add cls static model, test=doc

docs/source/released_model.md

@@ -67,9 +67,9 @@ GE2E + FastSpeech2 | AISHELL-3  |[ge2e-fastspeech2-aishell3](https://github.com/
 
 ## Audio Classification Models
 
-Model Type | Dataset| Example Link | Pretrained Models
+Model Type | Dataset| Example Link | Pretrained Models | Static Models 
 :-------------:| :------------:| :-----: | :-----:
-PANN | Audioset| [audioset_tagging_cnn](https://github.com/qiuqiangkong/audioset_tagging_cnn) | [panns_cnn6.pdparams](https://bj.bcebos.com/paddleaudio/models/panns_cnn6.pdparams), [panns_cnn10.pdparams](https://bj.bcebos.com/paddleaudio/models/panns_cnn10.pdparams), [panns_cnn14.pdparams](https://bj.bcebos.com/paddleaudio/models/panns_cnn14.pdparams)
+PANN | Audioset| [audioset_tagging_cnn](https://github.com/qiuqiangkong/audioset_tagging_cnn) | [panns_cnn6.pdparams](https://bj.bcebos.com/paddleaudio/models/panns_cnn6.pdparams), [panns_cnn10.pdparams](https://bj.bcebos.com/paddleaudio/models/panns_cnn10.pdparams), [panns_cnn14.pdparams](https://bj.bcebos.com/paddleaudio/models/panns_cnn14.pdparams) | [panns_cnn6_static.tar.gz](https://paddlespeech.bj.bcebos.com/cls/inference_model/panns_cnn6_static.tar.gz), [panns_cnn10_static.tar.gz](https://paddlespeech.bj.bcebos.com/cls/inference_model/panns_cnn10_static.tar.gz), [panns_cnn14_static.tar.gz](https://paddlespeech.bj.bcebos.com/cls/inference_model/panns_cnn14_static.tar.gz) 
 PANN | ESC-50 |[pann-esc50](../../examples/esc50/cls0)|[esc50_cnn6.tar.gz](https://paddlespeech.bj.bcebos.com/cls/esc50/esc50_cnn6.tar.gz), [esc50_cnn10.tar.gz](https://paddlespeech.bj.bcebos.com/cls/esc50/esc50_cnn10.tar.gz), [esc50_cnn14.tar.gz](https://paddlespeech.bj.bcebos.com/cls/esc50/esc50_cnn14.tar.gz)
 
 ## Punctuation Restoration Models

paddlespeech/server/engine/cls/paddleinference/cls_engine.py

@@ -92,7 +92,6 @@ class CLSServerExecutor(CLSExecutor):
         res_path = os.path.join(MODEL_HOME, tag)
         decompressed_path = download_and_decompress(pretrained_models[tag],
                                                     res_path)
-        print("aaaaaaaaaaaaa: ", decompressed_path)
         decompressed_path = os.path.abspath(decompressed_path)
         logger.info(
             'Use pretrained model stored in: {}'.format(decompressed_path))