FunASR/funasr/layers/stft.py

from distutils.version import LooseVersion
from typing import Optional
from typing import Tuple
from typing import Union

import torch
from torch_complex.tensor import ComplexTensor
from typeguard import check_argument_types

from funasr.modules.nets_utils import make_pad_mask
from funasr.layers.complex_utils import is_complex
from funasr.layers.inversible_interface import InversibleInterface
import librosa
import numpy as np

is_torch_1_9_plus = LooseVersion(torch.__version__) >= LooseVersion("1.9.0")


is_torch_1_7_plus = LooseVersion(torch.__version__) >= LooseVersion("1.7")


class Stft(torch.nn.Module, InversibleInterface):
    def __init__(
        self,
        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,
    ):
        assert check_argument_types()
        super().__init__()
        self.n_fft = n_fft
        if win_length is None:
            self.win_length = n_fft
        else:
            self.win_length = win_length
        self.hop_length = hop_length
        self.center = center
        self.normalized = normalized
        self.onesided = onesided
        if window is not None and not hasattr(torch, f"{window}_window"):
            raise ValueError(f"{window} window is not implemented")
        self.window = window

    def extra_repr(self):
        return (
            f"n_fft={self.n_fft}, "
            f"win_length={self.win_length}, "
            f"hop_length={self.hop_length}, "
            f"center={self.center}, "
            f"normalized={self.normalized}, "
            f"onesided={self.onesided}"
        )

    def forward(
        self, input: torch.Tensor, ilens: torch.Tensor = None
    ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
        """STFT forward function.

        Args:
            input: (Batch, Nsamples) or (Batch, Nsample, Channels)
            ilens: (Batch)
        Returns:
            output: (Batch, Frames, Freq, 2) or (Batch, Frames, Channels, Freq, 2)

        """
        bs = input.size(0)
        if input.dim() == 3:
            multi_channel = True
            # input: (Batch, Nsample, Channels) -> (Batch * Channels, Nsample)
            input = input.transpose(1, 2).reshape(-1, input.size(1))
        else:
            multi_channel = False

        # NOTE(kamo):
        #   The default behaviour of torch.stft is compatible with librosa.stft
        #   about padding and scaling.
        #   Note that it's different from scipy.signal.stft

        # output: (Batch, Freq, Frames, 2=real_imag)
        # or (Batch, Channel, Freq, Frames, 2=real_imag)
        if self.window is not None:
            window_func = getattr(torch, f"{self.window}_window")
            window = window_func(
                self.win_length, dtype=input.dtype, device=input.device
            )
        else:
            window = None

        # For the compatibility of ARM devices, which do not support
        # torch.stft() due to the lake of MKL.
        if input.is_cuda or torch.backends.mkl.is_available():
            stft_kwargs = dict(
                n_fft=self.n_fft,
                win_length=self.win_length,
                hop_length=self.hop_length,
                center=self.center,
                window=window,
                normalized=self.normalized,
                onesided=self.onesided,
            )
            if is_torch_1_7_plus:
                stft_kwargs["return_complex"] = False
            output = torch.stft(input, **stft_kwargs)
        else:
            if self.training:
                raise NotImplementedError(
                    "stft is implemented with librosa on this device, which does not "
                    "support the training mode."
                )

            # use stft_kwargs to flexibly control different PyTorch versions' kwargs
            stft_kwargs = dict(
                n_fft=self.n_fft,
                win_length=self.win_length,
                hop_length=self.hop_length,
                center=self.center,
                window=window,
            )

            if window is not None:
                # pad the given window to n_fft
                n_pad_left = (self.n_fft - window.shape[0]) // 2
                n_pad_right = self.n_fft - window.shape[0] - n_pad_left
                stft_kwargs["window"] = torch.cat(
                    [torch.zeros(n_pad_left), window, torch.zeros(n_pad_right)], 0
                ).numpy()
            else:
                win_length = (
                    self.win_length if self.win_length is not None else self.n_fft
                )
                stft_kwargs["window"] = torch.ones(win_length)

            output = []
            # iterate over istances in a batch
            for i, instance in enumerate(input):
                stft = librosa.stft(input[i].numpy(), **stft_kwargs)
                output.append(torch.tensor(np.stack([stft.real, stft.imag], -1)))
            output = torch.stack(output, 0)
            if not self.onesided:
                len_conj = self.n_fft - output.shape[1]
                conj = output[:, 1 : 1 + len_conj].flip(1)
                conj[:, :, :, -1].data *= -1
                output = torch.cat([output, conj], 1)
            if self.normalized:
                output = output * (stft_kwargs["window"].shape[0] ** (-0.5))

        # output: (Batch, Freq, Frames, 2=real_imag)
        # -> (Batch, Frames, Freq, 2=real_imag)
        output = output.transpose(1, 2)
        if multi_channel:
            # output: (Batch * Channel, Frames, Freq, 2=real_imag)
            # -> (Batch, Frame, Channel, Freq, 2=real_imag)
            output = output.view(bs, -1, output.size(1), output.size(2), 2).transpose(
                1, 2
            )

        if ilens is not None:
            if self.center:
                pad = self.n_fft // 2
                ilens = ilens + 2 * pad

            olens = (ilens - self.n_fft) // self.hop_length + 1
            output.masked_fill_(make_pad_mask(olens, output, 1), 0.0)
        else:
            olens = None

        return output, olens

    def inverse(
        self, input: Union[torch.Tensor, ComplexTensor], ilens: torch.Tensor = None
    ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
        """Inverse STFT.

        Args:
            input: Tensor(batch, T, F, 2) or ComplexTensor(batch, T, F)
            ilens: (batch,)
        Returns:
            wavs: (batch, samples)
            ilens: (batch,)
        """
        if LooseVersion(torch.__version__) >= LooseVersion("1.6.0"):
            istft = torch.functional.istft
        else:
            try:
                import torchaudio
            except ImportError:
                raise ImportError(
                    "Please install torchaudio>=0.3.0 or use torch>=1.6.0"
                )

            if not hasattr(torchaudio.functional, "istft"):
                raise ImportError(
                    "Please install torchaudio>=0.3.0 or use torch>=1.6.0"
                )
            istft = torchaudio.functional.istft

        if self.window is not None:
            window_func = getattr(torch, f"{self.window}_window")
            if is_complex(input):
                datatype = input.real.dtype
            else:
                datatype = input.dtype
            window = window_func(self.win_length, dtype=datatype, device=input.device)
        else:
            window = None

        if is_complex(input):
            input = torch.stack([input.real, input.imag], dim=-1)
        elif input.shape[-1] != 2:
            raise TypeError("Invalid input type")
        input = input.transpose(1, 2)

        wavs = istft(
            input,
            n_fft=self.n_fft,
            hop_length=self.hop_length,
            win_length=self.win_length,
            window=window,
            center=self.center,
            normalized=self.normalized,
            onesided=self.onesided,
            length=ilens.max() if ilens is not None else ilens,
        )

        return wavs, ilens