127 lines
4.3 KiB
Python
127 lines
4.3 KiB
Python
# Copyright (c) 2021 Binbin Zhang
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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 sys
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from typing import Optional
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import torch
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from kws.model.cmvn import GlobalCMVN
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from kws.model.subsampling import LinearSubsampling1, Conv1dSubsampling1
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from kws.model.tcn import TCN, CnnBlock, DsCnnBlock
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from kws.model.mdtc import MDTC
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from kws.utils.cmvn import load_cmvn
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class KWSModel(torch.nn.Module):
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"""Our model consists of four parts:
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1. global_cmvn: Optional, (idim, idim)
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2. preprocessing: feature dimention projection, (idim, hdim)
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3. backbone: backbone or feature extractor of the whole network, (hdim, hdim)
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4. classifier: output layer or classifier of KWS model, (hdim, odim)
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"""
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def __init__(
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self,
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idim: int,
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odim: int,
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hdim: int,
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global_cmvn: Optional[torch.nn.Module],
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preprocessing: Optional[torch.nn.Module],
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backbone: torch.nn.Module,
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):
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super().__init__()
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self.idim = idim
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self.odim = odim
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self.hdim = hdim
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self.global_cmvn = global_cmvn
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self.preprocessing = preprocessing
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self.backbone = backbone
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self.classifier = torch.nn.Linear(hdim, odim)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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if self.global_cmvn is not None:
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x = self.global_cmvn(x)
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if self.preprocessing:
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x = self.preprocessing(x)
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x, _ = self.backbone(x)
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x = self.classifier(x)
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return x
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def init_model(configs):
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cmvn = configs.get('cmvn', {})
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if 'cmvn_file' in cmvn and cmvn['cmvn_file'] is not None:
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mean, istd = load_cmvn(cmvn['cmvn_file'])
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global_cmvn = GlobalCMVN(
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torch.from_numpy(mean).float(),
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torch.from_numpy(istd).float(),
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cmvn['norm_var'],
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)
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else:
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global_cmvn = None
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input_dim = configs['input_dim']
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output_dim = configs['output_dim']
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hidden_dim = configs['hidden_dim']
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prep_type = configs['preprocessing']['type']
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if prep_type == 'linear':
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preprocessing = LinearSubsampling1(input_dim, hidden_dim)
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elif prep_type == 'cnn1d_s1':
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preprocessing = Conv1dSubsampling1(input_dim, hidden_dim)
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elif prep_type == 'none':
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preprocessing = None
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else:
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print('Unknown preprocessing type {}'.format(prep_type))
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sys.exit(1)
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backbone_type = configs['backbone']['type']
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if backbone_type == 'gru':
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num_layers = configs['backbone']['num_layers']
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backbone = torch.nn.GRU(hidden_dim,
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hidden_dim,
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num_layers=num_layers,
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batch_first=True)
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elif backbone_type == 'tcn':
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# Depthwise Separable
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num_layers = configs['backbone']['num_layers']
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ds = configs['backbone'].get('ds', False)
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if ds:
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block_class = DsCnnBlock
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else:
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block_class = CnnBlock
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kernel_size = configs['backbone'].get('kernel_size', 8)
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dropout = configs['backbone'].get('drouput', 0.1)
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backbone = TCN(num_layers, hidden_dim, kernel_size, dropout,
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block_class)
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elif backbone_type == 'mdtc':
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stack_size = configs['backbone']['stack_size']
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num_stack = configs['backbone']['num_stack']
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kernel_size = configs['backbone']['kernel_size']
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hidden_dim = configs['backbone']['hidden_dim']
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backbone = MDTC(num_stack,
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stack_size,
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input_dim,
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hidden_dim,
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kernel_size,
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causal=True)
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else:
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print('Unknown body type {}'.format(backbone_type))
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sys.exit(1)
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kws_model = KWSModel(input_dim, output_dim, hidden_dim, global_cmvn,
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preprocessing, backbone)
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return kws_model
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