Update to torch-vggish

Signed-off-by: Jael Gu <mengjia.gu@zilliz.com>

README.md

@@ -4,7 +4,7 @@ Authors: Jael Gu
 
 ## Overview
 
-This pipeline extracts features of a given audio file using a VGGish model implemented in Tensorflow. This is a supervised model pre-trained with [AudioSet](https://research.google.com/audioset/), which contains over 2 million sound clips.
+This pipeline extracts features of a given audio file using a VGGish model implemented in Pytorch. This is a supervised model pre-trained with [AudioSet](https://research.google.com/audioset/), which contains over 2 million sound clips.
 
 ## Interface
 
@@ -44,4 +44,4 @@ $ pip3 install towhee
 
 ## How it works
 
-This pipeline includes a main operator: [audio embedding](https://hub.towhee.io/towhee/audio-embedding-operator-template) (implemented as [towhee/tf-vggish-audioset](https://hub.towhee.io/towhee/tf-vggish-audioset)). The audio embedding operator encodes fixed-length clips of an audio data and finally output a set of vectors of the given audio.
+This pipeline includes a main operator: [audio-embedding](https://towhee.io/operators?limit=30&page=1&filter=3%3Aaudio-embedding) (default: [towhee/torch-vggish](https://hub.towhee.io/towhee/torch-vggish)). The audio embedding operator encodes audio file and finally output a set of vectors of the given audio.

audio_embedding_vggish.yaml

@@ -16,9 +16,8 @@ operators:
             type: map
     -
         name: 'embedding_model'
-        function: 'towhee/tf-vggish-audioset'
+        function: 'towhee/torch-vggish'
         init_args:
-            sample_rate: 16000
         inputs:
             -
                 df: 'audio'