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audio-embedding
Audio Embedding with Vggish
Author: Jael Gu
Desription
The audio embedding operator converts an input audio into a dense vector which can be used to represent the audio clip's semantics. This operator is built on top of VGGish with Pytorch. The model is a VGG variant pre-trained with a large scale of audio dataset AudioSet. As suggested, it is suitable to extract features at high level or warm up a larger model.
Code Example
Generate embeddings for the audio "test.wav".
Write the pipeline in simplified style:
from towhee import dc
dc.glob('test.wav')
.audio_decode()
.time_window(range=30)
.audio_embedding.vggish()
.show()
Write a same pipeline with explicit inputs/outputs name specifications:
from towhee import dc
dc.glob['path']('test.wav')
.audio_decode['path', 'audio']()
.time_window['audio', 'frames'](range=30)
.audio_embedding.vggish['frames', 'vecs']()
.select('vecs')
.show()
Factory Constructor
Create the operator via the following factory method
audio_embedding.vggish(weights_path=None, framework="pytorch")
Parameters:
weights_path: str
The path to model weights. If None, it will load default model weights.
framework: str
The framework of model implementation. Default value is "pytorch" since the model is implemented in Pytorch.
Interface
An audio embedding operator generates vectors in numpy.ndarray given an audio file path or a towhee audio.
Parameters:
Union[str, towhee.types.Audio]
The audio path or link in string. Or audio input data in towhee audio frames.
Returns:
numpy.ndarray
Audio embeddings in shape (num_clips, 128).
Jael Gu
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.gitattributes |
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README.md |
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__init__.py |
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mel_features.py |
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requirements.txt |
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vggish.pth |
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vggish.py |
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vggish_input.py |
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vggish_params.py |
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