| | |
|---|
| | | htk=htk, |
|---|
| | | ) |
|---|
| | | self.n_mels = n_mels |
|---|
| | | self.frontend_type = "default" |
|---|
| | | self.use_channel = use_channel |
|---|
| | | self.frontend_type = "default" |
|---|
| | | |
|---|
| | | def output_size(self) -> int: |
|---|
| | | return self.n_mels |
|---|
| | |
|---|
| | | def __init__( |
|---|
| | | self, |
|---|
| | | fs: Union[int, str] = 16000, |
|---|
| | | n_fft: int = 400, |
|---|
| | | frame_length: int = 25, |
|---|
| | | frame_shift: int = 10, |
|---|
| | | n_fft: int = 512, |
|---|
| | | win_length: int = None, |
|---|
| | | hop_length: int = None, |
|---|
| | | frame_length: int = None, |
|---|
| | | frame_shift: int = None, |
|---|
| | | window: Optional[str] = "hann", |
|---|
| | | center: bool = True, |
|---|
| | | normalized: bool = False, |
|---|
| | |
|---|
| | | use_channel: int = None, |
|---|
| | | lfr_m: int = 1, |
|---|
| | | lfr_n: int = 1, |
|---|
| | | cmvn_file: str = None |
|---|
| | | cmvn_file: str = None, |
|---|
| | | mc: bool = True |
|---|
| | | ): |
|---|
| | | assert check_argument_types() |
|---|
| | | super().__init__() |
|---|
| | |
|---|
| | | |
|---|
| | | # 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: |
|---|
| | | self.win_length = frame_length * 16 |
|---|
| | | self.hop_length = frame_shift * 16 |
|---|
| | | elif frame_length is None and frame_shift is None: |
|---|
| | | self.win_length = self.win_length |
|---|
| | | self.hop_length = self.hop_length |
|---|
| | | else: |
|---|
| | | logging.error( |
|---|
| | | "Only one of (win_length, hop_length) and (frame_length, frame_shift)" |
|---|
| | | "can be set." |
|---|
| | | ) |
|---|
| | | exit(1) |
|---|
| | | |
|---|
| | | if apply_stft: |
|---|
| | | self.stft = Stft( |
|---|
| | |
|---|
| | | htk=htk, |
|---|
| | | ) |
|---|
| | | self.n_mels = n_mels |
|---|
| | | self.frontend_type = "default" |
|---|
| | | self.use_channel = use_channel |
|---|
| | | self.mc = mc |
|---|
| | | if not self.mc: |
|---|
| | | if self.use_channel is not None: |
|---|
| | | logging.info("use the channel %d" % (self.use_channel)) |
|---|
| | | else: |
|---|
| | |
|---|
| | | mean, std = self._load_cmvn(self.cmvn_file) |
|---|
| | | self.register_buffer("mean", torch.from_numpy(mean)) |
|---|
| | | self.register_buffer("std", torch.from_numpy(std)) |
|---|
| | | self.frontend_type = "multichannelfrontend" |
|---|
| | | |
|---|
| | | def output_size(self) -> int: |
|---|
| | | return self.n_mels |
|---|
| | |
|---|
| | | # 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: |
|---|
| | | # 3. [Multi channel case]: Select a channel(sa_asr) |
|---|
| | | if input_stft.dim() == 4 and not self.mc: |
|---|
| | | # h: (B, T, C, F) -> h: (B, T, F) |
|---|
| | | if self.training: |
|---|
| | | if self.use_channel is not None: |
|---|
| | |
|---|
| | | # input_power: (Batch, [Channel,] Length, Freq) |
|---|
| | | # -> input_feats: (Batch, Length, Dim) |
|---|
| | | input_feats, _ = self.logmel(input_power, feats_lens) |
|---|
| | | |
|---|
| | | if self.mc: |
|---|
| | | # MFCCA |
|---|
| | | if input_feats.dim() ==4: |
|---|
| | | bt = input_feats.size(0) |
|---|
| | | channel_size = input_feats.size(2) |
|---|
| | | input_feats = input_feats.transpose(1,2).reshape(bt*channel_size,-1,80).contiguous() |
|---|
| | | feats_lens = feats_lens.repeat(1,channel_size).squeeze() |
|---|
| | | else: |
|---|
| | | channel_size = 1 |
|---|
| | | return input_feats, feats_lens, channel_size |
|---|
| | | else: |
|---|
| | | # 6. Apply CMVN |
|---|
| | | if self.cmvn_file is not None: |
|---|
| | | if feats_lens is None: |
|---|
