mirror of
https://github.com/k2-fsa/icefall.git
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b293db4baf
* modify preparation * small refacor * add tedlium3 conformer_ctc2 * modify decode * filter unk in decode * add scaling converter * address comments * fix lambda function lhotse * add implicit manifest shuffle * refactor ctc_greedy_search * import model arguments from train.py * style fix * fix ci test and last style issues * update RESULTS * fix RESULTS numbers * fix label smoothing loss * update model parameters number in RESULTS
121 lines
4.0 KiB
Python
121 lines
4.0 KiB
Python
#!/usr/bin/env python3
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# Copyright (c) 2021 University of Chinese Academy of Sciences (author: Han Zhu)
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# 2022 Xiaomi Corporation (author: Quandong Wang)
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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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import torch
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from scaling import (
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ActivationBalancer,
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BasicNorm,
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DoubleSwish,
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ScaledConv2d,
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ScaledLinear,
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)
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class Conv2dSubsampling(torch.nn.Module):
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"""Convolutional 2D subsampling (to 1/4 length).
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Convert an input of shape (N, T, idim) to an output
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with shape (N, T', odim), where
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T' = ((T-1)//2 - 1)//2, which approximates T' == T//4
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It is based on
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https://github.com/espnet/espnet/blob/master/espnet/nets/pytorch_backend/transformer/subsampling.py # noqa
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"""
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def __init__(
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self,
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in_channels: int,
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out_channels: int,
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layer1_channels: int = 8,
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layer2_channels: int = 32,
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layer3_channels: int = 128,
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) -> None:
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"""
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Args:
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in_channels:
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Number of channels in. The input shape is (N, T, in_channels).
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Caution: It requires: T >=7, in_channels >=7
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out_channels
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Output dim. The output shape is (N, ((T-1)//2 - 1)//2, out_channels)
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layer1_channels:
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Number of channels in layer1
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layer1_channels:
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Number of channels in layer2
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"""
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assert in_channels >= 7
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super().__init__()
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self.conv = torch.nn.Sequential(
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ScaledConv2d(
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in_channels=1,
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out_channels=layer1_channels,
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kernel_size=3,
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padding=1,
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),
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ActivationBalancer(channel_dim=1),
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DoubleSwish(),
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ScaledConv2d(
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in_channels=layer1_channels,
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out_channels=layer2_channels,
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kernel_size=3,
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stride=2,
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),
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ActivationBalancer(channel_dim=1),
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DoubleSwish(),
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ScaledConv2d(
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in_channels=layer2_channels,
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out_channels=layer3_channels,
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kernel_size=3,
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stride=2,
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),
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ActivationBalancer(channel_dim=1),
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DoubleSwish(),
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)
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self.out = ScaledLinear(
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layer3_channels * (((in_channels - 1) // 2 - 1) // 2), out_channels
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)
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# set learn_eps=False because out_norm is preceded by `out`, and `out`
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# itself has learned scale, so the extra degree of freedom is not
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# needed.
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self.out_norm = BasicNorm(out_channels, learn_eps=False)
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# constrain median of output to be close to zero.
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self.out_balancer = ActivationBalancer(
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channel_dim=-1, min_positive=0.45, max_positive=0.55
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)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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"""Subsample x.
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Args:
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x:
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Its shape is (N, T, idim).
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Returns:
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Return a tensor of shape (N, ((T-1)//2 - 1)//2, odim)
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"""
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# On entry, x is (N, T, idim)
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x = x.unsqueeze(1) # (N, T, idim) -> (N, 1, T, idim) i.e., (N, C, H, W)
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x = self.conv(x)
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# Now x is of shape (N, odim, ((T-1)//2 - 1)//2, ((idim-1)//2 - 1)//2)
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b, c, t, f = x.size()
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x = self.out(x.transpose(1, 2).contiguous().view(b, t, c * f))
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# Now x is of shape (N, ((T-1)//2 - 1))//2, odim)
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x = self.out_norm(x)
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x = self.out_balancer(x)
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return x
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