Source code for espnet2.tts.feats_extract.log_mel_fbank

from typing import Any, Dict, Optional, Tuple, Union

import humanfriendly
import torch
from typeguard import check_argument_types

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 """ def __init__( self, fs: Union[int, str] = 16000, n_fft: int = 1024, win_length: 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, ): assert check_argument_types() 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