Update README.md

README.md

@@ -1,9 +1,62 @@
 ---
+license: apache-2.0
+language:
+- en
 tags:
-- pytorch_model_hub_mixin
-- model_hub_mixin
+- hearing loss
+- challenge
+- signal processing
+- source separation
+- audio
+- audio-to-audio
+- NonCausal
 ---
 
+# Cadenza Challenge: CAD2-Task2
+
+A Causal separation model for the CAD2-Task2 system.
+
+This model is an ensemble of the following instruments:
+- Bassoon
+- Cello
+- Clarinet
+- Flute
+- Oboe
+- Sax
+- Viola
+- Violin
+
+Each model is based on the 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
+
+## Dataset
+The model was trained using EnsembleSet and CadenzaWoodwind datasets.
+
+## How to use
+
+```
+from dynamic_source_separator import DynamicSourceSeparator
+
+model = DynamicSourceSeparator.from_pretrained(
+    "cadenzachallenge/Dynamic_Source_Separator_Causal"
+).cpu()
+
+```
+
+## Description
+
 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. 
@@ -12,13 +65,11 @@ 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():
@@ -26,9 +77,6 @@ def dynamic_masked_loss(mixture, separated_sources, ground_truth_sources, indica
             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)
-
+Model and T002 recipe are shared in [Clarity toolkit](https://github.com/claritychallenge/clarity)