readme_merge

README.md

@@ -4,14 +4,14 @@
 
 Code for SilentCipher, a method of embedding watermarking in any audio with state-of-the-art robustness.<br>
 Currently this repository supports audio at 16kHz and 44.1kHz.<br>
-Checkout our [paper](https://arxiv.org/abs/2406.03822) for more details.
+Checkout our [paper](https://arxiv.org/abs/2406.03822) for more details.<br>
+<br>
+We have posted some examples from existing watermarking algorithms and how they compare to our watermarking method at [EXAMPLES](https://interspeech2024.github.io/silentcipher/) 
 
 [[`arXiv`](https://arxiv.org/abs/2406.03822)]
 [[`Colab notebook`](https://colab.research.google.com/github/sony/silentcipher/blob/master/examples/colab/demo.ipynb)]
 <!-- [[🤗`Hugging Face`](HUGGINGFACE)] -->
 
-![fig](assets/figure.png)
-
 # Summary
 
 In this paper, we address artefacts introduces by Deep learning-based watermarking methods and introduce a way to remove the need for perceptual losses which leads to stable training allowing us to achieve SOTA in terms of both perceptual quality and robustness against distortion. Unlike previous methods which work on 16kHz sampling rate, we also showcase our results on 44.1kHz sampling rates opening the path for practical applications.
@@ -44,11 +44,20 @@ pip install dist/<package>.whl
 
 # :gear: Models
 
+<<<<<<< HEAD
     Find the latest models for 44.1kHz and 16kHz sampling rate in the release section of this repository [RELEASE](https://github.com/sony/silentcipher/releases)
 
 **Note**: Soon the models will also be released on hugging face. Stay tuned !
 **Note**: We are working to release the training code for anyone wants to build their own watermarker. Stay tuned !
 
+=======
+**Note**: We will be uploading the model checkpoints soon. Stay Tuned!<br>
+
+# :running: Training Code
+
+**Note**: We are working to release the training code for anyone wants to build their own watermarker. Stay tuned !
+
+>>>>>>> 80f5ec1db99ce02891a6e3f6100818fb8a4dcd80
 <!-- You can find all the model checkpoints on the [Hugging Face Hub](HUGGINGFACE). We provide the checkpoints for the following models:
 
 - [SilentCipher-44.1khz](It takes a 44.1kHz audio signal as input along with the message to be embedded and generates a watermarked audio of the same size as output. This model may be useful for professional applications).
@@ -57,7 +66,14 @@ pip install dist/<package>.whl
 
 # :abacus: Usage
 
-SilentCipher provides a simple API to watermark and detect the watermarks from an audio sample. Example usage:
+SilentCipher provides a simple API to watermark and detect the watermarks from an audio sample.<br>
+<br>
+We showcase it in multiple ways as shown in the examples directory.<br>
+We provide a simple flask server as documented in [README_FLASK](https://github.com/sony/silentcipher/tree/master/examples/SilentCipherStandaloneServer)<br>
+You can also find a simple front-end and backend server which can be used to demonstrate the applications of silentcipher [README_UI](https://github.com/sony/silentcipher/tree/master/examples/WaterMarkingWebsite)<br>
+Some simple demo examples are also provided in the [COLAB DIR](https://github.com/sony/silentcipher/tree/master/examples/colab)
+
+Over here we provide an usage in python:
 
 ```python
 import librosa