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README.md

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 ## Introduce
 
-[Paraformer](https://arxiv.org/abs/2206.08317) is a non-autoregressive end-to-end speech recognition model. Compared to the currently mainstream autoregressive models, non-autoregressive models can output the target text for the entire sentence in parallel, making them particularly suitable for parallel inference using GPUs. Paraformer is currently the first known non-autoregressive model that can achieve the same performance as autoregressive end-to-end models on industrial-scale data. When combined with GPU inference, it can improve inference efficiency by 10 times, thereby reducing machine costs for speech recognition cloud services by nearly 10 times.
+[Paraformer](https://arxiv.org/abs/2206.08317) is an innovative non-autoregressive end-to-end speech recognition model that offers significant advantages over traditional autoregressive models. Unlike its counterparts, Paraformer can generate the target text for an entire sentence in parallel, making it ideal for parallel inference using GPUs. This capability leads to significant improvements in inference efficiency, which can reduce machine costs for speech recognition cloud services by almost 10 times. Furthermore, Paraformer can achieve the same performance as autoregressive models on industrial-scale data.
 
-This repo shows how to use Paraformer with `funasr_onnx` runtime, the model comes from [FunASR](https://github.com/alibaba-damo-academy/FunASR), which trained from 60000 hours Mandarin data. The performance of Paraformer obtained the first place in [SpeechIO Leadboard](https://github.com/SpeechColab/Leaderboard).
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-We have released a large number of industrial-level models, including speech recognition, voice activaty detection, punctuation restoration, speaker verification, speaker diarizatio and timestamp prediction(force alignment). If you are interest, please ref to [FunASR](https://github.com/alibaba-damo-academy/FunASR). The [docs](https://alibaba-damo-academy.github.io/FunASR/en/index.html)
+This repository demonstrates how to leverage Paraformer in conjunction with the funasr_onnx runtime. The underlying model is derived from [FunASR](https://github.com/alibaba-damo-academy/FunASR), which was trained on a massive 60,000-hour Mandarin dataset. Notably, Paraformer's performance secured the top spot on the [SpeechIO leaderboard](https://github.com/SpeechColab/Leaderboard), highlighting its exceptional capabilities in speech recognition.
 
+We have relesed numerous industrial-grade models, including speech recognition, voice activity detection, punctuation restoration, speaker verification, speaker diarization, and timestamp prediction (force alignment). To learn more about these models, kindly refer to the [documentation](https://alibaba-damo-academy.github.io/FunASR/en/index.html) available on FunASR. If you are interested in leveraging advanced AI technology for your speech-related projects, we invite you to explore the possibilities offered by [FunASR](https://github.com/alibaba-damo-academy/FunASR).
 
 ## Install funasr_onnx