# Audio Embedding with Neural Network Fingerprint

*Author: [Jael Gu](https://github.com/jaelgu)*

<br />

## 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](https://arxiv.org/abs/2010.11910).
The model is implemented in Pytorch.
We've also trained the nnfp model with [FMA dataset](https://github.com/mdeff/fma) (& some noise audio) and shared weights in this operator.
The nnfp operator is suitable to generate audio fingerprints.

<br />

## Code Example

Generate embeddings for the audio "test.wav". 

*Write the pipeline in simplified style*:

```python
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()
)
```
<img src="./result1.png" width="800px"/>

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

```python
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()
)
```
<img src="./result2.png" width="800px"/>

<br />

## 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.

<br />

## 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 input data should represent for an audio longer than 1s.


**Returns**:

*numpy.ndarray*

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