├── .gitignore ├── LICENSE ├── README.md ├── conformer ├── __init__.py ├── activation.py ├── attention.py ├── convolution.py ├── embedding.py ├── encoder.py ├── feed_forward.py ├── model.py └── modules.py ├── docs ├── .DS_Store ├── Makefile ├── Model.html ├── Modules.html ├── Submodules.html ├── _sources │ ├── Model.rst.txt │ ├── Modules.rst.txt │ ├── Submodules.rst.txt │ └── index.rst.txt ├── _static │ ├── basic.css │ ├── css │ │ ├── badge_only.css │ │ ├── fonts │ │ │ ├── Roboto-Slab-Bold.woff │ │ │ ├── Roboto-Slab-Bold.woff2 │ │ │ ├── Roboto-Slab-Regular.woff │ │ │ ├── Roboto-Slab-Regular.woff2 │ │ │ ├── fontawesome-webfont.eot │ │ │ ├── fontawesome-webfont.svg │ │ │ ├── fontawesome-webfont.ttf │ │ │ ├── fontawesome-webfont.woff │ │ │ ├── fontawesome-webfont.woff2 │ │ │ ├── lato-bold-italic.woff │ │ │ ├── lato-bold-italic.woff2 │ │ │ ├── lato-bold.woff │ │ │ ├── lato-bold.woff2 │ │ │ ├── lato-normal-italic.woff │ │ │ ├── lato-normal-italic.woff2 │ │ │ ├── lato-normal.woff │ │ │ └── lato-normal.woff2 │ │ └── theme.css │ ├── doctools.js │ ├── documentation_options.js │ ├── file.png │ ├── jquery-3.5.1.js │ ├── jquery.js │ ├── js │ │ ├── badge_only.js │ │ ├── html5shiv-printshiv.min.js │ │ ├── html5shiv.min.js │ │ └── theme.js │ ├── language_data.js │ ├── minus.png │ ├── plus.png │ ├── pygments.css │ ├── searchtools.js │ ├── underscore-1.3.1.js │ └── underscore.js ├── genindex.html ├── index.html ├── make.bat ├── objects.inv ├── search.html ├── searchindex.js └── source │ ├── Model.rst │ ├── Modules.rst │ ├── Submodules.rst │ ├── conf.py │ └── index.rst └── setup.py /.gitignore: -------------------------------------------------------------------------------- 1 | experiment/ 2 | experiment/* 3 | data/sample 4 | data/sample/* 5 | data/data_list/debug_list.csv 6 | data/data_list/sample_list.csv 7 | *.bin 8 | *.zip 9 | *.idea 10 | docs/training 11 | docs/training/* 12 | __pycache__/ 13 | venv/* 14 | *.pyc 15 | .idea 16 | .ipynb_checkpoints 17 | # Byte-compiled / optimized / DLL files 18 | __pycache__/ 19 | *.py[cod] 20 | *$py.class 21 | 22 | # C extensions 23 | *.so 24 | 25 | # Distribution / packaging 26 | .Python 27 | build/ 28 | develop-eggs/ 29 | dist/ 30 | downloads/ 31 | eggs/ 32 | .eggs/ 33 | lib/ 34 | lib64/ 35 | parts/ 36 | sdist/ 37 | var/ 38 | wheels/ 39 | pip-wheel-metadata/ 40 | share/python-wheels/ 41 | *.egg-info/ 42 | .installed.cfg 43 | *.egg 44 | MANIFEST 45 | 46 | # PyInstaller 47 | # Usually these files are written by a python script from a template 48 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 49 | *.manifest 50 | *.spec 51 | 52 | # Installer logs 53 | pip-log.txt 54 | pip-delete-this-directory.txt 55 | 56 | # Unit test / coverage reports 57 | htmlcov/ 58 | .tox/ 59 | .nox/ 60 | .coverage 61 | .coverage.* 62 | .cache 63 | nosetests.xml 64 | coverage.xml 65 | *.cover 66 | *.py,cover 67 | .hypothesis/ 68 | .pytest_cache/ 69 | 70 | # Translations 71 | *.mo 72 | *.pot 73 | 74 | # Django stuff: 75 | *.log 76 | local_settings.py 77 | db.sqlite3 78 | db.sqlite3-journal 79 | 80 | # Flask stuff: 81 | instance/ 82 | .webassets-cache 83 | 84 | # Scrapy stuff: 85 | .scrapy 86 | 87 | # Sphinx documentation 88 | docs/_build/ 89 | 90 | # PyBuilder 91 | target/ 92 | 93 | # Jupyter Notebook 94 | .ipynb_checkpoints 95 | 96 | # IPython 97 | profile_default/ 98 | ipython_config.py 99 | 100 | # pyenv 101 | .python-version 102 | 103 | # pipenv 104 | # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. 105 | # However, in case of collaboration, if having platform-specific dependencies or dependencies 106 | # having no cross-platform support, pipenv may install dependencies that don't work, or not 107 | # install all needed dependencies. 108 | #Pipfile.lock 109 | 110 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow 111 | __pypackages__/ 112 | 113 | # Celery stuff 114 | celerybeat-schedule 115 | celerybeat.pid 116 | 117 | # SageMath parsed files 118 | *.sage.py 119 | 120 | # Environments 121 | .env 122 | .venv 123 | env/ 124 | venv/ 125 | ENV/ 126 | env.bak/ 127 | venv.bak/ 128 | 129 | # Spyder project settings 130 | .spyderproject 131 | .spyproject 132 | 133 | # Rope project settings 134 | .ropeproject 135 | 136 | # mkdocs documentation 137 | /site 138 | 139 | # mypy 140 | .mypy_cache/ 141 | .dmypy.json 142 | dmypy.json 143 | 144 | # Pyre type checker 145 | .pyre/ 146 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | Apache License 2 | Version 2.0, January 2004 3 | http://www.apache.org/licenses/ 4 | 5 | TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 6 | 7 | 1. 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2 | 3 | 4 |

5 | 6 | **PyTorch implementation of Conformer: Convolution-augmented Transformer for Speech Recognition.** 7 | 8 | 9 |
10 | 11 | *** 12 | 13 |

14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | Transformer models are good at capturing content-based global interactions, while CNNs exploit local features effectively. Conformer combine convolution neural networks and transformers to model both local and global dependencies of an audio sequence in a parameter-efficient way. Conformer significantly outperforms the previous Transformer and CNN based models achieving state-of-the-art accuracies. 29 | 30 | 31 | 32 | This repository contains only model code, but you can train with conformer at [openspeech](https://github.com/openspeech-team/openspeech) 33 | 34 | ## Installation 35 | This project recommends Python 3.7 or higher. 36 | We recommend creating a new virtual environment for this project (using virtual env or conda). 37 | 38 | ### Prerequisites 39 | * Numpy: `pip install numpy` (Refer [here](https://github.com/numpy/numpy) for problem installing Numpy). 40 | * Pytorch: Refer to [PyTorch website](http://pytorch.org/) to install the version w.r.t. your environment. 41 | 42 | ### Install from source 43 | Currently we only support installation from source code using setuptools. Checkout the source code and run the 44 | following commands: 45 | 46 | ``` 47 | pip install -e . 48 | ``` 49 | 50 | ## Usage 51 | 52 | ```python 53 | import torch 54 | import torch.nn as nn 55 | from conformer import Conformer 56 | 57 | batch_size, sequence_length, dim = 3, 12345, 80 58 | 59 | cuda = torch.cuda.is_available() 60 | device = torch.device('cuda' if cuda else 'cpu') 61 | 62 | criterion = nn.CTCLoss().to(device) 63 | 64 | inputs = torch.rand(batch_size, sequence_length, dim).to(device) 65 | input_lengths = torch.LongTensor([12345, 12300, 12000]) 66 | targets = torch.LongTensor([[1, 3, 3, 3, 3, 3, 4, 5, 6, 2], 67 | [1, 3, 3, 3, 3, 3, 4, 5, 2, 0], 68 | [1, 3, 3, 3, 3, 3, 4, 2, 0, 0]]).to(device) 69 | target_lengths = torch.LongTensor([9, 8, 7]) 70 | 71 | model = Conformer(num_classes=10, 72 | input_dim=dim, 73 | encoder_dim=32, 74 | num_encoder_layers=3).to(device) 75 | 76 | # Forward propagate 77 | outputs, output_lengths = model(inputs, input_lengths) 78 | 79 | # Calculate CTC Loss 80 | loss = criterion(outputs.transpose(0, 1), targets, output_lengths, target_lengths) 81 | ``` 82 | 83 | ## Troubleshoots and Contributing 84 | If you have any questions, bug reports, and feature requests, please [open an issue](https://github.com/sooftware/conformer/issues) on github or 85 | contacts sh951011@gmail.com please. 86 | 87 | I appreciate any kind of feedback or contribution. Feel free to proceed with small issues like bug fixes, documentation improvement. For major contributions and new features, please discuss with the collaborators in corresponding issues. 88 | 89 | ## Code Style 90 | I follow [PEP-8](https://www.python.org/dev/peps/pep-0008/) for code style. Especially the style of docstrings is important to generate documentation. 91 | 92 | ## Reference 93 | - [Conformer: Convolution-augmented Transformer for Speech Recognition](https://arxiv.org/pdf/2005.08100.pdf) 94 | - [Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context](https://arxiv.org/abs/1901.02860) 95 | - [kimiyoung/transformer-xl](https://github.com/kimiyoung/transformer-xl) 96 | - [espnet/espnet](https://github.com/espnet/espnet) 97 | 98 | ## Author 99 | 100 | * Soohwan Kim [@sooftware](https://github.com/sooftware) 101 | * Contacts: sh951011@gmail.com 102 | -------------------------------------------------------------------------------- /conformer/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) 2021, Soohwan Kim. All rights reserved. 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | 15 | from .model import Conformer 16 | -------------------------------------------------------------------------------- /conformer/activation.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) 2021, Soohwan Kim. All rights reserved. 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | 15 | import torch.nn as nn 16 | from torch import Tensor 17 | 18 | 19 | class Swish(nn.Module): 20 | """ 21 | Swish is a smooth, non-monotonic function that consistently matches or outperforms ReLU on deep networks applied 22 | to a variety of challenging domains such as Image classification and Machine translation. 23 | """ 24 | def __init__(self): 25 | super(Swish, self).__init__() 26 | 27 | def forward(self, inputs: Tensor) -> Tensor: 28 | return inputs * inputs.sigmoid() 29 | 30 | 31 | class GLU(nn.Module): 32 | """ 33 | The gating mechanism is called Gated Linear Units (GLU), which was first introduced for natural language processing 34 | in the paper “Language Modeling with Gated Convolutional Networks” 35 | """ 36 | def __init__(self, dim: int) -> None: 37 | super(GLU, self).__init__() 38 | self.dim = dim 39 | 40 | def forward(self, inputs: Tensor) -> Tensor: 41 | outputs, gate = inputs.chunk(2, dim=self.dim) 42 | return outputs * gate.sigmoid() 43 | -------------------------------------------------------------------------------- /conformer/attention.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) 2021, Soohwan Kim. All rights reserved. 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | 15 | import math 16 | import torch 17 | import torch.nn as nn 18 | import torch.nn.functional as F 19 | from torch import Tensor 20 | from typing import Optional 21 | 22 | from .embedding import RelPositionalEncoding 23 | from .modules import Linear 24 | 25 | 26 | class RelativeMultiHeadAttention(nn.Module): 27 | """ 28 | Multi-head attention with relative positional encoding. 29 | This concept was proposed in the "Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context" 30 | 31 | Args: 32 | d_model (int): The dimension of model 33 | num_heads (int): The number of attention heads. 34 | dropout_p (float): probability of dropout 35 | 36 | Inputs: query, key, value, pos_embedding, mask 37 | - **query** (batch, time, dim): Tensor containing query vector 38 | - **key** (batch, time, dim): Tensor containing key vector 39 | - **value** (batch, time, dim): Tensor containing value vector 40 | - **pos_embedding** (batch, time, dim): Positional embedding tensor 41 | - **mask** (batch, 1, time2) or (batch, time1, time2): Tensor containing indices to be masked 42 | 43 | Returns: 44 | - **outputs**: Tensor produces by relative multi head attention module. 45 | """ 46 | def __init__( 47 | self, 48 | d_model: int = 512, 49 | num_heads: int = 16, 50 | dropout_p: float = 0.1, 51 | ): 52 | super(RelativeMultiHeadAttention, self).__init__() 53 | assert d_model % num_heads == 0, "d_model % num_heads should be zero." 54 | self.d_model = d_model 55 | self.d_head = int(d_model / num_heads) 56 | self.num_heads = num_heads 57 | self.sqrt_dim = math.sqrt(self.d_head) 58 | 59 | self.query_proj = Linear(d_model, d_model) 60 | self.key_proj = Linear(d_model, d_model) 61 | self.value_proj = Linear(d_model, d_model) 62 | self.pos_proj = Linear(d_model, d_model, bias=False) 63 | 64 | self.dropout = nn.Dropout(p=dropout_p) 65 | self.u_bias = nn.Parameter(torch.Tensor(self.num_heads, self.d_head)) 66 | self.v_bias = nn.Parameter(torch.Tensor(self.num_heads, self.d_head)) 67 | torch.nn.init.xavier_uniform_(self.u_bias) 68 | torch.nn.init.xavier_uniform_(self.v_bias) 69 | 70 | self.out_proj = Linear(d_model, d_model) 71 | 72 | def forward( 73 | self, 74 | query: Tensor, 75 | key: Tensor, 76 | value: Tensor, 77 | pos_embedding: Tensor, 78 | mask: Optional[Tensor] = None, 79 | ) -> Tensor: 80 | batch_size = value.size(0) 81 | 82 | query = self.query_proj(query).view(batch_size, -1, self.num_heads, self.d_head) 83 | key = self.key_proj(key).view(batch_size, -1, self.num_heads, self.d_head).permute(0, 2, 1, 3) 84 | value = self.value_proj(value).view(batch_size, -1, self.num_heads, self.d_head).permute(0, 2, 1, 3) 85 | pos_embedding = self.pos_proj(pos_embedding).view(batch_size, -1, self.num_heads, self.d_head) 86 | 87 | content_score = torch.matmul((query + self.u_bias).transpose(1, 2), key.transpose(2, 3)) 88 | pos_score = torch.matmul((query + self.v_bias).transpose(1, 2), pos_embedding.permute(0, 2, 3, 1)) 89 | pos_score = self._relative_shift(pos_score) 90 | 91 | score = (content_score + pos_score) / self.sqrt_dim 92 | 93 | if mask is not None: 94 | mask = mask.unsqueeze(1) 95 | score.masked_fill_(mask, -1e9) 96 | 97 | attn = F.softmax(score, -1) 98 | attn = self.dropout(attn) 99 | 100 | context = torch.matmul(attn, value).transpose(1, 2) 101 | context = context.contiguous().view(batch_size, -1, self.d_model) 102 | 103 | return self.out_proj(context) 104 | 105 | def _relative_shift(self, pos_score: Tensor) -> Tensor: 106 | batch_size, num_heads, seq_length1, seq_length2 = pos_score.size() 107 | zeros = pos_score.new_zeros(batch_size, num_heads, seq_length1, 1) 108 | padded_pos_score = torch.cat([zeros, pos_score], dim=-1) 109 | 110 | padded_pos_score = padded_pos_score.view(batch_size, num_heads, seq_length2 + 1, seq_length1) 111 | pos_score = padded_pos_score[:, :, 1:].view_as(pos_score)[:, :, :, : seq_length2 // 2 + 1] 112 | 113 | return pos_score 114 | 115 | 116 | class MultiHeadedSelfAttentionModule(nn.Module): 117 | """ 118 | Conformer employ multi-headed self-attention (MHSA) while integrating an important technique from Transformer-XL, 119 | the relative sinusoidal positional encoding scheme. The relative positional encoding allows the self-attention 120 | module to generalize better on different input length and the resulting encoder is more robust to the variance of 121 | the utterance length. Conformer use prenorm residual units with dropout which helps training 122 | and regularizing deeper models. 123 | 124 | Args: 125 | d_model (int): The dimension of model 126 | num_heads (int): The number of attention heads. 127 | dropout_p (float): probability of dropout 128 | 129 | Inputs: inputs, mask 130 | - **inputs** (batch, time, dim): Tensor containing input vector 131 | - **mask** (batch, 1, time2) or (batch, time1, time2): Tensor containing indices to be masked 132 | 133 | Returns: 134 | - **outputs** (batch, time, dim): Tensor produces by relative multi headed self attention module. 135 | """ 136 | def __init__(self, d_model: int, num_heads: int, dropout_p: float = 0.1): 137 | super(MultiHeadedSelfAttentionModule, self).__init__() 138 | self.positional_encoding = RelPositionalEncoding(d_model) 139 | self.layer_norm = nn.LayerNorm(d_model) 140 | self.attention = RelativeMultiHeadAttention(d_model, num_heads, dropout_p) 141 | self.dropout = nn.Dropout(p=dropout_p) 142 | 143 | def forward(self, inputs: Tensor, mask: Optional[Tensor] = None): 144 | batch_size = inputs.size(0) 145 | pos_embedding = self.positional_encoding(inputs) 146 | pos_embedding = pos_embedding.repeat(batch_size, 1, 1) 147 | 148 | inputs = self.layer_norm(inputs) 149 | outputs = self.attention(inputs, inputs, inputs, pos_embedding=pos_embedding, mask=mask) 150 | 151 | return self.dropout(outputs) 152 | -------------------------------------------------------------------------------- /conformer/convolution.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) 2021, Soohwan Kim. All rights reserved. 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | 15 | import torch 16 | import torch.nn as nn 17 | from torch import Tensor 18 | from typing import Tuple 19 | 20 | from .activation import Swish, GLU 21 | from .modules import Transpose 22 | 23 | 24 | class DepthwiseConv1d(nn.Module): 25 | """ 26 | When groups == in_channels and out_channels == K * in_channels, where K is a positive integer, 27 | this operation is termed in literature as depthwise convolution. 28 | 29 | Args: 30 | in_channels (int): Number of channels in the input 31 | out_channels (int): Number of channels produced by the convolution 32 | kernel_size (int or tuple): Size of the convolving kernel 33 | stride (int, optional): Stride of the convolution. Default: 1 34 | padding (int or tuple, optional): Zero-padding added to both sides of the input. Default: 0 35 | bias (bool, optional): If True, adds a learnable bias to the output. Default: True 36 | 37 | Inputs: inputs 38 | - **inputs** (batch, in_channels, time): Tensor containing input vector 39 | 40 | Returns: outputs 41 | - **outputs** (batch, out_channels, time): Tensor produces by depthwise 1-D convolution. 42 | """ 43 | def __init__( 44 | self, 45 | in_channels: int, 46 | out_channels: int, 47 | kernel_size: int, 48 | stride: int = 1, 49 | padding: int = 0, 50 | bias: bool = False, 51 | ) -> None: 52 | super(DepthwiseConv1d, self).__init__() 53 | assert out_channels % in_channels == 0, "out_channels should be constant multiple of in_channels" 54 | self.conv = nn.Conv1d( 55 | in_channels=in_channels, 56 | out_channels=out_channels, 57 | kernel_size=kernel_size, 58 | groups=in_channels, 59 | stride=stride, 60 | padding=padding, 61 | bias=bias, 62 | ) 63 | 64 | def forward(self, inputs: Tensor) -> Tensor: 65 | return self.conv(inputs) 66 | 67 | 68 | class PointwiseConv1d(nn.Module): 69 | """ 70 | When kernel size == 1 conv1d, this operation is termed in literature as pointwise convolution. 71 | This operation often used to match dimensions. 72 | 73 | Args: 74 | in_channels (int): Number of channels in the input 75 | out_channels (int): Number of channels produced by the convolution 76 | stride (int, optional): Stride of the convolution. Default: 1 77 | padding (int or tuple, optional): Zero-padding added to both sides of the input. Default: 0 78 | bias (bool, optional): If True, adds a learnable bias to the output. Default: True 79 | 80 | Inputs: inputs 81 | - **inputs** (batch, in_channels, time): Tensor containing input vector 82 | 83 | Returns: outputs 84 | - **outputs** (batch, out_channels, time): Tensor produces by pointwise 1-D convolution. 85 | """ 86 | def __init__( 87 | self, 88 | in_channels: int, 89 | out_channels: int, 90 | stride: int = 1, 91 | padding: int = 0, 92 | bias: bool = True, 93 | ) -> None: 94 | super(PointwiseConv1d, self).__init__() 95 | self.conv = nn.Conv1d( 96 | in_channels=in_channels, 97 | out_channels=out_channels, 98 | kernel_size=1, 99 | stride=stride, 100 | padding=padding, 101 | bias=bias, 102 | ) 103 | 104 | def forward(self, inputs: Tensor) -> Tensor: 105 | return self.conv(inputs) 106 | 107 | 108 | class ConformerConvModule(nn.Module): 109 | """ 110 | Conformer convolution module starts with a pointwise convolution and a gated linear unit (GLU). 111 | This is followed by a single 1-D depthwise convolution layer. Batchnorm is deployed just after the convolution 112 | to aid training deep models. 113 | 114 | Args: 115 | in_channels (int): Number of channels in the input 116 | kernel_size (int or tuple, optional): Size of the convolving kernel Default: 31 117 | dropout_p (float, optional): probability of dropout 118 | 119 | Inputs: inputs 120 | inputs (batch, time, dim): Tensor contains input sequences 121 | 122 | Outputs: outputs 123 | outputs (batch, time, dim): Tensor produces by conformer convolution module. 124 | """ 125 | def __init__( 126 | self, 127 | in_channels: int, 128 | kernel_size: int = 31, 129 | expansion_factor: int = 2, 130 | dropout_p: float = 0.1, 131 | ) -> None: 132 | super(ConformerConvModule, self).__init__() 133 | assert (kernel_size - 1) % 2 == 0, "kernel_size should be a odd number for 'SAME' padding" 134 | assert expansion_factor == 2, "Currently, Only Supports expansion_factor 2" 135 | 136 | self.sequential = nn.Sequential( 137 | nn.LayerNorm(in_channels), 138 | Transpose(shape=(1, 2)), 139 | PointwiseConv1d(in_channels, in_channels * expansion_factor, stride=1, padding=0, bias=True), 140 | GLU(dim=1), 141 | DepthwiseConv1d(in_channels, in_channels, kernel_size, stride=1, padding=(kernel_size - 1) // 2), 142 | nn.BatchNorm1d(in_channels), 143 | Swish(), 144 | PointwiseConv1d(in_channels, in_channels, stride=1, padding=0, bias=True), 145 | nn.Dropout(p=dropout_p), 146 | ) 147 | 148 | def forward(self, inputs: Tensor) -> Tensor: 149 | return self.sequential(inputs).transpose(1, 2) 150 | 151 | 152 | class Conv2dSubampling(nn.Module): 153 | """ 154 | Convolutional 2D subsampling (to 1/4 length) 155 | 156 | Args: 157 | in_channels (int): Number of channels in the input image 158 | out_channels (int): Number of channels produced by the convolution 159 | 160 | Inputs: inputs 161 | - **inputs** (batch, time, dim): Tensor containing sequence of inputs 162 | 163 | Returns: outputs, output_lengths 164 | - **outputs** (batch, time, dim): Tensor produced by the convolution 165 | - **output_lengths** (batch): list of sequence output lengths 166 | """ 167 | def __init__(self, in_channels: int, out_channels: int) -> None: 168 | super(Conv2dSubampling, self).__init__() 169 | self.sequential = nn.Sequential( 170 | nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=2), 171 | nn.ReLU(), 172 | nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=2), 173 | nn.ReLU(), 174 | ) 175 | 176 | def forward(self, inputs: Tensor, input_lengths: Tensor) -> Tuple[Tensor, Tensor]: 177 | outputs = self.sequential(inputs.unsqueeze(1)) 178 | batch_size, channels, subsampled_lengths, sumsampled_dim = outputs.size() 179 | 180 | outputs = outputs.permute(0, 2, 1, 3) 181 | outputs = outputs.contiguous().view(batch_size, subsampled_lengths, channels * sumsampled_dim) 182 | 183 | output_lengths = input_lengths >> 2 184 | output_lengths -= 1 185 | 186 | return outputs, output_lengths 187 | -------------------------------------------------------------------------------- /conformer/embedding.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) 2021, Soohwan Kim. All rights reserved. 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | 15 | import math 16 | import torch 17 | import torch.nn as nn 18 | from torch import Tensor 19 | 20 | 21 | class RelPositionalEncoding(nn.Module): 22 | """ 23 | Relative positional encoding module. 24 | Args: 25 | d_model: Embedding dimension. 26 | max_len: Maximum input length. 27 | """ 28 | 29 | def __init__(self, d_model: int = 512, max_len: int = 5000) -> None: 30 | super(RelPositionalEncoding, self).__init__() 31 | self.d_model = d_model 32 | self.pe = None 33 | self.extend_pe(torch.tensor(0.0).expand(1, max_len)) 34 | 35 | def extend_pe(self, x: Tensor) -> None: 36 | if self.pe is not None: 37 | if self.pe.size(1) >= x.size(1) * 2 - 1: 38 | if self.pe.dtype != x.dtype or self.pe.device != x.device: 39 | self.pe = self.pe.to(dtype=x.dtype, device=x.device) 40 | return 41 | 42 | pe_positive = torch.zeros(x.size(1), self.d_model) 43 | pe_negative = torch.zeros(x.size(1), self.d_model) 44 | position = torch.arange(0, x.size(1), dtype=torch.float32).unsqueeze(1) 45 | div_term = torch.exp( 46 | torch.arange(0, self.d_model, 2, dtype=torch.float32) * -(math.log(10000.0) / self.d_model) 47 | ) 48 | pe_positive[:, 0::2] = torch.sin(position * div_term) 49 | pe_positive[:, 1::2] = torch.cos(position * div_term) 50 | pe_negative[:, 0::2] = torch.sin(-1 * position * div_term) 51 | pe_negative[:, 1::2] = torch.cos(-1 * position * div_term) 52 | 53 | pe_positive = torch.flip(pe_positive, [0]).unsqueeze(0) 54 | pe_negative = pe_negative[1:].unsqueeze(0) 55 | pe = torch.cat([pe_positive, pe_negative], dim=1) 56 | self.pe = pe.to(device=x.device, dtype=x.dtype) 57 | 58 | def forward(self, x: Tensor) -> Tensor: 59 | """ 60 | Args: 61 | x : Input tensor B X T X C 62 | Returns: 63 | torch.Tensor: Encoded tensor B X T X C 64 | """ 65 | self.extend_pe(x) 66 | pos_emb = self.pe[ 67 | :, 68 | self.pe.size(1) // 2 - x.size(1) + 1 : self.pe.size(1) // 2 + x.size(1), 69 | ] 70 | return pos_emb 71 | -------------------------------------------------------------------------------- /conformer/encoder.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) 2021, Soohwan Kim. All rights reserved. 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | 15 | import torch 16 | import torch.nn as nn 17 | from torch import Tensor 18 | from typing import Tuple 19 | 20 | from .feed_forward import FeedForwardModule 21 | from .attention import MultiHeadedSelfAttentionModule 22 | from .convolution import ( 23 | ConformerConvModule, 24 | Conv2dSubampling, 25 | ) 26 | from .modules import ( 27 | ResidualConnectionModule, 28 | Linear, 29 | ) 30 | 31 | 32 | class ConformerBlock(nn.Module): 33 | """ 34 | Conformer block contains two Feed Forward modules sandwiching the Multi-Headed Self-Attention module 35 | and the Convolution module. This sandwich structure is inspired by Macaron-Net, which proposes replacing 36 | the original feed-forward layer in the Transformer block into two half-step feed-forward layers, 37 | one before the attention layer and one after. 38 | 39 | Args: 40 | encoder_dim (int, optional): Dimension of conformer encoder 41 | num_attention_heads (int, optional): Number of attention heads 42 | feed_forward_expansion_factor (int, optional): Expansion factor of feed forward module 43 | conv_expansion_factor (int, optional): Expansion factor of conformer convolution module 44 | feed_forward_dropout_p (float, optional): Probability of feed forward module dropout 45 | attention_dropout_p (float, optional): Probability of attention module dropout 46 | conv_dropout_p (float, optional): Probability of conformer convolution module dropout 47 | conv_kernel_size (int or tuple, optional): Size of the convolving kernel 48 | half_step_residual (bool): Flag indication whether to use half step residual or not 49 | 50 | Inputs: inputs 51 | - **inputs** (batch, time, dim): Tensor containing input vector 52 | 53 | Returns: outputs 54 | - **outputs** (batch, time, dim): Tensor produces by conformer block. 55 | """ 56 | def __init__( 57 | self, 58 | encoder_dim: int = 512, 59 | num_attention_heads: int = 8, 60 | feed_forward_expansion_factor: int = 4, 61 | conv_expansion_factor: int = 2, 62 | feed_forward_dropout_p: float = 0.1, 63 | attention_dropout_p: float = 0.1, 64 | conv_dropout_p: float = 0.1, 65 | conv_kernel_size: int = 31, 66 | half_step_residual: bool = True, 67 | ): 68 | super(ConformerBlock, self).__init__() 69 | if half_step_residual: 70 | self.feed_forward_residual_factor = 0.5 71 | else: 72 | self.feed_forward_residual_factor = 1 73 | 74 | self.sequential = nn.Sequential( 75 | ResidualConnectionModule( 76 | module=FeedForwardModule( 77 | encoder_dim=encoder_dim, 78 | expansion_factor=feed_forward_expansion_factor, 79 | dropout_p=feed_forward_dropout_p, 80 | ), 81 | module_factor=self.feed_forward_residual_factor, 82 | ), 83 | ResidualConnectionModule( 84 | module=MultiHeadedSelfAttentionModule( 85 | d_model=encoder_dim, 86 | num_heads=num_attention_heads, 87 | dropout_p=attention_dropout_p, 88 | ), 89 | ), 90 | ResidualConnectionModule( 91 | module=ConformerConvModule( 92 | in_channels=encoder_dim, 93 | kernel_size=conv_kernel_size, 94 | expansion_factor=conv_expansion_factor, 95 | dropout_p=conv_dropout_p, 96 | ), 97 | ), 98 | ResidualConnectionModule( 99 | module=FeedForwardModule( 100 | encoder_dim=encoder_dim, 101 | expansion_factor=feed_forward_expansion_factor, 102 | dropout_p=feed_forward_dropout_p, 103 | ), 104 | module_factor=self.feed_forward_residual_factor, 105 | ), 106 | nn.LayerNorm(encoder_dim), 107 | ) 108 | 109 | def forward(self, inputs: Tensor) -> Tensor: 110 | return self.sequential(inputs) 111 | 112 | 113 | class ConformerEncoder(nn.Module): 114 | """ 115 | Conformer encoder first processes the input with a convolution subsampling layer and then 116 | with a number of conformer blocks. 117 | 118 | Args: 119 | input_dim (int, optional): Dimension of input vector 120 | encoder_dim (int, optional): Dimension of conformer encoder 121 | num_layers (int, optional): Number of conformer blocks 122 | num_attention_heads (int, optional): Number of attention heads 123 | feed_forward_expansion_factor (int, optional): Expansion factor of feed forward module 124 | conv_expansion_factor (int, optional): Expansion factor of conformer convolution module 125 | feed_forward_dropout_p (float, optional): Probability of feed forward module dropout 126 | attention_dropout_p (float, optional): Probability of attention module dropout 127 | conv_dropout_p (float, optional): Probability of conformer convolution module dropout 128 | conv_kernel_size (int or tuple, optional): Size of the convolving kernel 129 | half_step_residual (bool): Flag indication whether to use half step residual or not 130 | 131 | Inputs: inputs, input_lengths 132 | - **inputs** (batch, time, dim): Tensor containing input vector 133 | - **input_lengths** (batch): list of sequence input lengths 134 | 135 | Returns: outputs, output_lengths 136 | - **outputs** (batch, out_channels, time): Tensor produces by conformer encoder. 137 | - **output_lengths** (batch): list of sequence output lengths 138 | """ 139 | def __init__( 140 | self, 141 | input_dim: int = 80, 142 | encoder_dim: int = 512, 143 | num_layers: int = 17, 144 | num_attention_heads: int = 8, 145 | feed_forward_expansion_factor: int = 4, 146 | conv_expansion_factor: int = 2, 147 | input_dropout_p: float = 0.1, 148 | feed_forward_dropout_p: float = 0.1, 149 | attention_dropout_p: float = 0.1, 150 | conv_dropout_p: float = 0.1, 151 | conv_kernel_size: int = 31, 152 | half_step_residual: bool = True, 153 | ): 154 | super(ConformerEncoder, self).__init__() 155 | self.conv_subsample = Conv2dSubampling(in_channels=1, out_channels=encoder_dim) 156 | self.input_projection = nn.Sequential( 157 | Linear(encoder_dim * (((input_dim - 1) // 2 - 1) // 2), encoder_dim), 158 | nn.Dropout(p=input_dropout_p), 159 | ) 160 | self.layers = nn.ModuleList([ConformerBlock( 161 | encoder_dim=encoder_dim, 162 | num_attention_heads=num_attention_heads, 163 | feed_forward_expansion_factor=feed_forward_expansion_factor, 164 | conv_expansion_factor=conv_expansion_factor, 165 | feed_forward_dropout_p=feed_forward_dropout_p, 166 | attention_dropout_p=attention_dropout_p, 167 | conv_dropout_p=conv_dropout_p, 168 | conv_kernel_size=conv_kernel_size, 169 | half_step_residual=half_step_residual, 170 | ) for _ in range(num_layers)]) 171 | 172 | def count_parameters(self) -> int: 173 | """ Count parameters of encoder """ 174 | return sum([p.numel() for p in self.parameters()]) 175 | 176 | def update_dropout(self, dropout_p: float) -> None: 177 | """ Update dropout probability of encoder """ 178 | for name, child in self.named_children(): 179 | if isinstance(child, nn.Dropout): 180 | child.p = dropout_p 181 | 182 | def forward(self, inputs: Tensor, input_lengths: Tensor) -> Tuple[Tensor, Tensor]: 183 | """ 184 | Forward propagate a `inputs` for encoder training. 185 | 186 | Args: 187 | inputs (torch.FloatTensor): A input sequence passed to encoder. Typically for inputs this will be a padded 188 | `FloatTensor` of size ``(batch, seq_length, dimension)``. 189 | input_lengths (torch.LongTensor): The length of input tensor. ``(batch)`` 190 | 191 | Returns: 192 | (Tensor, Tensor) 193 | 194 | * outputs (torch.FloatTensor): A output sequence of encoder. `FloatTensor` of size 195 | ``(batch, seq_length, dimension)`` 196 | * output_lengths (torch.LongTensor): The length of output tensor. ``(batch)`` 197 | """ 198 | outputs, output_lengths = self.conv_subsample(inputs, input_lengths) 199 | outputs = self.input_projection(outputs) 200 | 201 | for layer in self.layers: 202 | outputs = layer(outputs) 203 | 204 | return outputs, output_lengths 205 | -------------------------------------------------------------------------------- /conformer/feed_forward.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) 2021, Soohwan Kim. All rights reserved. 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | 15 | import torch 16 | import torch.nn as nn 17 | from torch import Tensor 18 | 19 | from .activation import Swish 20 | from .modules import Linear 21 | 22 | 23 | class FeedForwardModule(nn.Module): 24 | """ 25 | Conformer Feed Forward Module follow pre-norm residual units and apply layer normalization within the residual unit 26 | and on the input before the first linear layer. This module also apply Swish activation and dropout, which helps 27 | regularizing the network. 28 | 29 | Args: 30 | encoder_dim (int): Dimension of conformer encoder 31 | expansion_factor (int): Expansion factor of feed forward module. 32 | dropout_p (float): Ratio of dropout 33 | 34 | Inputs: inputs 35 | - **inputs** (batch, time, dim): Tensor contains input sequences 36 | 37 | Outputs: outputs 38 | - **outputs** (batch, time, dim): Tensor produces by feed forward module. 39 | """ 40 | def __init__( 41 | self, 42 | encoder_dim: int = 512, 43 | expansion_factor: int = 4, 44 | dropout_p: float = 0.1, 45 | ) -> None: 46 | super(FeedForwardModule, self).__init__() 47 | self.sequential = nn.Sequential( 48 | nn.LayerNorm(encoder_dim), 49 | Linear(encoder_dim, encoder_dim * expansion_factor, bias=True), 50 | Swish(), 51 | nn.Dropout(p=dropout_p), 52 | Linear(encoder_dim * expansion_factor, encoder_dim, bias=True), 53 | nn.Dropout(p=dropout_p), 54 | ) 55 | 56 | def forward(self, inputs: Tensor) -> Tensor: 57 | return self.sequential(inputs) 58 | -------------------------------------------------------------------------------- /conformer/model.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) 2021, Soohwan Kim. All rights reserved. 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | 15 | import torch 16 | import torch.nn as nn 17 | from torch import Tensor 18 | from typing import Tuple 19 | 20 | from .encoder import ConformerEncoder 21 | from .modules import Linear 22 | 23 | 24 | class Conformer(nn.Module): 25 | """ 26 | Conformer: Convolution-augmented Transformer for Speech Recognition 27 | The paper used a one-lstm Transducer decoder, currently still only implemented 28 | the conformer encoder shown in the paper. 29 | 30 | Args: 31 | num_classes (int): Number of classification classes 32 | input_dim (int, optional): Dimension of input vector 33 | encoder_dim (int, optional): Dimension of conformer encoder 34 | num_encoder_layers (int, optional): Number of conformer blocks 35 | num_attention_heads (int, optional): Number of attention heads 36 | feed_forward_expansion_factor (int, optional): Expansion factor of feed forward module 37 | conv_expansion_factor (int, optional): Expansion factor of conformer convolution module 38 | feed_forward_dropout_p (float, optional): Probability of feed forward module dropout 39 | attention_dropout_p (float, optional): Probability of attention module dropout 40 | conv_dropout_p (float, optional): Probability of conformer convolution module dropout 41 | conv_kernel_size (int or tuple, optional): Size of the convolving kernel 42 | half_step_residual (bool): Flag indication whether to use half step residual or not 43 | 44 | Inputs: inputs, input_lengths 45 | - **inputs** (batch, time, dim): Tensor containing input vector 46 | - **input_lengths** (batch): list of sequence input lengths 47 | 48 | Returns: outputs, output_lengths 49 | - **outputs** (batch, out_channels, time): Tensor produces by conformer. 50 | - **output_lengths** (batch): list of sequence output lengths 51 | """ 52 | def __init__( 53 | self, 54 | num_classes: int, 55 | input_dim: int = 80, 56 | encoder_dim: int = 512, 57 | num_encoder_layers: int = 17, 58 | num_attention_heads: int = 8, 59 | feed_forward_expansion_factor: int = 4, 60 | conv_expansion_factor: int = 2, 61 | input_dropout_p: float = 0.1, 62 | feed_forward_dropout_p: float = 0.1, 63 | attention_dropout_p: float = 0.1, 64 | conv_dropout_p: float = 0.1, 65 | conv_kernel_size: int = 31, 66 | half_step_residual: bool = True, 67 | ) -> None: 68 | super(Conformer, self).__init__() 69 | self.encoder = ConformerEncoder( 70 | input_dim=input_dim, 71 | encoder_dim=encoder_dim, 72 | num_layers=num_encoder_layers, 73 | num_attention_heads=num_attention_heads, 74 | feed_forward_expansion_factor=feed_forward_expansion_factor, 75 | conv_expansion_factor=conv_expansion_factor, 76 | input_dropout_p=input_dropout_p, 77 | feed_forward_dropout_p=feed_forward_dropout_p, 78 | attention_dropout_p=attention_dropout_p, 79 | conv_dropout_p=conv_dropout_p, 80 | conv_kernel_size=conv_kernel_size, 81 | half_step_residual=half_step_residual, 82 | ) 83 | self.fc = Linear(encoder_dim, num_classes, bias=False) 84 | 85 | def count_parameters(self) -> int: 86 | """ Count parameters of encoder """ 87 | return self.encoder.count_parameters() 88 | 89 | def update_dropout(self, dropout_p) -> None: 90 | """ Update dropout probability of model """ 91 | self.encoder.update_dropout(dropout_p) 92 | 93 | def forward(self, inputs: Tensor, input_lengths: Tensor) -> Tuple[Tensor, Tensor]: 94 | """ 95 | Forward propagate a `inputs` and `targets` pair for training. 96 | 97 | Args: 98 | inputs (torch.FloatTensor): A input sequence passed to encoder. Typically for inputs this will be a padded 99 | `FloatTensor` of size ``(batch, seq_length, dimension)``. 100 | input_lengths (torch.LongTensor): The length of input tensor. ``(batch)`` 101 | 102 | Returns: 103 | * predictions (torch.FloatTensor): Result of model predictions. 104 | """ 105 | encoder_outputs, encoder_output_lengths = self.encoder(inputs, input_lengths) 106 | outputs = self.fc(encoder_outputs) 107 | outputs = nn.functional.log_softmax(outputs, dim=-1) 108 | return outputs, encoder_output_lengths 109 | -------------------------------------------------------------------------------- /conformer/modules.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) 2021, Soohwan Kim. All rights reserved. 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | 15 | import torch 16 | import torch.nn as nn 17 | import torch.nn.init as init 18 | from torch import Tensor 19 | 20 | 21 | class ResidualConnectionModule(nn.Module): 22 | """ 23 | Residual Connection Module. 24 | outputs = (module(inputs) x module_factor + inputs x input_factor) 25 | """ 26 | def __init__(self, module: nn.Module, module_factor: float = 1.0, input_factor: float = 1.0): 27 | super(ResidualConnectionModule, self).__init__() 28 | self.module = module 29 | self.module_factor = module_factor 30 | self.input_factor = input_factor 31 | 32 | def forward(self, inputs: Tensor) -> Tensor: 33 | return (self.module(inputs) * self.module_factor) + (inputs * self.input_factor) 34 | 35 | 36 | class Linear(nn.Module): 37 | """ 38 | Wrapper class of torch.nn.Linear 39 | Weight initialize by xavier initialization and bias initialize to zeros. 40 | """ 41 | def __init__(self, in_features: int, out_features: int, bias: bool = True) -> None: 42 | super(Linear, self).__init__() 43 | self.linear = nn.Linear(in_features, out_features, bias=bias) 44 | init.xavier_uniform_(self.linear.weight) 45 | if bias: 46 | init.zeros_(self.linear.bias) 47 | 48 | def forward(self, x: Tensor) -> Tensor: 49 | return self.linear(x) 50 | 51 | 52 | class View(nn.Module): 53 | """ Wrapper class of torch.view() for Sequential module. """ 54 | def __init__(self, shape: tuple, contiguous: bool = False): 55 | super(View, self).__init__() 56 | self.shape = shape 57 | self.contiguous = contiguous 58 | 59 | def forward(self, x: Tensor) -> Tensor: 60 | if self.contiguous: 61 | x = x.contiguous() 62 | 63 | return x.view(*self.shape) 64 | 65 | 66 | class Transpose(nn.Module): 67 | """ Wrapper class of torch.transpose() for Sequential module. """ 68 | def __init__(self, shape: tuple): 69 | super(Transpose, self).__init__() 70 | self.shape = shape 71 | 72 | def forward(self, x: Tensor) -> Tensor: 73 | return x.transpose(*self.shape) 74 | -------------------------------------------------------------------------------- /docs/.DS_Store: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/sooftware/conformer/b4e7d44944214db6e5912f92ff43d5ea1919eaa2/docs/.DS_Store -------------------------------------------------------------------------------- /docs/Makefile: -------------------------------------------------------------------------------- 1 | # Minimal makefile for Sphinx documentation 2 | # 3 | 4 | # You can set these variables from the command line, and also 5 | # from the environment for the first two. 6 | SPHINXOPTS ?= 7 | SPHINXBUILD ?= sphinx-build 8 | SOURCEDIR = source 9 | BUILDDIR = build 10 | 11 | # Put it first so that "make" without argument is like "make help". 12 | help: 13 | @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) 14 | 15 | .PHONY: help Makefile 16 | 17 | # Catch-all target: route all unknown targets to Sphinx using the new 18 | # "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). 19 | %: Makefile 20 | @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) 21 | -------------------------------------------------------------------------------- /docs/Model.html: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | Model — conformer latest documentation 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | 42 | 43 | 44 | 45 | 46 | 47 | 48 | 49 |

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210 | 211 | 212 | 217 | 218 | 219 | 220 | 221 | 222 | 223 | 224 | -------------------------------------------------------------------------------- /docs/_sources/Model.rst.txt: -------------------------------------------------------------------------------- 1 | 2 | Model 3 | ===================================================== 4 | 5 | Conformer 6 | -------------------------------------------- 7 | 8 | .. automodule:: conformer.model 9 | :members: 10 | 11 | Encoder 12 | -------------------------------------------- 13 | 14 | .. automodule:: conformer.encoder 15 | :members: 16 | 17 | Decoder 18 | -------------------------------------------- 19 | 20 | .. automodule:: conformer.decoder 21 | :members: 22 | -------------------------------------------------------------------------------- /docs/_sources/Modules.rst.txt: -------------------------------------------------------------------------------- 1 | 2 | Conformer Modules 3 | ===================================================== 4 | 5 | Attention 6 | -------------------------------------------- 7 | 8 | .. automodule:: conformer.attention 9 | :members: 10 | 11 | Convolution 12 | -------------------------------------------- 13 | 14 | .. automodule:: conformer.convolution 15 | :members: 16 | 17 | Feed Forward 18 | -------------------------------------------- 19 | 20 | .. automodule:: conformer.feed_forward 21 | :members: -------------------------------------------------------------------------------- /docs/_sources/Submodules.rst.txt: -------------------------------------------------------------------------------- 1 | 2 | Submodules 3 | ===================================================== 4 | 5 | Activation 6 | -------------------------------------------- 7 | 8 | .. automodule:: conformer.activation 9 | :members: 10 | 11 | Modules 12 | -------------------------------------------- 13 | 14 | .. automodule:: conformer.modules 15 | :members: 16 | 17 | Embedding 18 | -------------------------------------------- 19 | 20 | .. automodule:: conformer.embedding 21 | :members: -------------------------------------------------------------------------------- /docs/_sources/index.rst.txt: -------------------------------------------------------------------------------- 1 | .. conformer documentation master file, created by 2 | sphinx-quickstart on Sun Jan 24 01:16:16 2021. 3 | You can adapt this file completely to your liking, but it should at least 4 | contain the root `toctree` directive. 5 | 6 | Welcome to Conformer's documentation! 7 | ===================================== 8 | 9 | .. toctree:: 10 | :maxdepth: 1 11 | :caption: PACKAGE 12 | 13 | Model 14 | Modules 15 | Submodules 16 | 17 | 18 | 19 | Indices and tables 20 | ================== 21 | 22 | * :ref:`genindex` 23 | * :ref:`modindex` 24 | * :ref:`search` 25 | -------------------------------------------------------------------------------- /docs/_static/basic.css: -------------------------------------------------------------------------------- 1 | /* 2 | * basic.css 3 | * ~~~~~~~~~ 4 | * 5 | * Sphinx stylesheet -- basic theme. 6 | * 7 | * :copyright: Copyright 2007-2020 by the Sphinx team, see AUTHORS. 8 | * :license: BSD, see LICENSE for details. 9 | * 10 | */ 11 | 12 | /* -- main layout ----------------------------------------------------------- */ 13 | 14 | div.clearer { 15 | clear: both; 16 | } 17 | 18 | div.section::after { 19 | display: block; 20 | content: ''; 21 | clear: left; 22 | } 23 | 24 | /* -- relbar ---------------------------------------------------------------- */ 25 | 26 | div.related { 27 | width: 100%; 28 | font-size: 90%; 29 | } 30 | 31 | div.related h3 { 32 | display: none; 33 | } 34 | 35 | div.related ul { 36 | margin: 0; 37 | padding: 0 0 0 10px; 38 | list-style: none; 39 | } 40 | 41 | div.related li { 42 | display: inline; 43 | } 44 | 45 | div.related li.right { 46 | float: right; 47 | margin-right: 5px; 48 | } 49 | 50 | /* -- sidebar --------------------------------------------------------------- */ 51 | 52 | div.sphinxsidebarwrapper { 53 | padding: 10px 5px 0 10px; 54 | } 55 | 56 | div.sphinxsidebar { 57 | float: left; 58 | width: 230px; 59 | margin-left: -100%; 60 | font-size: 90%; 61 | word-wrap: break-word; 62 | overflow-wrap : break-word; 63 | } 64 | 65 | div.sphinxsidebar ul { 66 | list-style: none; 67 | } 68 | 69 | div.sphinxsidebar ul ul, 70 | div.sphinxsidebar ul.want-points { 71 | margin-left: 20px; 72 | list-style: square; 73 | } 74 | 75 | div.sphinxsidebar ul ul { 76 | margin-top: 0; 77 | margin-bottom: 0; 78 | } 79 | 80 | div.sphinxsidebar form { 81 | margin-top: 10px; 82 | } 83 | 84 | div.sphinxsidebar input { 85 | border: 1px solid #98dbcc; 86 | font-family: sans-serif; 87 | font-size: 1em; 88 | } 89 | 90 | div.sphinxsidebar #searchbox form.search { 91 | overflow: hidden; 92 | } 93 | 94 | div.sphinxsidebar #searchbox input[type="text"] { 95 | float: left; 96 | width: 80%; 97 | padding: 0.25em; 98 | box-sizing: border-box; 99 | } 100 | 101 | div.sphinxsidebar #searchbox input[type="submit"] { 102 | float: left; 103 | width: 20%; 104 | border-left: none; 105 | padding: 0.25em; 106 | box-sizing: border-box; 107 | } 108 | 109 | 110 | img { 111 | border: 0; 112 | max-width: 100%; 113 | } 114 | 115 | /* -- search page ----------------------------------------------------------- */ 116 | 117 | ul.search { 118 | margin: 10px 0 0 20px; 119 | padding: 0; 120 | } 121 | 122 | ul.search li { 123 | padding: 5px 0 5px 20px; 124 | background-image: url(file.png); 125 | background-repeat: no-repeat; 126 | background-position: 0 7px; 127 | } 128 | 129 | ul.search li a { 130 | font-weight: bold; 131 | } 132 | 133 | ul.search li div.context { 134 | color: #888; 135 | margin: 2px 0 0 30px; 136 | text-align: left; 137 | } 138 | 139 | ul.keywordmatches li.goodmatch a { 140 | font-weight: bold; 141 | } 142 | 143 | /* -- index page ------------------------------------------------------------ */ 144 | 145 | table.contentstable { 146 | width: 90%; 147 | margin-left: auto; 148 | margin-right: auto; 149 | } 150 | 151 | table.contentstable p.biglink { 152 | line-height: 150%; 153 | } 154 | 155 | a.biglink { 156 | font-size: 1.3em; 157 | } 158 | 159 | span.linkdescr { 160 | font-style: italic; 161 | padding-top: 5px; 162 | font-size: 90%; 163 | } 164 | 165 | /* -- general index --------------------------------------------------------- */ 166 | 167 | table.indextable { 168 | width: 100%; 169 | } 170 | 171 | table.indextable td { 172 | text-align: left; 173 | vertical-align: top; 174 | } 175 | 176 | table.indextable ul { 177 | margin-top: 0; 178 | margin-bottom: 0; 179 | list-style-type: none; 180 | } 181 | 182 | table.indextable > tbody > tr > td > ul { 183 | padding-left: 0em; 184 | } 185 | 186 | table.indextable tr.pcap { 187 | height: 10px; 188 | } 189 | 190 | table.indextable tr.cap { 191 | margin-top: 10px; 192 | background-color: #f2f2f2; 193 | } 194 | 195 | img.toggler { 196 | margin-right: 3px; 197 | margin-top: 3px; 198 | cursor: pointer; 199 | } 200 | 201 | div.modindex-jumpbox { 202 | border-top: 1px solid #ddd; 203 | border-bottom: 1px solid #ddd; 204 | margin: 1em 0 1em 0; 205 | padding: 0.4em; 206 | } 207 | 208 | div.genindex-jumpbox { 209 | border-top: 1px solid #ddd; 210 | border-bottom: 1px solid #ddd; 211 | margin: 1em 0 1em 0; 212 | padding: 0.4em; 213 | } 214 | 215 | /* -- domain module index --------------------------------------------------- */ 216 | 217 | table.modindextable td { 218 | padding: 2px; 219 | border-collapse: collapse; 220 | } 221 | 222 | /* -- general body styles --------------------------------------------------- */ 223 | 224 | div.body { 225 | min-width: 450px; 226 | max-width: 800px; 227 | } 228 | 229 | div.body p, div.body dd, div.body li, div.body blockquote { 230 | -moz-hyphens: auto; 231 | -ms-hyphens: auto; 232 | -webkit-hyphens: auto; 233 | hyphens: auto; 234 | } 235 | 236 | a.headerlink { 237 | visibility: hidden; 238 | } 239 | 240 | a.brackets:before, 241 | span.brackets > a:before{ 242 | content: "["; 243 | } 244 | 245 | a.brackets:after, 246 | span.brackets > a:after { 247 | content: "]"; 248 | } 249 | 250 | h1:hover > a.headerlink, 251 | h2:hover > a.headerlink, 252 | h3:hover > a.headerlink, 253 | h4:hover > a.headerlink, 254 | h5:hover > a.headerlink, 255 | h6:hover > a.headerlink, 256 | dt:hover > a.headerlink, 257 | caption:hover > a.headerlink, 258 | p.caption:hover > a.headerlink, 259 | div.code-block-caption:hover > a.headerlink { 260 | visibility: visible; 261 | } 262 | 263 | div.body p.caption { 264 | text-align: inherit; 265 | } 266 | 267 | div.body td { 268 | text-align: left; 269 | } 270 | 271 | .first { 272 | margin-top: 0 !important; 273 | } 274 | 275 | p.rubric { 276 | margin-top: 30px; 277 | font-weight: bold; 278 | } 279 | 280 | img.align-left, .figure.align-left, object.align-left { 281 | clear: left; 282 | float: left; 283 | margin-right: 1em; 284 | } 285 | 286 | img.align-right, .figure.align-right, object.align-right { 287 | clear: right; 288 | 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blockquote { 338 | clear: left; 339 | } 340 | 341 | /* -- topics ---------------------------------------------------------------- */ 342 | 343 | div.topic { 344 | border: 1px solid #ccc; 345 | padding: 7px; 346 | margin: 10px 0 10px 0; 347 | } 348 | 349 | p.topic-title { 350 | font-size: 1.1em; 351 | font-weight: bold; 352 | margin-top: 10px; 353 | } 354 | 355 | /* -- admonitions ----------------------------------------------------------- */ 356 | 357 | div.admonition { 358 | margin-top: 10px; 359 | margin-bottom: 10px; 360 | padding: 7px; 361 | } 362 | 363 | div.admonition dt { 364 | font-weight: bold; 365 | } 366 | 367 | p.admonition-title { 368 | margin: 0px 10px 5px 0px; 369 | font-weight: bold; 370 | } 371 | 372 | div.body p.centered { 373 | text-align: center; 374 | margin-top: 25px; 375 | } 376 | 377 | /* -- content of sidebars/topics/admonitions -------------------------------- */ 378 | 379 | div.sidebar > :last-child, 380 | div.topic > :last-child, 381 | div.admonition > 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"warn", "error", "assert", "dir", 22 | "dirxml", "group", "groupEnd", "time", "timeEnd", "count", "trace", 23 | "profile", "profileEnd"]; 24 | window.console = {}; 25 | for (var i = 0; i < names.length; ++i) 26 | window.console[names[i]] = function() {}; 27 | } 28 | */ 29 | 30 | /** 31 | * small helper function to urldecode strings 32 | */ 33 | jQuery.urldecode = function(x) { 34 | return decodeURIComponent(x).replace(/\+/g, ' '); 35 | }; 36 | 37 | /** 38 | * small helper function to urlencode strings 39 | */ 40 | jQuery.urlencode = encodeURIComponent; 41 | 42 | /** 43 | * This function returns the parsed url parameters of the 44 | * current request. Multiple values per key are supported, 45 | * it will always return arrays of strings for the value parts. 46 | */ 47 | jQuery.getQueryParameters = function(s) { 48 | if (typeof s === 'undefined') 49 | s = document.location.search; 50 | var parts = s.substr(s.indexOf('?') + 1).split('&'); 51 | var result = {}; 52 | for (var i = 0; i < parts.length; i++) { 53 | var tmp = parts[i].split('=', 2); 54 | var key = jQuery.urldecode(tmp[0]); 55 | var value = jQuery.urldecode(tmp[1]); 56 | if (key in result) 57 | result[key].push(value); 58 | else 59 | result[key] = [value]; 60 | } 61 | return result; 62 | }; 63 | 64 | /** 65 | * highlight a given string on a jquery object by wrapping it in 66 | * span elements with the given class name. 67 | */ 68 | jQuery.fn.highlightText = function(text, className) { 69 | function highlight(node, addItems) { 70 | if (node.nodeType === 3) { 71 | var val = node.nodeValue; 72 | var pos = val.toLowerCase().indexOf(text); 73 | if (pos >= 0 && 74 | !jQuery(node.parentNode).hasClass(className) && 75 | !jQuery(node.parentNode).hasClass("nohighlight")) { 76 | var span; 77 | var isInSVG = jQuery(node).closest("body, svg, foreignObject").is("svg"); 78 | if (isInSVG) { 79 | span = document.createElementNS("http://www.w3.org/2000/svg", "tspan"); 80 | } else { 81 | span = document.createElement("span"); 82 | span.className = className; 83 | } 84 | span.appendChild(document.createTextNode(val.substr(pos, text.length))); 85 | node.parentNode.insertBefore(span, node.parentNode.insertBefore( 86 | document.createTextNode(val.substr(pos + text.length)), 87 | node.nextSibling)); 88 | node.nodeValue = val.substr(0, pos); 89 | if (isInSVG) { 90 | var rect = document.createElementNS("http://www.w3.org/2000/svg", "rect"); 91 | var bbox = node.parentElement.getBBox(); 92 | rect.x.baseVal.value = bbox.x; 93 | rect.y.baseVal.value = bbox.y; 94 | rect.width.baseVal.value = bbox.width; 95 | rect.height.baseVal.value = bbox.height; 96 | rect.setAttribute('class', className); 97 | addItems.push({ 98 | "parent": node.parentNode, 99 | "target": rect}); 100 | } 101 | } 102 | } 103 | else if (!jQuery(node).is("button, select, textarea")) { 104 | jQuery.each(node.childNodes, function() { 105 | highlight(this, addItems); 106 | }); 107 | } 108 | } 109 | var addItems = []; 110 | var result = this.each(function() { 111 | highlight(this, addItems); 112 | }); 113 | for (var i = 0; i < addItems.length; ++i) { 114 | jQuery(addItems[i].parent).before(addItems[i].target); 115 | } 116 | return result; 117 | }; 118 | 119 | /* 120 | * backward compatibility for jQuery.browser 121 | * This will be supported until firefox bug is fixed. 122 | */ 123 | if (!jQuery.browser) { 124 | jQuery.uaMatch = function(ua) { 125 | ua = ua.toLowerCase(); 126 | 127 | var match = /(chrome)[ \/]([\w.]+)/.exec(ua) || 128 | /(webkit)[ \/]([\w.]+)/.exec(ua) || 129 | /(opera)(?:.*version|)[ \/]([\w.]+)/.exec(ua) || 130 | /(msie) ([\w.]+)/.exec(ua) || 131 | ua.indexOf("compatible") < 0 && /(mozilla)(?:.*? rv:([\w.]+)|)/.exec(ua) || 132 | []; 133 | 134 | return { 135 | browser: match[ 1 ] || "", 136 | version: match[ 2 ] || "0" 137 | }; 138 | }; 139 | jQuery.browser = {}; 140 | jQuery.browser[jQuery.uaMatch(navigator.userAgent).browser] = true; 141 | } 142 | 143 | /** 144 | * Small JavaScript module for the documentation. 145 | */ 146 | var Documentation = { 147 | 148 | init : function() { 149 | this.fixFirefoxAnchorBug(); 150 | this.highlightSearchWords(); 151 | this.initIndexTable(); 152 | if (DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) { 153 | this.initOnKeyListeners(); 154 | } 155 | }, 156 | 157 | /** 158 | * i18n support 159 | */ 160 | TRANSLATIONS : {}, 161 | PLURAL_EXPR : function(n) { return n === 1 ? 0 : 1; }, 162 | LOCALE : 'unknown', 163 | 164 | // gettext and ngettext don't access this so that the functions 165 | // can safely bound to a different name (_ = Documentation.gettext) 166 | gettext : function(string) { 167 | var translated = Documentation.TRANSLATIONS[string]; 168 | if (typeof translated === 'undefined') 169 | return string; 170 | return (typeof translated === 'string') ? translated : translated[0]; 171 | }, 172 | 173 | ngettext : function(singular, plural, n) { 174 | var translated = Documentation.TRANSLATIONS[singular]; 175 | if (typeof translated === 'undefined') 176 | return (n == 1) ? singular : plural; 177 | return translated[Documentation.PLURALEXPR(n)]; 178 | }, 179 | 180 | addTranslations : function(catalog) { 181 | for (var key in catalog.messages) 182 | this.TRANSLATIONS[key] = catalog.messages[key]; 183 | this.PLURAL_EXPR = new Function('n', 'return +(' + catalog.plural_expr + ')'); 184 | this.LOCALE = catalog.locale; 185 | }, 186 | 187 | /** 188 | * add context elements like header anchor links 189 | */ 190 | addContextElements : function() { 191 | $('div[id] > :header:first').each(function() { 192 | $('\u00B6'). 193 | attr('href', '#' + this.id). 194 | attr('title', _('Permalink to this headline')). 195 | appendTo(this); 196 | }); 197 | $('dt[id]').each(function() { 198 | $('\u00B6'). 199 | attr('href', '#' + this.id). 200 | attr('title', _('Permalink to this definition')). 201 | appendTo(this); 202 | }); 203 | }, 204 | 205 | /** 206 | * workaround a firefox stupidity 207 | * see: https://bugzilla.mozilla.org/show_bug.cgi?id=645075 208 | */ 209 | fixFirefoxAnchorBug : function() { 210 | if (document.location.hash && $.browser.mozilla) 211 | window.setTimeout(function() { 212 | document.location.href += ''; 213 | }, 10); 214 | }, 215 | 216 | /** 217 | * highlight the search words provided in the url in the text 218 | */ 219 | highlightSearchWords : function() { 220 | var params = $.getQueryParameters(); 221 | var terms = (params.highlight) ? params.highlight[0].split(/\s+/) : []; 222 | if (terms.length) { 223 | var body = $('div.body'); 224 | if (!body.length) { 225 | body = $('body'); 226 | } 227 | window.setTimeout(function() { 228 | $.each(terms, function() { 229 | body.highlightText(this.toLowerCase(), 'highlighted'); 230 | }); 231 | }, 10); 232 | $('

' + _('Hide Search Matches') + '

') 234 | .appendTo($('#searchbox')); 235 | } 236 | }, 237 | 238 | /** 239 | * init the domain index toggle buttons 240 | */ 241 | initIndexTable : function() { 242 | var togglers = $('img.toggler').click(function() { 243 | var src = $(this).attr('src'); 244 | var idnum = $(this).attr('id').substr(7); 245 | $('tr.cg-' + idnum).toggle(); 246 | if (src.substr(-9) === 'minus.png') 247 | $(this).attr('src', src.substr(0, src.length-9) + 'plus.png'); 248 | else 249 | $(this).attr('src', src.substr(0, src.length-8) + 'minus.png'); 250 | }).css('display', ''); 251 | if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) { 252 | togglers.click(); 253 | } 254 | }, 255 | 256 | /** 257 | * helper function to hide the search marks again 258 | */ 259 | hideSearchWords : function() { 260 | $('#searchbox .highlight-link').fadeOut(300); 261 | $('span.highlighted').removeClass('highlighted'); 262 | }, 263 | 264 | /** 265 | * make the url absolute 266 | */ 267 | makeURL : function(relativeURL) { 268 | return DOCUMENTATION_OPTIONS.URL_ROOT + '/' + relativeURL; 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108 | if (re2.test(stem)) { 109 | w = stem; 110 | re2 = /(at|bl|iz)$/; 111 | re3 = new RegExp("([^aeiouylsz])\\1$"); 112 | re4 = new RegExp("^" + C + v + "[^aeiouwxy]$"); 113 | if (re2.test(w)) 114 | w = w + "e"; 115 | else if (re3.test(w)) { 116 | re = /.$/; 117 | w = w.replace(re,""); 118 | } 119 | else if (re4.test(w)) 120 | w = w + "e"; 121 | } 122 | } 123 | 124 | // Step 1c 125 | re = /^(.+?)y$/; 126 | if (re.test(w)) { 127 | var fp = re.exec(w); 128 | stem = fp[1]; 129 | re = new RegExp(s_v); 130 | if (re.test(stem)) 131 | w = stem + "i"; 132 | } 133 | 134 | // Step 2 135 | re = /^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/; 136 | if (re.test(w)) { 137 | var fp = re.exec(w); 138 | stem = fp[1]; 139 | suffix = fp[2]; 140 | re = new RegExp(mgr0); 141 | if (re.test(stem)) 142 | w = stem + step2list[suffix]; 143 | } 144 | 145 | // Step 3 146 | re = /^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/; 147 | if (re.test(w)) { 148 | var fp = re.exec(w); 149 | stem = fp[1]; 150 | suffix = fp[2]; 151 | re = new RegExp(mgr0); 152 | if (re.test(stem)) 153 | w = stem + step3list[suffix]; 154 | } 155 | 156 | // Step 4 157 | re = /^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/; 158 | re2 = /^(.+?)(s|t)(ion)$/; 159 | if (re.test(w)) { 160 | var fp = re.exec(w); 161 | stem = fp[1]; 162 | re = new RegExp(mgr1); 163 | if (re.test(stem)) 164 | w = stem; 165 | } 166 | else if (re2.test(w)) { 167 | var fp = re2.exec(w); 168 | stem = fp[1] + fp[2]; 169 | re2 = new RegExp(mgr1); 170 | if (re2.test(stem)) 171 | w = stem; 172 | } 173 | 174 | // Step 5 175 | re = /^(.+?)e$/; 176 | if (re.test(w)) { 177 | var fp = re.exec(w); 178 | stem = fp[1]; 179 | re = new RegExp(mgr1); 180 | re2 = new RegExp(meq1); 181 | re3 = new RegExp("^" + C + v + "[^aeiouwxy]$"); 182 | if (re.test(stem) || (re2.test(stem) && !(re3.test(stem)))) 183 | w = stem; 184 | } 185 | re = /ll$/; 186 | re2 = new RegExp(mgr1); 187 | if (re.test(w) && re2.test(w)) { 188 | re = /.$/; 189 | w = w.replace(re,""); 190 | } 191 | 192 | // and turn initial Y back to y 193 | if (firstch == "y") 194 | w = firstch.toLowerCase() + w.substr(1); 195 | return w; 196 | } 197 | } 198 | 199 | 200 | 201 | 202 | 203 | var splitChars = (function() { 204 | var result = {}; 205 | var singles = [96, 180, 187, 191, 215, 247, 749, 885, 903, 907, 909, 930, 1014, 1648, 206 | 1748, 1809, 2416, 2473, 2481, 2526, 2601, 2609, 2612, 2615, 2653, 2702, 207 | 2706, 2729, 2737, 2740, 2857, 2865, 2868, 2910, 2928, 2948, 2961, 2971, 208 | 2973, 3085, 3089, 3113, 3124, 3213, 3217, 3241, 3252, 3295, 3341, 3345, 209 | 3369, 3506, 3516, 3633, 3715, 3721, 3736, 3744, 3748, 3750, 3756, 3761, 210 | 3781, 3912, 4239, 4347, 4681, 4695, 4697, 4745, 4785, 4799, 4801, 4823, 211 | 4881, 5760, 5901, 5997, 6313, 7405, 8024, 8026, 8028, 8030, 8117, 8125, 212 | 8133, 8181, 8468, 8485, 8487, 8489, 8494, 8527, 11311, 11359, 11687, 11695, 213 | 11703, 11711, 11719, 11727, 11735, 12448, 12539, 43010, 43014, 43019, 43587, 214 | 43696, 43713, 64286, 64297, 64311, 64317, 64319, 64322, 64325, 65141]; 215 | var i, j, start, end; 216 | for (i = 0; i < singles.length; i++) { 217 | result[singles[i]] = true; 218 | } 219 | var ranges = [[0, 47], [58, 64], [91, 94], [123, 169], [171, 177], [182, 184], [706, 709], 220 | [722, 735], [741, 747], [751, 879], [888, 889], [894, 901], [1154, 1161], 221 | [1318, 1328], [1367, 1368], [1370, 1376], [1416, 1487], [1515, 1519], [1523, 1568], 222 | [1611, 1631], [1642, 1645], [1750, 1764], [1767, 1773], [1789, 1790], [1792, 1807], 223 | [1840, 1868], [1958, 1968], [1970, 1983], [2027, 2035], [2038, 2041], [2043, 2047], 224 | [2070, 2073], [2075, 2083], [2085, 2087], [2089, 2307], [2362, 2364], [2366, 2383], 225 | [2385, 2391], [2402, 2405], [2419, 2424], [2432, 2436], [2445, 2446], [2449, 2450], 226 | [2483, 2485], [2490, 2492], [2494, 2509], [2511, 2523], [2530, 2533], [2546, 2547], 227 | [2554, 2564], [2571, 2574], [2577, 2578], [2618, 2648], [2655, 2661], [2672, 2673], 228 | [2677, 2692], [2746, 2748], [2750, 2767], [2769, 2783], [2786, 2789], [2800, 2820], 229 | [2829, 2830], [2833, 2834], [2874, 2876], [2878, 2907], [2914, 2917], [2930, 2946], 230 | [2955, 2957], [2966, 2968], [2976, 2978], [2981, 2983], [2987, 2989], [3002, 3023], 231 | [3025, 3045], [3059, 3076], [3130, 3132], [3134, 3159], [3162, 3167], [3170, 3173], 232 | [3184, 3191], [3199, 3204], [3258, 3260], [3262, 3293], [3298, 3301], [3312, 3332], 233 | [3386, 3388], [3390, 3423], [3426, 3429], [3446, 3449], [3456, 3460], [3479, 3481], 234 | [3518, 3519], [3527, 3584], [3636, 3647], [3655, 3663], [3674, 3712], [3717, 3718], 235 | [3723, 3724], [3726, 3731], [3752, 3753], [3764, 3772], [3774, 3775], [3783, 3791], 236 | [3802, 3803], [3806, 3839], [3841, 3871], [3892, 3903], [3949, 3975], [3980, 4095], 237 | [4139, 4158], [4170, 4175], [4182, 4185], [4190, 4192], [4194, 4196], [4199, 4205], 238 | [4209, 4212], [4226, 4237], [4250, 4255], [4294, 4303], [4349, 4351], [4686, 4687], 239 | [4702, 4703], [4750, 4751], [4790, 4791], [4806, 4807], [4886, 4887], [4955, 4968], 240 | [4989, 4991], [5008, 5023], [5109, 5120], [5741, 5742], [5787, 5791], [5867, 5869], 241 | [5873, 5887], [5906, 5919], [5938, 5951], [5970, 5983], [6001, 6015], [6068, 6102], 242 | [6104, 6107], [6109, 6111], [6122, 6127], [6138, 6159], [6170, 6175], [6264, 6271], 243 | [6315, 6319], [6390, 6399], [6429, 6469], [6510, 6511], [6517, 6527], [6572, 6592], 244 | [6600, 6607], [6619, 6655], [6679, 6687], [6741, 6783], [6794, 6799], [6810, 6822], 245 | [6824, 6916], [6964, 6980], [6988, 6991], [7002, 7042], [7073, 7085], [7098, 7167], 246 | [7204, 7231], [7242, 7244], [7294, 7400], [7410, 7423], [7616, 7679], [7958, 7959], 247 | [7966, 7967], [8006, 8007], [8014, 8015], [8062, 8063], [8127, 8129], [8141, 8143], 248 | [8148, 8149], [8156, 8159], [8173, 8177], [8189, 8303], [8306, 8307], [8314, 8318], 249 | [8330, 8335], [8341, 8449], [8451, 8454], [8456, 8457], [8470, 8472], [8478, 8483], 250 | [8506, 8507], [8512, 8516], [8522, 8525], [8586, 9311], [9372, 9449], [9472, 10101], 251 | [10132, 11263], [11493, 11498], [11503, 11516], [11518, 11519], [11558, 11567], 252 | [11622, 11630], [11632, 11647], [11671, 11679], [11743, 11822], [11824, 12292], 253 | [12296, 12320], [12330, 12336], [12342, 12343], [12349, 12352], [12439, 12444], 254 | [12544, 12548], [12590, 12592], [12687, 12689], [12694, 12703], [12728, 12783], 255 | [12800, 12831], [12842, 12880], [12896, 12927], [12938, 12976], [12992, 13311], 256 | [19894, 19967], [40908, 40959], [42125, 42191], [42238, 42239], [42509, 42511], 257 | [42540, 42559], [42592, 42593], [42607, 42622], [42648, 42655], [42736, 42774], 258 | [42784, 42785], [42889, 42890], [42893, 43002], [43043, 43055], [43062, 43071], 259 | [43124, 43137], [43188, 43215], [43226, 43249], [43256, 43258], [43260, 43263], 260 | [43302, 43311], [43335, 43359], [43389, 43395], [43443, 43470], [43482, 43519], 261 | [43561, 43583], [43596, 43599], [43610, 43615], [43639, 43641], [43643, 43647], 262 | [43698, 43700], [43703, 43704], [43710, 43711], [43715, 43738], [43742, 43967], 263 | [44003, 44015], [44026, 44031], [55204, 55215], [55239, 55242], [55292, 55295], 264 | [57344, 63743], [64046, 64047], [64110, 64111], [64218, 64255], [64263, 64274], 265 | [64280, 64284], [64434, 64466], [64830, 64847], [64912, 64913], [64968, 65007], 266 | [65020, 65135], [65277, 65295], [65306, 65312], [65339, 65344], [65371, 65381], 267 | [65471, 65473], [65480, 65481], [65488, 65489], [65496, 65497]]; 268 | for (i = 0; i < ranges.length; i++) { 269 | start = ranges[i][0]; 270 | end = ranges[i][1]; 271 | for (j = start; j <= end; j++) { 272 | result[j] = true; 273 | } 274 | } 275 | return result; 276 | })(); 277 | 278 | function splitQuery(query) { 279 | var result = []; 280 | var start = -1; 281 | for (var i = 0; i < query.length; i++) { 282 | if (splitChars[query.charCodeAt(i)]) { 283 | if (start !== -1) { 284 | result.push(query.slice(start, i)); 285 | start = -1; 286 | } 287 | } else if (start === -1) { 288 | start = i; 289 | } 290 | } 291 | if (start !== -1) { 292 | result.push(query.slice(start)); 293 | } 294 | return result; 295 | } 296 | 297 | 298 | -------------------------------------------------------------------------------- /docs/_static/minus.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/sooftware/conformer/b4e7d44944214db6e5912f92ff43d5ea1919eaa2/docs/_static/minus.png -------------------------------------------------------------------------------- /docs/_static/plus.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/sooftware/conformer/b4e7d44944214db6e5912f92ff43d5ea1919eaa2/docs/_static/plus.png -------------------------------------------------------------------------------- /docs/_static/pygments.css: -------------------------------------------------------------------------------- 1 | pre { line-height: 125%; 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23 | }, 24 | */ 25 | 26 | // query matches the full name of an object 27 | objNameMatch: 11, 28 | // or matches in the last dotted part of the object name 29 | objPartialMatch: 6, 30 | // Additive scores depending on the priority of the object 31 | objPrio: {0: 15, // used to be importantResults 32 | 1: 5, // used to be objectResults 33 | 2: -5}, // used to be unimportantResults 34 | // Used when the priority is not in the mapping. 35 | objPrioDefault: 0, 36 | 37 | // query found in title 38 | title: 15, 39 | partialTitle: 7, 40 | // query found in terms 41 | term: 5, 42 | partialTerm: 2 43 | }; 44 | } 45 | 46 | if (!splitQuery) { 47 | function splitQuery(query) { 48 | return query.split(/\s+/); 49 | } 50 | } 51 | 52 | /** 53 | * Search Module 54 | */ 55 | var Search = { 56 | 57 | _index : null, 58 | _queued_query : null, 59 | _pulse_status : -1, 60 | 61 | htmlToText : function(htmlString) { 62 | var htmlElement = document.createElement('span'); 63 | htmlElement.innerHTML = htmlString; 64 | $(htmlElement).find('.headerlink').remove(); 65 | docContent = $(htmlElement).find('[role=main]')[0]; 66 | if(docContent === undefined) { 67 | console.warn("Content block not found. Sphinx search tries to obtain it " + 68 | "via '[role=main]'. Could you check your theme or template."); 69 | return ""; 70 | } 71 | return docContent.textContent || docContent.innerText; 72 | }, 73 | 74 | init : function() { 75 | var params = $.getQueryParameters(); 76 | if (params.q) { 77 | var query = params.q[0]; 78 | $('input[name="q"]')[0].value = query; 79 | this.performSearch(query); 80 | } 81 | }, 82 | 83 | loadIndex : function(url) { 84 | $.ajax({type: "GET", url: url, data: null, 85 | dataType: "script", cache: true, 86 | complete: function(jqxhr, textstatus) { 87 | if (textstatus != "success") { 88 | document.getElementById("searchindexloader").src = url; 89 | } 90 | }}); 91 | }, 92 | 93 | setIndex : function(index) { 94 | var q; 95 | this._index = index; 96 | if ((q = this._queued_query) !== null) { 97 | this._queued_query = null; 98 | Search.query(q); 99 | } 100 | }, 101 | 102 | hasIndex : function() { 103 | return this._index !== null; 104 | }, 105 | 106 | deferQuery : function(query) { 107 | this._queued_query = query; 108 | }, 109 | 110 | stopPulse : function() { 111 | this._pulse_status = 0; 112 | }, 113 | 114 | startPulse : function() { 115 | if (this._pulse_status >= 0) 116 | return; 117 | function pulse() { 118 | var i; 119 | Search._pulse_status = (Search._pulse_status + 1) % 4; 120 | var dotString = ''; 121 | for (i = 0; i < Search._pulse_status; i++) 122 | dotString += '.'; 123 | Search.dots.text(dotString); 124 | if (Search._pulse_status > -1) 125 | window.setTimeout(pulse, 500); 126 | } 127 | pulse(); 128 | }, 129 | 130 | /** 131 | * perform a search for something (or wait until index is loaded) 132 | */ 133 | performSearch : function(query) { 134 | // create the required interface elements 135 | this.out = $('#search-results'); 136 | this.title = $('

' + _('Searching') + '

').appendTo(this.out); 137 | this.dots = $('').appendTo(this.title); 138 | this.status = $('

 

').appendTo(this.out); 139 | this.output = $('