Update README

Author: enhuiz

README.md

@@ -1,9 +1,11 @@
 # Resemble Enhance
 
+[![PyPI](https://img.shields.io/pypi/v/resemble-enhance.svg)](https://pypi.org/project/resemble-enhance/)
+[![Hugging Face Space](https://img.shields.io/badge/Hugging%20Face%20%F0%9F%A4%97-Space-yellow)](https://huggingface.co/spaces/ResembleAI/resemble-enhance)
+[![License](https://img.shields.io/github/license/resemble-ai/Resemble-Enhance.svg)](https://github.com/resemble-ai/resemble-enhance/blob/main/LICENSE)
 
 https://github.com/resemble-ai/resemble-enhance/assets/660224/bc3ec943-e795-4646-b119-cce327c810f1
 
-
 Resemble Enhance is an AI-powered tool that aims to improve the overall quality of speech by performing denoising and enhancement. It consists of two modules: a denoiser, which separates speech from a noisy audio, and an enhancer, which further boosts the perceptual audio quality by restoring audio distortions and extending the audio bandwidth. The two models are trained on high-quality 44.1kHz speech data that guarantees the enhancement of your speech with high quality.
 
 ## Usage
@@ -26,9 +28,9 @@ resemble_enhance in_dir out_dir
 resemble_enhance in_dir out_dir --denoise_only
 ```
 
-### Gradio
+### Web Demo
 
-To serve the gradio demo, run:
+We provide a web demo built with Gradio, you can try it out [here](https://huggingface.co/spaces/ResembleAI/resemble-enhance), or also run it locally:
 
 ```
 python app.py
@@ -38,7 +40,7 @@ python app.py
 
 ### Data Preparation
 
-You need to prepare a foreground speech dataset and a background non-speech dataset. In addition, you need to prepare a RIR dataset.
+You need to prepare a foreground speech dataset and a background non-speech dataset. In addition, you need to prepare a RIR dataset ([examples](https://github.com/RoyJames/room-impulse-responses)).
 
 ```bash
 data
@@ -49,7 +51,7 @@ data
 │   ├── 00001.wav
 │   └── ...
 └── rir
-    ├── 00001.wav
+    ├── 00001.npy
     └── ...
 ```
 
@@ -65,6 +67,8 @@ python -m resemble_enhance.denoiser.train --yaml config/denoiser.yaml
 
 #### Enhancer
 
+Then, you can train the enhancer in two stages. The first stage is to train the autoencoder and vocoder. And the second stage is to train the latent conditional flow matching (CFM) model.
+
 ##### Stage 1
 
 ```bash