bands
X_wave[d] = librosa.resample(
X_wave[d + 1],
orig_sr=mp.param["band"][d + 1]["sr"],
target_sr=bp["sr"],
res_type=bp["res_type"],
)
y_wave[d] = librosa.resample(
y_wave[d + 1],
orig_sr=mp.param["band"][d + 1]["sr"],
target_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"],
),
orig_sr=bp["sr"],
target_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.resample(wave2, orig_sr=bp["sr"], target_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, n_fft=nfft, hop_length=hl)
spec_right = librosa.stft(wave_right, n_fft=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 argparse
import sys
import time
import cv2
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],
sr=bp["sr"],
mono=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],
orig_sr=mp.param["band"][d + 1]["sr"],
target_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))