2e0f255ada
* add timit recipe for icefall * add shared file * update the docs for timit recipe * Delete shared * update the timit recipe and check style * Update model.py * Do some changes * Update model.py * Update model.py * Add README.md and RESULTS.md * Update RESULTS.md * Update README.md * update the docs for timit recipe
482 lines
15 KiB
Python
482 lines
15 KiB
Python
# Copyright 2021 Xiaomi Corp. (authors: Fangjun Kuang
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# Mingshuang Luo)
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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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import torch.nn as nn
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from torch import Tensor
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from typing import Optional
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class TdnnLiGRU(nn.Module):
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def __init__(
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self, num_features: int, num_classes: int, subsampling_factor: int = 3
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) -> None:
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"""
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Args:
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num_features:
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The input dimension of the model.
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num_classes:
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The output dimension of the model.
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subsampling_factor:
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It reduces the number of output frames by this factor.
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"""
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super().__init__()
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self.num_features = num_features
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self.num_classes = num_classes
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self.subsampling_factor = subsampling_factor
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self.tdnn = nn.Sequential(
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nn.Conv1d(
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in_channels=num_features,
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out_channels=512,
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kernel_size=3,
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stride=1,
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padding=1,
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),
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nn.ReLU(inplace=True),
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nn.BatchNorm1d(num_features=512, affine=False),
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nn.Conv1d(
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in_channels=512,
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out_channels=512,
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kernel_size=3,
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stride=1,
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padding=1,
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),
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nn.ReLU(inplace=True),
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nn.BatchNorm1d(num_features=512, affine=False),
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nn.Conv1d(
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in_channels=512,
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out_channels=512,
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kernel_size=3,
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stride=1,
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padding=1,
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),
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nn.ReLU(inplace=True),
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nn.BatchNorm1d(num_features=512, affine=False),
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nn.Conv1d(
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in_channels=512,
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out_channels=512,
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kernel_size=3,
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stride=self.subsampling_factor, # stride: subsampling_factor!
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padding=1,
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),
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nn.ReLU(inplace=True),
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nn.BatchNorm1d(num_features=512, affine=False),
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)
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self.ligrus = nn.ModuleList(
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[
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LiGRU(
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input_shape=[None, None, 512],
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hidden_size=512,
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num_layers=1,
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bidirectional=True,
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)
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for _ in range(4)
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]
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)
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self.linears = nn.ModuleList(
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[nn.Linear(in_features=1024, out_features=512) for _ in range(4)]
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)
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self.bnorms = nn.ModuleList(
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[nn.BatchNorm1d(num_features=512, affine=False) for _ in range(4)]
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)
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self.dropout = nn.Dropout(0.2)
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self.linear = nn.Linear(in_features=512, out_features=self.num_classes)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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"""
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Args:
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x:
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Its shape is [N, C, T]
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Returns:
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The output tensor has shape [N, T, C]
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"""
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x = self.tdnn(x)
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x = x.permute(0, 2, 1)
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for ligru, linear, bnorm in zip(self.ligrus, self.linears, self.bnorms):
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x_new, _ = ligru(x)
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x_new = linear(x_new)
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x_new = bnorm(x_new.permute(0, 2, 1)).permute(0, 2, 1)
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# (N, T, C) -> (N, C, T) -> (N, T, C)
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x_new = self.dropout(x_new)
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x = x_new + x # skip connections
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x = self.linear(x)
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x = nn.functional.log_softmax(x, dim=-1)
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return x
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class LiGRU(torch.nn.Module):
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"""This function implements a Light GRU (liGRU).
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This LiGRU model is from speechbrain, please see
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https://github.com/speechbrain/speechbrain/blob/develop/speechbrain/nnet/RNN.py
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LiGRU is single-gate GRU model based on batch-norm + relu
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activations + recurrent dropout. For more info see:
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"M. Ravanelli, P. Brakel, M. Omologo, Y. Bengio,
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Light Gated Recurrent Units for Speech Recognition,
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in IEEE Transactions on Emerging Topics in Computational Intelligence,
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2018" (https://arxiv.org/abs/1803.10225)
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This is a custm RNN and to speed it up it must be compiled with
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the torch just-in-time compiler (jit) right before using it.
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You can compile it with:
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compiled_model = torch.jit.script(model)
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It accepts in input tensors formatted as (batch, time, fea).
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In the case of 4d inputs like (batch, time, fea, channel) the tensor is
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flattened as (batch, time, fea*channel).
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Arguments
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---------
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hidden_size : int
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Number of output neurons (i.e, the dimensionality of the output).
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values (i.e, time and frequency kernel sizes respectively).
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input_shape : tuple
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The shape of an example input.
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nonlinearity : str
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Type of nonlinearity (tanh, relu).
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normalization : str
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Type of normalization for the ligru model (batchnorm, layernorm).
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Every string different from batchnorm and layernorm will result
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in no normalization.
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num_layers : int
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Number of layers to employ in the RNN architecture.
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bias : bool
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If True, the additive bias b is adopted.
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dropout : float
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It is the dropout factor (must be between 0 and 1).
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bidirectional : bool
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If True, a bidirectional model that scans the sequence both
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right-to-left and left-to-right is used.
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Example
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-------
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>>> inp_tensor = torch.rand([4, 10, 20])
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>>> net = LiGRU(input_shape=inp_tensor.shape, hidden_size=5)
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>>> out_tensor, _ = net(inp_tensor)
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>>>
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torch.Size([4, 10, 5])
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"""
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def __init__(
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self,
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hidden_size,
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input_shape,
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nonlinearity="relu",
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normalization="batchnorm",
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num_layers=1,
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bias=True,
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dropout=0.0,
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bidirectional=False,
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):
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super().__init__()
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self.hidden_size = hidden_size
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self.nonlinearity = nonlinearity
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self.num_layers = num_layers
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self.normalization = normalization
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self.bias = bias
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self.dropout = dropout
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self.bidirectional = bidirectional
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self.reshape = False
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# Computing the feature dimensionality
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if len(input_shape) > 3:
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self.reshape = True
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self.fea_dim = float(torch.prod(torch.tensor(input_shape[2:])))
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self.batch_size = input_shape[0]
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self.rnn = self._init_layers()
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def _init_layers(self):
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"""Initializes the layers of the liGRU."""
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rnn = torch.nn.ModuleList([])
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current_dim = self.fea_dim
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for i in range(self.num_layers):
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rnn_lay = LiGRU_Layer(
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current_dim,
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self.hidden_size,
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self.num_layers,
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self.batch_size,
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dropout=self.dropout,
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nonlinearity=self.nonlinearity,
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normalization=self.normalization,
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bidirectional=self.bidirectional,
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)
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rnn.append(rnn_lay)
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if self.bidirectional:
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current_dim = self.hidden_size * 2
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else:
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current_dim = self.hidden_size
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return rnn
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def forward(self, x, hx: Optional[Tensor] = None):
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"""Returns the output of the liGRU.
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Arguments
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---------
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x : torch.Tensor
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The input tensor.
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hx : torch.Tensor
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Starting hidden state.
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"""
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# Reshaping input tensors for 4d inputs
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if self.reshape:
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if x.ndim == 4:
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x = x.reshape(x.shape[0], x.shape[1], x.shape[2] * x.shape[3])
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# run ligru
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output, hh = self._forward_ligru(x, hx=hx)
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return output, hh
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def _forward_ligru(self, x, hx: Optional[Tensor]):
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"""Returns the output of the vanilla liGRU.
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Arguments
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---------
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x : torch.Tensor
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Input tensor.
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hx : torch.Tensor
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"""
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h = []
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if hx is not None:
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if self.bidirectional:
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hx = hx.reshape(
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self.num_layers, self.batch_size * 2, self.hidden_size
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)
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# Processing the different layers
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for i, ligru_lay in enumerate(self.rnn):
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if hx is not None:
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x = ligru_lay(x, hx=hx[i])
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else:
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x = ligru_lay(x, hx=None)
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h.append(x[:, -1, :])
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h = torch.stack(h, dim=1)
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if self.bidirectional:
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h = h.reshape(h.shape[1] * 2, h.shape[0], self.hidden_size)
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else:
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h = h.transpose(0, 1)
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return x, h
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class LiGRU_Layer(torch.nn.Module):
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"""This function implements Light-Gated Recurrent Units (ligru) layer.
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Arguments
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---------
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input_size : int
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Feature dimensionality of the input tensors.
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batch_size : int
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Batch size of the input tensors.
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hidden_size : int
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Number of output neurons.
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num_layers : int
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Number of layers to employ in the RNN architecture.
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nonlinearity : str
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Type of nonlinearity (tanh, relu).
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normalization : str
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Type of normalization (batchnorm, layernorm).
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Every string different from batchnorm and layernorm will result
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in no normalization.
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dropout : float
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It is the dropout factor (must be between 0 and 1).
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bidirectional : bool
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if True, a bidirectional model that scans the sequence both
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right-to-left and left-to-right is used.
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"""
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def __init__(
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self,
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input_size,
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hidden_size,
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num_layers,
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batch_size,
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dropout=0.0,
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nonlinearity="relu",
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normalization="batchnorm",
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bidirectional=False,
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):
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super(LiGRU_Layer, self).__init__()
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self.hidden_size = int(hidden_size)
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self.input_size = int(input_size)
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self.batch_size = batch_size
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self.bidirectional = bidirectional
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self.dropout = dropout
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self.drop = torch.nn.Dropout(p=self.dropout, inplace=False)
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self.N_drop_masks = 16000
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self.drop_mask_cnt = 0
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self.drop_mask_te = torch.tensor([1.0]).float()
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self.w = nn.Linear(self.input_size, 2 * self.hidden_size, bias=False)
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self.u = nn.Linear(self.hidden_size, 2 * self.hidden_size, bias=False)
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# Initializing batch norm
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self.normalize = False
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if normalization == "batchnorm":
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self.norm = nn.BatchNorm1d(2 * self.hidden_size, momentum=0.05)
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self.normalize = True
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elif normalization == "layernorm":
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self.norm = torch.nn.LayerNorm(2 * self.hidden_size)
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self.normalize = True
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else:
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# Normalization is disabled here. self.norm is only formally
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# initialized to avoid jit issues.
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self.norm = torch.nn.LayerNorm(2 * self.hidden_size)
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self.normalize = True
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# Initial state
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self.register_buffer("h_init", torch.zeros(1, self.hidden_size))
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# Setting the activation function
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if nonlinearity == "tanh":
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self.act = torch.nn.Tanh()
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elif nonlinearity == "sin":
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self.act = torch.sin
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elif nonlinearity == "leaky_relu":
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self.act = torch.nn.LeakyReLU()
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else:
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self.act = torch.nn.ReLU()
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def forward(self, x, hx: Optional[Tensor] = None):
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# type: (Tensor, Optional[Tensor]) -> Tensor # noqa F821
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"""Returns the output of the liGRU layer.
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Arguments
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---------
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x : torch.Tensor
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Input tensor.
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"""
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if self.bidirectional:
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x_flip = x.flip(1)
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x = torch.cat([x, x_flip], dim=0)
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# Change batch size if needed
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self._change_batch_size(x)
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# Feed-forward affine transformations (all steps in parallel)
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w = self.w(x)
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# Apply batch normalization
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if self.normalize:
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w_bn = self.norm(w.reshape(w.shape[0] * w.shape[1], w.shape[2]))
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w = w_bn.reshape(w.shape[0], w.shape[1], w.shape[2])
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# Processing time steps
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if hx is not None:
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h = self._ligru_cell(w, hx)
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else:
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h = self._ligru_cell(w, self.h_init)
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if self.bidirectional:
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h_f, h_b = h.chunk(2, dim=0)
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h_b = h_b.flip(1)
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h = torch.cat([h_f, h_b], dim=2)
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return h
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def _ligru_cell(self, w, ht):
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"""Returns the hidden states for each time step.
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Arguments
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---------
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wx : torch.Tensor
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Linearly transformed input.
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"""
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hiddens = []
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# Sampling dropout mask
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drop_mask = self._sample_drop_mask(w)
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# Loop over time axis
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for k in range(w.shape[1]):
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gates = w[:, k] + self.u(ht)
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at, zt = gates.chunk(2, 1)
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zt = torch.sigmoid(zt)
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hcand = self.act(at) * drop_mask
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ht = zt * ht + (1 - zt) * hcand
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hiddens.append(ht)
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# Stacking hidden states
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h = torch.stack(hiddens, dim=1)
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return h
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def _init_drop(self, batch_size):
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"""Initializes the recurrent dropout operation. To speed it up,
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the dropout masks are sampled in advance.
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"""
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self.N_drop_masks = 16000
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self.drop_mask_cnt = 0
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self.register_buffer(
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"drop_masks",
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self.drop(torch.ones(self.N_drop_masks, self.hidden_size)).data,
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)
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self.register_buffer("drop_mask_te", torch.tensor([1.0]).float())
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def _sample_drop_mask(self, w):
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"""Selects one of the pre-defined dropout masks"""
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if self.training:
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# Sample new masks when needed
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if self.drop_mask_cnt + self.batch_size > self.N_drop_masks:
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self.drop_mask_cnt = 0
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self.drop_masks = self.drop(
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torch.ones(
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self.N_drop_masks, self.hidden_size, device=w.device
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)
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).data
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# Sampling the mask
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left_boundary = self.drop_mask_cnt
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right_boundary = self.drop_mask_cnt + self.batch_size
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drop_mask = self.drop_masks[left_boundary:right_boundary]
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self.drop_mask_cnt = self.drop_mask_cnt + self.batch_size
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else:
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self.drop_mask_te = self.drop_mask_te.to(w.device)
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drop_mask = self.drop_mask_te
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return drop_mask
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def _change_batch_size(self, x):
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"""This function changes the batch size when it is different from
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the one detected in the initialization method. This might happen in
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the case of multi-gpu or when we have different batch sizes in train
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and test. We also update the h_int and drop masks.
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"""
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if self.batch_size != x.shape[0]:
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self.batch_size = x.shape[0]
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if self.training:
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self.drop_masks = self.drop(
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torch.ones(
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self.N_drop_masks,
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self.hidden_size,
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device=x.device,
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)
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).data
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