Update README.md

README.md

@@ -1,6 +1,23 @@
 ---
 license: apache-2.0
+language:
+- zh
+metrics:
+- accuracy
+- cer
+pipeline_tag: automatic-speech-recognition
+tags:
+- Paraformer
+- FunASR
+- ASR
 ---
+## 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.
+
+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).
+
+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).
+
 
 ## Install `funasr_onnx`
 
@@ -46,4 +63,4 @@ Output: `List[str]`: recognition result
 
 ## Performance benchmark
 
-Please ref to [benchmark](https://github.com/alibaba-damo-academy/FunASR/blob/main/funasr/runtime/python/benchmark_onnx.md)
+Please ref to [benchmark](https://github.com/alibaba-damo-academy/FunASR/blob/main/funasr/runtime/python/benchmark_onnx.md)