From 765f9f886477f92aa86f5741a1d6d5a9da7d022f Mon Sep 17 00:00:00 2001
From: Azalea <22280294+hykilpikonna@users.noreply.github.com>
Date: Wed, 11 Oct 2023 20:06:24 -0400
Subject: [PATCH] [U] Publish pypi

---
 README.md      | 10 +++++++---
 pyproject.toml |  2 +-
 2 files changed, 8 insertions(+), 4 deletions(-)

diff --git a/README.md b/README.md
index c3ddcee..3351021 100644
--- a/README.md
+++ b/README.md
@@ -4,13 +4,17 @@
 
 CLAP (Contrastive Language-Audio Pretraining) is a model that learns acoustic concepts from natural language supervision and enables “Zero-Shot” inference. The model has been extensively evaluated in 26 audio downstream tasks achieving SoTA in several of them including classification, retrieval, and captioning.
 
-<img width="832" alt="clap_diagrams" src="./docs/diagram.png">
+<img width="832" alt="clap_diagrams" src="https://raw.githubusercontent.com/hykilpikonna/CLAP/main/docs/diagram.png">
 
 ## Setup
 
-First, install python 3.8 or higher (3.11 recommended). Then, install CLAP:
+First, install python 3.8 or higher (3.11 recommended). Then, install CLAP using either of the following:
 
 ```shell
+# Install pypi pacakge
+pip install msclap
+
+# Or Install latest (unstable) git source
 pip install git+https://github.com/microsoft/CLAP.git
 ```
 
@@ -52,7 +56,7 @@ captions = clap_model.generate_caption(file_paths: List[str])
 ```
 
 ## Examples
-Take a look at `CLAP\src\` for usage examples. 
+Take a look at [examples](./examples/) for usage examples. 
 
 To run Zero-Shot Classification on the ESC50 dataset try the following:
 
diff --git a/pyproject.toml b/pyproject.toml
index 2ec934e..3aee668 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "msclap"
-version = "1.3.0"
+version = "1.3.1"
 description = "CLAP (Contrastive Language-Audio Pretraining) is a model that learns acoustic concepts from natural language supervision and enables “Zero-Shot” inference. The model has been extensively evaluated in 26 audio downstream tasks achieving SoTA in several of them including classification, retrieval, and captioning."
 authors = ["Benjamin Elizalde and Soham Deshmukh and Huaming Wang"]
 license = "MIT"