mirror of
https://github.com/RVC-Boss/GPT-SoVITS.git
synced 2026-08-13 01:53:43 +08:00
270 lines
9.0 KiB
Python
270 lines
9.0 KiB
Python
import math
|
|
|
|
import librosa
|
|
import numpy as np
|
|
|
|
|
|
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_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 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 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 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, orig_sr=bp['sr'], target_sr=sr, res_type="sinc_fastest")
|
|
wave = librosa.core.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)
|