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README.md

@@ -1,61 +1,57 @@
 ---
-{}
+language:
+- en
+license: apache-2.0
+tags:
+- hearing loss
+- challenge
+- signal processing
+- source separation
+- audio
+- audio-to-audio
+- NonCausal
 ---
 
-    ---
-    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()
-
-    ```
+# 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()
+
+```