docs and GPU memory release

游雁

2023-07-14 579afd6ac81570438c7e95164333155a9cca163c

 README.md

@@ -14,13 +14,13 @@
[**News**](https://github.com/alibaba-damo-academy/FunASR#whats-new) 
| [**Highlights**](#highlights)
| [**Installation**](#installation)
| [**Usage**](#usage)
| [**Papers**](https://github.com/alibaba-damo-academy/FunASR#citations)
| [**Runtime**](https://github.com/alibaba-damo-academy/FunASR/tree/main/funasr/runtime)
| [**Model Zoo**](https://github.com/alibaba-damo-academy/FunASR/blob/main/docs/model_zoo/modelscope_models.md)
| [**Quick Start**](#quick-start)
| [**Runtime**](./funasr/runtime/readme.md)
| [**Model Zoo**](./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 @@

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 @@

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_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