From 3759ac24e2b8ef748de3c9ec7b67404b8830a3c9 Mon Sep 17 00:00:00 2001
From: Jael Gu <mengjia.gu@zilliz.com>
Date: Thu, 7 Apr 2022 17:37:24 +0800
Subject: [PATCH] Update README

Signed-off-by: Jael Gu <mengjia.gu@zilliz.com>
---
 README.md | 58 +++++++++++++++++++++++++++++--------------------------
 1 file changed, 31 insertions(+), 27 deletions(-)

diff --git a/README.md b/README.md
index 666f69b..e639c0d 100644
--- a/README.md
+++ b/README.md
@@ -2,6 +2,7 @@
 
 *Author: Jael Gu*
 
+<br />
 
 ## Desription
 
@@ -11,34 +12,36 @@ This operator is built on top of [VGGish](https://github.com/tensorflow/models/t
 The model is a [VGG](https://arxiv.org/abs/1409.1556) variant pre-trained with a large scale of audio dataset [AudioSet](https://research.google.com/audioset).
 As suggested, it is suitable to extract features at high level or warm up a larger model.
 
+<br />
+
 ## Code Example
 
 Generate embeddings for the audio "test.wav". 
 
- *Write the pipeline in simplified style*:
+*Write the pipeline in simplified style*:
 
 ```python
-from towhee import dc
+import towhee
 
-dc.glob('test.wav')
-  .audio_decode()
-  .time_window(range=10)
-  .audio_embedding.vggish()
-  .show()
+towhee.glob('test.wav') \
+      .audio_decode() \
+      .time_window(range=10) \
+      .audio_embedding.vggish() \
+      .show()
 ```
     | [-0.4931737, -0.40068552, -0.032327592, ...] shape=(10, 128) |
 
 *Write a same pipeline with explicit inputs/outputs name specifications:*
 
 ```python
-from towhee import dc
-
-dc.glob['path']('test.wav')
-  .audio_decode['path', 'audio']()
-  .time_window['audio', 'frames'](range=10)
-  .audio_embedding.vggish['frames', 'vecs']()
-  .select('vecs')
-  .to_vec()
+import towhee
+
+towhee.glob['path']('test.wav') \
+      .audio_decode['path', 'audio']() \
+      .time_window['audio', 'frames'](range=10) \
+      .audio_embedding.vggish['frames', 'vecs']() \
+      .select('vecs') \
+      .to_vec()
 ```
     [array([[-0.4931737 , -0.40068552, -0.03232759, ..., -0.33428153,
           0.1333081 , -0.25221825],
@@ -54,6 +57,8 @@ dc.glob['path']('test.wav')
         [-0.4886143 , -0.40098593, -0.03175077, ..., -0.3325425 ,
           0.13271847, -0.25159872]], dtype=float32)]
 
+<br />
+
 ## Factory Constructor
 
 Create the operator via the following factory method
@@ -62,34 +67,33 @@ Create the operator via the following factory method
 
 **Parameters:**
 
-​   *weights_path: str*
+*weights_path: str*
 
-​   The path to model weights. If None, it will load default model weights.
+The path to model weights. If None, it will load default model weights.
 
-​   *framework: str*
+*framework: str*
 
-​   The framework of model implementation.
+The framework of model implementation.
 Default value is "pytorch" since the model is implemented in Pytorch.
 
-## Interface
+<br />
 
-An audio embedding operator generates vectors in numpy.ndarray given an audio file path or a [towhee audio](link/to/AudioFrame/api/doc).
+## Interface
 
+An audio embedding operator generates vectors in numpy.ndarray given an audio file path or towhee audio frames.
 
 **Parameters:**
 
-​   *Union[str, towhee.types.Audio]*
+*Union[str, towhee.types.Audio (a sub-class of numpy.ndarray)]*
 
-​   The audio path or link in string.
+The audio path or link in string.
 Or audio input data in towhee audio frames.
 The input data should represent for an audio longer than 0.9s.
 
 
 **Returns**:
 
-​   *numpy.ndarray*
+*numpy.ndarray*
 
-​   Audio embeddings in shape (num_clips, 128).
+Audio embeddings in shape (num_clips, 128).
 Each embedding stands for features of an audio clip with length of 0.9s.
-
-