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91 lines
3.4 KiB
91 lines
3.4 KiB
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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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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# Modified from wekws(https://github.com/wenet-e2e/wekws)
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import paddle
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from tqdm import tqdm
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from yacs.config import CfgNode
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from paddlespeech.kws.exps.mdtc.collate import collate_features
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from paddlespeech.kws.models.mdtc import KWSModel
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from paddlespeech.s2t.training.cli import default_argument_parser
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from paddlespeech.s2t.utils.dynamic_import import dynamic_import
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if __name__ == '__main__':
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parser = default_argument_parser()
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parser.add_argument(
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"--ckpt",
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type=str,
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required=True,
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help='model checkpoint for evaluation.')
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parser.add_argument(
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"--score_file",
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type=str,
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default='./scores.txt',
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help='output file of trigger scores')
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args = parser.parse_args()
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# https://yaml.org/type/float.html
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config = CfgNode(new_allowed=True)
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if args.config:
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config.merge_from_file(args.config)
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# Dataset
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ds_class = dynamic_import(config['dataset'])
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test_ds = ds_class(
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data_dir=config['data_dir'],
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mode='test',
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feat_type=config['feat_type'],
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sample_rate=config['sample_rate'],
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frame_shift=config['frame_shift'],
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frame_length=config['frame_length'],
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n_mels=config['n_mels'], )
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test_sampler = paddle.io.BatchSampler(
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test_ds, batch_size=config['batch_size'], drop_last=False)
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test_loader = paddle.io.DataLoader(
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test_ds,
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batch_sampler=test_sampler,
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num_workers=config['num_workers'],
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return_list=True,
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use_buffer_reader=True,
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collate_fn=collate_features, )
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# Model
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backbone_class = dynamic_import(config['backbone'])
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backbone = backbone_class(
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stack_num=config['stack_num'],
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stack_size=config['stack_size'],
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in_channels=config['in_channels'],
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res_channels=config['res_channels'],
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kernel_size=config['kernel_size'], )
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model = KWSModel(backbone=backbone, num_keywords=config['num_keywords'])
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model.set_state_dict(paddle.load(args.ckpt))
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model.eval()
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with paddle.no_grad(), open(args.score_file, 'w', encoding='utf8') as f:
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for batch_idx, batch in enumerate(
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tqdm(test_loader, total=len(test_loader))):
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keys, feats, labels, lengths = batch
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logits = model(feats)
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num_keywords = logits.shape[2]
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for i in range(len(keys)):
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key = keys[i]
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score = logits[i][:lengths[i]]
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for keyword_i in range(num_keywords):
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keyword_scores = score[:, keyword_i]
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score_frames = ' '.join(
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['{:.6f}'.format(x) for x in keyword_scores.tolist()])
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f.write('{} {} {}\n'.format(key, keyword_i, score_frames))
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print('Result saved to: {}'.format(args.score_file))
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