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from typing import Union
import librosa
import numpy as np
import parselmouth
import torch
from modules.nsf_hifigan.nvSTFT import STFT
from utils.decomposed_waveform import DecomposedWaveform
from utils.pitch_utils import interp_f0
def get_mel_torch(
waveform, samplerate,
*,
num_mel_bins=128, hop_size=512, win_size=2048, fft_size=2048,
fmin=40, fmax=16000,
keyshift=0, speed=1, device=None
):
if device is None:
device = 'cuda' if torch.cuda.is_available() else 'cpu'
stft = STFT(samplerate, num_mel_bins, fft_size, win_size, hop_size, fmin, fmax, device=device)
with torch.no_grad():
wav_torch = torch.from_numpy(waveform).to(device)
mel_torch = stft.get_mel(wav_torch.unsqueeze(0), keyshift=keyshift, speed=speed).squeeze(0).T
return mel_torch.cpu().numpy()
@torch.no_grad()
def get_mel2ph_torch(lr, durs, length, timestep, device='cpu'):
ph_acc = torch.round(torch.cumsum(durs.to(device), dim=0) / timestep + 0.5).long()
ph_dur = torch.diff(ph_acc, dim=0, prepend=torch.LongTensor([0]).to(device))
mel2ph = lr(ph_dur[None])[0]
num_frames = mel2ph.shape[0]
if num_frames < length:
mel2ph = torch.cat((mel2ph, torch.full((length - num_frames,), fill_value=mel2ph[-1], device=device)), dim=0)
elif num_frames > length:
mel2ph = mel2ph[:length]
return mel2ph
def get_pitch_parselmouth(
waveform, samplerate, length,
*, hop_size, f0_min=65, f0_max=1100,
speed=1, interp_uv=False
):
"""
:param waveform: [T]
:param samplerate: sampling rate
:param length: Expected number of frames
:param hop_size: Frame width, in number of samples
:param f0_min: Minimum f0 in Hz
:param f0_max: Maximum f0 in Hz
:param speed: Change the speed
:param interp_uv: Interpolate unvoiced parts
:return: f0, uv
"""
hop_size = int(np.round(hop_size * speed))
time_step = hop_size / samplerate
l_pad = int(np.ceil(1.5 / f0_min * samplerate))
r_pad = hop_size * ((len(waveform) - 1) // hop_size + 1) - len(waveform) + l_pad + 1
waveform = np.pad(waveform, (l_pad, r_pad))
# noinspection PyArgumentList
s = parselmouth.Sound(waveform, sampling_frequency=samplerate).to_pitch_ac(
time_step=time_step, voicing_threshold=0.6,
pitch_floor=f0_min, pitch_ceiling=f0_max
)
assert np.abs(s.t1 - 1.5 / f0_min) < 0.001
f0 = s.selected_array['frequency'].astype(np.float32)
if len(f0) < length:
f0 = np.pad(f0, (0, length - len(f0)))
f0 = f0[: length]
uv = f0 == 0
if interp_uv:
f0, uv = interp_f0(f0, uv)
return f0, uv
def get_energy_librosa(waveform, length, *, hop_size, win_size, domain='db'):
"""
Definition of energy: RMS of the waveform, in dB representation
:param waveform: [T]
:param length: Expected number of frames
:param hop_size: Frame width, in number of samples
:param win_size: Window size, in number of samples
:param domain: db or amplitude
:return: energy
"""
energy = librosa.feature.rms(y=waveform, frame_length=win_size, hop_length=hop_size)[0]
if len(energy) < length:
energy = np.pad(energy, (0, length - len(energy)))
energy = energy[: length]
if domain == 'db':
energy = librosa.amplitude_to_db(energy)
elif domain == 'amplitude':
pass
else:
raise ValueError(f'Invalid domain: {domain}')
return energy
def get_breathiness(
waveform: Union[np.ndarray, DecomposedWaveform],
samplerate, f0, length,
*, hop_size=None, fft_size=None, win_size=None
):
"""
Definition of breathiness: RMS of the aperiodic part, in dB representation
:param waveform: All other analysis parameters will not take effect if a DeconstructedWaveform is given
:param samplerate: sampling rate
:param f0: reference f0
:param length: Expected number of frames
:param hop_size: Frame width, in number of samples
:param fft_size: Number of fft bins
:param win_size: Window size, in number of samples
:return: breathiness
"""
if not isinstance(waveform, DecomposedWaveform):
waveform = DecomposedWaveform(
waveform=waveform, samplerate=samplerate, f0=f0,
hop_size=hop_size, fft_size=fft_size, win_size=win_size
)
waveform_ap = waveform.aperiodic()
breathiness = get_energy_librosa(
waveform_ap, length=length,
hop_size=waveform.hop_size, win_size=waveform.win_size
)
return breathiness
def get_voicing(
waveform: Union[np.ndarray, DecomposedWaveform],
samplerate, f0, length,
*, hop_size=None, fft_size=None, win_size=None
):
"""
Definition of voicing: RMS of the harmonic part, in dB representation
:param waveform: All other analysis parameters will not take effect if a DeconstructedWaveform is given
:param samplerate: sampling rate
:param f0: reference f0
:param length: Expected number of frames
:param hop_size: Frame width, in number of samples
:param fft_size: Number of fft bins
:param win_size: Window size, in number of samples
:return: voicing
"""
if not isinstance(waveform, DecomposedWaveform):
waveform = DecomposedWaveform(
waveform=waveform, samplerate=samplerate, f0=f0,
hop_size=hop_size, fft_size=fft_size, win_size=win_size
)
waveform_sp = waveform.harmonic()
voicing = get_energy_librosa(
waveform_sp, length=length,
hop_size=waveform.hop_size, win_size=waveform.win_size
)
return voicing
def get_tension_base_harmonic(
waveform: Union[np.ndarray, DecomposedWaveform],
samplerate, f0, length,
*, hop_size=None, fft_size=None, win_size=None,
domain='logit'
):
"""
Definition of tension: radio of the real harmonic part (harmonic part except the base harmonic)
to the full harmonic part.
:param waveform: All other analysis parameters will not take effect if a DeconstructedWaveform is given
:param samplerate: sampling rate
:param f0: reference f0
:param length: Expected number of frames
:param hop_size: Frame width, in number of samples
:param fft_size: Number of fft bins
:param win_size: Window size, in number of samples
:param domain: The domain of the final ratio representation.
Can be 'ratio' (the raw ratio), 'db' (log decibel) or 'logit' (the reverse function of sigmoid)
:return: tension
"""
if not isinstance(waveform, DecomposedWaveform):
waveform = DecomposedWaveform(
waveform=waveform, samplerate=samplerate, f0=f0,
hop_size=hop_size, fft_size=fft_size, win_size=win_size
)
waveform_h = waveform.harmonic()
waveform_base_h = waveform.harmonic(0)
energy_base_h = get_energy_librosa(
waveform_base_h, length,
hop_size=waveform.hop_size, win_size=waveform.win_size,
domain='amplitude'
)
energy_h = get_energy_librosa(
waveform_h, length,
hop_size=waveform.hop_size, win_size=waveform.win_size,
domain='amplitude'
)
tension = np.sqrt(np.clip(energy_h ** 2 - energy_base_h ** 2, a_min=0, a_max=None)) / (energy_h + 1e-5)
if domain == 'ratio':
tension = np.clip(tension, a_min=0, a_max=1)
elif domain == 'db':
tension = np.clip(tension, a_min=1e-5, a_max=1)
tension = librosa.amplitude_to_db(tension)
elif domain == 'logit':
tension = np.clip(tension, a_min=1e-4, a_max=1 - 1e-4)
tension = np.log(tension / (1 - tension))
return tension
class SinusoidalSmoothingConv1d(torch.nn.Conv1d):
def __init__(self, kernel_size):
super().__init__(
in_channels=1,
out_channels=1,
kernel_size=kernel_size,
bias=False,
padding='same',
padding_mode='replicate'
)
smooth_kernel = torch.sin(torch.from_numpy(
np.linspace(0, 1, kernel_size).astype(np.float32) * np.pi
))
smooth_kernel /= smooth_kernel.sum()
self.weight.data = smooth_kernel[None, None]
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