# Audio Embedding with Vggish
*Author: Jael Gu*
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## Desription
The audio embedding operator converts an input audio into a dense vector which can be used to represent the audio clip's semantics.
Each vector represents for an audio clip with a fixed length of around 0.9s.
This operator is built on top of [VGGish ](https://github.com/tensorflow/models/tree/master/research/audioset/vggish ) with Pytorch.
The model is a [VGG ](https://arxiv.org/abs/1409.1556 ) variant pre-trained with a large scale of audio dataset [AudioSet ](https://research.google.com/audioset ).
As suggested, it is suitable to extract features at high level or warm up a larger model.
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## Code Example
Generate embeddings for the audio "test.wav".
*Write the pipeline in simplified style*:
```python
import towhee
towhee.glob('test.wav') \
.audio_decode() \
.time_window(range=10) \
.audio_embedding.vggish() \
.show()
```
| [-0.4931737, -0.40068552, -0.032327592, ...] shape=(10, 128) |
*Write a same pipeline with explicit inputs/outputs name specifications:*
```python
import towhee
towhee.glob['path']('test.wav') \
.audio_decode['path', 'audio']() \
.time_window['audio', 'frames'](range=10) \
.audio_embedding.vggish['frames', 'vecs']() \
.select('vecs') \
.to_vec()
```
[array([[-0.4931737 , -0.40068552, -0.03232759, ..., -0.33428153,
0.1333081 , -0.25221825],
[-0.49023268, -0.40161428, -0.03255743, ..., -0.33395663,
0.13261834, -0.25324696],
[-0.4992406 , -0.39848825, -0.03186834, ..., -0.33684137,
0.13326398, -0.25385314],
...,
[-0.49047503, -0.40119144, -0.03144619, ..., -0.33282205,
0.13334712, -0.2520305 ],
[-0.48861542, -0.40097567, -0.03173053, ..., -0.33255234,
0.13278192, -0.25157905],
[-0.4886143 , -0.40098593, -0.03175077, ..., -0.3325425 ,
0.13271847, -0.25159872]], dtype=float32)]
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## 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.
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## Interface
An audio embedding operator generates vectors in numpy.ndarray given an audio file path or towhee audio frames.
**Parameters:**
*Union[str, towhee.types.Audio (a sub-class of numpy.ndarray)]*
The audio path or link in string.
Or audio input data in towhee audio frames.
The input data should represent for an audio longer than 0.9s.
**Returns**:
*numpy.ndarray*
Audio embeddings in shape (num_clips, 128).
Each embedding stands for features of an audio clip with length of 0.9s.
