"""
from logging import getLogger
import torch
import torch.utils.data
import torchaudio
LOG = getLogger(__name__)
from ..hparams import HParams
def spectrogram_torch(audio: torch.Tensor, hps: HParams) -> torch.Tensor:
return torchaudio.transforms.Spectrogram(
n_fft=hps.data.filter_length,
win_length=hps.data.win_length,
hop_length=hps.data.hop_length,
power=1.0,
window_fn=torch.hann_window,
normalized=False,
).to(audio.device)(audio)
def spec_to_mel_torch(spec: torch.Tensor, hps: HParams) -> torch.Tensor:
return torchaudio.transforms.MelScale(
n_mels=hps.data.n_mel_channels,
sample_rate=hps.data.sampling_rate,
f_min=hps.data.mel_fmin,
f_max=hps.data.mel_fmax,
).to(spec.device)(spec)
def mel_spectrogram_torch(audio: torch.Tensor, hps: HParams) -> torch.Tensor:
return torchaudio.transforms.MelSpectrogram(
sample_rate=hps.data.sampling_rate,
n_fft=hps.data.filter_length,
n_mels=hps.data.n_mel_channels,
win_length=hps.data.win_length,
hop_length=hps.data.hop_length,
f_min=hps.data.mel_fmin,
f_max=hps.data.mel_fmax,
power=1.0,
window_fn=torch.hann_window,
normalized=False,
).to(audio.device)(audio)
"""
from logging import getLogger
import torch
import torch.utils.data
from librosa.filters import mel as librosa_mel_fn
LOG = getLogger(__name__)
MAX_WAV_VALUE = 32768.0
[docs]
def dynamic_range_compression_torch(x, C=1, clip_val=1e-5):
"""
PARAMS
------
C: compression factor
"""
return torch.log(torch.clamp(x, min=clip_val) * C)
[docs]
def dynamic_range_decompression_torch(x, C=1):
"""
PARAMS
------
C: compression factor used to compress
"""
return torch.exp(x) / C
[docs]
def spectral_normalize_torch(magnitudes):
output = dynamic_range_compression_torch(magnitudes)
return output
[docs]
def spectral_de_normalize_torch(magnitudes):
output = dynamic_range_decompression_torch(magnitudes)
return output
mel_basis = {}
hann_window = {}
[docs]
def spectrogram_torch(y, hps, center=False):
if torch.min(y) < -1.0:
LOG.info("min value is ", torch.min(y))
if torch.max(y) > 1.0:
LOG.info("max value is ", torch.max(y))
n_fft = hps.data.filter_length
hop_size = hps.data.hop_length
win_size = hps.data.win_length
global hann_window
dtype_device = str(y.dtype) + "_" + str(y.device)
wnsize_dtype_device = str(win_size) + "_" + dtype_device
if wnsize_dtype_device not in hann_window:
hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device)
y = torch.nn.functional.pad(
y.unsqueeze(1),
(int((n_fft - hop_size) / 2), int((n_fft - hop_size) / 2)),
mode="reflect",
)
y = y.squeeze(1)
spec = torch.stft(
y,
n_fft,
hop_length=hop_size,
win_length=win_size,
window=hann_window[wnsize_dtype_device],
center=center,
pad_mode="reflect",
normalized=False,
onesided=True,
return_complex=False,
)
spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
return spec
[docs]
def spec_to_mel_torch(spec, hps):
sampling_rate = hps.data.sampling_rate
n_fft = hps.data.filter_length
num_mels = hps.data.n_mel_channels
fmin = hps.data.mel_fmin
fmax = hps.data.mel_fmax
global mel_basis
dtype_device = str(spec.dtype) + "_" + str(spec.device)
fmax_dtype_device = str(fmax) + "_" + dtype_device
if fmax_dtype_device not in mel_basis:
mel = librosa_mel_fn(sr=sampling_rate, n_fft=n_fft, n_mels=num_mels, fmin=fmin, fmax=fmax)
mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=spec.dtype, device=spec.device)
spec = torch.matmul(mel_basis[fmax_dtype_device], spec)
spec = spectral_normalize_torch(spec)
return spec
[docs]
def mel_spectrogram_torch(y, hps, center=False):
sampling_rate = hps.data.sampling_rate
n_fft = hps.data.filter_length
num_mels = hps.data.n_mel_channels
fmin = hps.data.mel_fmin
fmax = hps.data.mel_fmax
hop_size = hps.data.hop_length
win_size = hps.data.win_length
if torch.min(y) < -1.0:
LOG.info(f"min value is {torch.min(y)}")
if torch.max(y) > 1.0:
LOG.info(f"max value is {torch.max(y)}")
global mel_basis, hann_window
dtype_device = str(y.dtype) + "_" + str(y.device)
fmax_dtype_device = str(fmax) + "_" + dtype_device
wnsize_dtype_device = str(win_size) + "_" + dtype_device
if fmax_dtype_device not in mel_basis:
mel = librosa_mel_fn(sr=sampling_rate, n_fft=n_fft, n_mels=num_mels, fmin=fmin, fmax=fmax)
mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=y.dtype, device=y.device)
if wnsize_dtype_device not in hann_window:
hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device)
y = torch.nn.functional.pad(
y.unsqueeze(1),
(int((n_fft - hop_size) / 2), int((n_fft - hop_size) / 2)),
mode="reflect",
)
y = y.squeeze(1)
spec = torch.stft(
y,
n_fft,
hop_length=hop_size,
win_length=win_size,
window=hann_window[wnsize_dtype_device],
center=center,
pad_mode="reflect",
normalized=False,
onesided=True,
return_complex=False,
)
spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
spec = torch.matmul(mel_basis[fmax_dtype_device], spec)
spec = spectral_normalize_torch(spec)
return spec