# Audio Embedding

## **Description**

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.




# More Resources

- [Exploring Multimodal Embeddings with FiftyOne and Milvus - Zilliz blog](https://zilliz.com/blog/exploring-multimodal-embeddings-with-fiftyone-and-milvus): This post explored how multimodal embeddings work with Voxel51 and Milvus.
- [How to Get the Right Vector Embeddings - Zilliz blog](https://zilliz.com/blog/how-to-get-the-right-vector-embeddings): A comprehensive introduction to vector embeddings and how to generate them with popular open-source models.
- [Audio Retrieval Based on Milvus - Zilliz blog](https://zilliz.com/blog/audio-retrieval-based-on-milvus): Create an audio retrieval system using Milvus, an open-source vector database. Classify and analyze sound data in real time.
- [Vector Database Use Case: Audio Similarity Search - Zilliz](https://zilliz.com/vector-database-use-cases/audio-similarity-search): Building agile and reliable audio similarity search with Zilliz vector database (fully managed Milvus).
- [Sparse and Dense Embeddings: A Guide for Effective Information Retrieval with Milvus | Zilliz Webinar](https://zilliz.com/event/sparse-and-dense-embeddings-webinar): Zilliz webinar covering what sparse and dense embeddings are and when you'd want to use one over the other.
- [Understanding Neural Network Embeddings - Zilliz blog](https://zilliz.com/learn/understanding-neural-network-embeddings): This article is dedicated to going a bit more in-depth into embeddings/embedding vectors, along with how they are used in modern ML algorithms and pipelines.
- [Sparse and Dense Embeddings: A Guide for Effective Information Retrieval with Milvus | Zilliz Webinar](https://zilliz.com/event/sparse-and-dense-embeddings-webinar/success): Zilliz webinar covering what sparse and dense embeddings are and when you'd want to use one over the other.
- [An Introduction to Vector Embeddings: What They Are and How to Use Them - Zilliz blog](https://zilliz.com/learn/everything-you-should-know-about-vector-embeddings): In this blog post, we will understand the concept of vector embeddings and explore its applications, best practices, and tools for working with embeddings.