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sincnet.py
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sincnet.py
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# The MIT License (MIT)
#
# Copyright (c) 2019- CNRS
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
#
# AUTHOR
# Hervé Bredin - http://herve.niderb.fr
from functools import lru_cache
import torch
import torch.nn as nn
import torch.nn.functional as F
from asteroid_filterbanks import Encoder, ParamSincFB
from pyannote.audio.utils.receptive_field import (
multi_conv_num_frames,
multi_conv_receptive_field_center,
multi_conv_receptive_field_size,
)
class SincNet(nn.Module):
def __init__(self, sample_rate: int = 16000, stride: int = 1):
super().__init__()
if sample_rate != 16000:
raise NotImplementedError("SincNet only supports 16kHz audio for now.")
# TODO: add support for other sample rate. it should be enough to multiply
# kernel_size by (sample_rate / 16000). but this needs to be double-checked.
self.sample_rate = sample_rate
self.stride = stride
self.wav_norm1d = nn.InstanceNorm1d(1, affine=True)
self.conv1d = nn.ModuleList()
self.pool1d = nn.ModuleList()
self.norm1d = nn.ModuleList()
self.conv1d.append(
Encoder(
ParamSincFB(
80,
251,
stride=self.stride,
sample_rate=sample_rate,
min_low_hz=50,
min_band_hz=50,
)
)
)
self.pool1d.append(nn.MaxPool1d(3, stride=3, padding=0, dilation=1))
self.norm1d.append(nn.InstanceNorm1d(80, affine=True))
self.conv1d.append(nn.Conv1d(80, 60, 5, stride=1))
self.pool1d.append(nn.MaxPool1d(3, stride=3, padding=0, dilation=1))
self.norm1d.append(nn.InstanceNorm1d(60, affine=True))
self.conv1d.append(nn.Conv1d(60, 60, 5, stride=1))
self.pool1d.append(nn.MaxPool1d(3, stride=3, padding=0, dilation=1))
self.norm1d.append(nn.InstanceNorm1d(60, affine=True))
@lru_cache
def num_frames(self, num_samples: int) -> int:
"""Compute number of output frames
Parameters
----------
num_samples : int
Number of input samples.
Returns
-------
num_frames : int
Number of output frames.
"""
kernel_size = [251, 3, 5, 3, 5, 3]
stride = [self.stride, 3, 1, 3, 1, 3]
padding = [0, 0, 0, 0, 0, 0]
dilation = [1, 1, 1, 1, 1, 1]
return multi_conv_num_frames(
num_samples,
kernel_size=kernel_size,
stride=stride,
padding=padding,
dilation=dilation,
)
def receptive_field_size(self, num_frames: int = 1) -> int:
"""Compute size of receptive field
Parameters
----------
num_frames : int, optional
Number of frames in the output signal
Returns
-------
receptive_field_size : int
Receptive field size.
"""
kernel_size = [251, 3, 5, 3, 5, 3]
stride = [self.stride, 3, 1, 3, 1, 3]
padding = [0, 0, 0, 0, 0, 0]
dilation = [1, 1, 1, 1, 1, 1]
return multi_conv_receptive_field_size(
num_frames,
kernel_size=kernel_size,
stride=stride,
padding=padding,
dilation=dilation,
)
def receptive_field_center(self, frame: int = 0) -> int:
"""Compute center of receptive field
Parameters
----------
frame : int, optional
Frame index
Returns
-------
receptive_field_center : int
Index of receptive field center.
"""
kernel_size = [251, 3, 5, 3, 5, 3]
stride = [self.stride, 3, 1, 3, 1, 3]
padding = [0, 0, 0, 0, 0, 0]
dilation = [1, 1, 1, 1, 1, 1]
return multi_conv_receptive_field_center(
frame,
kernel_size=kernel_size,
stride=stride,
padding=padding,
dilation=dilation,
)
def forward(self, waveforms: torch.Tensor) -> torch.Tensor:
"""Pass forward
Parameters
----------
waveforms : (batch, channel, sample)
"""
outputs = self.wav_norm1d(waveforms)
for c, (conv1d, pool1d, norm1d) in enumerate(
zip(self.conv1d, self.pool1d, self.norm1d)
):
outputs = conv1d(outputs)
# https://github.com/mravanelli/SincNet/issues/4
if c == 0:
outputs = torch.abs(outputs)
outputs = F.leaky_relu(norm1d(pool1d(outputs)))
return outputs