diff --git a/LICENSE b/LICENSE
index fda2785..e947739 100644
--- a/LICENSE
+++ b/LICENSE
@@ -1,6 +1,6 @@
 MIT License
 
-Copyright (c) 2023 Hao Hao Tan
+Copyright (c) 2024 Hao Hao Tan
 
 Permission is hereby granted, free of charge, to any person obtaining a copy
 of this software and associated documentation files (the "Software"), to deal
diff --git a/README.md b/README.md
index 5bb3b76..35831c2 100644
--- a/README.md
+++ b/README.md
@@ -1,17 +1,24 @@
 ## Frechet Audio Distance in PyTorch
 
-A lightweight library of Frechet Audio Distance calculation.
+A lightweight library of Frechet Audio Distance (FAD) calculation.
 
-Currently, we support embedding from:
-- `VGGish` by [S. Hershey et al.](https://arxiv.org/abs/1812.08466)
-- `PANN` by [Kong et al.](https://arxiv.org/abs/1912.10211)
-- `CLAP` by [Wu et al.](https://arxiv.org/abs/2211.06687)
+Currently, we support:
+- FAD score, with embeddings from:
+    - `VGGish` by [S. Hershey et al.](https://arxiv.org/abs/1812.08466)
+    
+    - `PANN` by [Kong et al.](https://arxiv.org/abs/1912.10211)
+    - `CLAP` by [Wu et al.](https://arxiv.org/abs/2211.06687)
+    - `EnCodec` by [Defossez et al.](https://arxiv.org/pdf/2210.13438.pdf)
+
+- CLAP score, for text and audio matching
 
 ### Installation
 
 `pip install frechet_audio_distance`
 
-### Demo
+### Example
+
+#### For FAD:
 
 ```python
 from frechet_audio_distance import FrechetAudioDistance
@@ -40,12 +47,43 @@ frechet = FrechetAudioDistance(
     verbose=False,
     enable_fusion=False,            # for CLAP only
 )
-fad_score = frechet.score("/path/to/background/set", "/path/to/eval/set", dtype="float32")
+# to use `EnCodec`
+frechet = FrechetAudioDistance(
+    model_name="encodec",
+    sample_rate=48000,
+    channels=2,
+    verbose=False,
+)
 
+fad_score = frechet.score(
+    "/path/to/background/set", 
+    "/path/to/eval/set", 
+    dtype="float32"
+)
 ```
 
 You can also have a look at [this notebook](https://github.com/gudgud96/frechet-audio-distance/blob/main/test/test_all.ipynb) for a better understanding of how each model is used.
 
+#### For CLAP score:
+
+```python
+from frechet_audio_distance import CLAPScore
+
+clap = CLAPScore(
+    submodel_name="630k-audioset",
+    verbose=True,
+    enable_fusion=False,
+)
+
+clap_score = clap.score(
+    text_path="./text1/text.csv",
+    audio_dir="./audio1",
+    text_column="caption",
+)
+```
+
+For more info, kindly refer to [this notebook](https://github.com/gudgud96/frechet-audio-distance/blob/main/test/test_clap_score.ipynb).
+
 ### Save pre-computed embeddings
 
 When computing the Frechet Audio Distance, you can choose to save the embeddings for future use. 
diff --git a/pyproject.toml b/pyproject.toml
index 2755621..2077549 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "frechet_audio_distance"
-version = "0.2.0"
+version = "0.3.0"
 authors = [
   { name="Hao Hao Tan", email="helloharry66@gmail.com" },
 ]
@@ -32,4 +32,7 @@ dependencies = [
 ]
 
 [project.urls]
-"Homepage" = "https://github.com/gudgud96/frechet-audio-distance"
\ No newline at end of file
+"Homepage" = "https://github.com/gudgud96/frechet-audio-distance"
+
+[tool.setuptools]
+py-modules = []
\ No newline at end of file