Spaces:
Running
Running
File size: 6,708 Bytes
35a4689 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 |
#!/usr/bin/python3
# -*- coding: utf-8 -*-
"""
DeepFilterNet 的原生实现不直接支持流式推理
社区开发者（如 Rikorose）提供了基于 Torch 的流式推理实现
https://github.com/grazder/DeepFilterNet/tree/1097015d53ced78fb234e7d7071a5dd4446e3952/torchDF
此文件试图实现一个支持流式推理的 dfnet
"""
import os
import math
from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple, Union
import numpy as np
import torch
import torch.nn as nn
from torch.nn import functional as F
from toolbox.torchaudio.configuration_utils import CONFIG_FILE
from toolbox.torchaudio.models.dfnet.configuration_dfnet import DfNetConfig
from toolbox.torchaudio.modules.conv_stft import ConvSTFT, ConviSTFT
from toolbox.torchaudio.modules.local_snr_target import LocalSnrTarget
from toolbox.torchaudio.modules.freq_bands.erb_bands import ErbBands
MODEL_FILE = "model.pt"
norm_layer_dict = {
"batch_norm_2d": torch.nn.BatchNorm2d
}
activation_layer_dict = {
"relu": torch.nn.ReLU,
"identity": torch.nn.Identity,
"sigmoid": torch.nn.Sigmoid,
}
class CausalConv2d(nn.Module):
def __init__(self,
in_channels: int,
out_channels: int,
kernel_size: Union[int, Iterable[int]],
fstride: int = 1,
dilation: int = 1,
pad_f_dim: bool = True,
bias: bool = True,
separable: bool = False,
norm_layer: str = "batch_norm_2d",
activation_layer: str = "relu",
):
super(CausalConv2d, self).__init__()
kernel_size = (kernel_size, kernel_size) if isinstance(kernel_size, int) else tuple(kernel_size)
if pad_f_dim:
fpad = kernel_size[1] // 2 + dilation - 1
else:
fpad = 0
# for last 2 dim, pad (left, right, top, bottom).
self.lookback = kernel_size[0] - 1
if self.lookback > 0:
self.tpad = nn.ConstantPad2d(padding=(0, 0, self.lookback, 0), value=0.0)
else:
self.tpad = nn.Identity()
groups = math.gcd(in_channels, out_channels) if separable else 1
if groups == 1:
separable = False
if max(kernel_size) == 1:
separable = False
self.conv = nn.Conv2d(
in_channels,
out_channels,
kernel_size=kernel_size,
padding=(0, fpad),
stride=(1, fstride), # stride over time is always 1
dilation=(1, dilation), # dilation over time is always 1
groups=groups,
bias=bias,
)
if separable:
self.convp = nn.Conv2d(
out_channels,
out_channels,
kernel_size=1,
bias=False,
)
else:
self.convp = nn.Identity()
if norm_layer is not None:
norm_layer = norm_layer_dict[norm_layer]
self.norm = norm_layer(out_channels)
else:
self.norm = nn.Identity()
if activation_layer is not None:
activation_layer = activation_layer_dict[activation_layer]
self.activation = activation_layer()
else:
self.activation = nn.Identity()
super().__init__()
def forward(self, inputs: torch.Tensor, cache: Tuple[torch.Tensor, torch.Tensor] = None):
"""
:param inputs: shape: [b, c, t, f]
:param cache: shape: [b, c, lookback, f];
:return:
"""
x = inputs
if cache is None:
x = self.tpad(x)
else:
x = torch.concat(tensors=[cache, x], dim=2)
new_cache = x[:, :, -self.lookback:, :]
x = self.conv(x)
x = self.convp(x)
x = self.norm(x)
x = self.activation(x)
return x, new_cache
class CausalConvTranspose2d(nn.Module):
def __init__(self,
in_channels: int,
out_channels: int,
kernel_size: Union[int, Iterable[int]],
fstride: int = 1,
dilation: int = 1,
pad_f_dim: bool = True,
bias: bool = True,
separable: bool = False,
norm_layer: str = "batch_norm_2d",
activation_layer: str = "relu",
):
super(CausalConvTranspose2d, self).__init__()
kernel_size = (kernel_size, kernel_size) if isinstance(kernel_size, int) else kernel_size
if pad_f_dim:
fpad = kernel_size[1] // 2
else:
fpad = 0
# for last 2 dim, pad (left, right, top, bottom).
self.lookback = kernel_size[0] - 1
groups = math.gcd(in_channels, out_channels) if separable else 1
if groups == 1:
separable = False
self.convt = nn.ConvTranspose2d(
in_channels,
out_channels,
kernel_size=kernel_size,
padding=(0, fpad),
output_padding=(0, 0),
stride=(1, fstride), # stride over time is always 1
dilation=(1, dilation), # dilation over time is always 1
groups=groups,
bias=bias,
)
if separable:
self.convp = nn.Conv2d(
out_channels,
out_channels,
kernel_size=1,
bias=False,
)
else:
self.convp = nn.Identity()
if norm_layer is not None:
norm_layer = norm_layer_dict[norm_layer]
self.norm = norm_layer(out_channels)
else:
self.norm = nn.Identity()
if activation_layer is not None:
activation_layer = activation_layer_dict[activation_layer]
self.activation = activation_layer()
else:
self.activation = nn.Identity()
def forward(self, inputs: torch.Tensor, cache: Tuple[torch.Tensor, torch.Tensor] = None):
"""
:param inputs: shape: [b, c, t, f]
:param cache: shape: [b, c, lookback, f];
:return:
"""
x = inputs
# x shape: [b, c, t, f]
x = self.convt(x)
# x shape: [b, c, t+lookback, f]
if cache is not None:
x = torch.concat(tensors=[
x[:, :, :self.lookback, :] + cache,
x[:, :, self.lookback:, :]
], dim=2)
x = x[:, :, :-self.lookback, :]
new_cache = x[:, :, -self.lookback:, :]
x = self.convp(x)
x = self.norm(x)
x = self.activation(x)
return x, new_cache
if __name__ == "__main__":
pass
|