vggish
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80 lines
2.5 KiB
80 lines
2.5 KiB
# Copyright 2021 Zilliz. 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 logging
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import warnings
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import os
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import sys
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import numpy
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from pathlib import Path
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from typing import Union, List, NamedTuple
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import torch
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from towhee.operator.base import NNOperator
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from towhee.models.vggish.torch_vggish import VGG
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from towhee import register
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sys.path.append(str(Path(__file__).parent))
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import vggish_input
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warnings.filterwarnings('ignore')
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log = logging.getLogger()
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AudioOutput = NamedTuple('AudioOutput', [('vec', 'ndarray')])
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class Vggish(NNOperator):
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"""
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"""
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def __init__(self, weights_path: str = None, framework: str = 'pytorch') -> None:
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super().__init__(framework=framework)
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self.device = "cuda" if torch.cuda.is_available() else "cpu"
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self.model = VGG()
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if not weights_path:
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path = str(Path(__file__).parent)
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weights_path = os.path.join(path, 'vggish.pth')
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state_dict = torch.load(weights_path, map_location=torch.device('cpu'))
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self.model.load_state_dict(state_dict)
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self.model.eval()
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self.model.to(self.device)
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def __call__(self, datas: List[NamedTuple('data', [('audio', 'ndarray'), ('sample_rate', 'int')])]) -> numpy.ndarray:
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audios = numpy.hstack([item.audio for item in datas])
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sr = datas[0].sample_rate
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audio_array = numpy.stack(audios)
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audio_tensors = self.preprocess(audio_array, sr).to(self.device)
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features = self.model(audio_tensors)
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outs = features.to("cpu")
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return [AudioOutput(outs.detach().numpy())]
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def preprocess(self, audio: Union[str, numpy.ndarray], sr: int = None):
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audio = audio.transpose()
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return vggish_input.waveform_to_examples(audio, sr, return_tensor=True)
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# if __name__ == '__main__':
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# encoder = Vggish()
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#
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# # audio_path = '/path/to/audio'
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# # vec = encoder(audio_path)
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#
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# audio_data = numpy.zeros((2, 441344))
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# sample_rate = 44100
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# vec = encoder(audio_data, sample_rate)
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# print(vec)
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