Paraformer语音识别-中文-通用-16k-离线-轻量

Highlights

模型为Paraformer语音识别-中文-通用-16k-离线的onnx量化导出版本，可以直接用来做生产部署，一键部署教程（点击此处）

模型转换及测试脚本

测试数据：https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/testaudio/asrexample_zh.pcm

from funasr_onnx import Paraformer
from pathlib import Path

model_dir = "damo/speech_paraformer_asr_nat-zh-cn-16k-common-vocab8358-tensorflow1"
model = Paraformer(model_dir, batch_size=1, quantize=True)

wav_path = path_to_asr_example_zh

result = model(wav_path)
print(result)

• model_dir: model_name in modelscope or local path downloaded from modelscope. If the local path is set, it should contain 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

参考教程：https://alibaba-damo-academy.github.io/FunASR/en/runtime/python/onnxruntime/README.html

相关论文以及引用信息

@inproceedings{gao2022paraformer,
  title={Paraformer: Fast and Accurate Parallel Transformer for Non-autoregressive End-to-End Speech Recognition},
  author={Gao, Zhifu and Zhang, Shiliang and McLoughlin, Ian and Yan, Zhijie},
  booktitle={INTERSPEECH},
  year={2022}
}