# Audio Embedding with CLMR
*Author: [Jael Gu ](https://github.com/jaelgu )*
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## 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 2s.
This operator is built on top of the original implementation of [CLMR ](https://github.com/Spijkervet/CLMR ).
The [default model weight ](clmr_checkpoint_10000.pt ) provided is pretrained on [Magnatagatune Dataset ](https://paperswithcode.com/dataset/magnatagatune ) with [SampleCNN ](sample_cnn.py ).
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## Code Example
Generate embeddings for the audio "test.wav".
*Write a pipeline with explicit inputs/outputs name specifications:*
```python
```
< img src = "./result.png" width = "800px" / >
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## Factory Constructor
Create the operator via the following factory method
***audio_embedding.clmr(framework="pytorch")***
**Parameters:**
*framework: str*
The framework of model implementation.
Default value is "pytorch" since the model is implemented in Pytorch.
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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 3s.
**Returns**:
*numpy.ndarray*
Audio embeddings in shape (num_clips, 512).
Each embedding stands for features of an audio clip with length of 2.7s.
