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.

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 and 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.