# Audio Classification with PANNS

*Author: Jael Gu*


## Desription

The audio classification operator classify the given audio data with 527 labels from the large-scale [AudioSet dataset](https://research.google.com/audioset/).
The pre-trained model used here is from the paper **PANNs: Large-Scale Pretrained Audio Neural Networks for Audio Pattern Recognition** ([paper link](https://arxiv.org/abs/1912.10211)).

```python
import numpy as np
from towhee import ops

audio_classifier = ops.audio_classification.panns()

# Path or url as input
tags, audio_embedding = audio_classifier("/audio/path/or/url/")

# Audio data as input
audio_data = np.zeros((2, 441344))
sample_rate = 44100
tags, audio_embedding = audio_classifier(audio_data, sample_rate)
```

## Factory Constructor

Create the operator via the following factory method

***ops.audio_classification.panns()***


## Interface

Given an audio (file path, link, or waveform),
the audio classification operator generates a list of labels 
and a vector in numpy.ndarray.


**Parameters:**

​	None.


**Returns**: *numpy.ndarray*

​	labels [(tag, score)], audio embedding in shape (2048,).



## Code Example

Generate embeddings for the audio "test.wav". 

 *Write the pipeline in simplified style*:

```python
from towhee import dc

dc.glob('test.wav')
  .audio_classification.panns()
  .show()
```

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

```python
from towhee import dc

dc.glob['path']('test.wav')
  .audio_classification.panns['path', 'vecs']()
  .select('vecs')
  .show()
```