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README.md

@@ -1,57 +1,61 @@
 ---
-language:
-- en
-license: apache-2.0
-tags:
-- hearing loss
-- challenge
-- signal processing
-- source separation
-- audio
-- audio-to-audio
-- Causal
+{}
 ---
 
-# Cadenza Challenge: CAD2-Task1
-
-A Causal Clarinet/Others separation model for the CAD2-Task2 baseline system.
-
-* Architecture: ConvTasNet (Kaituo XU) with multichannel support (Alexandre Defossez).
-* Parameters:
-  * B: 256
-  * C: 2
-  * H: 512
-  * L: 20
-  * N: 256
-  * P: 3
-  * R: 3
-  * X: 8
-  * audio_channels: 2
-  * causal: true
-  * mask_nonlinear: relu
-  * norm_type: cLN
-* training:
-  * sample_rate: 44100
-  * samples_per_track: 64
-  * segment: 5.0
-  * aggregate: 2
-  * batch_size: 4
-  * early_stop: true
-  * epochs: 200
-
-
-## Dataset
-The model was trained using EnsembleSet and CadenzaWoodwind datasets.
-
-## How to use
-
-```
-from tasnet import ConvTasNetStereo
-
-model = ConvTasNetStereo.from_pretrained(
-    "cadenzachallenge/ConvTasNet_Clarinet_Causal"
-).cpu()
-
-```
-    
-    
+    ---
+    license: apache-2.0
+    language:
+    - en
+    tags:
+    - hearing loss
+    - challenge
+    - signal processing
+    - source separation
+    - audio
+    - audio-to-audio
+    - NonCausal
+    ---
+
+    # Cadenza Challenge: CAD2-Task1
+
+    A NonCausal Clarinet/Others separation model for the CAD2-Task2 baseline system.
+
+    * Architecture: ConvTasNet (Kaituo XU) with multichannel support (Alexandre Defossez).
+    * Parameters:
+      * B: 256
+      * C: 2
+      * H: 512
+      * L: 20
+      * N: 256
+      * P: 3
+      * R: 3
+      * X: 8
+      * audio_channels: 2
+      * causal: false
+      * mask_nonlinear: relu
+      * norm_type: gLN
+    * training:
+      * sample_rate: 44100
+      * samples_per_track: 64
+      * segment: 5.0
+      * aggregate: 2
+      * batch_size: 4
+      * early_stop: true
+      * epochs: 200
+
+
+    ## Dataset
+    The model was trained using EnsembleSet and CadenzaWoodwind datasets.
+
+    ## How to use
+
+    ```
+    from tasnet import ConvTasNetStereo
+
+    model = ConvTasNetStereo.from_pretrained(
+        "cadenzachallenge/ConvTasNet_Clarinet_NonCausal"
+    ).cpu()
+
+    ```
+
+