---
title: speech_paraformer-tiny_asr_kws-zh-16k-vocab192-online
canonical_url: "https://www.modelscope.cn/models/dengcunqin/speech_paraformer-tiny_asr_kws-zh-16k-vocab192-online"
md_url: "https://www.modelscope.cn/models/dengcunqin/speech_paraformer-tiny_asr_kws-zh-16k-vocab192-online.md"
repository: dengcunqin/speech_paraformer-tiny_asr_kws-zh-16k-vocab192-online
chinese_name: "Paraformer语音唤醒-普通话-16k-实时-tiny"
last_updated: 2024-12-11
license: "Apache License 2.0"
pipeline_tag: auto-speech-recognition
tasks:
  - auto-speech-recognition
model_type:
  - Non-autoregressive
library_name:
  - pytorch
frameworks:
  - pytorch
language:
  - cn
domain:
  - audio
downloads: 954
stars: 3
tags:
  - FunASR
  - Paraformer
  - Alibaba
  - "INTERSPEECH 2022"
---

# speech_paraformer-tiny_asr_kws-zh-16k-vocab192-online

> speech_paraformer-tiny_asr_kws-zh-16k-vocab192-online - dengcunqin 在 ModelScope 开源的模型。模型介绍 基于Paraformer online large(iic/speechparaformer-largeasrnat-zh-cn-16k-common-vocab8404-online)，更换vocab为拼音，减小模型参数大小，通过在普通话1w小时音频数据集上进行训练1轮。此版本尚未训练完成，此次放出的为中间模型，最终模型还要训练一段…

dengcunqin/speech_paraformer-tiny_asr_kws-zh-16k-vocab192-online 是 ModelScope 魔搭社区上的auto-speech-recognition模型，采用 Apache License 2.0 许可。

- **Repository**: dengcunqin/speech_paraformer-tiny_asr_kws-zh-16k-vocab192-online
- **License**: Apache License 2.0
- **Tasks**: auto-speech-recognition
- **Tags**: FunASR, Paraformer, Alibaba, INTERSPEECH 2022
- **Downloads**: 954
- **Stars**: 3
- **Last updated**: 2024-12-11

Source: https://www.modelscope.cn/models/dengcunqin/speech_paraformer-tiny_asr_kws-zh-16k-vocab192-online

---

# 模型介绍
基于Paraformer online large(iic/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online)，更换vocab为拼音，减小模型参数大小，通过在普通话1w小时音频数据集上进行训练1轮。此版本尚未训练完成，此次放出的为中间模型，最终模型还要训练一段时间。 模型训练和推理代码已改为funasr1.0。可用于流式（在线）语音唤醒（需要的算力很小，速度比较快，边缘设备及手机均可流畅运行）。

## <strong>[FunASR开源项目介绍](https://github.com/alibaba-damo-academy/FunASR)</strong>
<strong>[FunASR](https://github.com/alibaba-damo-academy/FunASR)</strong>希望在语音识别的学术研究和工业应用之间架起一座桥梁。通过发布工业级语音识别模型的训练和微调，研究人员和开发人员可以更方便地进行语音识别模型的研究和生产，并推动语音识别生态的发展。让语音识别更有趣！

#### 基于ModelScope进行推理

- 流式语音识别api调用方式可参考如下范例：

```python
# -*- encoding: utf-8 -*-
# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
#  MIT License  (https://opensource.org/licenses/MIT)

from funasr import AutoModel

import soundfile
import os
chunk_size = [0, 10, 5] #[0, 10, 5] 600ms, [0, 8, 4] 480ms
encoder_chunk_look_back = 4 #number of chunks to lookback for encoder self-attention
decoder_chunk_look_back = 1 #number of encoder chunks to lookback for decoder cross-attention

model = AutoModel(model="dengcunqin/speech_paraformer-tiny_asr_kws-zh-16k-vocab192-online", model_revision="master")
cache = {}
wav_file = os.path.join(model.model_path, "example/asr_example.wav")
res = model.generate(input=wav_file,
            chunk_size=chunk_size,
            encoder_chunk_look_back=encoder_chunk_look_back,
            decoder_chunk_look_back=decoder_chunk_look_back,
            )
print(res)



wav_file = os.path.join(model.model_path, "example/asr_example.wav")
speech, sample_rate = soundfile.read(wav_file)

chunk_stride = chunk_size[1] * 960 # 600ms、480ms

cache = {}

total_chunk_num = int(len((speech)-1)/chunk_stride+1)
for i in range(total_chunk_num):
    speech_chunk = speech[i*chunk_stride:(i+1)*chunk_stride]
    is_final = i == total_chunk_num - 1
    res = model.generate(input=speech_chunk,
                cache=cache,
                is_final=is_final,
                chunk_size=chunk_size,
                encoder_chunk_look_back=encoder_chunk_look_back,
                decoder_chunk_look_back=decoder_chunk_look_back,
                )
    print(res)
```
### 微信交流
Funasr群找小邓

### 微信小程序
<img src="https://www.modelscope.cn/models/dengcunqin/speech_seaco_paraformer_large_asr_nat-zh-cantonese-en-16k-common-vocab11666-pytorch/resolve/master/%E5%BE%AE%E4%BF%A1%E5%B0%8F%E7%A8%8B%E5%BA%8F%E7%A0%81.jpg" alt="微信小程序"  width="500" />

## 使用方式以及适用范围

运行范围
- 支持Linux-x86_64、Mac和Windows运行。

使用方式
- 直接推理：可以直接对输入音频进行解码，输出目标文字。
- 微调：加载训练好的模型，采用私有或者开源数据进行模型训练。

使用范围与目标场景
- 适合于实时语音识别场景。


## 模型局限性以及可能的偏差

考虑到特征提取流程和工具以及训练工具差异，会对CER的数据带来一定的差异（<0.1%），推理GPU环境差异导致的RTF数值差异。



## 相关论文以及引用信息

```BibTeX
@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}
}
```