---
license: apache-2.0
---

## Install the `funasr_onnx`

```shell
pip install -U funasr_onnx
# For the users in China, you could install with the command:
# pip install -U funasr_onnx -i https://mirror.sjtu.edu.cn/pypi/web/simple
```

## Download the model

```shell
git clone https://huggingface.co/funasr/paraformer-large
```

## Inference with runtime

### Speech Recognition
#### Paraformer
 ```python
 from funasr_onnx import Paraformer

 model_dir = "./export/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch"
 model = Paraformer(model_dir, batch_size=1, quantize=True)

 wav_path = ['./export/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/example/asr_example.wav']

 result = model(wav_path)
 print(result)
 ```
- `model_dir`: the model path, which contains `model.onnx`, `config.yaml`, `am.mvn`
- `batch_size`: `1` (Default), the batch size duration inference
- `device_id`: `-1` (Default), infer on CPU. If you want to infer with GPU, set it to gpu_id (Please make sure that you have install the onnxruntime-gpu)
- `quantize`: `False` (Default), load the model of `model.onnx` in `model_dir`. If set `True`, load the model of `model_quant.onnx` in `model_dir`
- `intra_op_num_threads`: `4` (Default), sets the number of threads used for intraop parallelism on CPU

Input: wav formt file, support formats: `str, np.ndarray, List[str]`

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)