# Audio Classification with PANNS
*Author: [Jael Gu ](https://github.com/jaelgu )*
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## Description
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)).
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## Code Example
Predict labels and generate embeddings given the audio path "test.wav".
*Write a pipeline with explicit inputs/outputs name specifications:*
```python
from towhee import pipe, ops, DataCollection
p = (
pipe.input('path')
.map('path', 'frame', ops.audio_decode.ffmpeg())
.map('frame', ('labels', 'scores', 'vec'), ops.audio_classification.panns())
.output('path', 'labels', 'scores', 'vec')
)
DataCollection(p('./test.wav')).show()
```
< img src = "./result.png" width = "800px" / >
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## Factory Constructor
Create the operator via the following factory method
***audio_classification.panns(weights_path=None, framework='pytorch',
sample_rate=32000, topk=5)***
**Parameters:**
*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.
*sample_rate: int*
The target sample rate of audio data after convention, defaults to 32000.
*topk: int*
The number of labels & corresponding scores to be returned, sorting by possibility from high to low.
Default value is 5.
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## 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 2s.
**Returns**:
*labels, scores, vec: Tuple(List[str], List(float), numpy.ndarray)*
- labels: a list of topk predicted labels by model.
- scores: a list of scores corresponding to labels, representing for possibility.
- vec: a audio embedding generated by model, shape of which is (2048,)
