Pipeline: Audio Embedding using CLMR

Authors: Jael Gu

Overview

The pipeline uses a pre-trained CLMR model to extract embeddings of a given audio. It first transforms the input audio to a wave file with sample rate of 22050. Then the model splits the audio data into shorter clips with a fixed length. Finally it generates vectors of each clip, which composes the fingerprint of the input audio.

Interface

Input Arguments:

• filepath:
  • the input audio
  • supported types: str (path to the audio)

Pipeline Output:

The Operator returns a tuple Tuple[('embs', numpy.ndarray)] containing following fields:

• embs:
  • embeddings of input audio
  • data type: numpy.ndarray
  • shape: (num_clips,512)

How to use

1. Install Towhee

$ pip3 install towhee

You can refer to Getting Started with Towhee for more details. If you have any questions, you can submit an issue to the towhee repository.

2. Run it with Towhee

>>> from towhee import pipeline

>>> embedding_pipeline = pipeline('towhee/audio-embedding-clmr')
>>> embedding = embedding_pipeline('path/to/your/audio')

How it works

This pipeline includes a main operator: audio embedding (implemented as towhee/clmr-magnatagatune). The audio embedding operator encodes fixed-length clips of an audio data and finally output a set of vectors of the given audio.