FunASR/funasr/models/frontend/default.py

import copy
from typing import Optional
from typing import Tuple
from typing import Union

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

from funasr.layers.log_mel import LogMel
from funasr.layers.stft import Stft
from funasr.models.frontend.abs_frontend import AbsFrontend
from funasr.modules.frontends.frontend import Frontend
from funasr.utils.get_default_kwargs import get_default_kwargs


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"

    def output_size(self) -> int:
        return self.n_mels

    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




class MultiChannelFrontend(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,
            frame_length: int = None,
            frame_shift: int = None,
            lfr_m: int = None,
            lfr_n: int = None,
    ):
        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 = "multichannelfrontend"

    def output_size(self) -> int:
        return self.n_mels

    def forward(
            self, input: torch.Tensor, input_lengths: torch.Tensor
    ) -> Tuple[torch.Tensor, torch.Tensor]:
        # 1. Domain-conversion: e.g. Stft: time -> time-freq
        #import pdb;pdb.set_trace()
        if self.stft is not None:
            input_stft, feats_lens = self._compute_stft(input, input_lengths)
        else:
            if isinstance(input, ComplexTensor):
                input_stft = input
            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)
        # 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)
        bt = input_feats.size(0)
        if input_feats.dim() ==4:
            channel_size = input_feats.size(2)
            # batch * channel * T * D
            #pdb.set_trace()
            input_feats = input_feats.transpose(1,2).reshape(bt*channel_size,-1,80).contiguous()
            # input_feats = input_feats.transpose(1,2)
            # batch * channel
            feats_lens = feats_lens.repeat(1,channel_size).squeeze()
        else:
            channel_size = 1
        return input_feats, feats_lens, channel_size

    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