docs and GPU memory release

README.md

@@ -14,13 +14,13 @@
 [**News**](https://github.com/alibaba-damo-academy/FunASR#whats-new) 
 | [**Highlights**](#highlights)
 | [**Installation**](#installation)
-| [**Usage**](#usage)
+| [**Quick Start**](#quick-start)
-| [**Papers**](https://github.com/alibaba-damo-academy/FunASR#citations)
+| [**Runtime**](./funasr/runtime/readme.md)
-| [**Runtime**](https://github.com/alibaba-damo-academy/FunASR/tree/main/funasr/runtime)
+| [**Model Zoo**](./docs/model_zoo/modelscope_models.md)
-| [**Model Zoo**](https://github.com/alibaba-damo-academy/FunASR/blob/main/docs/model_zoo/modelscope_models.md)
 | [**Contact**](#contact)
-| [**M2MET2.0 Challenge**](https://github.com/alibaba-damo-academy/FunASR#multi-channel-multi-party-meeting-transcription-20-m2met20-challenge)
 
+
+<a name="whats-new"></a>
 ## What's new: 
 
 ### FunASR runtime-SDK
@@ -36,11 +36,13 @@ We are pleased to announce that the M2MeT2.0 challenge has been accepted by the
 
 For the release notes, please ref to [news](https://github.com/alibaba-damo-academy/FunASR/releases)
 
+<a name="highlights"></a>
 ## Highlights
 - FunASR is a fundamental speech recognition toolkit that offers a variety of features, including speech recognition (ASR), Voice Activity Detection (VAD), Punctuation Restoration, Language Models, Speaker Verification, Speaker diarization and multi-talker ASR.
 - We have released a vast collection of academic and industrial pretrained models on the [ModelScope](https://www.modelscope.cn/models?page=1&tasks=auto-speech-recognition), which can be accessed through our [Model Zoo](https://github.com/alibaba-damo-academy/FunASR/blob/main/docs/model_zoo/modelscope_models.md). The representative [Paraformer-large](https://www.modelscope.cn/models/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/summary) model has achieved SOTA performance in many speech recognition tasks. 
 - FunASR offers a user-friendly pipeline for fine-tuning pretrained models from the [ModelScope](https://www.modelscope.cn/models?page=1&tasks=auto-speech-recognition). Additionally, the optimized dataloader in FunASR enables faster training speeds for large-scale datasets. This feature enhances the efficiency of the speech recognition process for researchers and practitioners.
 
+<a name="Installation"></a>
 ## Installation
 
 Install from pip
@@ -70,7 +72,8 @@ pip3 install -U modelscope
 
 For more details, please ref to [installation](https://alibaba-damo-academy.github.io/FunASR/en/installation/installation.html)
 
-## Usage
+<a name="quick-start"></a>
+## Quick Start
 
 You could use FunASR by:
 
@@ -120,6 +123,8 @@ python funasr_wss_client.py --host "127.0.0.1" --port 10095 --mode 2pass --chunk
 #python funasr_wss_client.py --host "127.0.0.1" --port 10095 --mode 2pass --chunk_size "8,8,4" --audio_in "./data/wav.scp" --output_dir "./results"
 ```
 More examples could be found in [docs](https://alibaba-damo-academy.github.io/FunASR/en/runtime/websocket_python.html#id2)
+
+<a name="contact"></a>
 ## Contact
 
 If you have any questions about FunASR, please contact us by

README_zh.md

@@ -201,7 +201,7 @@ cd egs/aishell/paraformer
 
 
 ## 许可协议
-项目遵循[The MIT License](https://opensource.org/licenses/MIT)开源协议. 工业模型许可协议请参考（[点击此处](./MODEL_LICENSE)）
+项目遵循[The MIT License](https://opensource.org/licenses/MIT)开源协议。 工业模型许可协议请参考（[点击此处](./MODEL_LICENSE)）
 
 
 ## Stargazers over time

funasr/bin/asr_inference_launch.py

@@ -439,6 +439,7 @@ def inference_paraformer(
         logging.info(rtf_avg)
         if writer is not None:
             ibest_writer["rtf"]["rtf_avf"] = rtf_avg
+        torch.cuda.empty_cache()
         return asr_result_list
 
     return _forward
@@ -730,6 +731,7 @@ def inference_paraformer_vad_punc(
                     ibest_writer["time_stamp"][key] = "{}".format(time_stamp_postprocessed)
 
             logging.info("decoding, utt: {}, predictions: {}".format(key, text_postprocessed_punc))
+        torch.cuda.empty_cache()
         return asr_result_list
 
     return _forward

funasr/bin/vad_inference_launch.py

@@ -123,7 +123,7 @@ def inference_vad(
                 vad_results.append(item)
                 if writer is not None:
                     ibest_writer["text"][keys[i]] = "{}".format(results[i])
-
+        torch.cuda.empty_cache()
         return vad_results
 
     return _forward

funasr/version.txt

@@ -1 +1 @@
-0.6.9
+0.7.0