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73 lines
2.5 KiB
73 lines
2.5 KiB
# Copyright (c) 2021 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 argparse
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import os
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import paddle
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import paddle.nn.functional as F
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import yaml
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from paddlespeech.audio.backends import load as load_audio
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from paddlespeech.audio.features import LogMelSpectrogram
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from paddlespeech.audio.utils import logger
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from paddlespeech.cls.models import SoundClassifier
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from paddlespeech.utils.dynamic_import import dynamic_import
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# yapf: disable
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parser = argparse.ArgumentParser(__doc__)
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parser.add_argument("--cfg_path", type=str, required=True)
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args = parser.parse_args()
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# yapf: enable
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def extract_features(file: str, **feat_conf) -> paddle.Tensor:
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file = os.path.abspath(os.path.expanduser(file))
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waveform, _ = load_audio(file, sr=feat_conf['sr'])
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feature_extractor = LogMelSpectrogram(**feat_conf)
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feat = feature_extractor(paddle.to_tensor(waveform).unsqueeze(0))
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feat = paddle.transpose(feat, [0, 2, 1])
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return feat
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if __name__ == '__main__':
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args.cfg_path = os.path.abspath(os.path.expanduser(args.cfg_path))
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with open(args.cfg_path, 'r') as f:
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config = yaml.safe_load(f)
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model_conf = config['model']
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data_conf = config['data']
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feat_conf = config['feature']
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predicting_conf = config['predicting']
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ds_class = dynamic_import(data_conf['dataset'])
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backbone_class = dynamic_import(model_conf['backbone'])
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model = SoundClassifier(
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backbone=backbone_class(pretrained=False, extract_embedding=True),
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num_class=len(ds_class.label_list))
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model.set_state_dict(paddle.load(predicting_conf['checkpoint']))
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model.eval()
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feat = extract_features(predicting_conf['audio_file'], **feat_conf)
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logits = model(feat)
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probs = F.softmax(logits, axis=1).numpy()
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sorted_indices = (-probs[0]).argsort()
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msg = f"[{predicting_conf['audio_file']}]\n"
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for idx in sorted_indices[:predicting_conf['top_k']]:
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msg += f'{ds_class.label_list[idx]}: {probs[0][idx]}\n'
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logger.info(msg)
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