# 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.