def test_log_mel_equal():
layer1 = LinearSpectrogram(n_fft=4, hop_length=1)
layer2 = LogMelFbank(n_fft=4, hop_length=1, n_mels=2)
x = torch.randn(2, 4, 9)
y1, y1_lens = layer1(x, torch.LongTensor([4, 3]))
y2, _ = layer2(x, torch.LongTensor([4, 3]))
y1_2, _ = layer2.logmel(y1, y1_lens)
np.testing.assert_array_equal(
y2.detach().cpu().numpy(),
y1_2.detach().cpu().numpy(),
)
def __init__(
self,
fs: int = 22050,
n_fft: int = 1024,
hop_length: int = 256,
win_length: Optional[int] = None,
window: str = "hann",
n_mels: int = 80,
fmin: Optional[int] = 0,
fmax: Optional[int] = None,
center: bool = True,
normalized: bool = False,
onesided: bool = True,
log_base: Optional[float] = 10.0,
):
"""Initialize Mel-spectrogram loss.
Args:
fs (int): Sampling rate.
n_fft (int): FFT points.
hop_length (int): Hop length.
win_length (Optional[int]): Window length.
window (str): Window type.
n_mels (int): Number of Mel basis.
fmin (Optional[int]): Minimum frequency for Mel.
fmax (Optional[int]): Maximum frequency for Mel.
center (bool): Whether to use center window.
normalized (bool): Whether to use normalized one.
onesided (bool): Whether to use oneseded one.
log_base (Optional[float]): Log base value.
"""
super().__init__()
self.wav_to_mel = LogMelFbank(
fs=fs,
n_fft=n_fft,
hop_length=hop_length,
win_length=win_length,
window=window,
n_mels=n_mels,
fmin=fmin,
fmax=fmax,
center=center,
normalized=normalized,
onesided=onesided,
log_base=log_base,
)
def test_compatible_with_espnet1():
layer = LogMelFbank(n_fft=16,
hop_length=4,
n_mels=4,
fs="16k",
fmin=80,
fmax=7600)
x = torch.randn(1, 100)
y, _ = layer(x, torch.LongTensor([100]))
y = y.numpy()[0]
y2 = logmelspectrogram(x[0].numpy(),
n_fft=16,
n_shift=4,
n_mels=4,
fs=16000,
fmin=80,
fmax=7600)
np.testing.assert_allclose(y, y2, rtol=0, atol=1e-5)
class MelSpectrogramLoss(torch.nn.Module):
"""Mel-spectrogram loss."""
def __init__(
self,
fs: int = 22050,
n_fft: int = 1024,
hop_length: int = 256,
win_length: Optional[int] = None,
window: str = "hann",
n_mels: int = 80,
fmin: Optional[int] = 0,
fmax: Optional[int] = None,
center: bool = True,
normalized: bool = False,
onesided: bool = True,
log_base: Optional[float] = 10.0,
):
"""Initialize Mel-spectrogram loss.
Args:
fs (int): Sampling rate.
n_fft (int): FFT points.
hop_length (int): Hop length.
win_length (Optional[int]): Window length.
window (str): Window type.
n_mels (int): Number of Mel basis.
fmin (Optional[int]): Minimum frequency for Mel.
fmax (Optional[int]): Maximum frequency for Mel.
center (bool): Whether to use center window.
normalized (bool): Whether to use normalized one.
onesided (bool): Whether to use oneseded one.
log_base (Optional[float]): Log base value.
"""
super().__init__()
self.wav_to_mel = LogMelFbank(
fs=fs,
n_fft=n_fft,
hop_length=hop_length,
win_length=win_length,
window=window,
n_mels=n_mels,
fmin=fmin,
fmax=fmax,
center=center,
normalized=normalized,
onesided=onesided,
log_base=log_base,
)
def forward(
self,
y_hat: torch.Tensor,
y: torch.Tensor,
spec: Optional[torch.Tensor] = None,
) -> torch.Tensor:
"""Calculate Mel-spectrogram loss.
Args:
y_hat (Tensor): Generated waveform tensor (B, 1, T).
y (Tensor): Groundtruth waveform tensor (B, 1, T).
spec (Optional[Tensor]): Groundtruth linear amplitude spectrum tensor
(B, n_fft, T). if provided, use it instead of groundtruth waveform.
Returns:
Tensor: Mel-spectrogram loss value.
"""
mel_hat, _ = self.wav_to_mel(y_hat.squeeze(1))
if spec is None:
mel, _ = self.wav_to_mel(y.squeeze(1))
else:
mel, _ = self.wav_to_mel.logmel(spec)
mel_loss = F.l1_loss(mel_hat, mel)
return mel_loss
def test_forward():
layer = LogMelFbank(n_fft=4, hop_length=1, n_mels=2)
x = torch.randn(2, 4, 9)
y, _ = layer(x, torch.LongTensor([4, 3]))
assert y.shape == (2, 5, 9, 2)
def test_get_parameters():
layer = LogMelFbank(n_fft=4, hop_length=1, n_mels=2, fs="16k")
print(layer.get_parameters())
def test_output_size():
layer = LogMelFbank(n_fft=4, hop_length=1, n_mels=2, fs="16k")
print(layer.output_size())
def test_backward_not_leaf_in():
layer = LogMelFbank(n_fft=4, hop_length=1, n_mels=2)
x = torch.randn(2, 4, 9, requires_grad=True)
x = x + 2
y, _ = layer(x, torch.LongTensor([4, 3]))
y.sum().backward()