429 lines
14 KiB
Python
429 lines
14 KiB
Python
#!/usr/bin/env python3
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# -*- encoding: utf-8 -*-
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# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
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# MIT License (https://opensource.org/licenses/MIT)
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# Modified from 3D-Speaker (https://github.com/alibaba-damo-academy/3D-Speaker)
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from collections import OrderedDict
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import torch
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import torch.nn.functional as F
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import torch.utils.checkpoint as cp
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import torchaudio.compliance.kaldi as Kaldi
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def pad_list(xs, pad_value):
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"""Perform padding for the list of tensors.
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Args:
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xs (List): List of Tensors [(T_1, `*`), (T_2, `*`), ..., (T_B, `*`)].
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pad_value (float): Value for padding.
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Returns:
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Tensor: Padded tensor (B, Tmax, `*`).
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Examples:
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>>> x = [torch.ones(4), torch.ones(2), torch.ones(1)]
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>>> x
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[tensor([1., 1., 1., 1.]), tensor([1., 1.]), tensor([1.])]
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>>> pad_list(x, 0)
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tensor([[1., 1., 1., 1.],
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[1., 1., 0., 0.],
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[1., 0., 0., 0.]])
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"""
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n_batch = len(xs)
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max_len = max(x.size(0) for x in xs)
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pad = xs[0].new(n_batch, max_len, *xs[0].size()[1:]).fill_(pad_value)
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for i in range(n_batch):
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pad[i, : xs[i].size(0)] = xs[i]
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return pad
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def extract_feature(audio):
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features = []
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feature_times = []
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feature_lengths = []
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for au in audio:
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feature = Kaldi.fbank(au.unsqueeze(0), num_mel_bins=80)
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feature = feature - feature.mean(dim=0, keepdim=True)
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features.append(feature)
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feature_times.append(au.shape[0])
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feature_lengths.append(feature.shape[0])
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# padding for batch inference
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features_padded = pad_list(features, pad_value=0)
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# features = torch.cat(features)
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return features_padded, feature_lengths, feature_times
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class BasicResBlock(torch.nn.Module):
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expansion = 1
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def __init__(self, in_planes, planes, stride=1):
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super(BasicResBlock, self).__init__()
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self.conv1 = torch.nn.Conv2d(
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in_planes, planes, kernel_size=3, stride=(stride, 1), padding=1, bias=False
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)
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self.bn1 = torch.nn.BatchNorm2d(planes)
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self.conv2 = torch.nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False)
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self.bn2 = torch.nn.BatchNorm2d(planes)
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self.shortcut = torch.nn.Sequential()
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if stride != 1 or in_planes != self.expansion * planes:
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self.shortcut = torch.nn.Sequential(
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torch.nn.Conv2d(
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in_planes,
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self.expansion * planes,
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kernel_size=1,
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stride=(stride, 1),
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bias=False,
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),
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torch.nn.BatchNorm2d(self.expansion * planes),
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)
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def forward(self, x):
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out = F.relu(self.bn1(self.conv1(x)))
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out = self.bn2(self.conv2(out))
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out += self.shortcut(x)
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out = F.relu(out)
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return out
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class FCM(torch.nn.Module):
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def __init__(self, block=BasicResBlock, num_blocks=[2, 2], m_channels=32, feat_dim=80):
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super(FCM, self).__init__()
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self.in_planes = m_channels
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self.conv1 = torch.nn.Conv2d(1, m_channels, kernel_size=3, stride=1, padding=1, bias=False)
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self.bn1 = torch.nn.BatchNorm2d(m_channels)
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self.layer1 = self._make_layer(block, m_channels, num_blocks[0], stride=2)
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self.layer2 = self._make_layer(block, m_channels, num_blocks[0], stride=2)
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self.conv2 = torch.nn.Conv2d(
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m_channels, m_channels, kernel_size=3, stride=(2, 1), padding=1, bias=False
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)
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self.bn2 = torch.nn.BatchNorm2d(m_channels)
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self.out_channels = m_channels * (feat_dim // 8)
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def _make_layer(self, block, planes, num_blocks, stride):
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strides = [stride] + [1] * (num_blocks - 1)
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layers = []
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for stride in strides:
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layers.append(block(self.in_planes, planes, stride))
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self.in_planes = planes * block.expansion
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return torch.nn.Sequential(*layers)
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def forward(self, x):
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x = x.unsqueeze(1)
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out = F.relu(self.bn1(self.conv1(x)))
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out = self.layer1(out)
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out = self.layer2(out)
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out = F.relu(self.bn2(self.conv2(out)))
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shape = out.shape
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out = out.reshape(shape[0], shape[1] * shape[2], shape[3])
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return out
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def get_nonlinear(config_str, channels):
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nonlinear = torch.nn.Sequential()
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for name in config_str.split("-"):
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if name == "relu":
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nonlinear.add_module("relu", torch.nn.ReLU(inplace=True))
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elif name == "prelu":
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nonlinear.add_module("prelu", torch.nn.PReLU(channels))
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elif name == "batchnorm":
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nonlinear.add_module("batchnorm", torch.nn.BatchNorm1d(channels))
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elif name == "batchnorm_":
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nonlinear.add_module("batchnorm", torch.nn.BatchNorm1d(channels, affine=False))
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else:
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raise ValueError("Unexpected module ({}).".format(name))
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return nonlinear
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def statistics_pooling(x, dim=-1, keepdim=False, unbiased=True, eps=1e-2):
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mean = x.mean(dim=dim)
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std = x.std(dim=dim, unbiased=unbiased)
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stats = torch.cat([mean, std], dim=-1)
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if keepdim:
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stats = stats.unsqueeze(dim=dim)
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return stats
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class StatsPool(torch.nn.Module):
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def forward(self, x):
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return statistics_pooling(x)
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class TDNNLayer(torch.nn.Module):
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def __init__(
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self,
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in_channels,
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out_channels,
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kernel_size,
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stride=1,
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padding=0,
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dilation=1,
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bias=False,
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config_str="batchnorm-relu",
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):
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super(TDNNLayer, self).__init__()
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if padding < 0:
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assert (
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kernel_size % 2 == 1
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), "Expect equal paddings, but got even kernel size ({})".format(kernel_size)
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padding = (kernel_size - 1) // 2 * dilation
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self.linear = torch.nn.Conv1d(
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in_channels,
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out_channels,
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kernel_size,
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stride=stride,
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padding=padding,
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dilation=dilation,
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bias=bias,
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)
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self.nonlinear = get_nonlinear(config_str, out_channels)
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def forward(self, x):
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x = self.linear(x)
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x = self.nonlinear(x)
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return x
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class CAMLayer(torch.nn.Module):
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def __init__(
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self, bn_channels, out_channels, kernel_size, stride, padding, dilation, bias, reduction=2
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):
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super(CAMLayer, self).__init__()
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self.linear_local = torch.nn.Conv1d(
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bn_channels,
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out_channels,
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kernel_size,
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stride=stride,
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padding=padding,
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dilation=dilation,
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bias=bias,
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)
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self.linear1 = torch.nn.Conv1d(bn_channels, bn_channels // reduction, 1)
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self.relu = torch.nn.ReLU(inplace=True)
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self.linear2 = torch.nn.Conv1d(bn_channels // reduction, out_channels, 1)
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self.sigmoid = torch.nn.Sigmoid()
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def forward(self, x):
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y = self.linear_local(x)
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context = x.mean(-1, keepdim=True) + self.seg_pooling(x)
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context = self.relu(self.linear1(context))
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m = self.sigmoid(self.linear2(context))
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return y * m
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def seg_pooling(self, x, seg_len=100, stype="avg"):
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if stype == "avg":
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seg = F.avg_pool1d(x, kernel_size=seg_len, stride=seg_len, ceil_mode=True)
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elif stype == "max":
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seg = F.max_pool1d(x, kernel_size=seg_len, stride=seg_len, ceil_mode=True)
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else:
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raise ValueError("Wrong segment pooling type.")
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shape = seg.shape
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seg = seg.unsqueeze(-1).expand(*shape, seg_len).reshape(*shape[:-1], -1)
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seg = seg[..., : x.shape[-1]]
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return seg
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class CAMDenseTDNNLayer(torch.nn.Module):
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def __init__(
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self,
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in_channels,
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out_channels,
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bn_channels,
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kernel_size,
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stride=1,
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dilation=1,
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bias=False,
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config_str="batchnorm-relu",
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memory_efficient=False,
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):
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super(CAMDenseTDNNLayer, self).__init__()
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assert kernel_size % 2 == 1, "Expect equal paddings, but got even kernel size ({})".format(
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kernel_size
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)
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padding = (kernel_size - 1) // 2 * dilation
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self.memory_efficient = memory_efficient
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self.nonlinear1 = get_nonlinear(config_str, in_channels)
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self.linear1 = torch.nn.Conv1d(in_channels, bn_channels, 1, bias=False)
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self.nonlinear2 = get_nonlinear(config_str, bn_channels)
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self.cam_layer = CAMLayer(
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bn_channels,
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out_channels,
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kernel_size,
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stride=stride,
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padding=padding,
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dilation=dilation,
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bias=bias,
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)
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def bn_function(self, x):
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return self.linear1(self.nonlinear1(x))
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def forward(self, x):
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if self.training and self.memory_efficient:
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x = cp.checkpoint(self.bn_function, x)
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else:
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x = self.bn_function(x)
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x = self.cam_layer(self.nonlinear2(x))
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return x
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class CAMDenseTDNNBlock(torch.nn.ModuleList):
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def __init__(
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self,
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num_layers,
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in_channels,
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out_channels,
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bn_channels,
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kernel_size,
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stride=1,
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dilation=1,
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bias=False,
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config_str="batchnorm-relu",
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memory_efficient=False,
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):
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super(CAMDenseTDNNBlock, self).__init__()
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for i in range(num_layers):
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layer = CAMDenseTDNNLayer(
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in_channels=in_channels + i * out_channels,
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out_channels=out_channels,
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bn_channels=bn_channels,
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kernel_size=kernel_size,
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stride=stride,
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dilation=dilation,
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bias=bias,
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config_str=config_str,
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memory_efficient=memory_efficient,
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)
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self.add_module("tdnnd%d" % (i + 1), layer)
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def forward(self, x):
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for layer in self:
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x = torch.cat([x, layer(x)], dim=1)
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return x
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class TransitLayer(torch.nn.Module):
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def __init__(self, in_channels, out_channels, bias=True, config_str="batchnorm-relu"):
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super(TransitLayer, self).__init__()
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self.nonlinear = get_nonlinear(config_str, in_channels)
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self.linear = torch.nn.Conv1d(in_channels, out_channels, 1, bias=bias)
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def forward(self, x):
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x = self.nonlinear(x)
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x = self.linear(x)
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return x
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class DenseLayer(torch.nn.Module):
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def __init__(self, in_channels, out_channels, bias=False, config_str="batchnorm-relu"):
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super(DenseLayer, self).__init__()
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self.linear = torch.nn.Conv1d(in_channels, out_channels, 1, bias=bias)
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self.nonlinear = get_nonlinear(config_str, out_channels)
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def forward(self, x):
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if len(x.shape) == 2:
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x = self.linear(x.unsqueeze(dim=-1)).squeeze(dim=-1)
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else:
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x = self.linear(x)
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x = self.nonlinear(x)
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return x
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# @tables.register("model_classes", "CAMPPlus")
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class CAMPPlus(torch.nn.Module):
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def __init__(
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self,
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feat_dim=80,
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embedding_size=192,
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growth_rate=32,
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bn_size=4,
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init_channels=128,
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config_str="batchnorm-relu",
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memory_efficient=True,
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output_level="segment",
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**kwargs,
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):
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super().__init__()
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self.head = FCM(feat_dim=feat_dim)
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channels = self.head.out_channels
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self.output_level = output_level
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self.xvector = torch.nn.Sequential(
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OrderedDict(
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[
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(
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"tdnn",
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TDNNLayer(
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channels,
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init_channels,
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5,
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stride=2,
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dilation=1,
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padding=-1,
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config_str=config_str,
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),
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),
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]
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)
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)
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channels = init_channels
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for i, (num_layers, kernel_size, dilation) in enumerate(
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zip((12, 24, 16), (3, 3, 3), (1, 2, 2))
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):
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block = CAMDenseTDNNBlock(
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num_layers=num_layers,
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in_channels=channels,
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out_channels=growth_rate,
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bn_channels=bn_size * growth_rate,
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kernel_size=kernel_size,
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dilation=dilation,
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config_str=config_str,
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memory_efficient=memory_efficient,
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)
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self.xvector.add_module("block%d" % (i + 1), block)
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channels = channels + num_layers * growth_rate
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self.xvector.add_module(
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"transit%d" % (i + 1),
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TransitLayer(channels, channels // 2, bias=False, config_str=config_str),
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)
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channels //= 2
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self.xvector.add_module("out_nonlinear", get_nonlinear(config_str, channels))
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if self.output_level == "segment":
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self.xvector.add_module("stats", StatsPool())
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self.xvector.add_module(
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"dense", DenseLayer(channels * 2, embedding_size, config_str="batchnorm_")
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)
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else:
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assert (
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self.output_level == "frame"
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), "`output_level` should be set to 'segment' or 'frame'. "
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for m in self.modules():
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if isinstance(m, (torch.nn.Conv1d, torch.nn.Linear)):
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torch.nn.init.kaiming_normal_(m.weight.data)
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if m.bias is not None:
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torch.nn.init.zeros_(m.bias)
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def forward(self, x):
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x = x.permute(0, 2, 1) # (B,T,F) => (B,F,T)
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x = self.head(x)
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x = self.xvector(x)
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if self.output_level == "frame":
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x = x.transpose(1, 2)
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return x
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def inference(self, audio_list):
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speech, speech_lengths, speech_times = extract_feature(audio_list)
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results = self.forward(speech.to(torch.float32))
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return results
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