# Audio Embedding with data2vec
*author: David Wang*
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## Description
This operator extracts features for audio with [data2vec ](https://arxiv.org/abs/2202.03555 ). The core idea is to predict latent representations of the full input data based on a masked view of the input in a self-distillation setup using a standard Transformer architecture.
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## Code Example
Generate embeddings for the audio "test.wav".
*Write a pipeline with explicit inputs/outputs name specifications:*
```python
from towhee.dc2 import pipe, ops, DataCollection
p = (
pipe.input('path')
.map('path', 'frame', ops.audio_decode.ffmpeg(sample_rate=16000))
.map('frame', 'vecs', ops.audio_embedding.data2vec(model_name='facebook/data2vec-audio-base-960h'))
.output('path', 'vecs')
)
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
***data2vec(model_name='facebook/data2vec-audio-base')***
**Parameters:**
** *model_name***: *str*
The model name in string.
The default value is "facebook/data2vec-audio-base-960h".
Supported model name:
-
- facebook/data2vec-audio-base-960h
- facebook/data2vec-audio-large-960h
- facebook/data2vec-audio-base
- facebook/data2vec-audio-base-100h
- facebook/data2vec-audio-base-10m
- facebook/data2vec-audio-large
- facebook/data2vec-audio-large-100h
- facebook/data2vec-audio-large-10m
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## Interface
An audio embedding operator generates vectors in numpy.ndarray given an audio file path or 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 0.9s.
**Returns:** *numpy.ndarray*
The audio embedding extracted by model.
