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rwth-i6 · Jan 8, 2025 · d206dcf · d206dcf
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diff --git a/...oftmax/LBS-960/Figure1_orthosoftmax_jointly_train_five_models_FLOPs_aware_cmp_wise.config b/...oftmax/LBS-960/Figure1_orthosoftmax_jointly_train_five_models_FLOPs_aware_cmp_wise.config
diff --git a/...-softmax/LBS-960/Tab5_orthosoftmax_jointly_train_three_models_FLOPs_aware_cmp_wise.config b/...-softmax/LBS-960/Tab5_orthosoftmax_jointly_train_three_models_FLOPs_aware_cmp_wise.config
diff --git a/2024-orthogonal-softmax/README.md b/2024-orthogonal-softmax/README.md
@@ -4,4 +4,5 @@ Efficient Supernet Training with Orthogonal Softmax for Scalable ASR Model Compr
 
 We use [RETURNN](https://github.com/rwth-i6/returnn) for training and our setups are based on [Sisyphus](https://github.com/rwth-i6/sisyphus).
 
+We use models parts from [i6-models](https://github.com/rwth-i6/i6_models/tree/jing-dynamic-encoder-size)
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Expand Up		@@ -4,4 +4,5 @@ Efficient Supernet Training with Orthogonal Softmax for Scalable ASR Model Compr

		We use [RETURNN](https://github.com/rwth-i6/returnn) for training and our setups are based on [Sisyphus](https://github.com/rwth-i6/sisyphus).

		We use models parts from [i6-models](https://github.com/rwth-i6/i6_models/tree/jing-dynamic-encoder-size)