#!/usr/bin/env python3
# 2020, Technische Universität München; Ludwig Kürzinger
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-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