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# Copyright (c) 2023 Amphion.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
from librosa.filters import mel as librosa_mel_fn
def dynamic_range_compression_torch(x, C=1, clip_val=1e-5):
return torch.log(torch.clamp(x, min=clip_val) * C)
def spectral_normalize_torch(magnitudes):
output = dynamic_range_compression_torch(magnitudes)
return output
def extract_linear_features(y, cfg, center=False):
if torch.min(y) < -1.0:
print("min value is ", torch.min(y))
if torch.max(y) > 1.0:
print("max value is ", torch.max(y))
global hann_window
hann_window[str(y.device)] = torch.hann_window(cfg.win_size).to(y.device)
y = torch.nn.functional.pad(
y.unsqueeze(1),
(int((cfg.n_fft - cfg.hop_size) / 2), int((cfg.n_fft - cfg.hop_size) / 2)),
mode="reflect",
)
y = y.squeeze(1)
# complex tensor as default, then use view_as_real for future pytorch compatibility
spec = torch.stft(
y,
cfg.n_fft,
hop_length=cfg.hop_size,
win_length=cfg.win_size,
window=hann_window[str(y.device)],
center=center,
pad_mode="reflect",
normalized=False,
onesided=True,
return_complex=True,
)
spec = torch.view_as_real(spec)
spec = torch.sqrt(spec.pow(2).sum(-1) + (1e-9))
spec = torch.squeeze(spec, 0)
return spec
def mel_spectrogram_torch(y, cfg, center=False):
if torch.min(y) < -1.0:
print("min value is ", torch.min(y))
if torch.max(y) > 1.0:
print("max value is ", torch.max(y))
global mel_basis, hann_window
if cfg.fmax not in mel_basis:
mel = librosa_mel_fn(
sr=cfg.sample_rate,
n_fft=cfg.n_fft,
n_mels=cfg.n_mel,
fmin=cfg.fmin,
fmax=cfg.fmax,
)
mel_basis[str(cfg.fmax) + "_" + str(y.device)] = (
torch.from_numpy(mel).float().to(y.device)
)
hann_window[str(y.device)] = torch.hann_window(cfg.win_size).to(y.device)
y = torch.nn.functional.pad(
y.unsqueeze(1),
(int((cfg.n_fft - cfg.hop_size) / 2), int((cfg.n_fft - cfg.hop_size) / 2)),
mode="reflect",
)
y = y.squeeze(1)
spec = torch.stft(
y,
cfg.n_fft,
hop_length=cfg.hop_size,
win_length=cfg.win_size,
window=hann_window[str(y.device)],
center=center,
pad_mode="reflect",
normalized=False,
onesided=True,
return_complex=True,
)
spec = torch.view_as_real(spec)
spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
spec = torch.matmul(mel_basis[str(cfg.fmax) + "_" + str(y.device)], spec)
spec = spectral_normalize_torch(spec)
return spec
mel_basis = {}
hann_window = {}
def extract_mel_features(
y,
cfg,
center=False
# n_fft, n_mel, sampling_rate, hop_size, win_size, fmin, fmax, center=False
):
"""Extract mel features
Args:
y (tensor): audio data in tensor
cfg (dict): configuration in cfg.preprocess
center (bool, optional): In STFT, whether t-th frame is centered at time t*hop_length. Defaults to False.
Returns:
tensor: a tensor containing the mel feature calculated based on STFT result
"""
if torch.min(y) < -1.0:
print("min value is ", torch.min(y))
if torch.max(y) > 1.0:
print("max value is ", torch.max(y))
global mel_basis, hann_window
if cfg.fmax not in mel_basis:
mel = librosa_mel_fn(
sr=cfg.sample_rate,
n_fft=cfg.n_fft,
n_mels=cfg.n_mel,
fmin=cfg.fmin,
fmax=cfg.fmax,
)
mel_basis[str(cfg.fmax) + "_" + str(y.device)] = (
torch.from_numpy(mel).float().to(y.device)
)
hann_window[str(y.device)] = torch.hann_window(cfg.win_size).to(y.device)
y = torch.nn.functional.pad(
y.unsqueeze(1),
(int((cfg.n_fft - cfg.hop_size) / 2), int((cfg.n_fft - cfg.hop_size) / 2)),
mode="reflect",
)
y = y.squeeze(1)
# complex tensor as default, then use view_as_real for future pytorch compatibility
spec = torch.stft(
y,
cfg.n_fft,
hop_length=cfg.hop_size,
win_length=cfg.win_size,
window=hann_window[str(y.device)],
center=center,
pad_mode="reflect",
normalized=False,
onesided=True,
return_complex=True,
)
spec = torch.view_as_real(spec)
spec = torch.sqrt(spec.pow(2).sum(-1) + (1e-9))
spec = torch.matmul(mel_basis[str(cfg.fmax) + "_" + str(y.device)], spec)
spec = spectral_normalize_torch(spec)
return spec.squeeze(0)
def extract_mel_features_tts(
y,
cfg,
center=False,
taco=False,
_stft=None,
):
"""Extract mel features
Args:
y (tensor): audio data in tensor
cfg (dict): configuration in cfg.preprocess
center (bool, optional): In STFT, whether t-th frame is centered at time t*hop_length. Defaults to False.
taco: use tacotron mel
Returns:
tensor: a tensor containing the mel feature calculated based on STFT result
"""
if not taco:
if torch.min(y) < -1.0:
print("min value is ", torch.min(y))
if torch.max(y) > 1.0:
print("max value is ", torch.max(y))
global mel_basis, hann_window
if cfg.fmax not in mel_basis:
mel = librosa_mel_fn(
sr=cfg.sample_rate,
n_fft=cfg.n_fft,
n_mels=cfg.n_mel,
fmin=cfg.fmin,
fmax=cfg.fmax,
)
mel_basis[str(cfg.fmax) + "_" + str(y.device)] = (
torch.from_numpy(mel).float().to(y.device)
)
hann_window[str(y.device)] = torch.hann_window(cfg.win_size).to(y.device)
y = torch.nn.functional.pad(
y.unsqueeze(1),
(int((cfg.n_fft - cfg.hop_size) / 2), int((cfg.n_fft - cfg.hop_size) / 2)),
mode="reflect",
)
y = y.squeeze(1)
# complex tensor as default, then use view_as_real for future pytorch compatibility
spec = torch.stft(
y,
cfg.n_fft,
hop_length=cfg.hop_size,
win_length=cfg.win_size,
window=hann_window[str(y.device)],
center=center,
pad_mode="reflect",
normalized=False,
onesided=True,
return_complex=True,
)
spec = torch.view_as_real(spec)
spec = torch.sqrt(spec.pow(2).sum(-1) + (1e-9))
spec = torch.matmul(mel_basis[str(cfg.fmax) + "_" + str(y.device)], spec)
spec = spectral_normalize_torch(spec)
spec = spec.squeeze(0)
else:
audio = torch.clip(y, -1, 1)
audio = torch.autograd.Variable(audio, requires_grad=False)
spec, energy = _stft.mel_spectrogram(audio)
spec = torch.squeeze(spec, 0)
spec = torch.matmul(mel_basis[str(cfg.fmax) + "_" + str(y.device)], spec)
spec = spectral_normalize_torch(spec)
return spec.squeeze(0)
def amplitude_phase_spectrum(y, cfg):
hann_window = torch.hann_window(cfg.win_size).to(y.device)
y = torch.nn.functional.pad(
y.unsqueeze(1),
(int((cfg.n_fft - cfg.hop_size) / 2), int((cfg.n_fft - cfg.hop_size) / 2)),
mode="reflect",
)
y = y.squeeze(1)
stft_spec = torch.stft(
y,
cfg.n_fft,
hop_length=cfg.hop_size,
win_length=cfg.win_size,
window=hann_window,
center=False,
return_complex=True,
)
stft_spec = torch.view_as_real(stft_spec)
if stft_spec.size()[0] == 1:
stft_spec = stft_spec.squeeze(0)
if len(list(stft_spec.size())) == 4:
rea = stft_spec[:, :, :, 0] # [batch_size, n_fft//2+1, frames]
imag = stft_spec[:, :, :, 1] # [batch_size, n_fft//2+1, frames]
else:
rea = stft_spec[:, :, 0] # [n_fft//2+1, frames]
imag = stft_spec[:, :, 1] # [n_fft//2+1, frames]
log_amplitude = torch.log(
torch.abs(torch.sqrt(torch.pow(rea, 2) + torch.pow(imag, 2))) + 1e-5
) # [n_fft//2+1, frames]
phase = torch.atan2(imag, rea) # [n_fft//2+1, frames]
return log_amplitude, phase, rea, imag