From 90bd326269cfd594be859f8094135a33aa71a5a1 Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期六, 22 四月 2023 21:06:47 +0800
Subject: [PATCH] onnx docs

---
 funasr/runtime/python/onnxruntime/README.md |   65 ++++++++++++++++++++++++--------
 1 files changed, 48 insertions(+), 17 deletions(-)

diff --git a/funasr/runtime/python/onnxruntime/README.md b/funasr/runtime/python/onnxruntime/README.md
index 1f7fcaa..ed3deb6 100644
--- a/funasr/runtime/python/onnxruntime/README.md
+++ b/funasr/runtime/python/onnxruntime/README.md
@@ -19,7 +19,7 @@
 ```
 
 
-## Install the `funasr_onnx`
+## Install `funasr_onnx`
 
 install from pip
 ```shell
@@ -46,16 +46,22 @@
  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)
+ 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`
-- Input: wav formt file, support formats: `str, np.ndarray, List[str]`
-- Output: `List[str]`: recognition 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
 
 #### Paraformer-online
 
@@ -71,9 +77,16 @@
 result = model(wav_path)
 print(result)
 ```
-- Model_dir: the model path, which contains `model.onnx`, `config.yaml`, `am.mvn`
-- Input: wav formt file, support formats: `str, np.ndarray, List[str]`
-- Output: `List[str]`: recognition 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
+
 
 #### FSMN-VAD-online
 ```python
@@ -105,9 +118,16 @@
     if segments_result:
         print(segments_result)
 ```
-- Model_dir: the model path, which contains `model.onnx`, `config.yaml`, `am.mvn`
-- Input: wav formt file, support formats: `str, np.ndarray, List[str]`
-- Output: `List[str]`: recognition 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
+
 
 ### Punctuation Restoration
 #### CT-Transformer
@@ -121,9 +141,15 @@
 result = model(text_in)
 print(result[0])
 ```
-- Model_dir: the model path, which contains `model.onnx`, `config.yaml`, `am.mvn`
-- Input: wav formt file, support formats: `str, np.ndarray, List[str]`
-- Output: `List[str]`: recognition result
+- `model_dir`: the model path, which contains `model.onnx`, `config.yaml`, `am.mvn`
+- `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: `str`, raw text of asr result
+
+Output: `List[str]`: recognition result
+
 
 #### CT-Transformer-online
 ```python
@@ -143,9 +169,14 @@
 
 print(rec_result_all)
 ```
-- Model_dir: the model path, which contains `model.onnx`, `config.yaml`, `am.mvn`
-- Input: wav formt file, support formats: `str, np.ndarray, List[str]`
-- Output: `List[str]`: recognition result
+- `model_dir`: the model path, which contains `model.onnx`, `config.yaml`, `am.mvn`
+- `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: `str`, raw text of asr result
+
+Output: `List[str]`: recognition result
 
 ## Performance benchmark
 

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