Update README.md

README.md

@@ -9,4 +9,100 @@ tags:
 - watermark
 ---
 
-This is the 'WavMark-32bps' model as described in the paper 'WavMark: Watermarking for Audio Generation'.
+# WavMark
+> AI-based Audio Watermarking Tool
+
+- ⚡  **Leading Stability:** The watermark resist to **10**  types of common attacks like Gaussian noise, MP3 compression, low-pass filter, and speed variation; achieving over **29** times in robustness compared with the traditional method.
+- 🙉 **High Imperceptibility:** The watermarked audio has over 38dB SNR and 4.3 PESQ, which means it is inaudible to humans. Listen the examples: [https://wavmark.github.io/](https://wavmark.github.io/).
+- 😉 **Easy for Extending:** This project is entirely python based. You can easily leverage our underlying PyTorch model to implement a custom watermarking system with higher capacity or robustness.
+- 🤗 **Huggingface Spaces:**  Try our online demonstration: https://huggingface.co/spaces/M4869/WavMark
+
+## Installation
+```
+pip install wavmark
+```
+
+## Basic Usage
+The following code adds 16-bit watermark into the input file `example.wav` and subsequently performs decoding:
+```python
+import numpy as np
+import soundfile
+import torch
+import wavmark
+
+
+# 1.load model
+device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
+model = wavmark.load_model().to(device)
+
+# 2.create 16-bit payload
+payload = np.random.choice([0, 1], size=16)
+print("Payload:", payload)
+
+# 3.read host audio
+# the audio should be a single-channel 16kHz wav, you can read it using soundfile:
+signal, sample_rate = soundfile.read("example.wav")
+# Otherwise, you can use the following function to convert the host audio to single-channel 16kHz format:
+# from wavmark.utils import file_reader
+# signal = file_reader.read_as_single_channel("example.wav", aim_sr=16000)
+
+# 4.encode watermark
+watermarked_signal, _ = wavmark.encode_watermark(model, signal, payload, show_progress=True)
+# you can save it as a new wav:
+# soundfile.write("output.wav", watermarked_signal, 16000)
+
+# 5.decode watermark
+payload_decoded, _ = wavmark.decode_watermark(model, watermarked_signal, show_progress=True)
+BER = (payload != payload_decoded).mean() * 100
+
+print("Decode BER:%.1f" % BER)
+```
+
+
+
+
+## Low-level Access
+
+```python
+# 1.load model
+device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
+model = wavmark.load_model().to(device)
+
+# 2. take 16,000 samples
+signal, sample_rate = soundfile.read("example.wav")
+trunck = signal[0:16000]
+message_npy = np.random.choice([0, 1], size=32)
+
+# 3. do encode:
+with torch.no_grad():
+    signal = torch.FloatTensor(trunck).to(device)[None]
+    message_tensor = torch.FloatTensor(message_npy).to(device)[None]
+    signal_wmd_tensor = model.encode(signal, message_tensor)
+    signal_wmd_npy = signal_wmd_tensor.detach().cpu().numpy().squeeze()
+
+# 4.do decode:
+with torch.no_grad():
+    signal = torch.FloatTensor(signal_wmd_npy).to(device).unsqueeze(0)
+    message_decoded_npy = (model.decode(signal) >= 0.5).int().detach().cpu().numpy().squeeze()
+
+BER = (message_npy != message_decoded_npy).mean() * 100
+print("BER:", BER)
+```
+
+
+
+
+
+## Thanks
+The "[Audiowmark](https://uplex.de/audiowmark)" developed by Stefan Westerfeld has provided valuable ideas for the design of this project.
+## Citation
+```
+@misc{chen2023wavmark,
+      title={WavMark: Watermarking for Audio Generation}, 
+      author={Guangyu Chen and Yu Wu and Shujie Liu and Tao Liu and Xiaoyong Du and Furu Wei},
+      year={2023},
+      eprint={2308.12770},
+      archivePrefix={arXiv},
+      primaryClass={cs.SD}
+}
+```