Source code for espnet2.asr.frontend.default

import copy
from typing import Optional, Tuple, Union

import humanfriendly
import numpy as np
import torch
from torch_complex.tensor import ComplexTensor
from typeguard import check_argument_types

from espnet2.asr.frontend.abs_frontend import AbsFrontend
from espnet2.layers.log_mel import LogMel
from espnet2.layers.stft import Stft
from espnet2.utils.get_default_kwargs import get_default_kwargs
from espnet.nets.pytorch_backend.frontends.frontend import Frontend


[docs]class DefaultFrontend(AbsFrontend): """Conventional frontend structure for ASR. Stft -> WPE -> MVDR-Beamformer -> Power-spec -> Mel-Fbank -> CMVN """ def __init__( self, fs: Union[int, str] = 16000, n_fft: int = 512, win_length: int = None, hop_length: int = 128, window: Optional[str] = "hann", center: bool = True, normalized: bool = False, onesided: bool = True, n_mels: int = 80, fmin: int = None, fmax: int = None, htk: bool = False, frontend_conf: Optional[dict] = get_default_kwargs(Frontend), apply_stft: bool = True, ): assert check_argument_types() super().__init__() if isinstance(fs, str): fs = humanfriendly.parse_size(fs) # Deepcopy (In general, dict shouldn't be used as default arg) frontend_conf = copy.deepcopy(frontend_conf) self.hop_length = hop_length if apply_stft: self.stft = Stft( n_fft=n_fft, win_length=win_length, hop_length=hop_length, center=center, window=window, normalized=normalized, onesided=onesided, ) else: self.stft = None self.apply_stft = apply_stft if frontend_conf is not None: self.frontend = Frontend(idim=n_fft // 2 + 1, **frontend_conf) else: self.frontend = None self.logmel = LogMel( fs=fs, n_fft=n_fft, n_mels=n_mels, fmin=fmin, fmax=fmax, htk=htk, ) self.n_mels = n_mels self.frontend_type = "default"
[docs] def output_size(self) -> int: return self.n_mels
[docs] def forward( self, input: torch.Tensor, input_lengths: torch.Tensor ) -> Tuple[torch.Tensor, torch.Tensor]: # 1. Domain-conversion: e.g. Stft: time -> time-freq if self.stft is not None: input_stft, feats_lens = self._compute_stft(input, input_lengths) else: input_stft = ComplexTensor(input[..., 0], input[..., 1]) feats_lens = input_lengths # 2. [Option] Speech enhancement if self.frontend is not None: assert isinstance(input_stft, ComplexTensor), type(input_stft) # input_stft: (Batch, Length, [Channel], Freq) input_stft, _, mask = self.frontend(input_stft, feats_lens) # 3. [Multi channel case]: Select a channel if input_stft.dim() == 4: # h: (B, T, C, F) -> h: (B, T, F) if self.training: # Select 1ch randomly ch = np.random.randint(input_stft.size(2)) input_stft = input_stft[:, :, ch, :] else: # Use the first channel input_stft = input_stft[:, :, 0, :] # 4. STFT -> Power spectrum # h: ComplexTensor(B, T, F) -> torch.Tensor(B, T, F) input_power = input_stft.real**2 + input_stft.imag**2 # 5. Feature transform e.g. Stft -> Log-Mel-Fbank # input_power: (Batch, [Channel,] Length, Freq) # -> input_feats: (Batch, Length, Dim) input_feats, _ = self.logmel(input_power, feats_lens) return input_feats, feats_lens
def _compute_stft( self, input: torch.Tensor, input_lengths: torch.Tensor ) -> torch.Tensor: 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 # Change torch.Tensor to ComplexTensor # input_stft: (..., F, 2) -> (..., F) input_stft = ComplexTensor(input_stft[..., 0], input_stft[..., 1]) return input_stft, feats_lens