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Title: GitHub - LightricksResearch/ast: Code for the Interspeech 2021 paper "AST: Audio Spectrogram Transformer".

Open Graph Title: GitHub - LightricksResearch/ast: Code for the Interspeech 2021 paper "AST: Audio Spectrogram Transformer".

X Title: GitHub - LightricksResearch/ast: Code for the Interspeech 2021 paper "AST: Audio Spectrogram Transformer".

Description: Code for the Interspeech 2021 paper "AST: Audio Spectrogram Transformer". - LightricksResearch/ast

Open Graph Description: Code for the Interspeech 2021 paper "AST: Audio Spectrogram Transformer". - LightricksResearch/ast

X Description: Code for the Interspeech 2021 paper "AST: Audio Spectrogram Transformer". - LightricksResearch/ast

Mail addresses
yuangong@mit.edu

Opengraph URL: https://github.com/LightricksResearch/ast

X: @github

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AST_ Audio Spectrogram Transformer _ Papers With Code.pdfhttps://patch-diff.githubusercontent.com/LightricksResearch/ast/blob/master/AST_%20Audio%20Spectrogram%20Transformer%20_%20Papers%20With%20Code.pdf
AST_ Audio Spectrogram Transformer _ Papers With Code.pdfhttps://patch-diff.githubusercontent.com/LightricksResearch/ast/blob/master/AST_%20Audio%20Spectrogram%20Transformer%20_%20Papers%20With%20Code.pdf
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https://patch-diff.githubusercontent.com/LightricksResearch/ast#ast-audio-spectrogram-transformer
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Introductionhttps://patch-diff.githubusercontent.com/LightricksResearch/ast#Introduction
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ESC-50 Recipehttps://patch-diff.githubusercontent.com/LightricksResearch/ast#ESC-50-Recipe
Speechcommands Recipehttps://patch-diff.githubusercontent.com/LightricksResearch/ast#Speechcommands-V2-Recipe
AudioSet Recipehttps://patch-diff.githubusercontent.com/LightricksResearch/ast#Audioset-Recipe
Pretrained Modelshttps://patch-diff.githubusercontent.com/LightricksResearch/ast#Pretrained-Models
Use Pretrained Model For Downstream Taskshttps://patch-diff.githubusercontent.com/LightricksResearch/ast#Use-Pretrained-Model-For-Downstream-Tasks
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[here]https://github.com/YuanGongND/ltu
https://colab.research.google.com/github/YuanGongND/ast/blob/master/colab/AST_Inference_Demo.ipynb
bughttps://github.com/YuanGongND/ast/issues/58
herehttps://colab.research.google.com/github/YuanGongND/ast/blob/master/colab/torchaudio_SpecMasking_1_1.ipynb
CMKD: CNN/Transformer-Based Cross-Model Knowledge Distillation for Audio Classificationhttps://arxiv.org/abs/2203.06760
Self-Supervised AST (SSAST)https://arxiv.org/abs/2110.09784
[here]https://github.com/YuanGongND/ssast
PSLA training pipelinehttps://arxiv.org/abs/2102.01243
[here]https://github.com/YuanGongND/psla
https://patch-diff.githubusercontent.com/LightricksResearch/ast#introduction
https://github.com/YuanGongND/ast/blob/master/ast.png?raw=true
AST: Audio Spectrogram Transformerhttps://arxiv.org/abs/2104.01778
ISCA SIGML talkhttps://www.youtube.com/watch?v=CSRDbqGY0Vw
https://colab.research.google.com/github/YuanGongND/ast/blob/master/colab/AST_Inference_Demo.ipynb
https://patch-diff.githubusercontent.com/LightricksResearch/ast#citing
https://patch-diff.githubusercontent.com/LightricksResearch/ast#getting-started
herehttps://github.com/YuanGongND/ast/blob/102f0477099f83e04f6f2b30a498464b78bbaf46/src/dataloader.py#L191
https://colab.research.google.com/github/YuanGongND/ast/blob/master/colab/AST_Inference_Demo.ipynb
https://patch-diff.githubusercontent.com/LightricksResearch/ast#esc-50-recipe
https://patch-diff.githubusercontent.com/LightricksResearch/ast#speechcommands-v2-recipe
https://patch-diff.githubusercontent.com/LightricksResearch/ast#audioset-recipe
PSLA paperhttps://arxiv.org/abs/2102.01243
PSLA paperhttps://arxiv.org/abs/2102.01243
https://patch-diff.githubusercontent.com/LightricksResearch/ast#pretrained-models
Full AudioSet, 10 tstride, 10 fstride, with Weight Averaging (0.459 mAP)https://www.dropbox.com/s/ca0b1v2nlxzyeb4/audioset_10_10_0.4593.pth?dl=1
Full AudioSet, 10 tstride, 10 fstride, without Weight Averaging, Model 1 (0.450 mAP)https://www.dropbox.com/s/1tv0hovue1bxupk/audioset_10_10_0.4495.pth?dl=1
Full AudioSet, 10 tstride, 10 fstride, without Weight Averaging, Model 2 (0.448 mAP)https://www.dropbox.com/s/6u5sikl4b9wo4u5/audioset_10_10_0.4483.pth?dl=1
Full AudioSet, 10 tstride, 10 fstride, without Weight Averaging, Model 3 (0.448 mAP)https://www.dropbox.com/s/kt6i0v9fvfm1mbq/audioset_10_10_0.4475.pth?dl=1
Full AudioSet, 12 tstride, 12 fstride, without Weight Averaging, Model (0.447 mAP)https://www.dropbox.com/s/snfhx3tizr4nuc8/audioset_12_12_0.4467.pth?dl=1
Full AudioSet, 14 tstride, 14 fstride, without Weight Averaging, Model (0.443 mAP)https://www.dropbox.com/s/z18s6pemtnxm4k7/audioset_14_14_0.4431.pth?dl=1
Full AudioSet, 16 tstride, 16 fstride, without Weight Averaging, Model (0.442 mAP)https://www.dropbox.com/s/mdsa4t1xmcimia6/audioset_16_16_0.4422.pth?dl=1
Speechcommands V2-35, 10 tstride, 10 fstride, without Weight Averaging, Model (98.12% accuracy on evaluation set)https://www.dropbox.com/s/q0tbqpwv44pquwy/speechcommands_10_10_0.9812.pth?dl=1
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https://patch-diff.githubusercontent.com/LightricksResearch/ast#use-pretrained-model-for-downstream-tasks
PSLA paperhttps://arxiv.org/abs/2102.01243
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