# 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 the VGGish model with Pytorch.
It is originally implemented in [Tensorflow](https://github.com/tensorflow/models/tree/master/research/audioset/vggish).
The model is 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.

```python
import numpy as np
from towhee import ops

audio_encoder = ops.audio_embedding.vggish()

# Path or url as input
audio_embedding = audio_encoder("/audio/path/or/url/")

# Audio data as input
audio_data = np.zeros((2, 441344))
sample_rate = 44100
audio_embedding = audio_encoder(audio_data, sample_rate)
```

## Factory Constructor

Create the operator via the following factory method

***ops.audio_embedding.vggish()***


## Interface

An audio embedding operator generates vectors in numpy.ndarray given an audio file path.


**Parameters:**

​	None.


**Returns**: *numpy.ndarray*

​	Audio embeddings in shape (num_clips, 128).



## Code Example

Generate embeddings for the audio "test.wav". 

 *Write the pipeline in simplified style*:

```python
from towhee import dc

dc.glob('test.wav')
  .audio_embedding.vggish()
  .show()
```

*Write a same pipeline with explicit inputs/outputs name specifications:*

```python
from towhee import dc

dc.glob['path']('test.wav')
  .audio_embedding.vggish['path', 'vecs']()
  .select('vecs')
  .show()
```