| | |
|---|
| | | from funasr.models.specaug.abs_specaug import AbsSpecAug |
|---|
| | | from funasr.layers.abs_normalize import AbsNormalize |
|---|
| | | from funasr.torch_utils.device_funcs import force_gatherable |
|---|
| | | from funasr.train.abs_espnet_model import AbsESPnetModel |
|---|
| | | from funasr.models.base_model import FunASRModel |
|---|
| | | |
|---|
| | | if LooseVersion(torch.__version__) >= LooseVersion("1.6.0"): |
|---|
| | | from torch.cuda.amp import autocast |
|---|
| | |
|---|
| | | import pdb |
|---|
| | | import random |
|---|
| | | import math |
|---|
| | | class MFCCA(AbsESPnetModel): |
|---|
| | | |
|---|
| | | class MFCCA(FunASRModel): |
|---|
| | | """ |
|---|
| | | Author: Audio, Speech and Language Processing Group (ASLP@NPU), Northwestern Polytechnical University |
|---|
| | | MFCCA:Multi-Frame Cross-Channel attention for multi-speaker ASR in Multi-party meeting scenario |
|---|
| | |
|---|
| | | frontend: Optional[AbsFrontend], |
|---|
| | | specaug: Optional[AbsSpecAug], |
|---|
| | | normalize: Optional[AbsNormalize], |
|---|
| | | preencoder: Optional[AbsPreEncoder], |
|---|
| | | encoder: AbsEncoder, |
|---|
| | | decoder: AbsDecoder, |
|---|
| | | ctc: CTC, |
|---|
| | |
|---|
| | | report_wer: bool = True, |
|---|
| | | sym_space: str = "<space>", |
|---|
| | | sym_blank: str = "<blank>", |
|---|
| | | preencoder: Optional[AbsPreEncoder] = None, |
|---|
| | | ): |
|---|
| | | assert check_argument_types() |
|---|
| | | assert 0.0 <= ctc_weight <= 1.0, ctc_weight |
|---|
| | |
|---|
| | | self.token_list = token_list.copy() |
|---|
| | | |
|---|
| | | self.mask_ratio = mask_ratio |
|---|
| | | |
|---|
| | | |
|---|
| | | self.frontend = frontend |
|---|
| | | self.specaug = specaug |
|---|
| | |
|---|
| | | text_lengths: torch.Tensor, |
|---|
| | | ) -> Tuple[torch.Tensor, Dict[str, torch.Tensor], torch.Tensor]: |
|---|
| | | """Frontend + Encoder + Decoder + Calc loss |
|---|
| | | |
|---|
| | | Args: |
|---|
| | | speech: (Batch, Length, ...) |
|---|
| | | speech_lengths: (Batch, ) |
|---|
| | |
|---|
| | | self, speech: torch.Tensor, speech_lengths: torch.Tensor |
|---|
| | | ) -> Tuple[torch.Tensor, torch.Tensor]: |
|---|
| | | """Frontend + Encoder. Note that this method is used by asr_inference.py |
|---|
| | | |
|---|
| | | Args: |
|---|
| | | speech: (Batch, Length, ...) |
|---|
| | | speech_lengths: (Batch, ) |
|---|
