From f45471f59e88052f82d31fa6b6dc17966422b744 Mon Sep 17 00:00:00 2001
From: Jael Gu <mengjia.gu@zilliz.com>
Date: Thu, 7 Apr 2022 17:39:49 +0800
Subject: [PATCH] Update README

Signed-off-by: Jael Gu <mengjia.gu@zilliz.com>
---
 README.md | 49 ++++++++++++++++++++++++++++---------------------
 1 file changed, 28 insertions(+), 21 deletions(-)

diff --git a/README.md b/README.md
index 6ac0f91..8f2ab36 100644
--- a/README.md
+++ b/README.md
@@ -2,6 +2,7 @@
 
 *Author: Jael Gu*
 
+<br />
 
 ## Desription
 
@@ -10,34 +11,36 @@ Each vector represents for an audio clip with a fixed length of around 2s.
 This operator is built on top of the original implementation of [CLMR](https://github.com/Spijkervet/CLMR).
 The [default model weight](clmr_checkpoint_10000.pt) provided is pretrained on [Magnatagatune Dataset](https://paperswithcode.com/dataset/magnatagatune) with [SampleCNN](sample_cnn.py).
 
+<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.clmr()
-  .show()
+towhee.glob('test.wav') \
+      .audio_decode() \
+      .time_window(range=10) \
+      .audio_embedding.clmr() \
+      .show()
 ```
     | [-2.1045141, 0.55381, 0.4537212, ...] shape=(6, 512) |
 
 *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.clmr['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.clmr['frames', 'vecs']() \
+      .select('vecs') \
+      .to_vec()
 ```
     [array([[-2.1045141 ,  0.55381   ,  0.4537212 , ...,  0.18805158,
           0.3079657 , -1.216063  ],
@@ -52,6 +55,8 @@ dc.glob['path']('test.wav')
         [-2.0771549 ,  0.5641221 ,  0.43814805, ...,  0.1822092 ,
           0.33022994, -1.2070588 ]], dtype=float32)]
 
+<br />
+
 ## Factory Constructor
 
 Create the operator via the following factory method
@@ -60,26 +65,28 @@ Create the operator via the following factory method
 
 **Parameters:**
 
-​   *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.
 
+<br />
+
 ## Interface
 
 An audio embedding operator generates vectors in numpy.ndarray given an audio file path or a [towhee audio](link/to/AudioFrame/api/doc).
 
 **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 2s.
 
 **Returns**:
 
-​   *numpy.ndarray*
+*numpy.ndarray*
 
-​   Audio embeddings in shape (num_clips, 512).
+Audio embeddings in shape (num_clips, 512).
 Each embedding stands for features of an audio clip with length of 2s.
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