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-# panns
+# 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()
+```
+