from typing import Any, Dict, Optional, Tuple, Union
import humanfriendly
import torch
from typeguard import typechecked
from espnet2.layers.log_mel import LogMel
from espnet2.layers.stft import Stft
from espnet2.tts.feats_extract.abs_feats_extract import AbsFeatsExtract
[docs]class LogMelFbank(AbsFeatsExtract):
"""Conventional frontend structure for TTS.
Stft -> amplitude-spec -> Log-Mel-Fbank
"""
@typechecked
def __init__(
self,
fs: Union[int, str] = 16000,
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,
n_mels: int = 80,
fmin: Optional[int] = 80,
fmax: Optional[int] = 7600,
htk: bool = False,
log_base: Optional[float] = 10.0,
):
super().__init__()
if isinstance(fs, str):
fs = humanfriendly.parse_size(fs)
self.fs = fs
self.n_mels = n_mels
self.n_fft = n_fft
self.hop_length = hop_length
self.win_length = win_length
self.window = window
self.fmin = fmin
self.fmax = fmax
self.stft = Stft(
n_fft=n_fft,
win_length=win_length,
hop_length=hop_length,
window=window,
center=center,
normalized=normalized,
onesided=onesided,
)
self.logmel = LogMel(
fs=fs,
n_fft=n_fft,
n_mels=n_mels,
fmin=fmin,
fmax=fmax,
htk=htk,
log_base=log_base,
)
[docs] def output_size(self) -> int:
return self.n_mels
[docs] def get_parameters(self) -> Dict[str, Any]:
"""Return the parameters required by Vocoder"""
return dict(
fs=self.fs,
n_fft=self.n_fft,
n_shift=self.hop_length,
window=self.window,
n_mels=self.n_mels,
win_length=self.win_length,
fmin=self.fmin,
fmax=self.fmax,
)
[docs] def forward(
self, input: torch.Tensor, input_lengths: torch.Tensor = None
) -> Tuple[torch.Tensor, torch.Tensor]:
# 1. Domain-conversion: e.g. 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
# NOTE(kamo): We use different definition for log-spec between TTS and ASR
# TTS: log_10(abs(stft))
# ASR: log_e(power(stft))
# 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))
input_feats, _ = self.logmel(input_amp, feats_lens)
return input_feats, feats_lens