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104 lines
3.4 KiB
104 lines
3.4 KiB
3 years ago
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# 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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import os
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import sys
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import time
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import paddle
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from mdtc import KWSModel
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from mdtc import MDTC
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from tqdm import tqdm
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from paddleaudio.datasets import HeySnips
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def collate_features(batch):
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# (key, feat, label) in one sample
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collate_start = time.time()
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keys = []
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feats = []
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labels = []
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lengths = []
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for sample in batch:
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keys.append(sample[0])
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feats.append(sample[1])
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labels.append(sample[2])
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lengths.append(sample[1].shape[0])
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max_length = max(lengths)
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for i in range(len(feats)):
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feats[i] = paddle.nn.functional.pad(
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feats[i], [0, max_length - feats[i].shape[0], 0, 0],
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data_format='NLC')
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return keys, paddle.stack(feats), paddle.to_tensor(
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labels), paddle.to_tensor(lengths)
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if __name__ == '__main__':
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# Dataset
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feat_conf = {
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# 'n_mfcc': 80,
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'n_mels': 80,
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'frame_shift': 10,
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'frame_length': 25,
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# 'dither': 1.0,
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}
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test_ds = HeySnips(
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mode='test', feat_type='kaldi_fbank', sample_rate=16000, **feat_conf)
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test_sampler = paddle.io.BatchSampler(
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test_ds, batch_size=32, 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=16,
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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 = MDTC(
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stack_num=3,
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stack_size=4,
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in_channels=80,
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res_channels=32,
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kernel_size=5,
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causal=True, )
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model = KWSModel(backbone=backbone, num_keywords=1)
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model = paddle.DataParallel(model)
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# kws_checkpoint = '/ssd3/chenxiaojie06/PaddleSpeech/DeepSpeech/paddlespeech/kws/models/checkpoint/epoch_10_0.8903940343290826/model.pdparams'
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kws_checkpoint = os.path.join(
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os.path.abspath(sys.argv[1]), 'model.pdparams')
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model.set_state_dict(paddle.load(kws_checkpoint))
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model.eval()
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score_abs_path = os.path.join(os.path.abspath(sys.argv[1]), 'score.txt')
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with paddle.no_grad(), open(score_abs_path, 'w', encoding='utf8') as fout:
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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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fout.write(
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'{} {} {}\n'.format(key, keyword_i, score_frames))
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print('Scores saved to: {}'.format(score_abs_path))
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