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# 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 numpy as np
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import paddle
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import paddle.nn.functional as F
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from paddleaudio.backends import load as load_audio
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from paddleaudio.datasets import ESC50
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from paddleaudio.features import LogMelSpectrogram
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from paddleaudio.features import melspectrogram
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from paddlespeech.cls.models import cnn14
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from paddlespeech.cls.models import SoundClassifier
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# yapf: disable
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parser = argparse.ArgumentParser(__doc__)
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parser.add_argument("--wav", type=str, required=True, help="Audio file to infer.")
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parser.add_argument("--feat_backend", type=str, choices=['numpy', 'paddle'], default='numpy', help="Choose backend to extract features from audio files.")
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parser.add_argument("--top_k", type=int, default=1, help="Show top k predicted results")
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parser.add_argument("--checkpoint", type=str, required=True, help="Checkpoint of model.")
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args = parser.parse_args()
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# yapf: enable
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def extract_features(file: str, feat_backend: str='numpy',
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**kwargs) -> paddle.Tensor:
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waveform, sr = load_audio(file, sr=None)
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if args.feat_backend == 'numpy':
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feat = melspectrogram(waveform, sr, **kwargs).transpose()
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feat = np.expand_dims(feat, 0)
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feat = paddle.to_tensor(feat)
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else:
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feature_extractor = LogMelSpectrogram(sr=sr, **kwargs)
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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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model = SoundClassifier(
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backbone=cnn14(pretrained=False, extract_embedding=True),
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num_class=len(ESC50.label_list))
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model.set_state_dict(paddle.load(args.checkpoint))
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model.eval()
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feat = extract_features(args.wav, args.feat_backend)
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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'[{args.wav}]\n'
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for idx in sorted_indices[:args.top_k]:
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msg += f'{ESC50.label_list[idx]}: {probs[0][idx]}\n'
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print(msg)
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