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import librosa
import numpy as np
from scipy.interpolate import interp1d
def uv_energy_corrector(wav_data_16k, f0_func, f0_min=50, f0_max=1000):
hop_size = 256
win_size = hop_size * 6
sr = 16000
spec = np.abs(librosa.stft(wav_data_16k, n_fft=win_size, hop_length=hop_size,
win_length=win_size, pad_mode="constant").T)
T = spec.shape[0]
x_h256 = np.arange(0, 1, 1 / T)[:T]
x_h256[-1] = 1
f0 = f0_func(x_h256)
freqs = librosa.fft_frequencies(sr=sr, n_fft=win_size)
x_idx = np.arange(T)
def find_nearest_stft_bin(f0_):
return np.abs(freqs[None, :] - f0_[:, None]).argmin(-1)
def get_energy_mask(f0_lambda, hars=None, win_size=3):
if hars is None:
hars = [1]
mask = np.zeros([T, 10000]).astype(bool)
mask_bins = []
for multiple in hars:
f0_bin_idx = find_nearest_stft_bin(f0_lambda(f0, multiple))
for delta in range(-win_size // 2, 1 + win_size // 2):
y_idx = f0_bin_idx + delta
if np.max(y_idx) < spec.shape[1]:
mask_bins.append(spec[x_idx, y_idx])
mask[x_idx, y_idx] = 1
mask_bins = np.stack(mask_bins, 1)
energy_ = np.mean(mask_bins, 1)
return energy_, mask
# find uv first (for obtaining mean_energy_mharfhar)
energy_har, mask_har = get_energy_mask(lambda f0, m: f0 * m, [1, 2], 3)
energy_mhalfhar, mask_mhalfhar = get_energy_mask(lambda f0, m: f0 * (m - 0.5), [1], 5)
r_energy = energy_har / np.clip(energy_mhalfhar, 1e-8, None)
uv = np.zeros_like(f0).astype(bool)
uv |= r_energy < 10
uv |= (f0 > f0_max) | (f0 < f0_min)
func_uv = interp1d(x_h256, uv, 'nearest', fill_value='extrapolate')
return func_uv
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