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114 lines
3.1 KiB
Python
114 lines
3.1 KiB
Python
# Copyright 2022 Xiaomi Corp. (authors: Yifan Yang)
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#
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# See ../../../../LICENSE for clarification regarding multiple authors
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from typing import Optional
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import torch
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import torch.nn as nn
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from acoustic_model.utils_py.scaling_zipformer import Balancer, ScaledConv1d
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class LConv(nn.Module):
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"""A convolution module to prevent information loss."""
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def __init__(
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self,
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channels: int,
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kernel_size: int = 7,
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bias: bool = True,
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):
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"""
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Args:
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channels:
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Dimension of the input embedding, and of the lconv output.
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"""
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super().__init__()
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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.deriv_balancer1 = Balancer(
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2 * channels,
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channel_dim=1,
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min_abs=0.05,
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max_abs=10.0,
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min_positive=0.05,
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max_positive=1.0,
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)
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self.depthwise_conv = nn.Conv1d(
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2 * channels,
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2 * channels,
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kernel_size=kernel_size,
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stride=1,
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padding=(kernel_size - 1) // 2,
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groups=2 * channels,
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bias=bias,
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)
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self.deriv_balancer2 = Balancer(
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2 * channels,
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channel_dim=1,
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min_positive=0.05,
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max_positive=1.0,
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min_abs=0.05,
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max_abs=20.0,
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)
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self.pointwise_conv2 = ScaledConv1d(
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2 * 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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initial_scale=0.05,
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)
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def forward(
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self,
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x: torch.Tensor,
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src_key_padding_mask: Optional[torch.Tensor] = None,
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) -> torch.Tensor:
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"""
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Args:
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x: A 3-D tensor of shape (N, T, C).
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Returns:
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Return a tensor of shape (N, T, C).
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"""
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# exchange the temporal dimension and the feature dimension
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x = x.permute(0, 2, 1) # (#batch, channels, time).
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x = self.pointwise_conv1(x) # (batch, 2*channels, time)
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x = self.deriv_balancer1(x)
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if src_key_padding_mask is not None:
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x = x.masked_fill(src_key_padding_mask.unsqueeze(1).expand_as(x), 0.0)
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x = self.depthwise_conv(x)
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x = self.deriv_balancer2(x)
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x = self.pointwise_conv2(x) # (batch, channels, time)
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return x.permute(0, 2, 1)
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