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  This model generate a short textual description of any audio file.
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  ```bash
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  pip install conette
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  ```
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  ```py
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  from conette import CoNeTTEConfig, CoNeTTEModel
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  config = CoNeTTEConfig.from_pretrained("Labbeti/conette")
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  model = CoNeTTEModel.from_pretrained("Labbeti/conette", config=config)
 
 
 
 
 
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  ```
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  It was created by [@Labbeti](https://hf.co/Labbeti).
 
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  This model generate a short textual description of any audio file.
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+ ## Installation
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  ```bash
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  pip install conette
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  ```
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+ ## Usage
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  ```py
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  from conette import CoNeTTEConfig, CoNeTTEModel
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  config = CoNeTTEConfig.from_pretrained("Labbeti/conette")
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  model = CoNeTTEModel.from_pretrained("Labbeti/conette", config=config)
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+
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+ path = "/my/path/to/audio.wav"
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+ outputs = model(path)
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+ cands = outputs["cands"][0]
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+ print(cands)
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  ```
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+ ## Performance
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+ TODO
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+
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+ ## Additional information
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+
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+ The encoder part of the architecture is based on a ConvNeXt model for audio classification, available here: https://huggingface.co/topel/ConvNeXt-Tiny-AT.
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+ The encoder weights used are named "convnext_tiny_465mAP_BL_AC_70kit.pth", available on Zenodo: https://zenodo.org/record/8020843.
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+
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  It was created by [@Labbeti](https://hf.co/Labbeti).