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""" SincNet model """
from functools import lru_cache
import numpy as np
import logging
import torch
import torch.nn as nn
import torch.nn.functional as F
logger = logging.getLogger(__name__)
class SincNetFilterConvLayer(nn.Module):
"""SincNet fast convolution filter layer"""
def __init__(self, out_channels: int, kernel_size: int, sample_rate=16000,
stride=1, padding=0, dilation=1, min_low_hz=50, min_band_hz=50,
in_channels=1, requires_grad=False):
"""
Args:
out_channels : `int` number of filters.
kernel_size : `int` filter length.
sample_rate : `int`, optional sample rate. Defaults to 16000.
"""
super(SincNetFilterConvLayer, self).__init__()
if in_channels != 1:
raise ValueError(f"SincNetFilterConvLayer only support in_channels = 1, was in_channels = {in_channels}")
self._out_channels = out_channels
self._kernel_size = kernel_size
if kernel_size % 2 == 0:
self._kernel_size += 1 # Forcing the filters to be odd
self._stride = stride
self._padding = padding
self._dilation = dilation
self._sample_rate = sample_rate
self._min_low_hz = min_low_hz
self._min_band_hz = min_band_hz
# initialize filterbanks such that they are equally spaced in Mel scale
low_hz = 30
high_hz = self._sample_rate / 2 - (self._min_low_hz + self._min_band_hz)
mel = np.linspace(
2595 * np.log10(1 + low_hz / 700), # Convert Hz to Mel
2595 * np.log10(1 + high_hz / 700), # Convert Hz to Mel
self._out_channels // 2 + 1
)
hz = 700 * (10 ** (mel / 2595) - 1) # Convert Mel to Hz
self._low_hz = nn.Parameter(
torch.Tensor(hz[:-1]).view(-1, 1),
requires_grad=requires_grad
)
self._band_hz = nn.Parameter(
torch.Tensor(np.diff(hz)).view(-1, 1),
requires_grad=requires_grad
)
self.register_buffer(
"_window",
torch.from_numpy(np.hamming(self._kernel_size)[: self._kernel_size // 2]).float()
)
self.register_buffer(
"_n",
(2* np.pi * torch.arange(-(self._kernel_size // 2), 0.0).view(1, -1) / self._sample_rate)
)
@property
@lru_cache(maxsize=1)
def filters(self) -> torch.Tensor:
low = self._min_low_hz + torch.abs(self._low_hz)
high = torch.clamp(low + self._min_band_hz + torch.abs(self._band_hz), self._min_low_hz, self._sample_rate/2)
band = (high-low)[:,0]
f_times_t_low = torch.matmul(low, self._n)
f_times_t_high = torch.matmul(high, self._n)
band_pass_left = ((torch.sin(f_times_t_high)-torch.sin(f_times_t_low))/(self._n/2))*self._window
band_pass_center = 2 * band.view(-1, 1)
band_pass_right = torch.flip(band_pass_left, dims=[1])
band_pass = torch.cat([band_pass_left,band_pass_center,band_pass_right],dim=1)
band_pass = band_pass / (2*band[:,None])
return band_pass.view(self._out_channels, 1, self._kernel_size)
def forward(self, waveforms: torch.Tensor) -> torch.Tensor:
"""
Args:
waveforms : (batch_size, 1, n_samples) batch of waveforms.
Returns:
features : (batch_size, out_channels, n_samples_out) batch of sinc filters activations.
"""
return F.conv1d(waveforms, self.filters, stride=self._stride,
padding=self._padding, dilation=self._dilation,
).abs_() # https://github.com/mravanelli/SincNet/issues/4
class SincNet(nn.Module):
"""SincNet"""
def __init__(
self,
num_sinc_filters: int = 80,
sinc_filter_length: int = 251,
num_conv_filters: int = 60,
conv_filter_length: int = 5,
pool_kernel_size: int = 3,
pool_stride: int = 3,
sample_rate: int = 16000,
sinc_filter_stride: int = 10,
sinc_filter_padding: int = 0,
sinc_filter_dilation: int = 1,
min_low_hz: int = 50,
min_band_hz: int = 50,
sinc_filter_in_channels: int = 1,
num_wavform_channels: int = 1,
):
super().__init__()
if sample_rate != 16000:
raise NotImplementedError(f"SincNet only supports 16kHz audio (sample_rate = 16000), was sample_rate = {sample_rate}")
self.wav_norm1d = nn.InstanceNorm1d(num_wavform_channels, affine=True)
self.conv1d = nn.ModuleList([
SincNetFilterConvLayer(
num_sinc_filters,
sinc_filter_length,
sample_rate=sample_rate,
stride=sinc_filter_stride,
padding=sinc_filter_padding,
dilation=sinc_filter_dilation,
min_low_hz=min_low_hz,
min_band_hz=min_band_hz,
in_channels=sinc_filter_in_channels,
),
nn.Conv1d(num_sinc_filters, num_conv_filters, conv_filter_length),
nn.Conv1d(num_conv_filters, num_conv_filters, conv_filter_length),
])
self.pool1d = nn.ModuleList([
nn.MaxPool1d(pool_kernel_size, stride=pool_stride),
nn.MaxPool1d(pool_kernel_size, stride=pool_stride),
nn.MaxPool1d(pool_kernel_size, stride=pool_stride),
])
self.norm1d = nn.ModuleList([
nn.InstanceNorm1d(num_sinc_filters, affine=True),
nn.InstanceNorm1d(num_conv_filters, affine=True),
nn.InstanceNorm1d(num_conv_filters, affine=True),
])
def forward(self, waveforms: torch.Tensor) -> torch.Tensor:
"""
Args:
waveforms : (batch, channel, sample)
"""
outputs = self.wav_norm1d(waveforms)
for _, (conv1d, pool1d, norm1d) in enumerate(
zip(self.conv1d, self.pool1d, self.norm1d)
):
outputs = conv1d(outputs)
outputs = F.leaky_relu(norm1d(pool1d(outputs)))
return outputs
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