# Copyright 2020 Tomoki Hayashi
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# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
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"""Conformer encoder definition."""
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import logging
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from typing import List
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from typing import Optional
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from typing import Tuple
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from typing import Union
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import torch
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from torch import nn
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from typeguard import check_argument_types
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from funasr.models.ctc import CTC
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from funasr.models.encoder.abs_encoder import AbsEncoder
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from funasr.modules.attention import (
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MultiHeadedAttention, # noqa: H301
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RelPositionMultiHeadedAttention, # noqa: H301
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LegacyRelPositionMultiHeadedAttention, # noqa: H301
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)
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from funasr.modules.embedding import (
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PositionalEncoding, # noqa: H301
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ScaledPositionalEncoding, # noqa: H301
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RelPositionalEncoding, # noqa: H301
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LegacyRelPositionalEncoding, # noqa: H301
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)
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from funasr.modules.layer_norm import LayerNorm
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from funasr.modules.multi_layer_conv import Conv1dLinear
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from funasr.modules.multi_layer_conv import MultiLayeredConv1d
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from funasr.modules.nets_utils import get_activation
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from funasr.modules.nets_utils import make_pad_mask
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from funasr.modules.positionwise_feed_forward import (
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PositionwiseFeedForward, # noqa: H301
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)
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from funasr.modules.repeat import repeat
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from funasr.modules.subsampling import Conv2dSubsampling
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from funasr.modules.subsampling import Conv2dSubsampling2
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from funasr.modules.subsampling import Conv2dSubsampling6
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from funasr.modules.subsampling import Conv2dSubsampling8
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from funasr.modules.subsampling import TooShortUttError
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from funasr.modules.subsampling import check_short_utt
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class ConvolutionModule(nn.Module):
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"""ConvolutionModule in Conformer model.
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Args:
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channels (int): The number of channels of conv layers.
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kernel_size (int): Kernerl size of conv layers.
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"""
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def __init__(self, channels, kernel_size, activation=nn.ReLU(), bias=True):
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"""Construct an ConvolutionModule object."""
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super(ConvolutionModule, self).__init__()
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# kernerl_size should be a odd number for 'SAME' padding
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assert (kernel_size - 1) % 2 == 0
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self.pointwise_conv1 = nn.Conv1d(
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channels,
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2 * channels,
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kernel_size=1,
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stride=1,
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padding=0,
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bias=bias,
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)
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self.depthwise_conv = nn.Conv1d(
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channels,
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channels,
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kernel_size,
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stride=1,
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padding=(kernel_size - 1) // 2,
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groups=channels,
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bias=bias,
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)
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self.norm = nn.BatchNorm1d(channels)
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self.pointwise_conv2 = nn.Conv1d(
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channels,
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channels,
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kernel_size=1,
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stride=1,
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padding=0,
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bias=bias,
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)
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self.activation = activation
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def forward(self, x):
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"""Compute convolution module.
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Args:
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x (torch.Tensor): Input tensor (#batch, time, channels).
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Returns:
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torch.Tensor: Output tensor (#batch, time, channels).
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"""
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# exchange the temporal dimension and the feature dimension
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x = x.transpose(1, 2)
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# GLU mechanism
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x = self.pointwise_conv1(x) # (batch, 2*channel, dim)
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x = nn.functional.glu(x, dim=1) # (batch, channel, dim)
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# 1D Depthwise Conv
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x = self.depthwise_conv(x)
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x = self.activation(self.norm(x))
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x = self.pointwise_conv2(x)
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return x.transpose(1, 2)
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class EncoderLayer(nn.Module):
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"""Encoder layer module.
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Args:
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size (int): Input dimension.
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self_attn (torch.nn.Module): Self-attention module instance.
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`MultiHeadedAttention` or `RelPositionMultiHeadedAttention` instance
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can be used as the argument.
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feed_forward (torch.nn.Module): Feed-forward module instance.
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`PositionwiseFeedForward`, `MultiLayeredConv1d`, or `Conv1dLinear` instance
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can be used as the argument.
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feed_forward_macaron (torch.nn.Module): Additional feed-forward module instance.
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`PositionwiseFeedForward`, `MultiLayeredConv1d`, or `Conv1dLinear` instance
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can be used as the argument.
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conv_module (torch.nn.Module): Convolution module instance.
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`ConvlutionModule` instance can be used as the argument.
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dropout_rate (float): Dropout rate.
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normalize_before (bool): Whether to use layer_norm before the first block.
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concat_after (bool): Whether to concat attention layer's input and output.
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if True, additional linear will be applied.
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i.e. x -> x + linear(concat(x, att(x)))
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if False, no additional linear will be applied. i.e. x -> x + att(x)
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stochastic_depth_rate (float): Proability to skip this layer.
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During training, the layer may skip residual computation and return input
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as-is with given probability.
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"""
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def __init__(
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self,
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size,
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self_attn,
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feed_forward,
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feed_forward_macaron,
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conv_module,
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dropout_rate,
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normalize_before=True,
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concat_after=False,
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stochastic_depth_rate=0.0,
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):
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"""Construct an EncoderLayer object."""
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super(EncoderLayer, self).__init__()
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self.self_attn = self_attn
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self.feed_forward = feed_forward
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self.feed_forward_macaron = feed_forward_macaron
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self.conv_module = conv_module
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self.norm_ff = LayerNorm(size) # for the FNN module
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self.norm_mha = LayerNorm(size) # for the MHA module
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if feed_forward_macaron is not None:
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self.norm_ff_macaron = LayerNorm(size)
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self.ff_scale = 0.5
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else:
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self.ff_scale = 1.0
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if self.conv_module is not None:
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self.norm_conv = LayerNorm(size) # for the CNN module
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self.norm_final = LayerNorm(size) # for the final output of the block
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self.dropout = nn.Dropout(dropout_rate)
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self.size = size
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self.normalize_before = normalize_before
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self.concat_after = concat_after
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if self.concat_after:
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self.concat_linear = nn.Linear(size + size, size)
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self.stochastic_depth_rate = stochastic_depth_rate
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def forward(self, x_input, mask, cache=None):
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"""Compute encoded features.
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Args:
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x_input (Union[Tuple, torch.Tensor]): Input tensor w/ or w/o pos emb.
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- w/ pos emb: Tuple of tensors [(#batch, time, size), (1, time, size)].
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- w/o pos emb: Tensor (#batch, time, size).
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mask (torch.Tensor): Mask tensor for the input (#batch, time).
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cache (torch.Tensor): Cache tensor of the input (#batch, time - 1, size).
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Returns:
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torch.Tensor: Output tensor (#batch, time, size).
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torch.Tensor: Mask tensor (#batch, time).
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"""
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if isinstance(x_input, tuple):
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x, pos_emb = x_input[0], x_input[1]
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else:
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x, pos_emb = x_input, None
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skip_layer = False
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# with stochastic depth, residual connection `x + f(x)` becomes
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# `x <- x + 1 / (1 - p) * f(x)` at training time.
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stoch_layer_coeff = 1.0
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if self.training and self.stochastic_depth_rate > 0:
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skip_layer = torch.rand(1).item() < self.stochastic_depth_rate
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stoch_layer_coeff = 1.0 / (1 - self.stochastic_depth_rate)
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if skip_layer:
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if cache is not None:
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x = torch.cat([cache, x], dim=1)
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if pos_emb is not None:
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return (x, pos_emb), mask
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return x, mask
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# whether to use macaron style
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if self.feed_forward_macaron is not None:
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residual = x
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if self.normalize_before:
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x = self.norm_ff_macaron(x)
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x = residual + stoch_layer_coeff * self.ff_scale * self.dropout(
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self.feed_forward_macaron(x)
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)
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if not self.normalize_before:
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x = self.norm_ff_macaron(x)
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# multi-headed self-attention module
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residual = x
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if self.normalize_before:
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x = self.norm_mha(x)
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if cache is None:
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x_q = x
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else:
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assert cache.shape == (x.shape[0], x.shape[1] - 1, self.size)
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x_q = x[:, -1:, :]
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residual = residual[:, -1:, :]
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mask = None if mask is None else mask[:, -1:, :]
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if pos_emb is not None:
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x_att = self.self_attn(x_q, x, x, pos_emb, mask)
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else:
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x_att = self.self_attn(x_q, x, x, mask)
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if self.concat_after:
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x_concat = torch.cat((x, x_att), dim=-1)
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x = residual + stoch_layer_coeff * self.concat_linear(x_concat)
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else:
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x = residual + stoch_layer_coeff * self.dropout(x_att)
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if not self.normalize_before:
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x = self.norm_mha(x)
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# convolution module
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if self.conv_module is not None:
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residual = x
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if self.normalize_before:
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x = self.norm_conv(x)
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x = residual + stoch_layer_coeff * self.dropout(self.conv_module(x))
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if not self.normalize_before:
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x = self.norm_conv(x)
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# feed forward module
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residual = x
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if self.normalize_before:
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x = self.norm_ff(x)
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x = residual + stoch_layer_coeff * self.ff_scale * self.dropout(
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self.feed_forward(x)
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)
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if not self.normalize_before:
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x = self.norm_ff(x)
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if self.conv_module is not None:
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x = self.norm_final(x)
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if cache is not None:
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x = torch.cat([cache, x], dim=1)
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if pos_emb is not None:
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return (x, pos_emb), mask
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return x, mask
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class ConformerEncoder(AbsEncoder):
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"""Conformer encoder module.
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Args:
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input_size (int): Input dimension.
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output_size (int): Dimension of attention.
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attention_heads (int): The number of heads of multi head attention.
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linear_units (int): The number of units of position-wise feed forward.
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num_blocks (int): The number of decoder blocks.
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dropout_rate (float): Dropout rate.
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attention_dropout_rate (float): Dropout rate in attention.
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positional_dropout_rate (float): Dropout rate after adding positional encoding.
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input_layer (Union[str, torch.nn.Module]): Input layer type.
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normalize_before (bool): Whether to use layer_norm before the first block.
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concat_after (bool): Whether to concat attention layer's input and output.
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If True, additional linear will be applied.
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i.e. x -> x + linear(concat(x, att(x)))
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If False, no additional linear will be applied. i.e. x -> x + att(x)
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positionwise_layer_type (str): "linear", "conv1d", or "conv1d-linear".
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positionwise_conv_kernel_size (int): Kernel size of positionwise conv1d layer.
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rel_pos_type (str): Whether to use the latest relative positional encoding or
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the legacy one. The legacy relative positional encoding will be deprecated
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in the future. More Details can be found in
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https://github.com/espnet/espnet/pull/2816.
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encoder_pos_enc_layer_type (str): Encoder positional encoding layer type.
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encoder_attn_layer_type (str): Encoder attention layer type.
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activation_type (str): Encoder activation function type.
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macaron_style (bool): Whether to use macaron style for positionwise layer.
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use_cnn_module (bool): Whether to use convolution module.
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zero_triu (bool): Whether to zero the upper triangular part of attention matrix.
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cnn_module_kernel (int): Kernerl size of convolution module.
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padding_idx (int): Padding idx for input_layer=embed.
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"""
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def __init__(
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self,
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input_size: int,
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output_size: int = 256,
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attention_heads: int = 4,
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linear_units: int = 2048,
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num_blocks: int = 6,
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dropout_rate: float = 0.1,
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positional_dropout_rate: float = 0.1,
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attention_dropout_rate: float = 0.0,
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input_layer: str = "conv2d",
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normalize_before: bool = True,
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concat_after: bool = False,
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positionwise_layer_type: str = "linear",
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positionwise_conv_kernel_size: int = 3,
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macaron_style: bool = False,
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rel_pos_type: str = "legacy",
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pos_enc_layer_type: str = "rel_pos",
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selfattention_layer_type: str = "rel_selfattn",
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activation_type: str = "swish",
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use_cnn_module: bool = True,
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zero_triu: bool = False,
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cnn_module_kernel: int = 31,
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padding_idx: int = -1,
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interctc_layer_idx: List[int] = [],
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interctc_use_conditioning: bool = False,
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stochastic_depth_rate: Union[float, List[float]] = 0.0,
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):
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assert check_argument_types()
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super().__init__()
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self._output_size = output_size
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if rel_pos_type == "legacy":
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if pos_enc_layer_type == "rel_pos":
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pos_enc_layer_type = "legacy_rel_pos"
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if selfattention_layer_type == "rel_selfattn":
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selfattention_layer_type = "legacy_rel_selfattn"
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elif rel_pos_type == "latest":
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assert selfattention_layer_type != "legacy_rel_selfattn"
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assert pos_enc_layer_type != "legacy_rel_pos"
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else:
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raise ValueError("unknown rel_pos_type: " + rel_pos_type)
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activation = get_activation(activation_type)
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if pos_enc_layer_type == "abs_pos":
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pos_enc_class = PositionalEncoding
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elif pos_enc_layer_type == "scaled_abs_pos":
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pos_enc_class = ScaledPositionalEncoding
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elif pos_enc_layer_type == "rel_pos":
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assert selfattention_layer_type == "rel_selfattn"
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pos_enc_class = RelPositionalEncoding
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elif pos_enc_layer_type == "legacy_rel_pos":
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assert selfattention_layer_type == "legacy_rel_selfattn"
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pos_enc_class = LegacyRelPositionalEncoding
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logging.warning(
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"Using legacy_rel_pos and it will be deprecated in the future."
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)
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else:
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raise ValueError("unknown pos_enc_layer: " + pos_enc_layer_type)
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if input_layer == "linear":
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self.embed = torch.nn.Sequential(
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torch.nn.Linear(input_size, output_size),
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torch.nn.LayerNorm(output_size),
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torch.nn.Dropout(dropout_rate),
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pos_enc_class(output_size, positional_dropout_rate),
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)
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elif input_layer == "conv2d":
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self.embed = Conv2dSubsampling(
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input_size,
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output_size,
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dropout_rate,
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pos_enc_class(output_size, positional_dropout_rate),
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)
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elif input_layer == "conv2d2":
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self.embed = Conv2dSubsampling2(
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input_size,
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output_size,
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dropout_rate,
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pos_enc_class(output_size, positional_dropout_rate),
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)
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elif input_layer == "conv2d6":
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self.embed = Conv2dSubsampling6(
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input_size,
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output_size,
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dropout_rate,
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pos_enc_class(output_size, positional_dropout_rate),
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)
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elif input_layer == "conv2d8":
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self.embed = Conv2dSubsampling8(
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input_size,
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output_size,
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dropout_rate,
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pos_enc_class(output_size, positional_dropout_rate),
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)
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elif input_layer == "embed":
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self.embed = torch.nn.Sequential(
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torch.nn.Embedding(input_size, output_size, padding_idx=padding_idx),
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pos_enc_class(output_size, positional_dropout_rate),
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)
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elif isinstance(input_layer, torch.nn.Module):
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self.embed = torch.nn.Sequential(
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input_layer,
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pos_enc_class(output_size, positional_dropout_rate),
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)
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elif input_layer is None:
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self.embed = torch.nn.Sequential(
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pos_enc_class(output_size, positional_dropout_rate)
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)
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else:
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raise ValueError("unknown input_layer: " + input_layer)
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self.normalize_before = normalize_before
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if positionwise_layer_type == "linear":
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positionwise_layer = PositionwiseFeedForward
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positionwise_layer_args = (
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output_size,
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linear_units,
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dropout_rate,
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activation,
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)
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elif positionwise_layer_type == "conv1d":
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positionwise_layer = MultiLayeredConv1d
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positionwise_layer_args = (
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output_size,
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linear_units,
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positionwise_conv_kernel_size,
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dropout_rate,
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)
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elif positionwise_layer_type == "conv1d-linear":
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positionwise_layer = Conv1dLinear
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positionwise_layer_args = (
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output_size,
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linear_units,
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positionwise_conv_kernel_size,
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dropout_rate,
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)
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else:
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raise NotImplementedError("Support only linear or conv1d.")
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if selfattention_layer_type == "selfattn":
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encoder_selfattn_layer = MultiHeadedAttention
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encoder_selfattn_layer_args = (
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attention_heads,
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output_size,
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attention_dropout_rate,
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)
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elif selfattention_layer_type == "legacy_rel_selfattn":
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assert pos_enc_layer_type == "legacy_rel_pos"
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encoder_selfattn_layer = LegacyRelPositionMultiHeadedAttention
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encoder_selfattn_layer_args = (
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attention_heads,
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output_size,
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attention_dropout_rate,
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)
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logging.warning(
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"Using legacy_rel_selfattn and it will be deprecated in the future."
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)
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elif selfattention_layer_type == "rel_selfattn":
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assert pos_enc_layer_type == "rel_pos"
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encoder_selfattn_layer = RelPositionMultiHeadedAttention
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encoder_selfattn_layer_args = (
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attention_heads,
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output_size,
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attention_dropout_rate,
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zero_triu,
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)
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else:
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raise ValueError("unknown encoder_attn_layer: " + selfattention_layer_type)
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convolution_layer = ConvolutionModule
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convolution_layer_args = (output_size, cnn_module_kernel, activation)
|
|
if isinstance(stochastic_depth_rate, float):
|
stochastic_depth_rate = [stochastic_depth_rate] * num_blocks
|
|
if len(stochastic_depth_rate) != num_blocks:
|
raise ValueError(
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f"Length of stochastic_depth_rate ({len(stochastic_depth_rate)}) "
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f"should be equal to num_blocks ({num_blocks})"
|
)
|
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self.encoders = repeat(
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num_blocks,
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lambda lnum: EncoderLayer(
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output_size,
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encoder_selfattn_layer(*encoder_selfattn_layer_args),
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positionwise_layer(*positionwise_layer_args),
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positionwise_layer(*positionwise_layer_args) if macaron_style else None,
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convolution_layer(*convolution_layer_args) if use_cnn_module else None,
|
dropout_rate,
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normalize_before,
|
concat_after,
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stochastic_depth_rate[lnum],
|
),
|
)
|
if self.normalize_before:
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self.after_norm = LayerNorm(output_size)
|
|
self.interctc_layer_idx = interctc_layer_idx
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if len(interctc_layer_idx) > 0:
|
assert 0 < min(interctc_layer_idx) and max(interctc_layer_idx) < num_blocks
|
self.interctc_use_conditioning = interctc_use_conditioning
|
self.conditioning_layer = None
|
|
def output_size(self) -> int:
|
return self._output_size
|
|
def forward(
|
self,
|
xs_pad: torch.Tensor,
|
ilens: torch.Tensor,
|
prev_states: torch.Tensor = None,
|
ctc: CTC = None,
|
) -> Tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor]]:
|
"""Calculate forward propagation.
|
|
Args:
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xs_pad (torch.Tensor): Input tensor (#batch, L, input_size).
|
ilens (torch.Tensor): Input length (#batch).
|
prev_states (torch.Tensor): Not to be used now.
|
|
Returns:
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torch.Tensor: Output tensor (#batch, L, output_size).
|
torch.Tensor: Output length (#batch).
|
torch.Tensor: Not to be used now.
|
|
"""
|
masks = (~make_pad_mask(ilens)[:, None, :]).to(xs_pad.device)
|
|
if (
|
isinstance(self.embed, Conv2dSubsampling)
|
or isinstance(self.embed, Conv2dSubsampling2)
|
or isinstance(self.embed, Conv2dSubsampling6)
|
or isinstance(self.embed, Conv2dSubsampling8)
|
):
|
short_status, limit_size = check_short_utt(self.embed, xs_pad.size(1))
|
if short_status:
|
raise TooShortUttError(
|
f"has {xs_pad.size(1)} frames and is too short for subsampling "
|
+ f"(it needs more than {limit_size} frames), return empty results",
|
xs_pad.size(1),
|
limit_size,
|
)
|
xs_pad, masks = self.embed(xs_pad, masks)
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else:
|
xs_pad = self.embed(xs_pad)
|
|
intermediate_outs = []
|
if len(self.interctc_layer_idx) == 0:
|
xs_pad, masks = self.encoders(xs_pad, masks)
|
else:
|
for layer_idx, encoder_layer in enumerate(self.encoders):
|
xs_pad, masks = encoder_layer(xs_pad, masks)
|
|
if layer_idx + 1 in self.interctc_layer_idx:
|
encoder_out = xs_pad
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if isinstance(encoder_out, tuple):
|
encoder_out = encoder_out[0]
|
|
# intermediate outputs are also normalized
|
if self.normalize_before:
|
encoder_out = self.after_norm(encoder_out)
|
|
intermediate_outs.append((layer_idx + 1, encoder_out))
|
|
if self.interctc_use_conditioning:
|
ctc_out = ctc.softmax(encoder_out)
|
|
if isinstance(xs_pad, tuple):
|
x, pos_emb = xs_pad
|
x = x + self.conditioning_layer(ctc_out)
|
xs_pad = (x, pos_emb)
|
else:
|
xs_pad = xs_pad + self.conditioning_layer(ctc_out)
|
|
if isinstance(xs_pad, tuple):
|
xs_pad = xs_pad[0]
|
if self.normalize_before:
|
xs_pad = self.after_norm(xs_pad)
|
|
olens = masks.squeeze(1).sum(1)
|
if len(intermediate_outs) > 0:
|
return (xs_pad, intermediate_outs), olens, None
|
return xs_pad, olens, None
|
