Update Readme

Add more description of how the T002 model was trained

README.md

@@ -4,6 +4,31 @@ tags:
 - model_hub_mixin
 ---
 
-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]
+Audio source separation model used in Sytem T002 for [Cadenza2 Task2 Challenge](https://cadenzachallenge.org/docs/cadenza2/Rebalancing%20Classical/rebalancing)
+
+The model is a finetune of the 8 ConvTasNet models from the Task2 baseline. 
+The training optimised the estimated sources and the recosntructed mixture
+
+$$
+  Loss = \sum_{}^{Sources}(L_1(estimated~source, ref~source)) + L_1(reconstructed~mixture, original~mixture)
+$$
+
+```Python
+def dynamic_masked_loss(mixture, separated_sources, ground_truth_sources, indicator):
+    # Reconstruction Loss
+    reconstruction = sum(separated_sources.values())
+    reconstruction_loss = nn.L1Loss()(reconstruction, mixture)
+
+    # Separation Loss
+    separation_loss = 0
+    for instrument, active in indicator.items():
+        if active:
+            separation_loss += nn.L1Loss()(
+                separated_sources[instrument], ground_truth_sources[instrument]
+            )
+
+    return reconstruction_loss + separation_loss
+```
+
+Model and T002 recipe are shared in [Clarity toolkit](https://github.com/claritychallenge/clarity)
+