from typing import Any, Dict, Optional, Tuple
import torch
from typeguard import typechecked
from espnet2.layers.stft import Stft
from espnet2.tts.feats_extract.abs_feats_extract import AbsFeatsExtract
[docs]class LinearSpectrogram(AbsFeatsExtract):
"""Linear amplitude spectrogram.
Stft -> amplitude-spec
"""
@typechecked
def __init__(
self,
n_fft: int = 1024,
win_length: Optional[int] = None,
hop_length: int = 256,
window: Optional[str] = "hann",
center: bool = True,
normalized: bool = False,
onesided: bool = True,
):
super().__init__()
self.n_fft = n_fft
self.hop_length = hop_length
self.win_length = win_length
self.window = window
self.stft = Stft(
n_fft=n_fft,
win_length=win_length,
hop_length=hop_length,
window=window,
center=center,
normalized=normalized,
onesided=onesided,
)
self.n_fft = n_fft
[docs] def output_size(self) -> int:
return self.n_fft // 2 + 1
[docs] def get_parameters(self) -> Dict[str, Any]:
"""Return the parameters required by Vocoder."""
return dict(
n_fft=self.n_fft,
n_shift=self.hop_length,
win_length=self.win_length,
window=self.window,
)
[docs] def forward(
self, input: torch.Tensor, input_lengths: torch.Tensor = None
) -> Tuple[torch.Tensor, torch.Tensor]:
# 1. Stft: time -> time-freq
input_stft, feats_lens = self.stft(input, input_lengths)
assert input_stft.dim() >= 4, input_stft.shape
# "2" refers to the real/imag parts of Complex
assert input_stft.shape[-1] == 2, input_stft.shape
# STFT -> Power spectrum -> Amp spectrum
# input_stft: (..., F, 2) -> (..., F)
input_power = input_stft[..., 0] ** 2 + input_stft[..., 1] ** 2
input_amp = torch.sqrt(torch.clamp(input_power, min=1.0e-10))
return input_amp, feats_lens