330 lines
13 KiB
Python
330 lines
13 KiB
Python
# Copyright 2019 Shigeki Karita
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# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
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"""Transformer encoder definition."""
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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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import torch
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from torch import nn
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import logging
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from funasr.models.transformer.attention import MultiHeadedAttention
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from funasr.models.transformer.embedding import PositionalEncoding
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from funasr.models.transformer.layer_norm import LayerNorm
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from funasr.models.transformer.utils.multi_layer_conv import Conv1dLinear
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from funasr.models.transformer.utils.multi_layer_conv import MultiLayeredConv1d
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from funasr.models.transformer.utils.nets_utils import make_pad_mask
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from funasr.models.transformer.positionwise_feed_forward import PositionwiseFeedForward
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from funasr.models.transformer.utils.repeat import repeat
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from funasr.models.ctc.ctc import CTC
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from funasr.models.transformer.utils.subsampling import Conv2dSubsampling
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from funasr.models.transformer.utils.subsampling import Conv2dSubsampling2
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from funasr.models.transformer.utils.subsampling import Conv2dSubsampling6
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from funasr.models.transformer.utils.subsampling import Conv2dSubsampling8
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from funasr.models.transformer.utils.subsampling import TooShortUttError
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from funasr.models.transformer.utils.subsampling import check_short_utt
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from funasr.register import tables
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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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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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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.norm1 = LayerNorm(size)
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self.norm2 = LayerNorm(size)
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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, mask, cache=None):
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"""Compute encoded features.
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Args:
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x_input (torch.Tensor): Input 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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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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return x, mask
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residual = x
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if self.normalize_before:
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x = self.norm1(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 self.concat_after:
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x_concat = torch.cat((x, self.self_attn(x_q, x, x, mask)), 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(self.self_attn(x_q, x, x, mask))
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if not self.normalize_before:
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x = self.norm1(x)
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residual = x
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if self.normalize_before:
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x = self.norm2(x)
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x = residual + stoch_layer_coeff * self.dropout(self.feed_forward(x))
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if not self.normalize_before:
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x = self.norm2(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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return x, mask
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@tables.register("encoder_classes", "TransformerEncoder")
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class TransformerEncoder(nn.Module):
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"""Transformer encoder module.
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Args:
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input_size: input dim
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output_size: dimension of attention
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attention_heads: the number of heads of multi head attention
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linear_units: the number of units of position-wise feed forward
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num_blocks: the number of decoder blocks
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dropout_rate: dropout rate
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attention_dropout_rate: dropout rate in attention
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positional_dropout_rate: dropout rate after adding positional encoding
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input_layer: input layer type
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pos_enc_class: PositionalEncoding or ScaledPositionalEncoding
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normalize_before: whether to use layer_norm before the first block
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concat_after: 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.
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i.e. x -> x + att(x)
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positionwise_layer_type: linear of conv1d
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positionwise_conv_kernel_size: kernel size of positionwise conv1d layer
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padding_idx: 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: Optional[str] = "conv2d",
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pos_enc_class=PositionalEncoding,
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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 = 1,
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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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):
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super().__init__()
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self._output_size = output_size
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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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torch.nn.ReLU(),
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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(input_size, output_size, dropout_rate)
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elif input_layer == "conv2d2":
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self.embed = Conv2dSubsampling2(input_size, output_size, dropout_rate)
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elif input_layer == "conv2d6":
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self.embed = Conv2dSubsampling6(input_size, output_size, dropout_rate)
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elif input_layer == "conv2d8":
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self.embed = Conv2dSubsampling8(input_size, output_size, dropout_rate)
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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 input_layer is None:
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if input_size == output_size:
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self.embed = None
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else:
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self.embed = torch.nn.Linear(input_size, output_size)
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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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)
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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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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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MultiHeadedAttention(attention_heads, output_size, attention_dropout_rate),
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positionwise_layer(*positionwise_layer_args),
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dropout_rate,
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normalize_before,
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concat_after,
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),
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)
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if self.normalize_before:
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self.after_norm = LayerNorm(output_size)
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self.interctc_layer_idx = interctc_layer_idx
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if len(interctc_layer_idx) > 0:
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assert 0 < min(interctc_layer_idx) and max(interctc_layer_idx) < num_blocks
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self.interctc_use_conditioning = interctc_use_conditioning
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self.conditioning_layer = None
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def output_size(self) -> int:
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return self._output_size
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def forward(
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self,
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xs_pad: torch.Tensor,
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ilens: torch.Tensor,
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prev_states: torch.Tensor = None,
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ctc: CTC = None,
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) -> Tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor]]:
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"""Embed positions in tensor.
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Args:
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xs_pad: input tensor (B, L, D)
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ilens: input length (B)
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prev_states: Not to be used now.
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Returns:
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position embedded tensor and mask
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"""
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masks = (~make_pad_mask(ilens)[:, None, :]).to(xs_pad.device)
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if self.embed is None:
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xs_pad = xs_pad
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elif (
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isinstance(self.embed, Conv2dSubsampling)
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or isinstance(self.embed, Conv2dSubsampling2)
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or isinstance(self.embed, Conv2dSubsampling6)
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or isinstance(self.embed, Conv2dSubsampling8)
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):
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short_status, limit_size = check_short_utt(self.embed, xs_pad.size(1))
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if short_status:
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raise TooShortUttError(
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f"has {xs_pad.size(1)} frames and is too short for subsampling "
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+ f"(it needs more than {limit_size} frames), return empty results",
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xs_pad.size(1),
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limit_size,
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)
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xs_pad, masks = self.embed(xs_pad, masks)
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else:
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xs_pad = self.embed(xs_pad)
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intermediate_outs = []
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if len(self.interctc_layer_idx) == 0:
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xs_pad, masks = self.encoders(xs_pad, masks)
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else:
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for layer_idx, encoder_layer in enumerate(self.encoders):
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xs_pad, masks = encoder_layer(xs_pad, masks)
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if layer_idx + 1 in self.interctc_layer_idx:
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encoder_out = xs_pad
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# intermediate outputs are also normalized
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if self.normalize_before:
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encoder_out = self.after_norm(encoder_out)
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intermediate_outs.append((layer_idx + 1, encoder_out))
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if self.interctc_use_conditioning:
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ctc_out = ctc.softmax(encoder_out)
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xs_pad = xs_pad + self.conditioning_layer(ctc_out)
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if self.normalize_before:
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xs_pad = self.after_norm(xs_pad)
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olens = masks.squeeze(1).sum(1)
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if len(intermediate_outs) > 0:
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return (xs_pad, intermediate_outs), olens, None
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return xs_pad, olens, None
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