---
tasks:
- auto-speech-recognition
domain:
- audio
model-type:
- Non-autoregressive
frameworks:
- pytorch
backbone:
- transformer/conformer
metrics:
- CER
license: Apache License 2.0
language: 
- cn
tags:
- FunASR
- Paraformer
- Alibaba
- INTERSPEECH 2022
datasets:
  train:
  - 60,000 hour industrial Mandarin task
  test:
  - AISHELL-1 dev/test
  - AISHELL-2 dev_android/dev_ios/dev_mic/test_android/test_ios/test_mic
  - WentSpeech dev/test_meeting/test_net
  - SpeechIO TIOBE
  - 60,000 hour industrial Mandarin task
indexing:
   results:
   - task:
       name: Automatic Speech Recognition
     dataset:
       name: 60,000 hour industrial Mandarin task
       type: audio    # optional
       args: 16k sampling rate, 8404 characters  # optional
     metrics:
       - type: CER
         value: 8.53%  # float
         description: greedy search, withou lm, avg.
         args: default
       - type: RTF
         value: 0.0251  # float
         description: GPU inference on V100
         args: batch_size=1
widgets:
  - task: auto-speech-recognition
    inputs:
      - type: audio
        name: input
        title: 音频
    examples:
      - name: 1
        title: 示例1
        inputs:
          - name: input
            data: git://example/asr_example.wav
    inferencespec:
      cpu: 8 #CPU数量
      memory: 4096

finetune-support: True
---

# 模型介绍
基于Paraformer online large(iic/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online)，更换vocab，增加粤语部分字，通过在普通话1w小时、粤语100小时、英语1w小时音频数据集上进行训练1轮。此版本已不再更新，后续请关注vocab 11666版本。

## <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-large_asr_nat-zh-cantonese-en-16k-vocab8501-online", model_revision="master")
cache = {}
wav_file = os.path.join(model.model_path, "example/asr_example_普通话.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_粤语.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)
```

## 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}
}
```