The audio embedding pipeline 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 with Pytorch.
## Code Example
- Create audio embedding pipeline with the default configuration.
```python
from towhee import AutoPipes
p = AutoPipes.pipeline('audio-embedding')
res = p('test.wav')
res.get()
```
## **Interface**
**AudioEmbeddingConfig**
> You can find some parameters in [audio_decode.ffmpeg](https://towhee.io/audio-decode/ffmpeg) and [audio_embedding.vggish](https://towhee.io/audio-embedding/vggish) operators.
***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.
***device***: int
The number of GPU device, defaults to -1, which means using CPU.
