update installation url, revise training instructions

README.md

@@ -4,10 +4,14 @@
 
 </div>
 
+AIMLESS (Artificial Intelligence and Music League for Effective Source Separation) is a special interest group in audio source separation at C4DM, consisting of PhD students from the AIM CDT program.
+This repository contains our training code for the [SDX23 Sound Demixing Challenge](https://www.aicrowd.com/challenges/sound-demixing-challenge-2023).
+
+
 ## Quick Start
 
 The conda environment we used for training is described in `environment.yml`.
-The below commands should be runnable if you're using EECS servers.
+The below commands should be runnable if you're using QMUL EECS servers.
 If you want to run it on your local machine, change the `root` param in the config to where you downloaded the MUSDB18-HQ dataset.
 
 ### Frequency-masking-based model
@@ -24,11 +28,18 @@ python main.py fit --config cfg/xumx.yaml
 python main.py fit --config cfg/demucs.yaml
 ```
 
+## Install the repository as a package
+
+This step is required if you want to test our submission repositories (see section [Reproduce the winning submission](#reproduce-the-winning-submission)) locally.
+```sh
+pip install git+https://github.com/aim-qmul/sdx23-aimless
+```
+
 ## Reproduce the winning submission
 
 ### CDX Leaderboard A, submission ID 220319
 
-This section describes how to reproduce the models we used in our [winning submission](https://gitlab.aicrowd.com/yoyololicon/cdx-submissions/-/issues/90) on CDX leaderboard A.
+This section describes how to reproduce the [best perform model](https://gitlab.aicrowd.com/yoyololicon/cdx-submissions/-/issues/90) we used on CDX leaderboard A.
 The submission consists of one HDemucs predicting all the targets and one BandSplitRNN predicitng the music from the mixture.
 
 To train the HDemucs:
@@ -44,7 +55,7 @@ python main.py fit --config cfg/cdx_a/bandsplit_rnn.yaml --data.init_args.root /
 
 We trained the models with no more than 4 GPUs, depending on the resources we had at the time.
 
-After training, please go to our [submission repository](https://gitlab.aicrowd.com/yoyololicon/cdx-submissions/) and checkout the tag `submission-hdemucs+rnn-music-99k`.
+After training, please go to our [submission repository](https://gitlab.aicrowd.com/yoyololicon/cdx-submissions/).
 Then, copy the last checkpoint of HDemucs (usually located at `lightning_logs/version_**/checkpoints/last.ckpt`) to `my_submission/lightning_logs/hdemucs-64-sdr/checkpoints/last.ckpt` in the submission repository.
 Similarly, copy the last checkpoint of BandSplitRNN to `my_submission/lightning_logs/bandsplitRNN-music/checkpoints/last.ckpt`.
 After these steps, you have reproduced our submission!
@@ -56,9 +67,9 @@ The separated music from the BandSplitRNN is enhanced by Wiener Filtering, and t
 
 ### MDX Leaderboard A (Label Noise), submission ID 220426
 
-This section describes how to reproduce the model we used in our [winning submission](https://gitlab.aicrowd.com/yoyololicon/mdx23-submissions/-/issues/76) on MDX leaderboard A.
+This section describes how to reproduce the [best perform model](https://gitlab.aicrowd.com/yoyololicon/mdx23-submissions/-/issues/76) we used on MDX leaderboard A.
 
-Firstly, we manually inspected the [label noise dataset](https://www.aicrowd.com/challenges/sound-demixing-challenge-2023/problems/music-demixing-track-mdx-23/dataset_files) and labeled the clean songs (no label noise).
+Firstly, we manually inspected the [label noise dataset](https://www.aicrowd.com/challenges/sound-demixing-challenge-2023/problems/music-demixing-track-mdx-23/dataset_files)(thanks @mhrice for the hard work!) and labeled the clean songs (no label noise).
 The labels are recorded in `data/lightning/label_noise.csv`.
 Then, a HDemucs was trained only on the clean labels with the following settings:
 
@@ -77,7 +88,7 @@ Other details:
 * Model is trained for ~800 epochs (approx. 2 weeks on 4 RTX A50000)
 * During the last ~200 epochs, the learning rate is reduced to 0.001, gradient accumulation is increased to 64, and the effect randomization chance is increased by a factor of 1.666 (e.g. 30% to 50% etc.)
 
-After training, please go to our [submission repository](https://gitlab.aicrowd.com/yoyololicon/mdx23-submissions/) and checkout the tag `submission-cm-acc64-4d-lr001-e1213-last`.
+After training, please go to our [submission repository](https://gitlab.aicrowd.com/yoyololicon/mdx23-submissions/).
 Then, copy the checkpoint to `my_submission/acc64_4devices_lr0001_e1213_last.ckpt` in the submission repository.
 After these steps, you have reproduced our submission!
 
@@ -108,9 +119,4 @@ Split song in the browser with pretrained Hybrid Demucs.
 Then open [http://localhost:8501/](http://localhost:8501/) in your browser. 
 
 
-## Install the repository as a package
 
-```sh
-pip install git+https://yoyololicon:[email protected]/yoyololicon/mdx23-aim-playground
-```
-For the value of `ACCESS_TOKEN` please refer to [#24](https://github.com/yoyololicon/mdx23-aim-playground/issues/24#issuecomment-1420952853).