| | |
| | | from typing import Tuple |
| | | from typing import Union |
| | | import logging |
| | | import humanfriendly |
| | | import numpy as np |
| | | import torch |
| | | import torch.nn as nn |
| | |
| | | from funasr.frontends.utils.stft import Stft |
| | | from funasr.frontends.utils.frontend import Frontend |
| | | from funasr.models.transformer.utils.nets_utils import make_pad_mask |
| | | from funasr.register import tables |
| | | |
| | | |
| | | @tables.register("frontend_classes", "DefaultFrontend") |
| | | class DefaultFrontend(nn.Module): |
| | | """Conventional frontend structure for ASR. |
| | | Stft -> WPE -> MVDR-Beamformer -> Power-spec -> Mel-Fbank -> CMVN |
| | |
| | | |
| | | def __init__( |
| | | self, |
| | | fs: int = 16000, |
| | | fs: Union[int, str] = 16000, |
| | | n_fft: int = 512, |
| | | win_length: int = None, |
| | | hop_length: int = 128, |
| | |
| | | frontend_conf: Optional[dict] = None, |
| | | apply_stft: bool = True, |
| | | use_channel: int = None, |
| | | **kwargs, |
| | | ): |
| | | 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 |
| | | self.fs = fs |
| | | |
| | | if apply_stft: |
| | | self.stft = Stft( |
| | |
| | | |
| | | def __init__( |
| | | self, |
| | | fs: int = 16000, |
| | | fs: Union[int, str] = 16000, |
| | | n_fft: int = 512, |
| | | win_length: int = None, |
| | | hop_length: int = None, |
| | |
| | | mc: bool = True |
| | | ): |
| | | 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) |
| | | if win_length is None and hop_length is None: |