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README.md

@@ -1,9 +1,57 @@
 ---
+language:
+- en
+license: apache-2.0
 tags:
-- pytorch_model_hub_mixin
-- model_hub_mixin
+- hearing loss
+- challenge
+- signal processing
+- source separation
+- audio
+- audio-to-audio
+- Causal
 ---
 
-This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration:
-- Library: [More Information Needed]
-- Docs: [More Information Needed]
+# 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()
+
+```
+    
+