Audio Embedding with Neural Network Fingerprint

Author: Jael Gu

Description

The audio embedding operator 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 1s. This operator generates audio embeddings with fingerprinting method introduced by Neural Audio Fingerprint. The model is implemented in Pytorch. We've also trained the nnfp model with FMA dataset (& some noise audio) and shared weights in this operator. The nnfp operator is suitable for audio fingerprinting.

Code Example

Generate embeddings for the audio "test.wav".

Write the pipeline in simplified style:

import towhee

(
    towhee.glob('test.wav')
          .audio_decode.ffmpeg()
          .runas_op(func=lambda x:[y[0] for y in x])
          .audio_embedding.nnfp()  # use default model
          .show()
)

Write a same pipeline with explicit inputs/outputs name specifications:

import towhee

(
    towhee.glob['path']('test.wav')
          .audio_decode.ffmpeg['path', 'frames']()
          .runas_op['frames', 'frames'](func=lambda x:[y[0] for y in x])
          .audio_embedding.nnfp['frames', 'vecs']()
          .select['path', 'vecs']()
          .show()
)

Factory Constructor

Create the operator via the following factory method

audio_embedding.nnfp(params=None, checkpoint_path=None, framework='pytorch')

Parameters:

params: dict

A dictionary of model parameters. If None, it will use default parameters to create model.

checkpoint_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.

Interface

An audio embedding operator generates vectors in numpy.ndarray given towhee audio frames.

Parameters:

data: List[towhee.types.audio_frame.AudioFrame]

Input audio data is a list of towhee audio frames. The audio input should be at least 1s.

Returns:

numpy.ndarray

Audio embeddings in shape (num_clips, 128). Each embedding stands for features of an audio clip with length of 1s.