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Model: iic/speech_paraformer_asr_nat-zh-cn-8k-common-vocab8358-tensorflow1 Source: Original Platform
2026-04-13 14:33:02 +08:00
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+---
+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: https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav
+    inferencespec:
+      cpu: 8 #CPU数量
+      memory: 4096
+finetune-support: True
+---
+
+
+# Paraformer-large模型介绍
+
+## Highlights
+- 热词版本：[Paraformer-large热词版模型](https://www.modelscope.cn/models/damo/speech_paraformer-large-contextual_asr_nat-zh-cn-16k-common-vocab8404/summary)支持热词定制功能，基于提供的热词列表进行激励增强，提升热词的召回率和准确率。
+- 长音频版本：[Paraformer-large长音频模型](https://www.modelscope.cn/models/damo/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch/summary)，集成VAD、ASR、标点与时间戳功能，可直接对时长为数小时音频进行识别，并输出带标点文字与时间戳。
+
+## <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！
+
+[**github仓库**](https://github.com/alibaba-damo-academy/FunASR)
+| [**最新动态**](https://github.com/alibaba-damo-academy/FunASR#whats-new) 
+| [**环境安装**](https://github.com/alibaba-damo-academy/FunASR#installation)
+| [**服务部署**](https://www.funasr.com)
+| [**模型库**](https://github.com/alibaba-damo-academy/FunASR/tree/main/model_zoo)
+| [**联系我们**](https://github.com/alibaba-damo-academy/FunASR#contact)
+
+
+## 模型原理介绍
+
+Paraformer是达摩院语音团队提出的一种高效的非自回归端到端语音识别框架。本项目为Paraformer中文通用语音识别模型，采用工业级数万小时的标注音频进行模型训练，保证了模型的通用识别效果。模型可以被应用于语音输入法、语音导航、智能会议纪要等场景。
+
+<p align="center">
+<img src="fig/struct.png" alt="Paraformer模型结构"  width="500" />
+
+
+Paraformer模型结构如上图所示，由 Encoder、Predictor、Sampler、Decoder 与 Loss function 五部分组成。Encoder可以采用不同的网络结构，例如self-attention，conformer，SAN-M等。Predictor 为两层FFN，预测目标文字个数以及抽取目标文字对应的声学向量。Sampler 为无可学习参数模块，依据输入的声学向量和目标向量，生产含有语义的特征向量。Decoder 结构与自回归模型类似，为双向建模（自回归为单向建模）。Loss function 部分，除了交叉熵（CE）与 MWER 区分性优化目标，还包括了 Predictor 优化目标 MAE。
+
+
+其核心点主要有：  
+- Predictor 模块：基于 Continuous integrate-and-fire (CIF) 的 预测器 (Predictor) 来抽取目标文字对应的声学特征向量，可以更加准确的预测语音中目标文字个数。  
+- Sampler：通过采样，将声学特征向量与目标文字向量变换成含有语义信息的特征向量，配合双向的 Decoder 来增强模型对于上下文的建模能力。  
+- 基于负样本采样的 MWER 训练准则。  
+
+更详细的细节见：
+- 论文： [Paraformer: Fast and Accurate Parallel Transformer for Non-autoregressive End-to-End Speech Recognition](https://arxiv.org/abs/2206.08317)
+- 论文解读：[Paraformer: 高识别率、高计算效率的单轮非自回归端到端语音识别模型](https://mp.weixin.qq.com/s/xQ87isj5_wxWiQs4qUXtVw)
+
+
+## 基于ModelScope进行推理
+
+- 推理支持音频格式如下：
+  - wav文件路径，例如：data/test/audios/asr_example.wav
+  - pcm文件路径，例如：data/test/audios/asr_example.pcm
+  - wav文件url，例如：https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav
+  - wav二进制数据，格式bytes，例如：用户直接从文件里读出bytes数据或者是麦克风录出bytes数据。
+  - 已解析的audio音频，例如：audio, rate = soundfile.read("asr_example_zh.wav")，类型为numpy.ndarray或者torch.Tensor。
+  - wav.scp文件，需符合如下要求：
+
+```sh
+cat wav.scp
+asr_example1  data/test/audios/asr_example1.wav
+asr_example2  data/test/audios/asr_example2.wav
+...
+```
+
+- 若输入格式wav文件url，api调用方式可参考如下范例：
+
+```python
+from modelscope.pipelines import pipeline
+from modelscope.utils.constant import Tasks
+
+inference_pipeline = pipeline(
+    task=Tasks.auto_speech_recognition,
+    model='iic/speech_paraformer_asr_nat-zh-cn-8k-common-vocab8358-tensorflow1', model_revision="v2.0.4")
+
+rec_result = inference_pipeline('https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_8K.wav')
+print(rec_result)
+```
+
+- 输入音频为pcm格式，调用api时需要传入音频采样率参数audio_fs，例如：
+
+```python
+rec_result = inference_pipeline('https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_8K.pcm', audio_fs=16000)
+```
+
+- 输入音频为wav格式，api调用方式可参考如下范例:
+
+```python
+rec_result = inference_pipeline('asr_example_8K.wav')
+```
+
+- 若输入格式为文件wav.scp(注：文件名需要以.scp结尾)，可添加 output_dir 参数将识别结果写入文件中，api调用方式可参考如下范例:
+
+```python
+inference_pipeline("wav.scp", output_dir='./output_dir')
+```
+识别结果输出路径结构如下：
+
+```sh
+tree output_dir/
+output_dir/
+└── 1best_recog
+    ├── score
+    └── text
+
+1 directory, 3 files
+```
+score：识别路径得分
+
+text：语音识别结果文件
+
+
+- 若输入音频为已解析的audio音频，api调用方式可参考如下范例：
+
+```python
+import soundfile
+
+waveform, sample_rate = soundfile.read("asr_example_8K.wav")
+rec_result = inference_pipeline(waveform)
+```
+
+- ASR、VAD、PUNC模型自由组合
+
+可根据使用需求对VAD和PUNC标点模型进行自由组合，使用方式如下：
+```python
+inference_pipeline = pipeline(
+    task=Tasks.auto_speech_recognition,
+    model='iic/speech_paraformer_asr_nat-zh-cn-8k-common-vocab8358-tensorflow1', model_revision="v2.0.4",
+    vad_model='iic/iic/speech_fsmn_vad_zh-cn-8k-common', vad_model_revision="v2.0.4",
+    punc_model='iic/punc_ct-transformer_zh-cn-common-vocab272727-pytorch', punc_model_revision="v2.0.4",
+    # spk_model="iic/speech_campplus_sv_zh-cn_16k-common",
+    # spk_model_revision="v2.0.2",
+)
+```
+若不使用PUNC模型，可配置punc_model=""，或不传入punc_model参数，如需加入LM模型，可增加配置lm_model='damo/speech_transformer_lm_zh-cn-common-vocab8404-pytorch'，并设置lm_weight和beam_size参数。
+
+## 基于FunASR进行推理
+
+下面为快速上手教程，测试音频（[中文](https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/vad_example.wav)，[英文](https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_en.wav)）
+
+### 可执行命令行
+在命令行终端执行：
+
+```shell
+funasr +model=paraformer-zh +vad_model="fsmn-vad" +punc_model="ct-punc" +input=vad_example.wav
+```
+
+注：支持单条音频文件识别，也支持文件列表，列表为kaldi风格wav.scp：`wav_id   wav_path`
+
+### python示例
+#### 非实时语音识别
+```python
+from funasr import AutoModel
+# paraformer-zh is a multi-functional asr model
+# use vad, punc, spk or not as you need
+model = AutoModel(model="paraformer-zh", model_revision="v2.0.4",
+                  vad_model="fsmn-vad", vad_model_revision="v2.0.4",
+                  punc_model="ct-punc-c", punc_model_revision="v2.0.4",
+                  # spk_model="cam++", spk_model_revision="v2.0.2",
+                  )
+res = model.generate(input=f"{model.model_path}/example/asr_example.wav", 
+            batch_size_s=300, 
+            hotword='魔搭')
+print(res)
+```
+注：`model_hub`：表示模型仓库，`ms`为选择modelscope下载，`hf`为选择huggingface下载。
+
+#### 实时语音识别
+
+```python
+from funasr import AutoModel
+
+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="paraformer-zh-streaming", model_revision="v2.0.4")
+
+import soundfile
+import os
+
+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
+
+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)
+```
+
+注：`chunk_size`为流式延时配置，`[0,10,5]`表示上屏实时出字粒度为`10*60=600ms`，未来信息为`5*60=300ms`。每次推理输入为`600ms`（采样点数为`16000*0.6=960`），输出为对应文字，最后一个语音片段输入需要设置`is_final=True`来强制输出最后一个字。
+
+#### 语音端点检测（非实时）
+```python
+from funasr import AutoModel
+
+model = AutoModel(model="fsmn-vad", model_revision="v2.0.4")
+
+wav_file = f"{model.model_path}/example/asr_example.wav"
+res = model.generate(input=wav_file)
+print(res)
+```
+
+#### 语音端点检测（实时）
+```python
+from funasr import AutoModel
+
+chunk_size = 200 # ms
+model = AutoModel(model="fsmn-vad", model_revision="v2.0.4")
+
+import soundfile
+
+wav_file = f"{model.model_path}/example/vad_example.wav"
+speech, sample_rate = soundfile.read(wav_file)
+chunk_stride = int(chunk_size * sample_rate / 1000)
+
+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)
+    if len(res[0]["value"]):
+        print(res)
+```
+
+#### 标点恢复
+```python
+from funasr import AutoModel
+
+model = AutoModel(model="ct-punc", model_revision="v2.0.4")
+
+res = model.generate(input="那今天的会就到这里吧 happy new year 明年见")
+print(res)
+```
+
+#### 时间戳预测
+```python
+from funasr import AutoModel
+
+model = AutoModel(model="fa-zh", model_revision="v2.0.4")
+
+wav_file = f"{model.model_path}/example/asr_example.wav"
+text_file = f"{model.model_path}/example/text.txt"
+res = model.generate(input=(wav_file, text_file), data_type=("sound", "text"))
+print(res)
+```
+
+更多详细用法（[示例](https://github.com/alibaba-damo-academy/FunASR/tree/main/examples/industrial_data_pretraining)）
+
+
+## 微调
+
+详细用法（[示例](https://github.com/alibaba-damo-academy/FunASR/tree/main/examples/industrial_data_pretraining)）
+
+
+## Benchmark
+  结合大数据、大模型优化的Paraformer在一序列语音识别的benchmark上获得当前SOTA的效果，以下展示学术数据集AISHELL-1、AISHELL-2、WenetSpeech，公开评测项目SpeechIO TIOBE白盒测试场景的效果。在学术界常用的中文语音识别评测任务中，其表现远远超于目前公开发表论文中的结果，远好于单独封闭数据集上的模型。
+
+### AISHELL-1
+
+| AISHELL-1 test                                   | w/o LM                                | w/ LM                                 |
+|:------------------------------------------------:|:-------------------------------------:|:-------------------------------------:|
+| <div style="width: 150pt">Espnet</div>           | <div style="width: 150pt">4.90</div>  | <div style="width: 150pt">4.70</div>  | 
+| <div style="width: 150pt">Wenet</div>            | <div style="width: 150pt">4.61</div>  | <div style="width: 150pt">4.36</div>  | 
+| <div style="width: 150pt">K2</div>               | <div style="width: 150pt">-</div>     | <div style="width: 150pt">4.26</div>  | 
+| <div style="width: 150pt">Blockformer</div>      | <div style="width: 150pt">4.29</div>  | <div style="width: 150pt">4.05</div>  |
+| <div style="width: 150pt">Paraformer-large</div> | <div style="width: 150pt">1.95</div>  | <div style="width: 150pt">1.68</div>  | 
+
+### AISHELL-2
+
+|           | dev_ios| test_android| test_ios|test_mic|
+|:-------------------------------------------------:|:-------------------------------------:|:-------------------------------------:|:------------------------------------:|:------------------------------------:|
+| <div style="width: 150pt">Espnet</div>            | <div style="width: 70pt">5.40</div>  |<div style="width: 70pt">6.10</div>  |<div style="width: 70pt">5.70</div>  |<div style="width: 70pt">6.10</div>  |
+| <div style="width: 150pt">WeNet</div>             | <div style="width: 70pt">-</div>     |<div style="width: 70pt">-</div>     |<div style="width: 70pt">5.39</div>  |<div style="width: 70pt">-</div>    |
+| <div style="width: 150pt">Paraformer-large</div>  | <div style="width: 70pt">2.80</div>  |<div style="width: 70pt">3.13</div>  |<div style="width: 70pt">2.85</div>  |<div style="width: 70pt">3.06</div>  |
+
+
+### Wenetspeech
+
+|           | dev| test_meeting| test_net|
+|:-------------------------------------------------:|:-------------------------------------:|:-------------------------------------:|:------------------------------------:|
+| <div style="width: 150pt">Espnet</div>            | <div style="width: 100pt">9.70</div>  |<div style="width: 100pt">15.90</div>  |<div style="width: 100pt">8.80</div>  |
+| <div style="width: 150pt">WeNet</div>             | <div style="width: 100pt">8.60</div>  |<div style="width: 100pt">17.34</div>  |<div style="width: 100pt">9.26</div>  |
+| <div style="width: 150pt">K2</div>                | <div style="width: 100pt">7.76</div>  |<div style="width: 100pt">13.41</div>  |<div style="width: 100pt">8.71</div>  |
+| <div style="width: 150pt">Paraformer-large</div>  | <div style="width: 100pt">3.57</div>  |<div style="width: 100pt">6.97</div>   |<div style="width: 100pt">6.74</div>  |
+
+### SpeechIO TIOBE
+
+Paraformer-large模型结合Transformer-LM模型做shallow fusion，在公开评测项目SpeechIO TIOBE白盒测试场景上获得当前SOTA的效果，目前[Transformer-LM模型](https://modelscope.cn/models/damo/speech_transformer_lm_zh-cn-common-vocab8404-pytorch/summary)已在ModelScope上开源，以下展示SpeechIO TIOBE白盒测试场景without LM、with Transformer-LM的效果：
+
+- Decode config w/o LM: 
+  - Decode without LM
+  - Beam size: 1
+- Decode config w/ LM:
+  - Decode with [Transformer-LM](https://modelscope.cn/models/damo/speech_transformer_lm_zh-cn-common-vocab8404-pytorch/summary)
+  - Beam size: 10
+  - LM weight: 0.15
+
+| testset | w/o LM | w/ LM |
+|:------------------:|:----:|:----:|
+|<div style="width: 200pt">SPEECHIO_ASR_ZH00001</div>| <div style="width: 150pt">0.49</div> | <div style="width: 150pt">0.35</div> |
+|<div style="width: 200pt">SPEECHIO_ASR_ZH00002</div>| <div style="width: 150pt">3.23</div> | <div style="width: 150pt">2.86</div> |
+|<div style="width: 200pt">SPEECHIO_ASR_ZH00003</div>| <div style="width: 150pt">1.13</div> | <div style="width: 150pt">0.80</div> |
+|<div style="width: 200pt">SPEECHIO_ASR_ZH00004</div>| <div style="width: 150pt">1.33</div> | <div style="width: 150pt">1.10</div> |
+|<div style="width: 200pt">SPEECHIO_ASR_ZH00005</div>| <div style="width: 150pt">1.41</div> | <div style="width: 150pt">1.18</div> |
+|<div style="width: 200pt">SPEECHIO_ASR_ZH00006</div>| <div style="width: 150pt">5.25</div> | <div style="width: 150pt">4.85</div> |
+|<div style="width: 200pt">SPEECHIO_ASR_ZH00007</div>| <div style="width: 150pt">5.51</div> | <div style="width: 150pt">4.97</div> |
+|<div style="width: 200pt">SPEECHIO_ASR_ZH00008</div>| <div style="width: 150pt">3.69</div> | <div style="width: 150pt">3.18</div> |
+|<div style="width: 200pt">SPEECHIO_ASR_ZH00009</div>| <div style="width: 150pt">3.02</div> | <div style="width: 150pt">2.78</div> |
+|<div style="width: 200pt">SPEECHIO_ASR_ZH000010</div>| <div style="width: 150pt">3.35</div> | <div style="width: 150pt">2.99</div> |
+|<div style="width: 200pt">SPEECHIO_ASR_ZH000011</div>| <div style="width: 150pt">1.54</div> | <div style="width: 150pt">1.25</div> |
+|<div style="width: 200pt">SPEECHIO_ASR_ZH000012</div>| <div style="width: 150pt">2.06</div> | <div style="width: 150pt">1.68</div> |
+|<div style="width: 200pt">SPEECHIO_ASR_ZH000013</div>| <div style="width: 150pt">2.57</div> | <div style="width: 150pt">2.25</div> |
+|<div style="width: 200pt">SPEECHIO_ASR_ZH000014</div>| <div style="width: 150pt">3.86</div> | <div style="width: 150pt">3.08</div> |
+|<div style="width: 200pt">SPEECHIO_ASR_ZH000015</div>| <div style="width: 150pt">3.34</div> | <div style="width: 150pt">2.67</div> |
+
+
+## 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
+- 适合与离线语音识别场景，如录音文件转写，配合GPU推理效果更加，推荐输入语音时长在20s以下，若想解码长音频，推荐使用[Paraformer-large长音频模型](https://www.modelscope.cn/models/damo/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch/summary)，集成VAD、ASR、标点与时间戳功能，可直接对时长为数小时音频进行识别，并输出带标点文字与时间戳。
+
+
+## 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}
+}
+```
--- a/am.mvn
+++ b/am.mvn
--- a/config.yaml
+++ b/config.yaml
@@ -0,0 +1,123 @@
+
+# network architecture
+model: Paraformer
+model_conf:
+    ctc_weight: 0.0
+    lsm_weight: 0.1     # label smoothing option
+    length_normalized_loss: true
+    predictor_weight: 1.0
+    predictor_bias: 1
+    sampling_ratio: 0.75
+
+# encoder
+encoder: SANMEncoder
+encoder_conf:
+    output_size: 320
+    attention_heads: 4
+    linear_units: 1280
+    num_blocks: 40
+    dropout_rate: 0.1
+    positional_dropout_rate: 0.1
+    attention_dropout_rate: 0.1
+    input_layer: pe
+    pos_enc_class: SinusoidalPositionEncoder
+    normalize_before: true
+    kernel_size: 11
+    sanm_shfit: 0
+    selfattention_layer_type: sanm
+
+# decoder
+decoder: ParaformerSANMDecoder
+decoder_conf:
+    attention_heads: 4
+    linear_units: 1280
+    num_blocks: 12
+    dropout_rate: 0.1
+    positional_dropout_rate: 0.1
+    self_attention_dropout_rate: 0.1
+    src_attention_dropout_rate: 0.1
+    att_layer_num: 12
+    kernel_size: 11
+    sanm_shfit: 0
+
+predictor: CifPredictorV2
+predictor_conf:
+    idim: 320
+    threshold: 1.0
+    l_order: 1
+    r_order: 1
+    tail_threshold: 0.45
+
+# frontend related
+frontend: WavFrontend
+frontend_conf:
+    fs: 8000
+    window: hamming
+    n_mels: 80
+    frame_length: 25
+    frame_shift: 10
+    lfr_m: 7
+    lfr_n: 6
+
+specaug: SpecAugLFR
+specaug_conf:
+    apply_time_warp: false
+    time_warp_window: 5
+    time_warp_mode: bicubic
+    apply_freq_mask: true
+    freq_mask_width_range:
+    - 0
+    - 30
+    lfr_rate: 6
+    num_freq_mask: 1
+    apply_time_mask: true
+    time_mask_width_range:
+    - 0
+    - 12
+    num_time_mask: 1
+
+train_conf:
+  accum_grad: 1
+  grad_clip: 5
+  max_epoch: 150
+  val_scheduler_criterion:
+      - valid
+      - acc
+  best_model_criterion:
+  -   - valid
+      - acc
+      - max
+  keep_nbest_models: 10
+  log_interval: 50
+
+optim: adam
+optim_conf:
+   lr: 0.0005
+scheduler: warmuplr
+scheduler_conf:
+   warmup_steps: 30000
+
+dataset: AudioDataset
+dataset_conf:
+    index_ds: IndexDSJsonl
+    batch_sampler: DynamicBatchLocalShuffleSampler
+    batch_type: example # example or length
+    batch_size: 1 # if batch_type is example, batch_size is the numbers of samples; if length, batch_size is source_token_len+target_token_len;
+    max_token_length: 2048 # filter samples if source_token_len+target_token_len > max_token_length, 
+    buffer_size: 500
+    shuffle: True
+    num_workers: 0
+
+tokenizer: CharTokenizer
+tokenizer_conf:
+  unk_symbol: <unk>
+  split_with_space: true
+  
+
+input_size: 560
+ctc_conf:
+    dropout_rate: 0.0
+    ctc_type: builtin
+    reduce: true
+    ignore_nan_grad: true
+normalize: null
--- a/configuration.json
+++ b/configuration.json
@@ -0,0 +1,14 @@
+{
+  "framework": "pytorch",
+  "task" : "auto-speech-recognition",
+  "model": {"type" : "funasr"},
+  "pipeline": {"type":"funasr-pipeline"},
+  "model_name_in_hub": {
+    "ms":"iic/speech_paraformer_asr_nat-zh-cn-8k-common-vocab8358-tensorflow1", 
+    "hf":""},
+  "file_path_metas": {
+    "init_param":"model.pt", 
+    "config":"config.yaml",
+    "tokenizer_conf": {"token_list": "tokens.json", "seg_dict_file": "seg_dict"},
+    "frontend_conf":{"cmvn_file": "am.mvn"}}
+}
--- a/example/asr_example.wav
+++ b/example/asr_example.wav
--- a/example/asr_example_8K.wav
+++ b/example/asr_example_8K.wav
--- a/fig/error_type.png
+++ b/fig/error_type.png
--- a/fig/struct.png
+++ b/fig/struct.png
--- a/model.pt
+++ b/model.pt
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9d8d136b17cd3c20def31612e12e921babaefcc2750844a85ca5d4b2b2eef8e5
+size 284469859
--- a/320432
+++ b/320432
--- a/tokens.json
+++ b/tokens.json