Source code for espnet2.asr.frontend.windowing

#!/usr/bin/env python3
#  2020, Technische Universität München;  Ludwig Kürzinger
#  Apache 2.0  (

"""Sliding Window for raw audio input data."""

from typing import Optional, Tuple

import torch
from typeguard import typechecked

from espnet2.asr.frontend.abs_frontend import AbsFrontend

[docs]class SlidingWindow(AbsFrontend): """Sliding Window. Provides a sliding window over a batched continuous raw audio tensor. Optionally, provides padding (Currently not implemented). Combine this module with a pre-encoder compatible with raw audio data, for example Sinc convolutions. Known issues: Output length is calculated incorrectly if audio shorter than win_length. WARNING: trailing values are discarded - padding not implemented yet. There is currently no additional window function applied to input values. """ @typechecked def __init__( self, win_length: int = 400, hop_length: int = 160, channels: int = 1, padding: Optional[int] = None, fs=None, ): """Initialize. Args: win_length: Length of frame. hop_length: Relative starting point of next frame. channels: Number of input channels. padding: Padding (placeholder, currently not implemented). fs: Sampling rate (placeholder for compatibility, not used). """ super().__init__() self.fs = fs self.win_length = win_length self.hop_length = hop_length self.channels = channels self.padding = padding
[docs] def forward( self, input: torch.Tensor, input_lengths: torch.Tensor ) -> Tuple[torch.Tensor, torch.Tensor]: """Apply a sliding window on the input. Args: input: Input (B, T, C*D) or (B, T*C*D), with D=C=1. input_lengths: Input lengths within batch. Returns: Tensor: Output with dimensions (B, T, C, D), with D=win_length. Tensor: Output lengths within batch. """ input_size = input.size() B = input_size[0] T = input_size[1] C = self.channels D = self.win_length # (B, T, C) --> (T, B, C) continuous = input.view(B, T, C).permute(1, 0, 2) windowed = continuous.unfold(0, D, self.hop_length) # (T, B, C, D) --> (B, T, C, D) output = windowed.permute(1, 0, 2, 3).contiguous() # After unfold(), windowed lengths change: output_lengths = ( torch.div( input_lengths - self.win_length, self.hop_length, rounding_mode="trunc" ) + 1 ) return output, output_lengths
[docs] def output_size(self) -> int: """Return output length of feature dimension D, i.e. the window length.""" return self.win_length