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tinyCLAP

contrastive learning
CLAP
audio classification
zero-shot classification
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@@ -81,9 +81,10 @@ This command will automatically download the checkpoint, if present in the zoo o
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  A list of available models with their computational cost is described in the follwing table:
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- | alpha | beta | t0 | N | Params [M] | ESC-50 | UrbanSound8K | TUT17 |
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- |:-----:|:----:|:--:|:-:|:----------:|:------:|:------------:|:-----:|
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- | 1.5 | 0.75 | 6 | 7 | 4.4 | 77.3% | 69.7% | 21.9% |
 
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  **Note**: I am currently computing the confidence intervals for these results. Due to seed variability and CUDA version mismatch, the numbers might be slightly different from those reported in the paper.
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  The original paper's checkpoints are available through [this link](https://www.dropbox.com/scl/fi/e3aj76vxwlb4w6hs3mclv/tinyCLAP_results.zip?rlkey=7fl426tz1vf686oyosvja8i9s&dl=0).
 
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  A list of available models with their computational cost is described in the follwing table:
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+ | audioenc_name_student | Params [M] | ESC-50 | UrbanSound8K | TUT17 |
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+ |:-----:|:----------:|:------:|:------------:|:-----:|
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+ | Cnn14 | 82.8 | 81.3% | 72.3% | 23.7% |
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+ | phinet_alpha_1.50_beta_0.75_t0_6_N_7 | 4.4 | 77.3% | 69.7% | 21.9% |
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  **Note**: I am currently computing the confidence intervals for these results. Due to seed variability and CUDA version mismatch, the numbers might be slightly different from those reported in the paper.
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  The original paper's checkpoints are available through [this link](https://www.dropbox.com/scl/fi/e3aj76vxwlb4w6hs3mclv/tinyCLAP_results.zip?rlkey=7fl426tz1vf686oyosvja8i9s&dl=0).