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README.md
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# Wav2Small2.0 - Arousal / Dominance / Valence
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Please note that this model is for research purpose only. A commercial [license](https://www.audeering.com/products/devaice/) can be acquired with audEERING. The model expects a raw audio signal 16KHz as input, and outputs: arousal, dominance valence in range [0, 1]
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# How To
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```python
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import torch
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import numpy as np
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import torch.nn.functional as F
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import librosa
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from transformers
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from torch import nn
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real = self.conv_real(x)
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imag = self.conv_imag(x)
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return real ** 2 + imag ** 2 # bs,
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class LogmelFilterBank(nn.Module):
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# Wav2Small2.0 - Arousal / Dominance / Valence
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Please note that this model is for research purpose only. A commercial [license](https://www.audeering.com/products/devaice/) can be acquired with audEERING. The model expects a raw audio signal 16KHz as input, and outputs: arousal, dominance valence in range [0, 1]. The model is created following the [Wav2Small paper](https://arxiv.org/abs/2408.13920) and has a total of 17K params.
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# How To
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```python
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import torch
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import numpy as np
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import librosa
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from transformers import Wav2Vec2PreTrainedModel, PretrainedConfig
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from torch import nn
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real = self.conv_real(x)
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imag = self.conv_imag(x)
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return real ** 2 + imag ** 2 # bs, freq, time-frames
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class LogmelFilterBank(nn.Module):
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