# Audio Embedding with Vggish
*Author: [Jael Gu ](https://github.com/jaelgu )*
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## Description
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 a pipeline with explicit inputs/outputs name specifications:*
```python
from towhee import pipe, ops, DataCollection
p = (
pipe.input('path')
.map('path', 'frame', ops.audio_decode.ffmpeg())
.map('frame', 'vecs', ops.audio_embedding.vggish())
.output('path', 'vecs')
)
DataCollection(p('./test.wav')).show()
```
< img src = "./result.png" width = "800px" / >
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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 towhee audio frames.
**Parameters:**
*data: List[towhee.types.audio_frame.AudioFrame]*
Input audio data is a list of 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.
