diff --git a/.gitattributes b/.gitattributes
index 8b5bcaf693ffda18b3e1a431a12933eac17e3943..6b56304db606e71caf99c0d1b26a5d5450148ab1 100644
--- a/.gitattributes
+++ b/.gitattributes
@@ -38,3 +38,4 @@ results/check/speech_j.joblib filter=lfs diff=lfs merge=lfs -text
 results/track_record/collator_print_first.joblib filter=lfs diff=lfs merge=lfs -text
 results/track_record/outputs_loss.joblib filter=lfs diff=lfs merge=lfs -text
 results/track_record/text_projection.joblib filter=lfs diff=lfs merge=lfs -text
+model/fsmn_vad/example/vad_example.wav filter=lfs diff=lfs merge=lfs -text
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+Revision:master,CreatedAt:1727321787
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diff --git a/model/SenseVoiceSmall/README.md b/model/SenseVoiceSmall/README.md
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@@ -0,0 +1,219 @@
+---
+frameworks:
+- Pytorch
+license: Apache License 2.0
+tasks:
+- auto-speech-recognition
+
+#model-type:
+##如 gpt、phi、llama、chatglm、baichuan 等
+#- gpt
+
+#domain:
+##如 nlp、cv、audio、multi-modal
+#- nlp
+
+#language:
+##语言代码列表 https://help.aliyun.com/document_detail/215387.html?spm=a2c4g.11186623.0.0.9f8d7467kni6Aa
+#- cn 
+
+#metrics:
+##如 CIDEr、Blue、ROUGE 等
+#- CIDEr
+
+#tags:
+##各种自定义，包括 pretrained、fine-tuned、instruction-tuned、RL-tuned 等训练方法和其他
+#- pretrained
+
+#tools:
+##如 vllm、fastchat、llamacpp、AdaSeq 等
+#- vllm
+---
+
+# Highlights
+**SenseVoice**专注于高精度多语言语音识别、情感辨识和音频事件检测
+- **多语言识别：** 采用超过40万小时数据训练，支持超过50种语言，识别效果上优于Whisper模型。
+- **富文本识别：** 
+  - 具备优秀的情感识别，能够在测试数据上达到和超过目前最佳情感识别模型的效果。
+  - 支持声音事件检测能力，支持音乐、掌声、笑声、哭声、咳嗽、喷嚏等多种常见人机交互事件进行检测。
+- **高效推理：** SenseVoice-Small模型采用非自回归端到端框架，推理延迟极低，10s音频推理仅耗时70ms，15倍优于Whisper-Large。
+- **微调定制：** 具备便捷的微调脚本与策略，方便用户根据业务场景修复长尾样本问题。
+- **服务部署：** 具有完整的服务部署链路，支持多并发请求，支持客户端语言有，python、c++、html、java与c#等。
+
+
+## <strong>[SenseVoice开源项目介绍](https://github.com/FunAudioLLM/SenseVoice)</strong>
+<strong>[SenseVoice](https://github.com/FunAudioLLM/SenseVoice)</strong>开源模型是多语言音频理解模型，具有包括语音识别、语种识别、语音情感识别，声学事件检测能力。
+
+[**github仓库**](https://github.com/FunAudioLLM/SenseVoice)
+| [**最新动态**](https://github.com/FunAudioLLM/SenseVoice/blob/main/README_zh.md#%E6%9C%80%E6%96%B0%E5%8A%A8%E6%80%81)
+| [**环境安装**](https://github.com/FunAudioLLM/SenseVoice/blob/main/README_zh.md#%E7%8E%AF%E5%A2%83%E5%AE%89%E8%A3%85)
+
+# 模型结构图
+SenseVoice多语言音频理解模型，支持语音识别、语种识别、语音情感识别、声学事件检测、逆文本正则化等能力，采用工业级数十万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。模型可以被应用于中文、粤语、英语、日语、韩语音频识别，并输出带有情感和事件的富文本转写结果。
+
+<p align="center">
+<img src="fig/sensevoice.png" alt="SenseVoice模型结构"  width="1500" />
+</p>
+
+SenseVoice-Small是基于非自回归端到端框架模型，为了指定任务，我们在语音特征前添加四个嵌入作为输入传递给编码器：
+- LID：用于预测音频语种标签。
+- SER：用于预测音频情感标签。
+- AED：用于预测音频包含的事件标签。
+- ITN：用于指定识别输出文本是否进行逆文本正则化。
+
+
+# 依赖环境
+
+推理之前，请务必更新funasr与modelscope版本
+
+```shell
+pip install -U funasr modelscope
+```
+
+# 用法
+
+
+## 推理
+
+### modelscope pipeline推理
+```python
+from modelscope.pipelines import pipeline
+from modelscope.utils.constant import Tasks
+
+inference_pipeline = pipeline(
+    task=Tasks.auto_speech_recognition,
+    model='iic/SenseVoiceSmall',
+    model_revision="master",
+    device="cuda:0",)
+
+rec_result = inference_pipeline('https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav')
+print(rec_result)
+```
+
+### 使用funasr推理
+
+支持任意格式音频输入，支持任意时长输入
+
+```python
+from funasr import AutoModel
+from funasr.utils.postprocess_utils import rich_transcription_postprocess
+
+model_dir = "iic/SenseVoiceSmall"
+
+
+model = AutoModel(
+    model=model_dir,
+    trust_remote_code=True,
+    remote_code="./model.py",  
+    vad_model="fsmn-vad",
+    vad_kwargs={"max_single_segment_time": 30000},
+    device="cuda:0",
+)
+
+# en
+res = model.generate(
+    input=f"{model.model_path}/example/en.mp3",
+    cache={},
+    language="auto",  # "zn", "en", "yue", "ja", "ko", "nospeech"
+    use_itn=True,
+    batch_size_s=60,
+    merge_vad=True,  #
+    merge_length_s=15,
+)
+text = rich_transcription_postprocess(res[0]["text"])
+print(text)
+```
+参数说明：
+- `model_dir`：模型名称，或本地磁盘中的模型路径。
+- `trust_remote_code`：
+  - `True`表示model代码实现从`remote_code`处加载，`remote_code`指定`model`具体代码的位置（例如，当前目录下的`model.py`），支持绝对路径与相对路径，以及网络url。
+  - `False`表示，model代码实现为 [FunASR](https://github.com/modelscope/FunASR) 内部集成版本，此时修改当前目录下的`model.py`不会生效，因为加载的是funasr内部版本，模型代码[点击查看](https://github.com/modelscope/FunASR/tree/main/funasr/models/sense_voice)。
+- `vad_model`：表示开启VAD，VAD的作用是将长音频切割成短音频，此时推理耗时包括了VAD与SenseVoice总耗时，为链路耗时，如果需要单独测试SenseVoice模型耗时，可以关闭VAD模型。
+- `vad_kwargs`：表示VAD模型配置,`max_single_segment_time`: 表示`vad_model`最大切割音频时长, 单位是毫秒ms。
+- `use_itn`：输出结果中是否包含标点与逆文本正则化。
+- `batch_size_s` 表示采用动态batch，batch中总音频时长，单位为秒s。
+- `merge_vad`：是否将 vad 模型切割的短音频碎片合成，合并后长度为`merge_length_s`，单位为秒s。
+- `ban_emo_unk`：禁用emo_unk标签，禁用后所有的句子都会被赋与情感标签。默认`False`
+
+```python
+model = AutoModel(model=model_dir, trust_remote_code=True, device="cuda:0")
+
+res = model.generate(
+    input=f"{model.model_path}/example/en.mp3",
+    cache={},
+    language="auto", # "zn", "en", "yue", "ja", "ko", "nospeech"
+    use_itn=True,
+    batch_size=64, 
+)
+```
+
+更多详细用法，请参考 [文档](https://github.com/modelscope/FunASR/blob/main/docs/tutorial/README.md)
+
+
+
+## 模型下载
+上面代码会自动下载模型，如果您需要离线下载好模型，可以通过下面代码，手动下载，之后指定模型本地路径即可。
+
+SDK下载
+```bash
+#安装ModelScope
+pip install modelscope
+```
+```python
+#SDK模型下载
+from modelscope import snapshot_download
+model_dir = snapshot_download('iic/SenseVoiceSmall')
+```
+Git下载
+```
+#Git模型下载
+git clone https://www.modelscope.cn/iic/SenseVoiceSmall.git
+```
+
+## 服务部署
+
+Undo
+
+# Performance
+
+## 语音识别效果
+我们在开源基准数据集（包括 AISHELL-1、AISHELL-2、Wenetspeech、Librispeech和Common Voice）上比较了SenseVoice与Whisper的多语言语音识别性能和推理效率。在中文和粤语识别效果上，SenseVoice-Small模型具有明显的效果优势。
+
+<p align="center">
+<img src="fig/asr_results.png" alt="SenseVoice模型在开源测试集上的表现"  width="2500" />
+</p>
+
+
+
+## 情感识别效果
+由于目前缺乏被广泛使用的情感识别测试指标和方法，我们在多个测试集的多种指标进行测试，并与近年来Benchmark上的多个结果进行了全面的对比。所选取的测试集同时包含中文/英文两种语言以及表演、影视剧、自然对话等多种风格的数据，在不进行目标数据微调的前提下，SenseVoice能够在测试数据上达到和超过目前最佳情感识别模型的效果。
+
+<p align="center">
+<img src="fig/ser_table.png" alt="SenseVoice模型SER效果1"  width="1500" />
+</p>
+
+同时，我们还在测试集上对多个开源情感识别模型进行对比，结果表明，SenseVoice-Large模型可以在几乎所有数据上都达到了最佳效果，而SenseVoice-Small模型同样可以在多数数据集上取得超越其他开源模型的效果。
+
+<p align="center">
+<img src="fig/ser_figure.png" alt="SenseVoice模型SER效果2"  width="500" />
+</p>
+
+## 事件检测效果
+
+尽管SenseVoice只在语音数据上进行训练，它仍然可以作为事件检测模型进行单独使用。我们在环境音分类ESC-50数据集上与目前业内广泛使用的BEATS与PANN模型的效果进行了对比。SenseVoice模型能够在这些任务上取得较好的效果，但受限于训练数据与训练方式，其事件分类效果专业的事件检测模型相比仍然有一定的差距。
+
+<p align="center">
+<img src="fig/aed_figure.png" alt="SenseVoice模型AED效果"  width="500" />
+</p>
+
+
+
+## 推理效率
+SenseVoice-Small模型采用非自回归端到端架构，推理延迟极低。在参数量与Whisper-Small模型相当的情况下，比Whisper-Small模型推理速度快7倍，比Whisper-Large模型快17倍。同时SenseVoice-small模型在音频时长增加的情况下，推理耗时也无明显增加。
+
+
+<p align="center">
+<img src="fig/inference.png" alt="SenseVoice模型的推理效率"  width="1500" />
+</p>
+
+<p style="color: lightgrey;">如果您是本模型的贡献者，我们邀请您根据<a href="https://modelscope.cn/docs/ModelScope%E6%A8%A1%E5%9E%8B%E6%8E%A5%E5%85%A5%E6%B5%81%E7%A8%8B%E6%A6%82%E8%A7%88" style="color: lightgrey; text-decoration: underline;">模型贡献文档</a>，及时完善模型卡片内容。</p>
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new file mode 100644
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+<LearnRateCoef> 0 [ 0.155775 0.154484 0.1527379 0.1518718 0.1506028 0.1489256 0.147067 0.1447061 0.1436307 0.1443568 0.1451849 0.1455157 0.1452821 0.1445717 0.1439195 0.1435867 0.1436018 0.1438781 0.1442086 0.1448844 0.1454756 0.145663 0.146268 0.1467386 0.1472724 0.147664 0.1480913 0.1483739 0.1488841 0.1493636 0.1497088 0.1500379 0.1502916 0.1505389 0.1506787 0.1507102 0.1505992 0.1505445 0.1505938 0.1508133 0.1509569 0.1512396 0.1514625 0.1516195 0.1516156 0.1515561 0.1514966 0.1513976 0.1512612 0.151076 0.1510596 0.1510431 0.151077 0.1511168 0.1511917 0.151023 0.1508045 0.1505885 0.1503493 0.1502373 0.1501726 0.1500762 0.1500065 0.1499782 0.150057 0.1502658 0.150469 0.1505335 0.1505505 0.1505328 0.1504275 0.1502438 0.1499674 0.1497118 0.1494661 0.1493102 0.1493681 0.1495501 0.1499738 0.1509654 0.155775 0.154484 0.1527379 0.1518718 0.1506028 0.1489256 0.147067 0.1447061 0.1436307 0.1443568 0.1451849 0.1455157 0.1452821 0.1445717 0.1439195 0.1435867 0.1436018 0.1438781 0.1442086 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+</Nnet> 
diff --git a/model/SenseVoiceSmall/chn_jpn_yue_eng_ko_spectok.bpe.model b/model/SenseVoiceSmall/chn_jpn_yue_eng_ko_spectok.bpe.model
new file mode 100644
index 0000000000000000000000000000000000000000..72734d2b95d9bf34a2df2018846cf87edac3332d
--- /dev/null
+++ b/model/SenseVoiceSmall/chn_jpn_yue_eng_ko_spectok.bpe.model
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:aa87f86064c3730d799ddf7af3c04659151102cba548bce325cf06ba4da4e6a8
+size 377341
diff --git a/model/SenseVoiceSmall/config.yaml b/model/SenseVoiceSmall/config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..6638d28a02df3113c9327596a036488a3dc10a7e
--- /dev/null
+++ b/model/SenseVoiceSmall/config.yaml
@@ -0,0 +1,98 @@
+encoder: SenseVoiceEncoderSmall
+encoder_conf:
+    output_size: 512
+    attention_heads: 4
+    linear_units: 2048
+    num_blocks: 50
+    tp_blocks: 20
+    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
+
+
+model: SenseVoiceSmall
+model_conf:
+    length_normalized_loss: true
+    sos: 1
+    eos: 2
+    ignore_id: -1
+
+tokenizer: SentencepiecesTokenizer
+tokenizer_conf:
+  bpemodel: null
+  unk_symbol: <unk>
+  split_with_space: true
+
+frontend: WavFrontend
+frontend_conf:
+    fs: 32000
+    window: hamming
+    n_mels: 80
+    frame_length: 25
+    frame_shift: 10
+    lfr_m: 7
+    lfr_n: 6
+    cmvn_file: null
+    dither: 0.0
+
+
+dataset: SenseVoiceCTCDataset
+dataset_conf:
+  index_ds: IndexDSJsonl
+  batch_sampler: EspnetStyleBatchSampler
+  data_split_num: 32
+  batch_type: token
+  batch_size: 14000
+  max_token_length: 2000
+  min_token_length: 60
+  max_source_length: 2000
+  min_source_length: 60
+  max_target_length: 200
+  min_target_length: 0
+  shuffle: true
+  num_workers: 4
+  sos: ${model_conf.sos}
+  eos: ${model_conf.eos}
+  IndexDSJsonl: IndexDSJsonl
+  retry: 20
+
+train_conf:
+  accum_grad: 1
+  grad_clip: 5
+  max_epoch: 20
+  keep_nbest_models: 10
+  avg_nbest_model: 10
+  log_interval: 100
+  resume: true
+  validate_interval: 10000
+  save_checkpoint_interval: 10000
+
+optim: adamw
+optim_conf:
+  lr: 0.00002
+scheduler: warmuplr
+scheduler_conf:
+  warmup_steps: 25000
+
+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
diff --git a/model/SenseVoiceSmall/configuration.json b/model/SenseVoiceSmall/configuration.json
new file mode 100644
index 0000000000000000000000000000000000000000..264d8171d2d6bfebf3c48d04bd325b34d9be1d9a
--- /dev/null
+++ b/model/SenseVoiceSmall/configuration.json
@@ -0,0 +1,14 @@
+{
+  "framework": "pytorch",
+  "task" : "auto-speech-recognition",
+  "model": {"type" : "funasr"},
+  "pipeline": {"type":"funasr-pipeline"},
+  "model_name_in_hub": {
+    "ms":"", 
+    "hf":""},
+  "file_path_metas": {
+    "init_param":"model.pt", 
+    "config":"config.yaml",
+    "tokenizer_conf": {"bpemodel": "chn_jpn_yue_eng_ko_spectok.bpe.model"},
+    "frontend_conf":{"cmvn_file": "am.mvn"}}
+}
\ No newline at end of file
diff --git a/model/SenseVoiceSmall/example/.DS_Store b/model/SenseVoiceSmall/example/.DS_Store
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diff --git a/model/SenseVoiceSmall/example/en.mp3 b/model/SenseVoiceSmall/example/en.mp3
new file mode 100644
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Binary files /dev/null and b/model/SenseVoiceSmall/example/en.mp3 differ
diff --git a/model/SenseVoiceSmall/example/ja.mp3 b/model/SenseVoiceSmall/example/ja.mp3
new file mode 100644
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Binary files /dev/null and b/model/SenseVoiceSmall/example/ja.mp3 differ
diff --git a/model/SenseVoiceSmall/example/ko.mp3 b/model/SenseVoiceSmall/example/ko.mp3
new file mode 100644
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Binary files /dev/null and b/model/SenseVoiceSmall/example/ko.mp3 differ
diff --git a/model/SenseVoiceSmall/example/yue.mp3 b/model/SenseVoiceSmall/example/yue.mp3
new file mode 100644
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Binary files /dev/null and b/model/SenseVoiceSmall/example/yue.mp3 differ
diff --git a/model/SenseVoiceSmall/example/zh.mp3 b/model/SenseVoiceSmall/example/zh.mp3
new file mode 100644
index 0000000000000000000000000000000000000000..1ae2c89b29112ee5e23bcebc353ea6687c38e6bd
Binary files /dev/null and b/model/SenseVoiceSmall/example/zh.mp3 differ
diff --git a/model/SenseVoiceSmall/fig/aed_figure.png b/model/SenseVoiceSmall/fig/aed_figure.png
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Binary files /dev/null and b/model/SenseVoiceSmall/fig/aed_figure.png differ
diff --git a/model/SenseVoiceSmall/fig/asr_results.png b/model/SenseVoiceSmall/fig/asr_results.png
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Binary files /dev/null and b/model/SenseVoiceSmall/fig/asr_results.png differ
diff --git a/model/SenseVoiceSmall/fig/inference.png b/model/SenseVoiceSmall/fig/inference.png
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Binary files /dev/null and b/model/SenseVoiceSmall/fig/inference.png differ
diff --git a/model/SenseVoiceSmall/fig/sensevoice.png b/model/SenseVoiceSmall/fig/sensevoice.png
new file mode 100644
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diff --git a/model/SenseVoiceSmall/fig/ser_figure.png b/model/SenseVoiceSmall/fig/ser_figure.png
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Binary files /dev/null and b/model/SenseVoiceSmall/fig/ser_figure.png differ
diff --git a/model/SenseVoiceSmall/fig/ser_table.png b/model/SenseVoiceSmall/fig/ser_table.png
new file mode 100644
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diff --git a/model/SenseVoiceSmall/model.pt b/model/SenseVoiceSmall/model.pt
new file mode 100644
index 0000000000000000000000000000000000000000..504783eeb5c68405034d837e954fc733d6677eba
--- /dev/null
+++ b/model/SenseVoiceSmall/model.pt
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:833ca2dcfdf8ec91bd4f31cfac36d6124e0c459074d5e909aec9cabe6204a3ea
+size 936291369
diff --git a/model/SenseVoiceSmall/model.py b/model/SenseVoiceSmall/model.py
new file mode 100644
index 0000000000000000000000000000000000000000..ee5cb93bc5287fa615203a959b481144c6539b58
--- /dev/null
+++ b/model/SenseVoiceSmall/model.py
@@ -0,0 +1,895 @@
+import time
+import torch
+from torch import nn
+import torch.nn.functional as F
+from typing import Iterable, Optional
+
+from funasr.register import tables
+from funasr.models.ctc.ctc import CTC
+from funasr.utils.datadir_writer import DatadirWriter
+from funasr.models.paraformer.search import Hypothesis
+from funasr.train_utils.device_funcs import force_gatherable
+from funasr.losses.label_smoothing_loss import LabelSmoothingLoss
+from funasr.metrics.compute_acc import compute_accuracy, th_accuracy
+from funasr.utils.load_utils import load_audio_text_image_video, extract_fbank
+
+
+class SinusoidalPositionEncoder(torch.nn.Module):
+    """ """
+
+    def __int__(self, d_model=80, dropout_rate=0.1):
+        pass
+
+    def encode(
+        self, positions: torch.Tensor = None, depth: int = None, dtype: torch.dtype = torch.float32
+    ):
+        batch_size = positions.size(0)
+        positions = positions.type(dtype)
+        device = positions.device
+        log_timescale_increment = torch.log(torch.tensor([10000], dtype=dtype, device=device)) / (
+            depth / 2 - 1
+        )
+        inv_timescales = torch.exp(
+            torch.arange(depth / 2, device=device).type(dtype) * (-log_timescale_increment)
+        )
+        inv_timescales = torch.reshape(inv_timescales, [batch_size, -1])
+        scaled_time = torch.reshape(positions, [1, -1, 1]) * torch.reshape(
+            inv_timescales, [1, 1, -1]
+        )
+        encoding = torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], dim=2)
+        return encoding.type(dtype)
+
+    def forward(self, x):
+        batch_size, timesteps, input_dim = x.size()
+        positions = torch.arange(1, timesteps + 1, device=x.device)[None, :]
+        position_encoding = self.encode(positions, input_dim, x.dtype).to(x.device)
+
+        return x + position_encoding
+
+
+class PositionwiseFeedForward(torch.nn.Module):
+    """Positionwise feed forward layer.
+
+    Args:
+        idim (int): Input dimenstion.
+        hidden_units (int): The number of hidden units.
+        dropout_rate (float): Dropout rate.
+
+    """
+
+    def __init__(self, idim, hidden_units, dropout_rate, activation=torch.nn.ReLU()):
+        """Construct an PositionwiseFeedForward object."""
+        super(PositionwiseFeedForward, self).__init__()
+        self.w_1 = torch.nn.Linear(idim, hidden_units)
+        self.w_2 = torch.nn.Linear(hidden_units, idim)
+        self.dropout = torch.nn.Dropout(dropout_rate)
+        self.activation = activation
+
+    def forward(self, x):
+        """Forward function."""
+        return self.w_2(self.dropout(self.activation(self.w_1(x))))
+
+
+class MultiHeadedAttentionSANM(nn.Module):
+    """Multi-Head Attention layer.
+
+    Args:
+        n_head (int): The number of heads.
+        n_feat (int): The number of features.
+        dropout_rate (float): Dropout rate.
+
+    """
+
+    def __init__(
+        self,
+        n_head,
+        in_feat,
+        n_feat,
+        dropout_rate,
+        kernel_size,
+        sanm_shfit=0,
+        lora_list=None,
+        lora_rank=8,
+        lora_alpha=16,
+        lora_dropout=0.1,
+    ):
+        """Construct an MultiHeadedAttention object."""
+        super().__init__()
+        assert n_feat % n_head == 0
+        # We assume d_v always equals d_k
+        self.d_k = n_feat // n_head
+        self.h = n_head
+        # self.linear_q = nn.Linear(n_feat, n_feat)
+        # self.linear_k = nn.Linear(n_feat, n_feat)
+        # self.linear_v = nn.Linear(n_feat, n_feat)
+
+        self.linear_out = nn.Linear(n_feat, n_feat)
+        self.linear_q_k_v = nn.Linear(in_feat, n_feat * 3)
+        self.attn = None
+        self.dropout = nn.Dropout(p=dropout_rate)
+
+        self.fsmn_block = nn.Conv1d(
+            n_feat, n_feat, kernel_size, stride=1, padding=0, groups=n_feat, bias=False
+        )
+        # padding
+        left_padding = (kernel_size - 1) // 2
+        if sanm_shfit > 0:
+            left_padding = left_padding + sanm_shfit
+        right_padding = kernel_size - 1 - left_padding
+        self.pad_fn = nn.ConstantPad1d((left_padding, right_padding), 0.0)
+
+    def forward_fsmn(self, inputs, mask, mask_shfit_chunk=None):
+        b, t, d = inputs.size()
+        if mask is not None:
+            mask = torch.reshape(mask, (b, -1, 1))
+            if mask_shfit_chunk is not None:
+                mask = mask * mask_shfit_chunk
+            inputs = inputs * mask
+
+        x = inputs.transpose(1, 2)
+        x = self.pad_fn(x)
+        x = self.fsmn_block(x)
+        x = x.transpose(1, 2)
+        x += inputs
+        x = self.dropout(x)
+        if mask is not None:
+            x = x * mask
+        return x
+
+    def forward_qkv(self, x):
+        """Transform query, key and value.
+
+        Args:
+            query (torch.Tensor): Query tensor (#batch, time1, size).
+            key (torch.Tensor): Key tensor (#batch, time2, size).
+            value (torch.Tensor): Value tensor (#batch, time2, size).
+
+        Returns:
+            torch.Tensor: Transformed query tensor (#batch, n_head, time1, d_k).
+            torch.Tensor: Transformed key tensor (#batch, n_head, time2, d_k).
+            torch.Tensor: Transformed value tensor (#batch, n_head, time2, d_k).
+
+        """
+        b, t, d = x.size()
+        q_k_v = self.linear_q_k_v(x)
+        q, k, v = torch.split(q_k_v, int(self.h * self.d_k), dim=-1)
+        q_h = torch.reshape(q, (b, t, self.h, self.d_k)).transpose(
+            1, 2
+        )  # (batch, head, time1, d_k)
+        k_h = torch.reshape(k, (b, t, self.h, self.d_k)).transpose(
+            1, 2
+        )  # (batch, head, time2, d_k)
+        v_h = torch.reshape(v, (b, t, self.h, self.d_k)).transpose(
+            1, 2
+        )  # (batch, head, time2, d_k)
+
+        return q_h, k_h, v_h, v
+
+    def forward_attention(self, value, scores, mask, mask_att_chunk_encoder=None):
+        """Compute attention context vector.
+
+        Args:
+            value (torch.Tensor): Transformed value (#batch, n_head, time2, d_k).
+            scores (torch.Tensor): Attention score (#batch, n_head, time1, time2).
+            mask (torch.Tensor): Mask (#batch, 1, time2) or (#batch, time1, time2).
+
+        Returns:
+            torch.Tensor: Transformed value (#batch, time1, d_model)
+                weighted by the attention score (#batch, time1, time2).
+
+        """
+        n_batch = value.size(0)
+        if mask is not None:
+            if mask_att_chunk_encoder is not None:
+                mask = mask * mask_att_chunk_encoder
+
+            mask = mask.unsqueeze(1).eq(0)  # (batch, 1, *, time2)
+
+            min_value = -float(
+                "inf"
+            )  # float(numpy.finfo(torch.tensor(0, dtype=scores.dtype).numpy().dtype).min)
+            scores = scores.masked_fill(mask, min_value)
+            attn = torch.softmax(scores, dim=-1).masked_fill(
+                mask, 0.0
+            )  # (batch, head, time1, time2)
+        else:
+            attn = torch.softmax(scores, dim=-1)  # (batch, head, time1, time2)
+
+        p_attn = self.dropout(attn)
+        x = torch.matmul(p_attn, value)  # (batch, head, time1, d_k)
+        x = (
+            x.transpose(1, 2).contiguous().view(n_batch, -1, self.h * self.d_k)
+        )  # (batch, time1, d_model)
+
+        return self.linear_out(x)  # (batch, time1, d_model)
+
+    def forward(self, x, mask, mask_shfit_chunk=None, mask_att_chunk_encoder=None):
+        """Compute scaled dot product attention.
+
+        Args:
+            query (torch.Tensor): Query tensor (#batch, time1, size).
+            key (torch.Tensor): Key tensor (#batch, time2, size).
+            value (torch.Tensor): Value tensor (#batch, time2, size).
+            mask (torch.Tensor): Mask tensor (#batch, 1, time2) or
+                (#batch, time1, time2).
+
+        Returns:
+            torch.Tensor: Output tensor (#batch, time1, d_model).
+
+        """
+        q_h, k_h, v_h, v = self.forward_qkv(x)
+        fsmn_memory = self.forward_fsmn(v, mask, mask_shfit_chunk)
+        q_h = q_h * self.d_k ** (-0.5)
+        scores = torch.matmul(q_h, k_h.transpose(-2, -1))
+        att_outs = self.forward_attention(v_h, scores, mask, mask_att_chunk_encoder)
+        return att_outs + fsmn_memory
+
+    def forward_chunk(self, x, cache=None, chunk_size=None, look_back=0):
+        """Compute scaled dot product attention.
+
+        Args:
+            query (torch.Tensor): Query tensor (#batch, time1, size).
+            key (torch.Tensor): Key tensor (#batch, time2, size).
+            value (torch.Tensor): Value tensor (#batch, time2, size).
+            mask (torch.Tensor): Mask tensor (#batch, 1, time2) or
+                (#batch, time1, time2).
+
+        Returns:
+            torch.Tensor: Output tensor (#batch, time1, d_model).
+
+        """
+        q_h, k_h, v_h, v = self.forward_qkv(x)
+        if chunk_size is not None and look_back > 0 or look_back == -1:
+            if cache is not None:
+                k_h_stride = k_h[:, :, : -(chunk_size[2]), :]
+                v_h_stride = v_h[:, :, : -(chunk_size[2]), :]
+                k_h = torch.cat((cache["k"], k_h), dim=2)
+                v_h = torch.cat((cache["v"], v_h), dim=2)
+
+                cache["k"] = torch.cat((cache["k"], k_h_stride), dim=2)
+                cache["v"] = torch.cat((cache["v"], v_h_stride), dim=2)
+                if look_back != -1:
+                    cache["k"] = cache["k"][:, :, -(look_back * chunk_size[1]) :, :]
+                    cache["v"] = cache["v"][:, :, -(look_back * chunk_size[1]) :, :]
+            else:
+                cache_tmp = {
+                    "k": k_h[:, :, : -(chunk_size[2]), :],
+                    "v": v_h[:, :, : -(chunk_size[2]), :],
+                }
+                cache = cache_tmp
+        fsmn_memory = self.forward_fsmn(v, None)
+        q_h = q_h * self.d_k ** (-0.5)
+        scores = torch.matmul(q_h, k_h.transpose(-2, -1))
+        att_outs = self.forward_attention(v_h, scores, None)
+        return att_outs + fsmn_memory, cache
+
+
+class LayerNorm(nn.LayerNorm):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+    def forward(self, input):
+        output = F.layer_norm(
+            input.float(),
+            self.normalized_shape,
+            self.weight.float() if self.weight is not None else None,
+            self.bias.float() if self.bias is not None else None,
+            self.eps,
+        )
+        return output.type_as(input)
+
+
+def sequence_mask(lengths, maxlen=None, dtype=torch.float32, device=None):
+    if maxlen is None:
+        maxlen = lengths.max()
+    row_vector = torch.arange(0, maxlen, 1).to(lengths.device)
+    matrix = torch.unsqueeze(lengths, dim=-1)
+    mask = row_vector < matrix
+    mask = mask.detach()
+
+    return mask.type(dtype).to(device) if device is not None else mask.type(dtype)
+
+
+class EncoderLayerSANM(nn.Module):
+    def __init__(
+        self,
+        in_size,
+        size,
+        self_attn,
+        feed_forward,
+        dropout_rate,
+        normalize_before=True,
+        concat_after=False,
+        stochastic_depth_rate=0.0,
+    ):
+        """Construct an EncoderLayer object."""
+        super(EncoderLayerSANM, self).__init__()
+        self.self_attn = self_attn
+        self.feed_forward = feed_forward
+        self.norm1 = LayerNorm(in_size)
+        self.norm2 = LayerNorm(size)
+        self.dropout = nn.Dropout(dropout_rate)
+        self.in_size = in_size
+        self.size = size
+        self.normalize_before = normalize_before
+        self.concat_after = concat_after
+        if self.concat_after:
+            self.concat_linear = nn.Linear(size + size, size)
+        self.stochastic_depth_rate = stochastic_depth_rate
+        self.dropout_rate = dropout_rate
+
+    def forward(self, x, mask, cache=None, mask_shfit_chunk=None, mask_att_chunk_encoder=None):
+        """Compute encoded features.
+
+        Args:
+            x_input (torch.Tensor): Input tensor (#batch, time, size).
+            mask (torch.Tensor): Mask tensor for the input (#batch, time).
+            cache (torch.Tensor): Cache tensor of the input (#batch, time - 1, size).
+
+        Returns:
+            torch.Tensor: Output tensor (#batch, time, size).
+            torch.Tensor: Mask tensor (#batch, time).
+
+        """
+        skip_layer = False
+        # with stochastic depth, residual connection `x + f(x)` becomes
+        # `x <- x + 1 / (1 - p) * f(x)` at training time.
+        stoch_layer_coeff = 1.0
+        if self.training and self.stochastic_depth_rate > 0:
+            skip_layer = torch.rand(1).item() < self.stochastic_depth_rate
+            stoch_layer_coeff = 1.0 / (1 - self.stochastic_depth_rate)
+
+        if skip_layer:
+            if cache is not None:
+                x = torch.cat([cache, x], dim=1)
+            return x, mask
+
+        residual = x
+        if self.normalize_before:
+            x = self.norm1(x)
+
+        if self.concat_after:
+            x_concat = torch.cat(
+                (
+                    x,
+                    self.self_attn(
+                        x,
+                        mask,
+                        mask_shfit_chunk=mask_shfit_chunk,
+                        mask_att_chunk_encoder=mask_att_chunk_encoder,
+                    ),
+                ),
+                dim=-1,
+            )
+            if self.in_size == self.size:
+                x = residual + stoch_layer_coeff * self.concat_linear(x_concat)
+            else:
+                x = stoch_layer_coeff * self.concat_linear(x_concat)
+        else:
+            if self.in_size == self.size:
+                x = residual + stoch_layer_coeff * self.dropout(
+                    self.self_attn(
+                        x,
+                        mask,
+                        mask_shfit_chunk=mask_shfit_chunk,
+                        mask_att_chunk_encoder=mask_att_chunk_encoder,
+                    )
+                )
+            else:
+                x = stoch_layer_coeff * self.dropout(
+                    self.self_attn(
+                        x,
+                        mask,
+                        mask_shfit_chunk=mask_shfit_chunk,
+                        mask_att_chunk_encoder=mask_att_chunk_encoder,
+                    )
+                )
+        if not self.normalize_before:
+            x = self.norm1(x)
+
+        residual = x
+        if self.normalize_before:
+            x = self.norm2(x)
+        x = residual + stoch_layer_coeff * self.dropout(self.feed_forward(x))
+        if not self.normalize_before:
+            x = self.norm2(x)
+
+        return x, mask, cache, mask_shfit_chunk, mask_att_chunk_encoder
+
+    def forward_chunk(self, x, cache=None, chunk_size=None, look_back=0):
+        """Compute encoded features.
+
+        Args:
+            x_input (torch.Tensor): Input tensor (#batch, time, size).
+            mask (torch.Tensor): Mask tensor for the input (#batch, time).
+            cache (torch.Tensor): Cache tensor of the input (#batch, time - 1, size).
+
+        Returns:
+            torch.Tensor: Output tensor (#batch, time, size).
+            torch.Tensor: Mask tensor (#batch, time).
+
+        """
+
+        residual = x
+        if self.normalize_before:
+            x = self.norm1(x)
+
+        if self.in_size == self.size:
+            attn, cache = self.self_attn.forward_chunk(x, cache, chunk_size, look_back)
+            x = residual + attn
+        else:
+            x, cache = self.self_attn.forward_chunk(x, cache, chunk_size, look_back)
+
+        if not self.normalize_before:
+            x = self.norm1(x)
+
+        residual = x
+        if self.normalize_before:
+            x = self.norm2(x)
+        x = residual + self.feed_forward(x)
+        if not self.normalize_before:
+            x = self.norm2(x)
+
+        return x, cache
+
+
+@tables.register("encoder_classes", "SenseVoiceEncoderSmall")
+class SenseVoiceEncoderSmall(nn.Module):
+    """
+    Author: Speech Lab of DAMO Academy, Alibaba Group
+    SCAMA: Streaming chunk-aware multihead attention for online end-to-end speech recognition
+    https://arxiv.org/abs/2006.01713
+    """
+
+    def __init__(
+        self,
+        input_size: int,
+        output_size: int = 256,
+        attention_heads: int = 4,
+        linear_units: int = 2048,
+        num_blocks: int = 6,
+        tp_blocks: int = 0,
+        dropout_rate: float = 0.1,
+        positional_dropout_rate: float = 0.1,
+        attention_dropout_rate: float = 0.0,
+        stochastic_depth_rate: float = 0.0,
+        input_layer: Optional[str] = "conv2d",
+        pos_enc_class=SinusoidalPositionEncoder,
+        normalize_before: bool = True,
+        concat_after: bool = False,
+        positionwise_layer_type: str = "linear",
+        positionwise_conv_kernel_size: int = 1,
+        padding_idx: int = -1,
+        kernel_size: int = 11,
+        sanm_shfit: int = 0,
+        selfattention_layer_type: str = "sanm",
+        **kwargs,
+    ):
+        super().__init__()
+        self._output_size = output_size
+
+        self.embed = SinusoidalPositionEncoder()
+
+        self.normalize_before = normalize_before
+
+        positionwise_layer = PositionwiseFeedForward
+        positionwise_layer_args = (
+            output_size,
+            linear_units,
+            dropout_rate,
+        )
+
+        encoder_selfattn_layer = MultiHeadedAttentionSANM
+        encoder_selfattn_layer_args0 = (
+            attention_heads,
+            input_size,
+            output_size,
+            attention_dropout_rate,
+            kernel_size,
+            sanm_shfit,
+        )
+        encoder_selfattn_layer_args = (
+            attention_heads,
+            output_size,
+            output_size,
+            attention_dropout_rate,
+            kernel_size,
+            sanm_shfit,
+        )
+
+        self.encoders0 = nn.ModuleList(
+            [
+                EncoderLayerSANM(
+                    input_size,
+                    output_size,
+                    encoder_selfattn_layer(*encoder_selfattn_layer_args0),
+                    positionwise_layer(*positionwise_layer_args),
+                    dropout_rate,
+                )
+                for i in range(1)
+            ]
+        )
+        self.encoders = nn.ModuleList(
+            [
+                EncoderLayerSANM(
+                    output_size,
+                    output_size,
+                    encoder_selfattn_layer(*encoder_selfattn_layer_args),
+                    positionwise_layer(*positionwise_layer_args),
+                    dropout_rate,
+                )
+                for i in range(num_blocks - 1)
+            ]
+        )
+
+        self.tp_encoders = nn.ModuleList(
+            [
+                EncoderLayerSANM(
+                    output_size,
+                    output_size,
+                    encoder_selfattn_layer(*encoder_selfattn_layer_args),
+                    positionwise_layer(*positionwise_layer_args),
+                    dropout_rate,
+                )
+                for i in range(tp_blocks)
+            ]
+        )
+
+        self.after_norm = LayerNorm(output_size)
+
+        self.tp_norm = LayerNorm(output_size)
+
+    def output_size(self) -> int:
+        return self._output_size
+
+    def forward(
+        self,
+        xs_pad: torch.Tensor,
+        ilens: torch.Tensor,
+    ):
+        """Embed positions in tensor."""
+        masks = sequence_mask(ilens, device=ilens.device)[:, None, :]
+
+        xs_pad *= self.output_size() ** 0.5
+
+        xs_pad = self.embed(xs_pad)
+
+        # forward encoder1
+        for layer_idx, encoder_layer in enumerate(self.encoders0):
+            encoder_outs = encoder_layer(xs_pad, masks)
+            xs_pad, masks = encoder_outs[0], encoder_outs[1]
+
+        for layer_idx, encoder_layer in enumerate(self.encoders):
+            encoder_outs = encoder_layer(xs_pad, masks)
+            xs_pad, masks = encoder_outs[0], encoder_outs[1]
+
+        xs_pad = self.after_norm(xs_pad)
+
+        # forward encoder2
+        olens = masks.squeeze(1).sum(1).int()
+
+        for layer_idx, encoder_layer in enumerate(self.tp_encoders):
+            encoder_outs = encoder_layer(xs_pad, masks)
+            xs_pad, masks = encoder_outs[0], encoder_outs[1]
+
+        xs_pad = self.tp_norm(xs_pad)
+        return xs_pad, olens
+
+
+@tables.register("model_classes", "SenseVoiceSmall")
+class SenseVoiceSmall(nn.Module):
+    """CTC-attention hybrid Encoder-Decoder model"""
+
+    def __init__(
+        self,
+        specaug: str = None,
+        specaug_conf: dict = None,
+        normalize: str = None,
+        normalize_conf: dict = None,
+        encoder: str = None,
+        encoder_conf: dict = None,
+        ctc_conf: dict = None,
+        input_size: int = 80,
+        vocab_size: int = -1,
+        ignore_id: int = -1,
+        blank_id: int = 0,
+        sos: int = 1,
+        eos: int = 2,
+        length_normalized_loss: bool = False,
+        **kwargs,
+    ):
+
+        super().__init__()
+
+        if specaug is not None:
+            specaug_class = tables.specaug_classes.get(specaug)
+            specaug = specaug_class(**specaug_conf)
+        if normalize is not None:
+            normalize_class = tables.normalize_classes.get(normalize)
+            normalize = normalize_class(**normalize_conf)
+        encoder_class = tables.encoder_classes.get(encoder)
+        encoder = encoder_class(input_size=input_size, **encoder_conf)
+        encoder_output_size = encoder.output_size()
+
+        if ctc_conf is None:
+            ctc_conf = {}
+        ctc = CTC(odim=vocab_size, encoder_output_size=encoder_output_size, **ctc_conf)
+
+        self.blank_id = blank_id
+        self.sos = sos if sos is not None else vocab_size - 1
+        self.eos = eos if eos is not None else vocab_size - 1
+        self.vocab_size = vocab_size
+        self.ignore_id = ignore_id
+        self.specaug = specaug
+        self.normalize = normalize
+        self.encoder = encoder
+        self.error_calculator = None
+
+        self.ctc = ctc
+
+        self.length_normalized_loss = length_normalized_loss
+        self.encoder_output_size = encoder_output_size
+
+        self.lid_dict = {"auto": 0, "zh": 3, "en": 4, "yue": 7, "ja": 11, "ko": 12, "nospeech": 13}
+        self.lid_int_dict = {24884: 3, 24885: 4, 24888: 7, 24892: 11, 24896: 12, 24992: 13}
+        self.textnorm_dict = {"withitn": 14, "woitn": 15}
+        self.textnorm_int_dict = {25016: 14, 25017: 15}
+        self.embed = torch.nn.Embedding(7 + len(self.lid_dict) + len(self.textnorm_dict), input_size)
+        self.emo_dict = {"unk": 25009, "happy": 25001, "sad": 25002, "angry": 25003, "neutral": 25004}
+        
+        self.criterion_att = LabelSmoothingLoss(
+            size=self.vocab_size,
+            padding_idx=self.ignore_id,
+            smoothing=kwargs.get("lsm_weight", 0.0),
+            normalize_length=self.length_normalized_loss,
+        )
+    
+    @staticmethod
+    def from_pretrained(model:str=None, **kwargs):
+        from funasr import AutoModel
+        model, kwargs = AutoModel.build_model(model=model, trust_remote_code=True, **kwargs)
+        
+        return model, kwargs
+
+    def forward(
+        self,
+        speech: torch.Tensor,
+        speech_lengths: torch.Tensor,
+        text: torch.Tensor,
+        text_lengths: torch.Tensor,
+        **kwargs,
+    ):
+        """Encoder + Decoder + Calc loss
+        Args:
+                speech: (Batch, Length, ...)
+                speech_lengths: (Batch, )
+                text: (Batch, Length)
+                text_lengths: (Batch,)
+        """
+        # import pdb;
+        # pdb.set_trace()
+        if len(text_lengths.size()) > 1:
+            text_lengths = text_lengths[:, 0]
+        if len(speech_lengths.size()) > 1:
+            speech_lengths = speech_lengths[:, 0]
+
+        batch_size = speech.shape[0]
+
+        # 1. Encoder
+        encoder_out, encoder_out_lens = self.encode(speech, speech_lengths, text)
+
+        loss_ctc, cer_ctc = None, None
+        loss_rich, acc_rich = None, None
+        stats = dict()
+
+        loss_ctc, cer_ctc = self._calc_ctc_loss(
+            encoder_out[:, 4:, :], encoder_out_lens - 4, text[:, 4:], text_lengths - 4
+        )
+
+        loss_rich, acc_rich = self._calc_rich_ce_loss(
+            encoder_out[:, :4, :], text[:, :4]
+        )
+
+        loss = loss_ctc + loss_rich
+        # Collect total loss stats
+        stats["loss_ctc"] = torch.clone(loss_ctc.detach()) if loss_ctc is not None else None
+        stats["loss_rich"] = torch.clone(loss_rich.detach()) if loss_rich is not None else None
+        stats["loss"] = torch.clone(loss.detach()) if loss is not None else None
+        stats["acc_rich"] = acc_rich
+
+        # force_gatherable: to-device and to-tensor if scalar for DataParallel
+        if self.length_normalized_loss:
+            batch_size = int((text_lengths + 1).sum())
+        loss, stats, weight = force_gatherable((loss, stats, batch_size), loss.device)
+        return loss, stats, weight
+
+    def encode(
+        self,
+        speech: torch.Tensor,
+        speech_lengths: torch.Tensor,
+        text: torch.Tensor,
+        **kwargs,
+    ):
+        """Frontend + Encoder. Note that this method is used by asr_inference.py
+        Args:
+                speech: (Batch, Length, ...)
+                speech_lengths: (Batch, )
+                ind: int
+        """
+
+        # Data augmentation
+        if self.specaug is not None and self.training:
+            speech, speech_lengths = self.specaug(speech, speech_lengths)
+
+        # Normalization for feature: e.g. Global-CMVN, Utterance-CMVN
+        if self.normalize is not None:
+            speech, speech_lengths = self.normalize(speech, speech_lengths)
+
+
+        lids = torch.LongTensor([[self.lid_int_dict[int(lid)] if torch.rand(1) > 0.2 and int(lid) in self.lid_int_dict else 0 ] for lid in text[:, 0]]).to(speech.device)
+        language_query = self.embed(lids)
+        
+        styles = torch.LongTensor([[self.textnorm_int_dict[int(style)]] for style in text[:, 3]]).to(speech.device)
+        style_query = self.embed(styles)
+        speech = torch.cat((style_query, speech), dim=1)
+        speech_lengths += 1
+
+        event_emo_query = self.embed(torch.LongTensor([[1, 2]]).to(speech.device)).repeat(speech.size(0), 1, 1)
+        input_query = torch.cat((language_query, event_emo_query), dim=1)
+        speech = torch.cat((input_query, speech), dim=1)
+        speech_lengths += 3
+
+        encoder_out, encoder_out_lens = self.encoder(speech, speech_lengths)
+
+        return encoder_out, encoder_out_lens
+
+    def _calc_ctc_loss(
+        self,
+        encoder_out: torch.Tensor,
+        encoder_out_lens: torch.Tensor,
+        ys_pad: torch.Tensor,
+        ys_pad_lens: torch.Tensor,
+    ):
+        # Calc CTC loss
+        loss_ctc = self.ctc(encoder_out, encoder_out_lens, ys_pad, ys_pad_lens)
+
+        # Calc CER using CTC
+        cer_ctc = None
+        if not self.training and self.error_calculator is not None:
+            ys_hat = self.ctc.argmax(encoder_out).data
+            cer_ctc = self.error_calculator(ys_hat.cpu(), ys_pad.cpu(), is_ctc=True)
+        return loss_ctc, cer_ctc
+
+    def _calc_rich_ce_loss(
+        self,
+        encoder_out: torch.Tensor,
+        ys_pad: torch.Tensor,
+    ):
+        decoder_out = self.ctc.ctc_lo(encoder_out)
+        # 2. Compute attention loss
+        loss_rich = self.criterion_att(decoder_out, ys_pad.contiguous())
+        acc_rich = th_accuracy(
+            decoder_out.view(-1, self.vocab_size),
+            ys_pad.contiguous(),
+            ignore_label=self.ignore_id,
+        )
+
+        return loss_rich, acc_rich
+
+
+    def inference(
+        self,
+        data_in,
+        data_lengths=None,
+        key: list = ["wav_file_tmp_name"],
+        tokenizer=None,
+        frontend=None,
+        **kwargs,
+    ):
+
+
+        meta_data = {}
+        if (
+            isinstance(data_in, torch.Tensor) and kwargs.get("data_type", "sound") == "fbank"
+        ):  # fbank
+            speech, speech_lengths = data_in, data_lengths
+            if len(speech.shape) < 3:
+                speech = speech[None, :, :]
+            if speech_lengths is None:
+                speech_lengths = speech.shape[1]
+        else:
+            # extract fbank feats
+            time1 = time.perf_counter()
+            audio_sample_list = load_audio_text_image_video(
+                data_in,
+                fs=frontend.fs,
+                audio_fs=kwargs.get("fs", 16000),
+                data_type=kwargs.get("data_type", "sound"),
+                tokenizer=tokenizer,
+            )
+            time2 = time.perf_counter()
+            meta_data["load_data"] = f"{time2 - time1:0.3f}"
+            speech, speech_lengths = extract_fbank(
+                audio_sample_list, data_type=kwargs.get("data_type", "sound"), frontend=frontend
+            )
+            time3 = time.perf_counter()
+            meta_data["extract_feat"] = f"{time3 - time2:0.3f}"
+            meta_data["batch_data_time"] = (
+                speech_lengths.sum().item() * frontend.frame_shift * frontend.lfr_n / 1000
+            )
+
+        speech = speech.to(device=kwargs["device"])
+        speech_lengths = speech_lengths.to(device=kwargs["device"])
+
+        language = kwargs.get("language", "auto")
+        language_query = self.embed(
+            torch.LongTensor(
+                [[self.lid_dict[language] if language in self.lid_dict else 0]]
+            ).to(speech.device)
+        ).repeat(speech.size(0), 1, 1)
+        
+        use_itn = kwargs.get("use_itn", False)
+        textnorm = kwargs.get("text_norm", None)
+        if textnorm is None:
+            textnorm = "withitn" if use_itn else "woitn"
+        textnorm_query = self.embed(
+            torch.LongTensor([[self.textnorm_dict[textnorm]]]).to(speech.device)
+        ).repeat(speech.size(0), 1, 1)
+        speech = torch.cat((textnorm_query, speech), dim=1)
+        speech_lengths += 1
+
+        event_emo_query = self.embed(torch.LongTensor([[1, 2]]).to(speech.device)).repeat(
+            speech.size(0), 1, 1
+        )
+        input_query = torch.cat((language_query, event_emo_query), dim=1)
+        speech = torch.cat((input_query, speech), dim=1)
+        speech_lengths += 3
+
+        # Encoder
+        encoder_out, encoder_out_lens = self.encoder(speech, speech_lengths)
+        if isinstance(encoder_out, tuple):
+            encoder_out = encoder_out[0]
+
+        # c. Passed the encoder result and the beam search
+        ctc_logits = self.ctc.log_softmax(encoder_out)
+        if kwargs.get("ban_emo_unk", False):
+            ctc_logits[:, :, self.emo_dict["unk"]] = -float("inf")
+
+        results = []
+        b, n, d = encoder_out.size()
+        if isinstance(key[0], (list, tuple)):
+            key = key[0]
+        if len(key) < b:
+            key = key * b
+        for i in range(b):
+            x = ctc_logits[i, : encoder_out_lens[i].item(), :]
+            yseq = x.argmax(dim=-1)
+            yseq = torch.unique_consecutive(yseq, dim=-1)
+
+            ibest_writer = None
+            if kwargs.get("output_dir") is not None:
+                if not hasattr(self, "writer"):
+                    self.writer = DatadirWriter(kwargs.get("output_dir"))
+                ibest_writer = self.writer[f"1best_recog"]
+
+            mask = yseq != self.blank_id
+            token_int = yseq[mask].tolist()
+
+            # Change integer-ids to tokens
+            text = tokenizer.decode(token_int)
+
+            result_i = {"key": key[i], "text": text}
+            results.append(result_i)
+
+            if ibest_writer is not None:
+                ibest_writer["text"][key[i]] = text
+
+        return results, meta_data
+
+    def export(self, **kwargs):
+        from export_meta import export_rebuild_model
+
+        if "max_seq_len" not in kwargs:
+            kwargs["max_seq_len"] = 512
+        models = export_rebuild_model(model=self, **kwargs)
+        return models
diff --git a/model/SenseVoiceSmall/tokens.json b/model/SenseVoiceSmall/tokens.json
new file mode 100644
index 0000000000000000000000000000000000000000..bc0021233d7c0891e2885233ef7ce16ba80be86d
--- /dev/null
+++ b/model/SenseVoiceSmall/tokens.json
@@ -0,0 +1,25057 @@
+[
+    "<unk>",
+    "<s>",
+    "</s>",
+    "▁the",
+    "s",
+    "▁to",
+    "▁and",
+    "▁of",
+    "▁a",
+    "'",
+    "▁in",
+    "▁I",
+    "▁that",
+    "▁is",
+    "▁you",
+    "▁it",
+    "t",
+    "▁for",
+    "▁we",
+    "▁was",
+    "▁with",
+    "▁on",
+    "ing",
+    "▁be",
+    "▁are",
+    "▁have",
+    "ed",
+    "d",
+    "▁as",
+    "▁this",
+    "▁not",
+    "▁our",
+    "▁at",
+    "▁your",
+    "▁can",
+    "▁from",
+    "▁The",
+    "▁or",
+    "▁will",
+    "▁but",
+    "▁he",
+    "▁one",
+    "▁an",
+    "re",
+    "▁my",
+    "▁by",
+    "▁his",
+    "▁all",
+    "▁they",
+    "ly",
+    "▁had",
+    "▁her",
+    "▁me",
+    "▁more",
+    "I",
+    "▁were",
+    "▁about",
+    "▁out",
+    "▁do",
+    "▁so",
+    "▁up",
+    "m",
+    "▁has",
+    "▁what",
+    "y",
+    "▁there",
+    "▁time",
+    "▁would",
+    "The",
+    "▁like",
+    "▁their",
+    "er",
+    "▁It",
+    "▁been",
+    "▁just",
+    "▁if",
+    "▁them",
+    "▁some",
+    "▁which",
+    "▁We",
+    "▁she",
+    "▁He",
+    "ve",
+    "▁also",
+    "▁when",
+    "▁two",
+    "▁into",
+    "▁no",
+    "▁other",
+    "▁get",
+    "▁over",
+    "ll",
+    "▁him",
+    "▁us",
+    "n",
+    "▁And",
+    "▁know",
+    "▁think",
+    "▁who",
+    "al",
+    "▁could",
+    "▁see",
+    "▁than",
+    "▁A",
+    "▁very",
+    "▁don",
+    "▁back",
+    "▁how",
+    "e",
+    "▁people",
+    "▁new",
+    "▁these",
+    "▁But",
+    "r",
+    "▁make",
+    "▁any",
+    "▁first",
+    "We",
+    "▁then",
+    "▁only",
+    "▁now",
+    "▁way",
+    "in",
+    "▁You",
+    "▁",
+    "▁In",
+    "▁year",
+    "▁want",
+    "▁good",
+    "▁This",
+    "▁well",
+    "▁need",
+    "▁going",
+    "▁may",
+    "▁work",
+    "es",
+    "▁because",
+    "▁much",
+    "▁even",
+    "▁through",
+    "▁She",
+    "▁where",
+    "▁go",
+    "▁down",
+    "▁So",
+    "It",
+    "A",
+    "▁right",
+    "▁really",
+    "▁those",
+    "▁most",
+    "▁should",
+    "a",
+    "In",
+    "▁three",
+    "▁did",
+    "▁take",
+    "▁many",
+    "▁re",
+    "▁twenty",
+    "▁here",
+    "▁business",
+    "▁use",
+    "▁still",
+    "▁after",
+    "▁too",
+    "▁If",
+    "▁before",
+    "▁made",
+    "or",
+    "▁They",
+    "▁look",
+    "▁years",
+    "▁around",
+    "▁point",
+    "ers",
+    "And",
+    "▁being",
+    "▁day",
+    "▁hundred",
+    "▁last",
+    "▁little",
+    "▁didn",
+    "▁five",
+    "▁said",
+    "▁something",
+    "▁off",
+    "▁things",
+    "▁such",
+    "This",
+    "▁same",
+    "▁help",
+    "▁life",
+    "S",
+    "▁both",
+    "▁different",
+    "ic",
+    "o",
+    "▁find",
+    "▁never",
+    "▁each",
+    "▁got",
+    "He",
+    "▁quarter",
+    "▁come",
+    "l",
+    "▁lot",
+    "▁say",
+    "▁own",
+    "▁its",
+    "▁four",
+    "▁That",
+    "▁long",
+    "▁un",
+    "▁again",
+    "▁great",
+    "▁used",
+    "▁end",
+    "▁S",
+    "▁There",
+    "▁few",
+    "▁better",
+    "▁always",
+    "▁part",
+    "▁while",
+    "▁might",
+    "But",
+    "▁home",
+    "▁best",
+    "▁between",
+    "▁am",
+    "le",
+    "ation",
+    "▁every",
+    "▁world",
+    "If",
+    "T",
+    "it",
+    "▁feel",
+    "▁love",
+    "▁place",
+    "an",
+    "C",
+    "▁next",
+    "▁market",
+    "able",
+    "▁B",
+    "ion",
+    "▁does",
+    "▁s",
+    "g",
+    "P",
+    "▁number",
+    "▁found",
+    "▁another",
+    "▁growth",
+    "▁second",
+    "▁believe",
+    "▁thing",
+    "You",
+    "▁away",
+    "▁put",
+    "▁continue",
+    "c",
+    "▁must",
+    "ity",
+    "▁results",
+    "▁give",
+    "ar",
+    "▁under",
+    "on",
+    "il",
+    "▁sure",
+    "▁thought",
+    "▁head",
+    "▁hand",
+    "▁important",
+    "▁thousand",
+    "▁man",
+    "▁For",
+    "▁without",
+    "So",
+    "▁data",
+    "▁T",
+    "▁keep",
+    "▁call",
+    "▁able",
+    "▁As",
+    "▁company",
+    "▁information",
+    "▁bit",
+    "▁de",
+    "▁What",
+    "M",
+    "▁let",
+    "▁why",
+    "▁E",
+    "▁during",
+    "D",
+    "▁case",
+    "i",
+    "▁high",
+    "▁six",
+    "▁kind",
+    "▁When",
+    "▁change",
+    "▁course",
+    "▁nineteen",
+    "▁team",
+    "▁start",
+    "p",
+    "and",
+    "F",
+    "▁set",
+    "▁though",
+    "As",
+    "el",
+    "B",
+    "▁done",
+    "▁hard",
+    "▁using",
+    "▁against",
+    "▁small",
+    "E",
+    "▁system",
+    "▁left",
+    "▁doing",
+    "th",
+    "▁face",
+    "▁side",
+    "▁water",
+    "▁enough",
+    "b",
+    "▁came",
+    "▁F",
+    "ch",
+    "R",
+    "▁fact",
+    "▁looking",
+    "▁car",
+    "▁since",
+    "en",
+    "▁went",
+    "ra",
+    "ent",
+    "▁took",
+    "L",
+    "▁order",
+    "▁room",
+    "▁ten",
+    "▁today",
+    "▁doesn",
+    "ment",
+    "▁process",
+    "▁non",
+    "▁line",
+    "▁No",
+    "▁past",
+    "▁D",
+    "▁far",
+    "ness",
+    "▁family",
+    "▁big",
+    "▁looked",
+    "▁show",
+    "ur",
+    "▁already",
+    "▁support",
+    "▁anything",
+    "▁experience",
+    "▁until",
+    "▁My",
+    "▁eight",
+    "▁tell",
+    "▁making",
+    "▁strong",
+    "▁open",
+    "▁actually",
+    "▁provide",
+    "▁play",
+    "▁working",
+    "▁seven",
+    "▁seen",
+    "▁game",
+    "G",
+    "▁care",
+    "▁across",
+    "▁real",
+    "She",
+    "▁expect",
+    "h",
+    "▁eyes",
+    "▁customers",
+    "▁wanted",
+    "▁future",
+    "▁mean",
+    "▁less",
+    "▁value",
+    "They",
+    "ri",
+    "us",
+    "▁together",
+    "▁night",
+    "at",
+    "▁free",
+    "▁money",
+    "▁ever",
+    "▁C",
+    "est",
+    "▁G",
+    "k",
+    "▁full",
+    "te",
+    "▁con",
+    "▁e",
+    "▁getting",
+    "▁level",
+    "ive",
+    "▁days",
+    "▁question",
+    "is",
+    "▁within",
+    "as",
+    "▁To",
+    "▁wasn",
+    "▁percent",
+    "▁mind",
+    "man",
+    "▁house",
+    "ry",
+    "O",
+    "▁power",
+    "▁large",
+    "▁try",
+    "▁top",
+    "▁understand",
+    "▁having",
+    "▁study",
+    "▁several",
+    "lo",
+    "▁everything",
+    "ro",
+    "▁P",
+    "▁cost",
+    "to",
+    "▁nothing",
+    "ies",
+    "▁light",
+    "▁run",
+    "▁forward",
+    "▁won",
+    "ter",
+    "▁saw",
+    "▁pre",
+    "▁started",
+    "▁service",
+    "▁possible",
+    "▁often",
+    "▁once",
+    "▁control",
+    "▁body",
+    "▁knew",
+    "▁product",
+    "▁felt",
+    "▁problem",
+    "▁talk",
+    "▁person",
+    "▁become",
+    "▁old",
+    "▁performance",
+    "u",
+    "▁group",
+    "▁read",
+    "▁co",
+    "▁state",
+    "There",
+    "▁half",
+    "▁book",
+    "▁later",
+    "▁nine",
+    "▁post",
+    "▁thirty",
+    "▁turn",
+    "▁trying",
+    "▁However",
+    "ia",
+    "▁Re",
+    "na",
+    "▁per",
+    "▁week",
+    "For",
+    "▁least",
+    "▁told",
+    "When",
+    "▁available",
+    "▁yet",
+    "N",
+    "id",
+    "▁move",
+    "▁food",
+    "ta",
+    "▁someone",
+    "▁R",
+    "▁probably",
+    "▁including",
+    "▁children",
+    "▁share",
+    "▁All",
+    "ist",
+    "▁patients",
+    "▁given",
+    "▁God",
+    "▁higher",
+    "▁impact",
+    "▁plan",
+    "▁quite",
+    "▁area",
+    "▁example",
+    "ted",
+    "▁increase",
+    "▁school",
+    "f",
+    "▁close",
+    "rs",
+    "▁products",
+    "▁result",
+    "That",
+    "▁job",
+    "ne",
+    "ate",
+    "ary",
+    "▁name",
+    "▁coming",
+    "▁live",
+    "age",
+    "▁local",
+    "▁model",
+    "ine",
+    "et",
+    "▁O",
+    "▁space",
+    "▁terms",
+    "▁door",
+    "▁Do",
+    "▁along",
+    "▁asked",
+    "▁financial",
+    "▁further",
+    "▁sales",
+    "co",
+    "▁due",
+    "ul",
+    "▁whole",
+    "se",
+    "▁clear",
+    "▁pretty",
+    "▁moment",
+    "▁months",
+    "▁matter",
+    "▁lower",
+    "▁New",
+    "▁else",
+    "▁early",
+    "▁significant",
+    "▁whether",
+    "▁key",
+    "▁present",
+    "ling",
+    "▁pro",
+    "me",
+    "▁H",
+    "ty",
+    "▁based",
+    "▁means",
+    "▁reason",
+    "▁At",
+    "▁development",
+    "we",
+    "▁times",
+    "la",
+    "▁focus",
+    "ie",
+    "▁almost",
+    "▁easy",
+    "▁idea",
+    "H",
+    "▁heart",
+    "▁myself",
+    "▁create",
+    "▁pay",
+    "ism",
+    "▁either",
+    "▁sense",
+    "ating",
+    "▁view",
+    "ma",
+    "▁story",
+    "▁design",
+    "▁N",
+    "▁front",
+    "ant",
+    "ton",
+    "▁bad",
+    "▁fifty",
+    "▁current",
+    "less",
+    "▁third",
+    "▁K",
+    "▁ask",
+    "▁His",
+    "▁turned",
+    "▁project",
+    "▁couple",
+    "▁rest",
+    "▁however",
+    "▁position",
+    "▁behind",
+    "▁p",
+    "ce",
+    "▁rate",
+    "▁dollars",
+    "tic",
+    "▁couldn",
+    "ous",
+    "▁country",
+    "x",
+    "▁makes",
+    "ol",
+    "▁Then",
+    "V",
+    "▁price",
+    "ally",
+    "ance",
+    "▁government",
+    "▁men",
+    "ge",
+    "um",
+    "▁hope",
+    "However",
+    "▁certain",
+    "▁everyone",
+    "▁health",
+    "▁isn",
+    "▁quality",
+    "li",
+    "▁others",
+    "▁needs",
+    "▁short",
+    "▁women",
+    "ized",
+    "▁add",
+    "Our",
+    "im",
+    "▁public",
+    "▁stop",
+    "▁needed",
+    "ial",
+    "ated",
+    "▁customer",
+    "▁taking",
+    "ad",
+    "ci",
+    "▁form",
+    "▁services",
+    "▁called",
+    "▁risk",
+    "▁Now",
+    "▁friends",
+    "▁yourself",
+    "▁ago",
+    "▁comes",
+    "▁dis",
+    "▁Not",
+    "▁L",
+    "▁addition",
+    "ian",
+    "the",
+    "▁true",
+    "▁These",
+    "▁low",
+    "▁cannot",
+    "▁f",
+    "▁season",
+    "▁One",
+    "▁After",
+    "▁human",
+    "▁site",
+    "▁capital",
+    "▁program",
+    "▁table",
+    "▁Or",
+    "▁amount",
+    "▁forty",
+    "▁size",
+    "▁Our",
+    "▁zero",
+    "▁opportunity",
+    "▁sort",
+    "▁maybe",
+    "w",
+    "▁list",
+    "W",
+    "▁content",
+    "▁longer",
+    "▁taken",
+    "▁specific",
+    "▁minutes",
+    "▁W",
+    "▁deal",
+    "led",
+    "▁single",
+    "▁revenue",
+    "▁potential",
+    "▁leave",
+    "▁mother",
+    "▁How",
+    "▁inside",
+    "▁questions",
+    "▁drive",
+    "▁above",
+    "▁treatment",
+    "▁remember",
+    "▁stay",
+    "▁changes",
+    "▁wrong",
+    "▁simple",
+    "▁especially",
+    "▁positive",
+    "▁similar",
+    "▁court",
+    "▁outside",
+    "▁interest",
+    "▁words",
+    "▁offer",
+    "un",
+    "▁young",
+    "▁tried",
+    "▁hands",
+    "▁bring",
+    "▁rather",
+    "▁ninety",
+    "▁social",
+    "To",
+    "▁seems",
+    "▁website",
+    "▁industry",
+    "ck",
+    "▁phone",
+    "▁black",
+    "▁type",
+    "▁grow",
+    "▁answer",
+    "▁learn",
+    "▁buy",
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+    "髏",
+    "髑",
+    "髒",
+    "髓",
+    "體",
+    "高",
+    "髙",
+    "髟",
+    "髡",
+    "髢",
+    "髣",
+    "髦",
+    "髪",
+    "髫",
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+    "髴",
+    "髷",
+    "髹",
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+    "鯣",
+    "鯥",
+    "鯧",
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+    "鯪",
+    "鯰",
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+    "鰓",
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+    "鰰",
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+    "鰶",
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+    "鰾",
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+    "鱆",
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+    "鱏",
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+    "鱓",
+    "鱗",
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+    "鱠",
+    "鱧",
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+    "鱰",
+    "鱵",
+    "鱶",
+    "鱸",
+    "鱼",
+    "鱿",
+    "鲀",
+    "鲁",
+    "鲂",
+    "鲅",
+    "鲇",
+    "鲈",
+    "鲊",
+    "鲋",
+    "鲌",
+    "鲍",
+    "鲐",
+    "鲑",
+    "鲒",
+    "鲔",
+    "鲖",
+    "鲚",
+    "鲛",
+    "鲜",
+    "鲞",
+    "鲟",
+    "鲠",
+    "鲡",
+    "鲢",
+    "鲣",
+    "鲤",
+    "鲥",
+    "鲦",
+    "鲧",
+    "鲨",
+    "鲩",
+    "鲫",
+    "鲭",
+    "鲮",
+    "鲱",
+    "鲲",
+    "鲳",
+    "鲴",
+    "鲵",
+    "鲶",
+    "鲷",
+    "鲸",
+    "鲻",
+    "鲼",
+    "鲽",
+    "鳀",
+    "鳃",
+    "鳄",
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+    "鳇",
+    "鳉",
+    "鳊",
+    "鳌",
+    "鳍",
+    "鳎",
+    "鳏",
+    "鳐",
+    "鳔",
+    "鳕",
+    "鳖",
+    "鳗",
+    "鳙",
+    "鳜",
+    "鳝",
+    "鳞",
+    "鳟",
+    "鳣",
+    "鳥",
+    "鳧",
+    "鳩",
+    "鳫",
+    "鳰",
+    "鳳",
+    "鳴",
+    "鳶",
+    "鴂",
+    "鴃",
+    "鴆",
+    "鴇",
+    "鴈",
+    "鴉",
+    "鴋",
+    "鴎",
+    "鴐",
+    "鴒",
+    "鴛",
+    "鴞",
+    "鴟",
+    "鴣",
+    "鴦",
+    "鴨",
+    "鴫",
+    "鴬",
+    "鴲",
+    "鴻",
+    "鴾",
+    "鴿",
+    "鵀",
+    "鵄",
+    "鵆",
+    "鵇",
+    "鵐",
+    "鵑",
+    "鵙",
+    "鵜",
+    "鵝",
+    "鵞",
+    "鵟",
+    "鵠",
+    "鵡",
+    "鵤",
+    "鵫",
+    "鵬",
+    "鵯",
+    "鵰",
+    "鵲",
+    "鵺",
+    "鵼",
+    "鶇",
+    "鶉",
+    "鶏",
+    "鶒",
+    "鶖",
+    "鶗",
+    "鶚",
+    "鶡",
+    "鶤",
+    "鶩",
+    "鶫",
+    "鶯",
+    "鶲",
+    "鶴",
+    "鶸",
+    "鶺",
+    "鶻",
+    "鷁",
+    "鷂",
+    "鷃",
+    "鷆",
+    "鷓",
+    "鷙",
+    "鷚",
+    "鷠",
+    "鷦",
+    "鷭",
+    "鷯",
+    "鷲",
+    "鷸",
+    "鷹",
+    "鷺",
+    "鷽",
+    "鸂",
+    "鸙",
+    "鸚",
+    "鸛",
+    "鸞",
+    "鸟",
+    "鸠",
+    "鸡",
+    "鸢",
+    "鸣",
+    "鸥",
+    "鸦",
+    "鸨",
+    "鸩",
+    "鸪",
+    "鸫",
+    "鸬",
+    "鸭",
+    "鸮",
+    "鸯",
+    "鸰",
+    "鸱",
+    "鸳",
+    "鸵",
+    "鸶",
+    "鸷",
+    "鸸",
+    "鸹",
+    "鸽",
+    "鸾",
+    "鸿",
+    "鹀",
+    "鹁",
+    "鹂",
+    "鹃",
+    "鹄",
+    "鹅",
+    "鹆",
+    "鹇",
+    "鹈",
+    "鹉",
+    "鹊",
+    "鹋",
+    "鹌",
+    "鹎",
+    "鹏",
+    "鹑",
+    "鹕",
+    "鹖",
+    "鹗",
+    "鹘",
+    "鹚",
+    "鹜",
+    "鹞",
+    "鹣",
+    "鹤",
+    "鹦",
+    "鹧",
+    "鹨",
+    "鹩",
+    "鹪",
+    "鹫",
+    "鹬",
+    "鹭",
+    "鹮",
+    "鹯",
+    "鹰",
+    "鹱",
+    "鹳",
+    "鹵",
+    "鹸",
+    "鹹",
+    "鹽",
+    "鹾",
+    "鹿",
+    "麁",
+    "麂",
+    "麇",
+    "麈",
+    "麋",
+    "麒",
+    "麓",
+    "麕",
+    "麗",
+    "麝",
+    "麞",
+    "麟",
+    "麥",
+    "麦",
+    "麩",
+    "麪",
+    "麭",
+    "麴",
+    "麵",
+    "麸",
+    "麹",
+    "麺",
+    "麻",
+    "麼",
+    "麽",
+    "麾",
+    "麿",
+    "黃",
+    "黄",
+    "黉",
+    "黌",
+    "黍",
+    "黎",
+    "黏",
+    "黐",
+    "黑",
+    "黒",
+    "黔",
+    "默",
+    "黙",
+    "黛",
+    "黜",
+    "黝",
+    "點",
+    "黟",
+    "黠",
+    "黡",
+    "黢",
+    "黥",
+    "黧",
+    "黨",
+    "黩",
+    "黯",
+    "黴",
+    "黹",
+    "黻",
+    "黼",
+    "黽",
+    "鼇",
+    "鼈",
+    "鼋",
+    "鼍",
+    "鼎",
+    "鼐",
+    "鼓",
+    "龝",
+    "齦",
+    "鼻",
+    "齋",
+    "ｘ",
+    "龉",
+    "龇",
+    "鼡",
+    "齧",
+    "龍",
+    "齡",
+    "鼯",
+    "鼱",
+    "鼠",
+    "齊",
+    "龙",
+    "龃",
+    "龐",
+    "鼬",
+    "龈",
+    "齟",
+    "龄",
+    "鼾",
+    "龊",
+    "龕",
+    "鼪",
+    "龢",
+    "齣",
+    "齉",
+    "齲",
+    "齎",
+    "鼹",
+    "𠱁",
+    "鼩",
+    "齷",
+    "龋",
+    "齑",
+    "龛",
+    "齁",
+    "齿",
+    "龜",
+    "齐",
+    "齔",
+    "鼢",
+    "龅",
+    "齒",
+    "龚",
+    "齬",
+    "龌",
+    "龠",
+    "齪",
+    "龟",
+    "ｙ",
+    "齢",
+    "鼕",
+    "鼙",
+    "！",
+    "을",
+    "의",
+    "이",
+    "에",
+    "는",
+    "를",
+    "가",
+    "은",
+    "▁수",
+    "도",
+    "한",
+    "고",
+    "로",
+    "▁이",
+    "과",
+    "에서",
+    "지",
+    "으로",
+    "기",
+    "다",
+    "하는",
+    "인",
+    "해",
+    "와",
+    "하고",
+    "▁있다",
+    "할",
+    "▁그",
+    "일",
+    "▁있는",
+    "리",
+    "▁한",
+    "어",
+    "사",
+    "시",
+    "서",
+    "자",
+    "만",
+    "게",
+    "▁있습니다",
+    "▁전",
+    "스",
+    "면",
+    "년",
+    "수",
+    "▁고",
+    "했다",
+    "▁가",
+    "대",
+    "적",
+    "라",
+    "나",
+    "주",
+    "하게",
+    "아",
+    "성",
+    "상",
+    "원",
+    "이다",
+    "장",
+    "▁일",
+    "▁주",
+    "▁더",
+    "▁내",
+    "적인",
+    "진",
+    "정",
+    "화",
+    "드",
+    "된",
+    "부",
+    "▁대",
+    "▁할",
+    "▁유",
+    "▁보",
+    "▁것",
+    "▁사",
+    "▁시",
+    "▁중",
+    "▁대한",
+    "▁지",
+    "▁때",
+    "들이",
+    "요",
+    "▁다",
+    "▁것이",
+    "전",
+    "▁정",
+    "제",
+    "구",
+    "야",
+    "▁부",
+    "▁안",
+    "치",
+    "▁및",
+    "▁나",
+    "월",
+    "트",
+    "한다",
+    "보",
+    "하기",
+    "▁한다",
+    "▁신",
+    "입니다",
+    "에게",
+    "▁것이다",
+    "소",
+    "▁말",
+    "하지",
+    "▁등",
+    "▁기",
+    "적으로",
+    "▁비",
+    "▁해",
+    "▁위해",
+    "합니다",
+    "신",
+    "에는",
+    "산",
+    "선",
+    "위",
+    "▁소",
+    "▁자",
+    "관",
+    "하",
+    "비",
+    "▁마",
+    "니",
+    "러",
+    "세",
+    "▁하는",
+    "까지",
+    "들",
+    "▁아",
+    "당",
+    "▁상",
+    "용",
+    "▁제",
+    "▁인",
+    "▁오",
+    "▁두",
+    "▁다른",
+    "려",
+    "단",
+    "▁하",
+    "▁공",
+    "며",
+    "우",
+    "동",
+    "▁무",
+    "▁후",
+    "하여",
+    "조",
+    "▁것은",
+    "▁미",
+    "▁원",
+    "명",
+    "히",
+    "▁바",
+    "르",
+    "실",
+    "마",
+    "▁김",
+    "▁많은",
+    "국",
+    "여",
+    "▁가장",
+    "▁동",
+    "분",
+    "▁잘",
+    "방",
+    "회",
+    "▁구",
+    "행",
+    "식",
+    "▁거",
+    "▁연",
+    "차",
+    "▁통해",
+    "계",
+    "▁세",
+    "던",
+    "체",
+    "▁조",
+    "▁한국",
+    "중",
+    "재",
+    "▁같은",
+    "▁함께",
+    "질",
+    "▁모",
+    "미",
+    "▁모든",
+    "▁경우",
+    "연",
+    "학",
+    "호",
+    "▁때문에",
+    "▁사용",
+    "▁것을",
+    "안",
+    "▁불",
+    "들은",
+    "▁하지만",
+    "▁도",
+    "▁될",
+    "들을",
+    "개",
+    "▁선",
+    "물",
+    "▁재",
+    "▁우리",
+    "▁입",
+    "▁성",
+    "▁의",
+    "▁강",
+    "▁스",
+    "무",
+    "▁생각",
+    "문",
+    "▁저",
+    "이나",
+    "해야",
+    "간",
+    "내",
+    "▁장",
+    "▁서",
+    "되는",
+    "운",
+    "부터",
+    "경",
+    "▁어",
+    "▁그리고",
+    "하면",
+    "▁여",
+    "들의",
+    "▁발",
+    "하다",
+    "▁단",
+    "▁당",
+    "오",
+    "금",
+    "▁명",
+    "공",
+    "▁물",
+    "크",
+    "▁것으로",
+    "▁했다",
+    "▁양",
+    "▁배",
+    "법",
+    "형",
+    "▁또",
+    "▁간",
+    "▁개",
+    "▁또한",
+    "▁그러나",
+    "▁이런",
+    "▁차",
+    "▁큰",
+    "민",
+    "▁다양한",
+    "생",
+    "었다",
+    "▁남",
+    "력",
+    "유",
+    "▁따라",
+    "지만",
+    "권",
+    "▁게",
+    "모",
+    "▁경",
+    "▁위",
+    "▁없는",
+    "▁하나",
+    "에서는",
+    "워",
+    "▁시작",
+    "▁파",
+    "▁아니라",
+    "▁대해",
+    "▁만",
+    "▁실",
+    "▁알",
+    "▁없다",
+    "하며",
+    "▁합니다",
+    "▁된다",
+    "감",
+    "▁많이",
+    "영",
+    "▁지역",
+    "▁반",
+    "건",
+    "▁데",
+    "▁방",
+    "▁위한",
+    "직",
+    "거",
+    "▁하고",
+    "▁어떤",
+    "▁세계",
+    "▁적",
+    "양",
+    "▁분",
+    "점",
+    "데",
+    "보다",
+    "▁좋은",
+    "습니다",
+    "▁열",
+    "▁미국",
+    "▁초",
+    "역",
+    "면서",
+    "터",
+    "된다",
+    "▁다시",
+    "심",
+    "파",
+    "▁어떻게",
+    "종",
+    "타",
+    "▁본",
+    "▁살",
+    "급",
+    "▁중국",
+    "▁진",
+    "▁손",
+    "래",
+    "▁줄",
+    "▁매",
+    "석",
+    "▁여행",
+    "▁피",
+    "▁이상",
+    "교",
+    "▁지난",
+    "▁집",
+    "▁모두",
+    "약",
+    "프",
+    "린",
+    "▁자동",
+    "라는",
+    "라고",
+    "▁감",
+    "포",
+    "▁있도록",
+    "▁진행",
+    "토",
+    "▁박",
+    "음",
+    "▁것입니다",
+    "▁못",
+    "천",
+    "지는",
+    "▁우",
+    "란",
+    "▁또는",
+    "▁노",
+    "▁포",
+    "▁화",
+    "군",
+    "달",
+    "입",
+    "업",
+    "▁이용",
+    "▁되",
+    "▁현재",
+    "▁타",
+    "▁걸",
+    "저",
+    "해서",
+    "▁그런",
+    "바",
+    "까",
+    "▁번",
+    "▁건",
+    "될",
+    "청",
+    "디",
+    "▁최",
+    "▁예",
+    "▁이후",
+    "증",
+    "임",
+    "▁일본",
+    "속",
+    "▁외",
+    "티",
+    "▁특히",
+    "격",
+    "▁볼",
+    "료",
+    "▁있을",
+    "▁지금",
+    "▁관련",
+    "▁아이",
+    "▁들",
+    "▁들어",
+    "레",
+    "▁문제",
+    "두",
+    "출",
+    "되어",
+    "▁이번",
+    "에도",
+    "▁산",
+    "작",
+    "설",
+    "▁사회",
+    "▁시간",
+    "▁사실",
+    "▁있었다",
+    "▁된",
+    "▁그는",
+    "즈",
+    "▁날",
+    "발",
+    "▁뒤",
+    "▁새로운",
+    "매",
+    "처럼",
+    "▁나는",
+    "온",
+    "난",
+    "▁교육",
+    "▁출",
+    "▁각",
+    "▁편",
+    "▁지원",
+    "▁호",
+    "▁심",
+    "▁영",
+    "▁계",
+    "▁서비스",
+    "는데",
+    "▁바로",
+    "들에게",
+    "▁정도",
+    "▁문",
+    "▁시장",
+    "이라는",
+    "길",
+    "품",
+    "▁선택",
+    "절",
+    "기를",
+    "▁이어",
+    "색",
+    "되고",
+    "목",
+    "았다",
+    "▁여러",
+    "▁가지",
+    "류",
+    "▁법",
+    "▁에",
+    "거나",
+    "▁서울",
+    "▁다음",
+    "키",
+    "▁달",
+    "▁확인",
+    "▁약",
+    "▁제공",
+    "▁교",
+    "립",
+    "▁사람",
+    "▁동안",
+    "피",
+    "릴",
+    "▁자신의",
+    "▁영화",
+    "▁과",
+    "▁없",
+    "배",
+    "▁치",
+    "▁맞",
+    "▁기업",
+    "▁국",
+    "▁이러한",
+    "▁않고",
+    "네",
+    "카",
+    "▁이야기",
+    "▁그래서",
+    "▁속",
+    "▁연구",
+    "그",
+    "▁만들어",
+    "▁눈",
+    "져",
+    "▁우리는",
+    "강",
+    "통",
+    "▁최근",
+    "▁국가",
+    "▁책",
+    "루",
+    "집",
+    "님",
+    "▁임",
+    "판",
+    "죠",
+    "른",
+    "▁시설",
+    "▁때문이다",
+    "울",
+    "하면서",
+    "▁추",
+    "▁않은",
+    "▁정보",
+    "했습니다",
+    "든",
+    "▁곳",
+    "각",
+    "▁총",
+    "▁않을",
+    "림",
+    "▁않는",
+    "태",
+    "향",
+    "후",
+    "▁생",
+    "▁결과",
+    "▁되는",
+    "▁학",
+    "▁찾아",
+    "결",
+    "등",
+    "▁있다는",
+    "였다",
+    "버",
+    "론",
+    "▁게임",
+    "▁말했다",
+    "▁채",
+    "▁사람이",
+    "▁있어",
+    "성을",
+    "현",
+    "▁보다",
+    "환",
+    "▁관",
+    "이라고",
+    "▁고객",
+    "▁몸",
+    "량",
+    "▁막",
+    "▁귀",
+    "▁먹",
+    "▁변",
+    "▁기술",
+    "▁로",
+    "▁너무",
+    "▁새",
+    "▁물론",
+    "▁제품",
+    "▁경제",
+    "날",
+    "▁직접",
+    "남",
+    "▁준비",
+    "합",
+    "▁사업",
+    "▁천",
+    "노",
+    "박",
+    "됩니다",
+    "▁용",
+    "엔",
+    "▁메",
+    "스트",
+    "▁운영",
+    "▁있",
+    "▁개발",
+    "자가",
+    "▁없이",
+    "▁코",
+    "▁내가",
+    "▁토",
+    "▁매우",
+    "담",
+    "▁금",
+    "언",
+    "시간",
+    "▁그런데",
+    "능",
+    "준",
+    "▁리",
+    "▁종",
+    "▁회사",
+    "▁복",
+    "▁사항을",
+    "▁국내",
+    "으며",
+    "▁사진",
+    "▁몇",
+    "으면",
+    "▁이렇게",
+    "처",
+    "름",
+    "▁노력",
+    "▁애",
+    "▁수도",
+    "케",
+    "락",
+    "이었다",
+    "▁첫",
+    "복",
+    "▁대표",
+    "▁더욱",
+    "됐다",
+    "▁통",
+    "▁역사",
+    "▁의미",
+    "▁호텔",
+    "▁온",
+    "갈",
+    "▁식",
+    "▁어느",
+    "▁이해",
+    "▁년",
+    "▁청",
+    "▁문화",
+    "했던",
+    "편",
+    "▁관리",
+    "▁여성",
+    "외",
+    "▁상태",
+    "▁바랍니다",
+    "▁처음",
+    "망",
+    "병",
+    "령",
+    "▁결정",
+    "졌다",
+    "▁있고",
+    "션",
+    "층",
+    "리는",
+    "▁북한",
+    "▁잡",
+    "▁좀",
+    "으",
+    "근",
+    "별",
+    "브",
+    "▁현",
+    "▁당시",
+    "▁순",
+    "승",
+    "▁것도",
+    "▁기대",
+    "▁허",
+    "평",
+    "더",
+    "▁역시",
+    "▁보고",
+    "▁회",
+    "반",
+    "▁필요",
+    "▁개인",
+    "▁설명",
+    "▁일부",
+    "▁정말",
+    "▁올",
+    "초",
+    "▁군",
+    "너",
+    "백",
+    "지고",
+    "함",
+    "▁계속",
+    "▁마음",
+    "▁가지고",
+    "▁사람들이",
+    "▁않는다",
+    "술",
+    "▁가격",
+    "▁권",
+    "▁담",
+    "▁길",
+    "▁작품",
+    "▁사랑",
+    "되지",
+    "▁목",
+    "▁부분",
+    "▁건강",
+    "▁돌",
+    "▁보면",
+    "▁해당",
+    "▁참",
+    "▁환경",
+    "북",
+    "▁아니",
+    "▁중요한",
+    "기도",
+    "▁난",
+    "▁자기",
+    "▁있으며",
+    "▁백",
+    "▁쓰",
+    "▁이미",
+    "▁참여",
+    "▁창",
+    "▁아닌",
+    "▁이를",
+    "창",
+    "▁존재",
+    "▁글",
+    "▁우리가",
+    "▁경기",
+    "▁대학",
+    "▁드",
+    "추",
+    "▁없습니다",
+    "겨",
+    "▁학교",
+    "들과",
+    "기에",
+    "열",
+    "▁추가",
+    "▁해결",
+    "▁치료",
+    "▁성장",
+    "독",
+    "▁전체",
+    "▁변화",
+    "율",
+    "▁아니다",
+    "▁높은",
+    "▁체",
+    "▁회원",
+    "투",
+    "코",
+    "▁농",
+    "▁요",
+    "예",
+    "▁평",
+    "▁제안",
+    "대로",
+    "▁왜",
+    "▁이제",
+    "▁있다고",
+    "▁월",
+    "▁나라",
+    "▁포함",
+    "▁공간",
+    "축",
+    "▁점",
+    "▁필요한",
+    "지를",
+    "▁등을",
+    "▁말씀",
+    "하거나",
+    "있는",
+    "▁쉽게",
+    "▁않",
+    "▁정책",
+    "▁같이",
+    "▁활동",
+    "▁않았다",
+    "▁같다",
+    "▁투자",
+    "▁생활",
+    "▁되어",
+    "주의",
+    "익",
+    "▁정치",
+    "▁죽",
+    "▁요구",
+    "▁활용",
+    "▁그렇게",
+    "살",
+    "책",
+    "▁커",
+    "진다",
+    "▁평가",
+    "례",
+    "▁성공",
+    "도록",
+    "▁정부",
+    "에서도",
+    "▁오늘",
+    "▁일이",
+    "쪽",
+    "▁안전",
+    "▁몰",
+    "▁표시",
+    "▁경험",
+    "었습니다",
+    "▁유지",
+    "▁해야",
+    "▁도시",
+    "▁올해",
+    "▁라고",
+    "▁않습니다",
+    "표",
+    "▁있기",
+    "어요",
+    "▁취",
+    "▁먼저",
+    "되",
+    "▁사이",
+    "▁검토",
+    "니까",
+    "▁인간",
+    "▁와",
+    "철",
+    "▁주장",
+    "침",
+    "다는",
+    "▁적용",
+    "▁번역",
+    "친",
+    "탄",
+    "▁시대",
+    "▁결국",
+    "▁삼",
+    "▁사건",
+    "▁수정",
+    "▁시스템",
+    "▁가족",
+    "▁따라서",
+    "▁독",
+    "▁늘",
+    "▁레",
+    "▁갈",
+    "▁앞으로",
+    "니다",
+    "사를",
+    "▁카",
+    "▁질문",
+    "▁역",
+    "번",
+    "습",
+    "▁광",
+    "▁만나",
+    "긴",
+    "▁북",
+    "▁하지",
+    "움",
+    "▁아래",
+    "▁절",
+    "▁읽",
+    "볼",
+    "▁결",
+    "▁자리",
+    "본",
+    "▁계획",
+    "돼",
+    "순",
+    "▁태",
+    "▁발전",
+    "▁기록",
+    "▁됩니다",
+    "▁팀",
+    "▁크",
+    "▁밝혔다",
+    "▁되었다",
+    "착",
+    "▁세상",
+    "▁국민",
+    "▁설",
+    "▁의견을",
+    "▁버",
+    "▁이것은",
+    "▁살아",
+    "▁발생",
+    "▁분석",
+    "었",
+    "쳐",
+    "자는",
+    "▁현실",
+    "▁듯",
+    "말",
+    "▁수준",
+    "▁운동",
+    "▁놓",
+    "▁있지만",
+    "광",
+    "▁그의",
+    "▁방문",
+    "▁배우",
+    "기가",
+    "▁끝",
+    "▁방법",
+    "▁뜻",
+    "▁합",
+    "▁서로",
+    "▁접",
+    "▁생산",
+    "▁않다",
+    "▁발표",
+    "▁하나님",
+    "▁하루",
+    "링",
+    "▁급",
+    "애",
+    "▁표현",
+    "▁병",
+    "▁싶",
+    "▁음식",
+    "▁정보를",
+    "▁좋아",
+    "▁아직",
+    "▁미래",
+    "▁인터넷",
+    "없이",
+    "▁모습",
+    "▁은",
+    "▁크게",
+    "씨",
+    "▁야",
+    "▁알고",
+    "렸다",
+    "▁판매",
+    "▁프로그램",
+    "▁교회",
+    "▁받아",
+    "팅",
+    "▁직",
+    "▁앞",
+    "▁관계",
+    "▁구성",
+    "다고",
+    "랑",
+    "▁자신",
+    "▁높",
+    "▁조금",
+    "았",
+    "▁승",
+    "족",
+    "▁만들",
+    "▁베",
+    "▁키",
+    "▁믿",
+    "▁작은",
+    "▁현대",
+    "▁예약",
+    "플",
+    "▁아이들",
+    "▁자연",
+    "규",
+    "▁전문",
+    "력을",
+    "범",
+    "항",
+    "▁만큼",
+    "이며",
+    "련",
+    "▁테",
+    "▁있었",
+    "▁대통령",
+    "장은",
+    "▁찾",
+    "▁패",
+    "▁이유",
+    "네요",
+    "성이",
+    "▁않았",
+    "▁기억",
+    "▁골",
+    "테",
+    "▁행",
+    "▁증가",
+    "▁밀",
+    "▁별",
+    "▁돌아",
+    "▁대부분",
+    "▁문제가",
+    "▁감사합니다",
+    "송",
+    "▁누",
+    "▁넘",
+    "▁마련",
+    "▁질",
+    "이라",
+    "▁증",
+    "▁가능",
+    "▁잠",
+    "▁형",
+    "▁감독",
+    "▁행복",
+    "▁환",
+    "황",
+    "▁제가",
+    "▁이름",
+    "▁공개",
+    "있",
+    "장을",
+    "▁민",
+    "▁황",
+    "▁거의",
+    "극",
+    "▁가운데",
+    "▁맛",
+    "악",
+    "셔",
+    "막",
+    "▁있으나",
+    "▁기존",
+    "▁소개",
+    "▁디자인",
+    "자를",
+    "▁기능",
+    "커",
+    "▁설치",
+    "▁산업",
+    "▁온라인",
+    "변",
+    "▁목표",
+    "▁번째",
+    "▁악",
+    "▁폭",
+    "자의",
+    "감을",
+    "불",
+    "록",
+    "▁보호",
+    "▁행사",
+    "▁표",
+    "▁침",
+    "택",
+    "▁투",
+    "▁향",
+    "▁스스로",
+    "▁기자",
+    "▁시간이",
+    "▁기간",
+    "▁없어",
+    "▁일을",
+    "희",
+    "런",
+    "줄",
+    "로서",
+    "▁기본",
+    "▁상품",
+    "▁특",
+    "▁내용",
+    "▁인해",
+    "▁탈",
+    "▁지난해",
+    "받",
+    "▁지나",
+    "▁있게",
+    "▁하면",
+    "리를",
+    "▁학생",
+    "▁협",
+    "▁여러분의",
+    "▁충",
+    "활",
+    "▁연결",
+    "▁공부",
+    "▁제대로",
+    "▁보내",
+    "곤",
+    "▁터",
+    "▁음악",
+    "▁주변",
+    "▁돈",
+    "▁방송",
+    "▁환자",
+    "▁이는",
+    "▁떠",
+    "▁고민",
+    "▁네",
+    "▁가능한",
+    "▁위치",
+    "곡",
+    "▁우리의",
+    "졌",
+    "▁무료",
+    "▁위험",
+    "▁며",
+    "▁이날",
+    "▁꼭",
+    "▁즉",
+    "▁같",
+    "▁주요",
+    "▁많다",
+    "▁삶",
+    "흥",
+    "찰",
+    "올",
+    "▁전에",
+    "▁그림",
+    "▁저는",
+    "▁암",
+    "▁벌",
+    "▁철",
+    "▁이라고",
+    "압",
+    "▁영향을",
+    "▁국제",
+    "염",
+    "▁보인다",
+    "십",
+    "▁함",
+    "▁학생들",
+    "확",
+    "▁힘",
+    "▁조사",
+    "객",
+    "▁면",
+    "▁등의",
+    "▁아주",
+    "▁작업",
+    "▁행동",
+    "▁과거",
+    "▁때문",
+    "퍼",
+    "▁왕",
+    "베",
+    "▁과정",
+    "▁우리나라",
+    "▁있기를",
+    "▁발견",
+    "레이",
+    "▁의원",
+    "▁꿈",
+    "채",
+    "▁사람은",
+    "▁모습을",
+    "▁구조",
+    "글",
+    "▁육",
+    "▁방식",
+    "▁시간을",
+    "▁숙박",
+    "▁했",
+    "점을",
+    "▁있어서",
+    "하였다",
+    "▁처",
+    "▁트",
+    "▁도움이",
+    "칠",
+    "팀",
+    "인데",
+    "▁음",
+    "▁자유",
+    "▁일반",
+    "▁친",
+    "▁놀",
+    "었던",
+    "▁팔",
+    "▁됐다",
+    "기는",
+    "▁가진",
+    "▁변경",
+    "률",
+    "▁해외",
+    "마다",
+    "▁항",
+    "했고",
+    "거리",
+    "▁현장",
+    "▁개선",
+    "▁후보",
+    "▁극",
+    "▁다만",
+    "▁집중",
+    "▁근",
+    "위원회",
+    "▁의사",
+    "▁얘기",
+    "▁구매",
+    "▁프로",
+    "▁관한",
+    "▁감사",
+    "▁이에",
+    "되었다",
+    "때",
+    "▁사이트",
+    "▁올라",
+    "▁맡",
+    "▁사람들",
+    "▁추진",
+    "▁서비스를",
+    "▁고려",
+    "협",
+    "께",
+    "▁풀",
+    "▁계획에",
+    "였",
+    "▁중입니다",
+    "▁여행자들",
+    "▁넘어",
+    "▁씨",
+    "▁되고",
+    "▁친구",
+    "▁붙",
+    "▁브랜드",
+    "훈",
+    "▁처리",
+    "▁둘",
+    "▁나오",
+    "▁순간",
+    "▁조금이나마",
+    "▁조직",
+    "▁같습니다",
+    "▁사용자",
+    "▁낮",
+    "▁상황",
+    "메",
+    "▁사고",
+    "▁한국어로",
+    "들도",
+    "왔다",
+    "▁자체",
+    "▁받고",
+    "▁힘들",
+    "▁뿐",
+    "밀",
+    "▁선수",
+    "▁없었다",
+    "▁여기",
+    "▁판",
+    "더라도",
+    "사가",
+    "▁업무",
+    "▁회원님의",
+    "액",
+    "▁작",
+    "▁그것은",
+    "하지만",
+    "▁개인정보",
+    "▁피부",
+    "▁예상",
+    "▁입장",
+    "리고",
+    "하십시오",
+    "▁거리",
+    "▁달라",
+    "▁풍",
+    "▁작가",
+    "▁비용",
+    "▁많",
+    "머",
+    "▁오래",
+    "▁리뷰는",
+    "▁완벽하지",
+    "▁사람들은",
+    "▁제작",
+    "▁업체",
+    "▁퀄리티가",
+    "▁이동",
+    "▁싶다",
+    "▁강화",
+    "숙",
+    "▁번역되었습니다",
+    "헌",
+    "▁분위기",
+    "▁싶은",
+    "▁보여",
+    "▁의해",
+    "▁문제를",
+    "▁느끼",
+    "▁항상",
+    "▁신청",
+    "▁윤",
+    "취",
+    "▁참고가",
+    "▁참고하실",
+    "▁판단",
+    "▁실제",
+    "▁지적",
+    "▁번역을",
+    "▁소리",
+    "▁정신",
+    "▁반대",
+    "▁대상",
+    "어서",
+    "▁얼마나",
+    "▁실제로",
+    "▁분야",
+    "▁숨",
+    "▁공격",
+    "위를",
+    "▁정도로",
+    "▁이야기를",
+    "낼",
+    "▁달리",
+    "▁기준",
+    "▁자신이",
+    "▁디",
+    "▁검색",
+    "▁번역의",
+    "▁따",
+    "장이",
+    "한다는",
+    "하실",
+    "▁브",
+    "▁효과",
+    "▁라",
+    "▁운",
+    "▁따르면",
+    "▁객실",
+    "▁클",
+    "▁경찰",
+    "사회",
+    "▁받은",
+    "▁그대로",
+    "다면",
+    "▁책임",
+    "혀",
+    "충",
+    "하도록",
+    "▁필",
+    "▁경쟁",
+    "▁들어가",
+    "것",
+    "널",
+    "했",
+    "▁부담",
+    "▁역할을",
+    "갔다",
+    "▁한번",
+    "▁가치",
+    "▁이용자",
+    "교육",
+    "▁전략",
+    "▁위해서",
+    "▁금융",
+    "낸",
+    "▁해서",
+    "▁속에서",
+    "견",
+    "▁피해",
+    "▁거래",
+    "▁최대",
+    "▁있는데",
+    "▁그러",
+    "라도",
+    "▁등장",
+    "▁내려",
+    "블",
+    "▁일정",
+    "▁대신",
+    "▁자료",
+    "▁필요하다",
+    "▁지방",
+    "냈다",
+    "▁지속",
+    "▁확대",
+    "▁않아",
+    "▁우선",
+    "시장",
+    "▁만드는",
+    "▁겪",
+    "▁밤",
+    "페",
+    "▁얼굴",
+    "▁대해서",
+    "▁비교",
+    "▁좋다",
+    "▁페",
+    "화를",
+    "▁말을",
+    "▁바람",
+    "츠",
+    "인지",
+    "▁가져",
+    "▁선거",
+    "효",
+    "탁",
+    "▁이미지",
+    "▁최고",
+    "냐",
+    "▁폐",
+    "주는",
+    "▁인식",
+    "로부터",
+    "명이",
+    "개월",
+    "로운",
+    "이고",
+    "▁누구",
+    "▁갖고",
+    "돌",
+    "▁교수",
+    "▁좋",
+    "▁이곳",
+    "▁인기",
+    "렸",
+    "▁등록",
+    "▁과학",
+    "린다",
+    "까요",
+    "▁하다",
+    "▁오히려",
+    "득",
+    "▁모델",
+    "▁광고",
+    "▁경우가",
+    "력이",
+    "척",
+    "틀",
+    "▁추천",
+    "총",
+    "람",
+    "▁잔",
+    "▁한다는",
+    "▁해도",
+    "▁송",
+    "걸",
+    "끼",
+    "▁땅",
+    "겠",
+    "하던",
+    "▁언론",
+    "▁나가",
+    "물을",
+    "이지만",
+    "▁남자",
+    "▁제시",
+    "▁그들은",
+    "▁단계",
+    "▁아버지",
+    "데요",
+    "지도",
+    "어야",
+    "▁국회",
+    "▁하기",
+    "▁꽃",
+    "덕",
+    "▁설정",
+    "▁그리",
+    "검",
+    "풍",
+    "센",
+    "씩",
+    "육",
+    "함을",
+    "▁축",
+    "하기도",
+    "▁하나님의",
+    "▁견",
+    "▁어디",
+    "▁전국",
+    "폭",
+    "▁보통",
+    "▁보는",
+    "▁오는",
+    "▁인생",
+    "골",
+    "▁어린이",
+    "콘",
+    "▁여자",
+    "▁자동차",
+    "럽",
+    "▁수행",
+    "▁어려운",
+    "▁마지막",
+    "▁원인",
+    "▁여전히",
+    "▁생각이",
+    "▁아침",
+    "▁섬",
+    "▁측",
+    "시는",
+    "▁오후",
+    "▁녹",
+    "▁그냥",
+    "▁보여주",
+    "▁위해서는",
+    "수를",
+    "스크",
+    "하세요",
+    "곳",
+    "▁목적",
+    "기로",
+    "렌",
+    "▁되면",
+    "▁소비자",
+    "▁대한민국",
+    "이란",
+    "▁사용할",
+    "▁경우에는",
+    "▁라는",
+    "▁동시에",
+    "▁가정",
+    "▁데이터",
+    "▁전쟁",
+    "▁못한",
+    "듯",
+    "▁인사",
+    "억",
+    "▁자주",
+    "칭",
+    "▁중심",
+    "▁요청",
+    "▁전혀",
+    "▁회장",
+    "▁이들",
+    "▁찍",
+    "▁엄",
+    "▁있지",
+    "▁머리",
+    "▁아무",
+    "상을",
+    "▁마치",
+    "▁조선",
+    "▁되었",
+    "▁규모",
+    "▁돼",
+    "사는",
+    "필",
+    "▁마사지",
+    "▁인정",
+    "▁영상",
+    "▁다음과",
+    "▁안정",
+    "새",
+    "▁반응",
+    "▁특정",
+    "▁답",
+    "탈",
+    "▁요즘",
+    "▁사례",
+    "▁핵심",
+    "스터",
+    "▁한편",
+    "▁저희",
+    "▁이전",
+    "▁최고의",
+    "▁장소",
+    "험",
+    "▁아마",
+    "▁빠",
+    "▁때는",
+    "▁슬",
+    "▁엄마",
+    "▁필요가",
+    "▁결혼",
+    "▁빛",
+    "▁상대",
+    "▁대화",
+    "▁주는",
+    "클",
+    "패",
+    "▁유럽",
+    "▁보니",
+    "▁공급",
+    "슬",
+    "▁책을",
+    "십시오",
+    "컨",
+    "▁좌",
+    "▁원하는",
+    "▁이유는",
+    "딩",
+    "▁생명",
+    "왕",
+    "▁말이",
+    "▁받을",
+    "▁홍",
+    "▁사회적",
+    "▁마음을",
+    "▁빈",
+    "▁것이라고",
+    "▁부모",
+    "▁공연",
+    "스를",
+    "▁울",
+    "▁그렇",
+    "시키는",
+    "했을",
+    "▁계약",
+    "려고",
+    "▁노동",
+    "▁넓",
+    "▁걱정",
+    "시켜",
+    "▁따른",
+    "▁만든",
+    "▁일상",
+    "▁요금",
+    "균",
+    "▁전화",
+    "▁전통",
+    "념",
+    "▁부족",
+    "▁공유",
+    "찬",
+    "쟁",
+    "지요",
+    "잡",
+    "▁제한",
+    "▁아니오",
+    "▁정상",
+    "▁욕",
+    "▁나와",
+    "▁생각하시나요",
+    "손",
+    "략",
+    "▁시민",
+    "하신",
+    "▁특징",
+    "암",
+    "▁제도",
+    "▁능력",
+    "▁시행",
+    "▁전달",
+    "▁상황이",
+    "▁식사",
+    "힌",
+    "사업",
+    "▁반드시",
+    "▁여러분",
+    "▁격",
+    "▁내부",
+    "킹",
+    "▁되지",
+    "▁감정",
+    "▁탄",
+    "▁준",
+    "▁에너지",
+    "▁제출해주셔",
+    "▁나서",
+    "접",
+    "▁플",
+    "라면",
+    "▁매일",
+    "▁내용을",
+    "▁생각해",
+    "밖",
+    "▁직원",
+    "▁있다면",
+    "▁수많은",
+    "▁너",
+    "▁수밖에",
+    "▁나이",
+    "났다",
+    "묘",
+    "▁지지",
+    "▁뭐",
+    "원을",
+    "▁알아",
+    "▁느낌",
+    "▁시도",
+    "주세요",
+    "났",
+    "▁현지",
+    "▁본인",
+    "가지",
+    "인이",
+    "봉",
+    "▁기사",
+    "보고",
+    "수록",
+    "▁넣",
+    "▁경영",
+    "생활",
+    "▁죄",
+    "▁주목",
+    "하자",
+    "▁중요",
+    "▁요소",
+    "치를",
+    "▁병원",
+    "자들이",
+    "▁조건",
+    "▁검찰",
+    "제를",
+    "명을",
+    "▁영어",
+    "서는",
+    "▁우려",
+    "▁두고",
+    "▁접근",
+    "일에",
+    "식을",
+    "없는",
+    "▁것처럼",
+    "끄",
+    "▁받는",
+    "랜드",
+    "▁많아",
+    "▁예술",
+    "▁동물",
+    "하는데",
+    "▁예정",
+    "기관",
+    "▁긴",
+    "측",
+    "▁의료",
+    "▁가능성이",
+    "춘",
+    "▁삶을",
+    "▁항공",
+    "했다고",
+    "벨",
+    "▁깨",
+    "▁건물",
+    "▁비판",
+    "▁지식",
+    "▁시절",
+    "▁이뤄",
+    "▁목소리",
+    "▁부산",
+    "벌",
+    "▁노래",
+    "▁관심을",
+    "▁가입",
+    "▁없을",
+    "▁억",
+    "얼",
+    "▁과정에서",
+    "▁실시",
+    "▁관심",
+    "▁알려",
+    "▁검사",
+    "▁제주",
+    "▁여기에",
+    "▁최소",
+    "▁느껴",
+    "▁석",
+    "▁사람의",
+    "음을",
+    "▁묻",
+    "수가",
+    "▁정리",
+    "보다는",
+    "▁높이",
+    "▁혹은",
+    "▁연기",
+    "▁쌓",
+    "▁콘텐츠",
+    "▁망",
+    "▁희망",
+    "센터",
+    "스러운",
+    "▁컴퓨터",
+    "년간",
+    "▁열린",
+    "▁시기",
+    "갔",
+    "정을",
+    "▁평화",
+    "▁곧",
+    "명의",
+    "▁앞서",
+    "▁방법을",
+    "▁그것",
+    "롭",
+    "권을",
+    "▁쪽",
+    "▁확보",
+    "▁모바일",
+    "▁대비",
+    "▁영국",
+    "▁가능성",
+    "치는",
+    "▁검",
+    "▁저렴한",
+    "▁퍼",
+    "▁밖에",
+    "맹",
+    "폐",
+    "▁생각을",
+    "▁받았다",
+    "▁웹",
+    "▁밝",
+    "폰",
+    "▁빌",
+    "▁속에",
+    "관계",
+    "▁통합",
+    "▁대중",
+    "리가",
+    "▁비해",
+    "텔",
+    "▁헤",
+    "사의",
+    "▁여름",
+    "문화",
+    "▁프로젝트",
+    "▁문제는",
+    "▁전망",
+    "원이",
+    "▁색",
+    "▁보기",
+    "▁등이",
+    "엘",
+    "▁있던",
+    "▁예정이다",
+    "▁나무",
+    "▁있어요",
+    "한다면",
+    "▁않으면",
+    "▁훨씬",
+    "▁인물",
+    "▁절대",
+    "▁떠나",
+    "핑",
+    "▁만족",
+    "▁돈을",
+    "▁매력",
+    "느냐",
+    "▁시즌",
+    "▁분들",
+    "▁술",
+    "되면",
+    "혹",
+    "▁보이는",
+    "▁그들의",
+    "▁속도",
+    "▁루",
+    "▁듣",
+    "▁모양",
+    "으나",
+    "겠습니다",
+    "▁주제",
+    "▁대응",
+    "▁중앙",
+    "▁얼마",
+    "녀",
+    "으니",
+    "▁어렵",
+    "▁지켜",
+    "▁촬영",
+    "▁있는지",
+    "트를",
+    "▁찬",
+    "▁체험",
+    "▁높아",
+    "되기",
+    "▁퇴",
+    "있다",
+    "스는",
+    "▁사전",
+    "▁신경",
+    "서를",
+    "▁못하고",
+    "▁하며",
+    "▁영어로",
+    "랐",
+    "▁부정",
+    "▁전자",
+    "▁일어나",
+    "▁현상",
+    "참",
+    "▁파일",
+    "▁위기",
+    "▁뇌",
+    "▁기도",
+    "▁참고",
+    "▁강조",
+    "▁칼",
+    "▁사실을",
+    "▁그동안",
+    "▁휴",
+    "▁언어",
+    "▁조치",
+    "▁신뢰",
+    "▁후에",
+    "▁형태",
+    "▁나누",
+    "▁가는",
+    "▁공식",
+    "▁그것을",
+    "▁사용하는",
+    "금을",
+    "▁기관",
+    "▁궁금",
+    "▁각종",
+    "▁행정",
+    "▁특성",
+    "▁당신이",
+    "▁그가",
+    "▁꾸",
+    "하시",
+    "놓",
+    "▁도전",
+    "▁마을",
+    "▁문서",
+    "▁규정",
+    "▁중에",
+    "혜",
+    "▁관광",
+    "하였",
+    "▁한다고",
+    "▁작성",
+    "▁도입",
+    "▁맞는",
+    "▁생기",
+    "▁시험",
+    "▁차량",
+    "▁사는",
+    "했지만",
+    "▁안에",
+    "▁파악",
+    "▁사람을",
+    "▁선정",
+    "▁앱",
+    "잃",
+    "▁논의",
+    "▁아파트",
+    "▁그때",
+    "▁인상",
+    "▁전시",
+    "▁증상",
+    "한다고",
+    "▁하나의",
+    "▁장관",
+    "▁답변",
+    "드는",
+    "▁품",
+    "▁수업",
+    "▁글로벌",
+    "▁행위",
+    "▁스타일",
+    "▁잊",
+    "이기도",
+    "자들은",
+    "▁있어야",
+    "▁나타나",
+    "▁딸",
+    "▁캐릭터",
+    "▁탐",
+    "▁제거",
+    "으로서",
+    "▁독일",
+    "▁있을까",
+    "벤",
+    "▁힘을",
+    "▁언급",
+    "▁이것",
+    "출장",
+    "▁참석",
+    "▁차지",
+    "▁일단",
+    "적이",
+    "르는",
+    "되었",
+    "▁즐기",
+    "▁모르",
+    "하시는",
+    "▁하늘",
+    "▁주차",
+    "▁상승",
+    "▁카드",
+    "▁싶어",
+    "▁수익",
+    "▁지금까지",
+    "▁사항",
+    "되면서",
+    "존",
+    "성과",
+    "▁분명",
+    "▁것과",
+    "▁당신",
+    "▁완전히",
+    "▁당신의",
+    "▁예산",
+    "▁것에",
+    "겠다",
+    "▁써",
+    "▁시작했다",
+    "자로",
+    "▁개정",
+    "슨",
+    "▁착",
+    "▁중요하다",
+    "▁혼자",
+    "▁청년",
+    "▁말한다",
+    "시설",
+    "▁반영",
+    "리아",
+    "▁형성",
+    "▁건설",
+    "▁소재",
+    "학생",
+    "▁보장",
+    "▁삼성",
+    "▁의견",
+    "감이",
+    "▁겨울",
+    "▁개념",
+    "적이고",
+    "▁이루어",
+    "▁고통",
+    "▁적이",
+    "▁대부분의",
+    "▁눈에",
+    "탕",
+    "▁기타",
+    "▁옷",
+    "▁만약",
+    "싸",
+    "들에",
+    "만원",
+    "▁출시",
+    "▁하여",
+    "▁영역",
+    "▁못하는",
+    "▁자녀",
+    "▁성과",
+    "▁보험",
+    "▁남성",
+    "▁핵",
+    "▁보도",
+    "▁협력",
+    "▁정부가",
+    "▁이용할",
+    "▁편안",
+    "▁주로",
+    "▁생각한다",
+    "관리",
+    "▁완성",
+    "▁방향",
+    "▁있으면",
+    "▁커피",
+    "▁열심히",
+    "▁없다는",
+    "아야",
+    "▁지구",
+    "▁혼",
+    "학교",
+    "텐",
+    "▁하는데",
+    "섭",
+    "▁특별한",
+    "쁘",
+    "▁스포츠",
+    "▁작성된",
+    "▁제외",
+    "▁바다",
+    "하라",
+    "▁실패",
+    "▁쓸",
+    "▁때문입니다",
+    "지지",
+    "▁감소",
+    "▁글을",
+    "▁종류",
+    "▁다니",
+    "▁곳이",
+    "▁차이",
+    "▁제기",
+    "▁찾을",
+    "▁혁신",
+    "털",
+    "옥",
+    "셨",
+    "▁충분히",
+    "▁트럼프",
+    "▁존",
+    "▁하였다",
+    "▁불안",
+    "만큼",
+    "보면",
+    "▁아닙니다",
+    "▁예방",
+    "르고",
+    "▁상황에서",
+    "▁어머니",
+    "▁외부",
+    "▁낙",
+    "▁아름다운",
+    "▁오랜",
+    "▁그룹",
+    "되며",
+    "▁회의",
+    "▁가득",
+    "▁대회",
+    "▁옆",
+    "▁아들",
+    "▁트립어드바이저",
+    "▁자신을",
+    "▁전문가",
+    "욕",
+    "▁반복",
+    "럼",
+    "▁위하여",
+    "▁머",
+    "▁푸",
+    "▁그렇지",
+    "▁삶의",
+    "▁당신은",
+    "전을",
+    "▁틀",
+    "▁공동",
+    "▁그리스도",
+    "스타",
+    "▁공공",
+    "대학교",
+    "▁적극",
+    "▁불편",
+    "▁반면",
+    "▁있었던",
+    "▁했습니다",
+    "▁뉴스",
+    "▁나오는",
+    "께서",
+    "보험",
+    "▁드라마",
+    "▁겁니다",
+    "문을",
+    "활동",
+    "▁익",
+    "▁하면서",
+    "▁빠르게",
+    "▁회복",
+    "▁비즈니스",
+    "▁어린",
+    "운동",
+    "▁역할",
+    "닝",
+    "▁제공하는",
+    "▁구입",
+    "쾌",
+    "▁길을",
+    "▁염",
+    "▁요리",
+    "▁혈",
+    "▁크기",
+    "▁있었습니다",
+    "▁문재인",
+    "▁담당",
+    "▁소식",
+    "▁뛰어",
+    "▁전환",
+    "▁구축",
+    "▁젊은",
+    "▁지정",
+    "▁기회",
+    "▁편의",
+    "▁학습",
+    "비를",
+    "▁지키",
+    "세를",
+    "▁등에",
+    "▁예수님",
+    "▁직장",
+    "개의",
+    "▁이상의",
+    "내는",
+    "리지",
+    "하려면",
+    "정부",
+    "▁선물",
+    "▁언제",
+    "▁늦",
+    "▁동의",
+    "▁미리",
+    "회의",
+    "▁단체",
+    "▁도착",
+    "사이트",
+    "▁장면",
+    "▁쓴",
+    "▁정의",
+    "려면",
+    "▁자세한",
+    "보는",
+    "▁번역입니다",
+    "▁대체",
+    "▁장기",
+    "▁위에",
+    "▁어려움",
+    "▁배경",
+    "▁찾는",
+    "년에",
+    "▁이루",
+    "▁리뷰의",
+    "▁수있는",
+    "▁같아요",
+    "▁무대",
+    "보세요",
+    "귀",
+    "▁스마트폰",
+    "▁벌어",
+    "▁왔다",
+    "라이",
+    "▁방안",
+    "준다",
+    "▁설계",
+    "▁이벤트",
+    "▁업",
+    "랜",
+    "오는",
+    "간다",
+    "▁다양",
+    "▁중심으로",
+    "▁사람들의",
+    "경제",
+    "▁가능하다",
+    "▁논란",
+    "기업",
+    "▁다가",
+    "리그",
+    "년대",
+    "▁기분",
+    "▁효율",
+    "▁벽",
+    "▁출장",
+    "았습니다",
+    "세요",
+    "▁무슨",
+    "▁잘못",
+    "▁주문",
+    "▁각각",
+    "산업",
+    "먹",
+    "▁만에",
+    "▁남북",
+    "▁드러",
+    "▁약속",
+    "▁전기",
+    "▁독립",
+    "서비스",
+    "답",
+    "▁정부는",
+    "▁결과를",
+    "▁다음은",
+    "▁블",
+    "▁생각합니다",
+    "▁소통",
+    "▁끼",
+    "▁프랑스",
+    "▁모습이",
+    "▁액",
+    "쿠",
+    "▁앞에",
+    "▁되기",
+    "정보",
+    "▁수술",
+    "▁불구하고",
+    "▁오전",
+    "▁화면",
+    "▁곡",
+    "▁대상으로",
+    "▁출신",
+    "갑",
+    "▁숙",
+    "▁러시아",
+    "▁쉬",
+    "▁프",
+    "웠",
+    "됐",
+    "▁마음이",
+    "▁못했다",
+    "▁관심이",
+    "▁종교",
+    "퇴",
+    "▁선생님",
+    "▁도움을",
+    "▁정부의",
+    "▁주택",
+    "혈",
+    "▁이와",
+    "닥",
+    "▁활동을",
+    "대를",
+    "하려는",
+    "▁관련된",
+    "▁받",
+    "▁필수",
+    "싱",
+    "▁기능을",
+    "▁끌",
+    "▁부동산",
+    "▁바뀌",
+    "이라면",
+    "집니다",
+    "▁대해서는",
+    "▁운전",
+    "좌",
+    "회를",
+    "▁시각",
+    "자와",
+    "▁초기",
+    "법을",
+    "▁상상",
+    "임을",
+    "쳤다",
+    "▁장점",
+    "점이",
+    "▁개의",
+    "지역",
+    "▁이들은",
+    "시킬",
+    "겠다는",
+    "므로",
+    "▁봉",
+    "▁무엇보다",
+    "▁여부",
+    "▁규제",
+    "▁가슴",
+    "▁봄",
+    "▁비슷한",
+    "▁나의",
+    "▁쉽지",
+    "▁인천",
+    "▁건축",
+    "▁기반",
+    "▁빠른",
+    "▁통해서",
+    "▁향상",
+    "으로부터",
+    "농",
+    "▁예수",
+    "▁소비",
+    "▁기획",
+    "▁인터뷰",
+    "억원",
+    "▁웃",
+    "▁좋습니다",
+    "▁홈페이지",
+    "▁평균",
+    "▁소설",
+    "▁죽음",
+    "▁훈련",
+    "▁성경",
+    "하다는",
+    "▁사망",
+    "원은",
+    "함으로써",
+    "▁그래도",
+    "자에게",
+    "▁과정을",
+    "▁마케팅",
+    "▁안내",
+    "일부터",
+    "▁나온",
+    "▁청와대",
+    "▁대구",
+    "▁개최",
+    "▁받았",
+    "▁제품을",
+    "▁특별",
+    "꺼",
+    "지가",
+    "▁홈",
+    "앙",
+    "▁체크",
+    "장에서",
+    "촌",
+    "▁유명",
+    "▁제조",
+    "기간",
+    "▁응",
+    "나요",
+    "리스",
+    "▁딱",
+    "▁바꾸",
+    "▁빨리",
+    "었고",
+    "▁없고",
+    "끈",
+    "▁살펴보",
+    "▁점점",
+    "심을",
+    "▁어렵다",
+    "▁이하",
+    "▁금액",
+    "▁승리",
+    "▁절차",
+    "▁남편",
+    "▁해석",
+    "▁여기서",
+    "▁클릭",
+    "▁나를",
+    "▁교통",
+    "었는데",
+    "▁편집",
+    "물이",
+    "웃",
+    "▁사이에",
+    "▁졸업",
+    "▁그녀",
+    "▁메시지",
+    "▁단순",
+    "첨",
+    "폴",
+    "혼",
+    "▁누구나",
+    "▁제공합니다",
+    "▁입니다",
+    "▁청소년",
+    "하다고",
+    "▁올리",
+    "▁받으",
+    "▁알게",
+    "하고자",
+    "▁지금은",
+    "되어야",
+    "▁쿠",
+    "▁거기",
+    "스가",
+    "▁됐",
+    "▁저자",
+    "▁먹는",
+    "하려고",
+    "자들의",
+    "▁그것이",
+    "▁디지털",
+    "▁바라보",
+    "▁즐길",
+    "▁레이",
+    "▁라이",
+    "▁미국의",
+    "보니",
+    "▁확",
+    "▁구글",
+    "▁감염",
+    "▁보유",
+    "▁늘어",
+    "▁주민",
+    "턴",
+    "▁참가",
+    "▁바라",
+    "둥",
+    "웠다",
+    "▁무료로",
+    "인은",
+    "쯤",
+    "▁이유로",
+    "조사",
+    "▁내용은",
+    "▁질병",
+    "힐",
+    "정책",
+    "에선",
+    "▁짓",
+    "▁대통령이",
+    "▁걷",
+    "▁타고",
+    "▁부분이",
+    "완",
+    "▁지도",
+    "드리",
+    "▁아내",
+    "▁뿐만",
+    "기술",
+    "▁통한",
+    "▁불가능",
+    "▁보안",
+    "▁정당",
+    "줍니다",
+    "▁확장",
+    "▁캐",
+    "▁합의",
+    "▁최종",
+    "▁저장",
+    "▁팬",
+    "▁주고",
+    "▁티",
+    "징",
+    "짐",
+    "▁기회를",
+    "▁낮은",
+    "▁고용",
+    "촉",
+    "킨",
+    "▁플랫폼",
+    "슈",
+    "▁노출",
+    "▁실행",
+    "▁바탕으로",
+    "소를",
+    "▁봐",
+    "시아",
+    "▁페이지",
+    "삼",
+    "▁포기",
+    "▁아시아",
+    "▁내년",
+    "▁약간",
+    "▁진짜",
+    "▁워",
+    "▁사람들에게",
+    "▁끝나",
+    "▁범",
+    "▁인도",
+    "▁일자리",
+    "출장마사지",
+    "휴",
+    "겠지만",
+    "▁인한",
+    "▁나온다",
+    "▁본다",
+    "▁조성",
+    "▁스타",
+    "▁평소",
+    "빙",
+    "▁성격",
+    "▁갈등",
+    "▁점을",
+    "▁흔",
+    "▁깊은",
+    "▁전했다",
+    "▁업계",
+    "▁방식으로",
+    "▁좋아하는",
+    "▁저녁",
+    "되었습니다",
+    "▁기계",
+    "▁근거",
+    "▁달러",
+    "셀",
+    "짝",
+    "▁기초",
+    "▁과연",
+    "롤",
+    "▁누군가",
+    "▁굳",
+    "▁의지",
+    "하겠다",
+    "▁잠시",
+    "▁상담",
+    "뿐만",
+    "▁얼",
+    "업체",
+    "▁맥",
+    "▁믿음",
+    "▁쓰는",
+    "▁목사",
+    "▁전반",
+    "▁항공권",
+    "▁해주",
+    "▁직업",
+    "▁인간의",
+    "봐야",
+    "▁타이",
+    "▁때까지",
+    "치고",
+    "▁상당히",
+    "렬",
+    "▁멀",
+    "▁예를",
+    "시킨",
+    "▁주인공",
+    "▁기념",
+    "▁되었습니다",
+    "뿐",
+    "▁소유",
+    "▁코로나",
+    "▁끊",
+    "▁은행",
+    "▁빠져",
+    "했는데",
+    "▁수요",
+    "▁다운로드",
+    "▁단어",
+    "▁빼",
+    "낭",
+    "▁대통령은",
+    "느",
+    "▁조정",
+    "▁싸",
+    "▁있으니",
+    "▁웃음",
+    "▁출연",
+    "부는",
+    "▁단지",
+    "▁그렇다면",
+    "▁중단",
+    "▁시점",
+    "▁부르",
+    "▁힘든",
+    "조차",
+    "▁앞에서",
+    "몬",
+    "▁냉",
+    "▁수출",
+    "▁원래",
+    "▁때마다",
+    "▁축구",
+    "▁외국인",
+    "▁흥미",
+    "▁중에서",
+    "빛",
+    "▁재미",
+    "▁권리",
+    "▁자랑",
+    "▁말이다",
+    "▁그들이",
+    "▁느낄",
+    "겁",
+    "뜨",
+    "▁복잡",
+    "▁정권",
+    "▁활성화",
+    "▁매출",
+    "▁재미있",
+    "▁조합",
+    "웨",
+    "▁괴",
+    "▁대하여",
+    "▁갖추",
+    "다가",
+    "▁친구들",
+    "▁생각하는",
+    "▁일은",
+    "▁듣고",
+    "▁살고",
+    "▁연락",
+    "▁홀",
+    "▁했던",
+    "▁옵션",
+    "▁장치",
+    "▁우주",
+    "▁있겠",
+    "▁주의",
+    "가요",
+    "듯이",
+    "▁완",
+    "하니",
+    "▁이름을",
+    "▁것이라는",
+    "▁파트너",
+    "▁상황을",
+    "▁먹고",
+    "▁관계자는",
+    "▁없다고",
+    "▁거부",
+    "▁토론",
+    "▁논",
+    "▁사태",
+    "▁진단",
+    "▁보수",
+    "▁유리",
+    "▁징",
+    "이라도",
+    "▁지급",
+    "▁경험을",
+    "▁홍보",
+    "▁공정",
+    "▁마시",
+    "일까지",
+    "▁꽤",
+    "▁대형",
+    "▁돌아가",
+    "▁리뷰",
+    "했다는",
+    "▁포인트",
+    "▁취업",
+    "▁새로",
+    "▁빨",
+    "▁실천",
+    "▁꼽",
+    "▁진출",
+    "▁수집",
+    "▁맞춰",
+    "▁센터",
+    "▁향후",
+    "사항",
+    "궁",
+    "▁기다리",
+    "▁흐름",
+    "톤",
+    "▁블로그",
+    "콜걸",
+    "▁인구",
+    "렉",
+    "▁더불어",
+    "▁효과를",
+    "▁가까운",
+    "▁곳에",
+    "보기",
+    "었지만",
+    "▁밥",
+    "▁창업",
+    "▁강한",
+    "▁공장",
+    "▁혜택",
+    "교회",
+    "▁펼쳐",
+    "▁불러",
+    "▁도구",
+    "▁원칙",
+    "죄",
+    "▁결코",
+    "▁얻을",
+    "▁여행자",
+    "▁이번에",
+    "▁카페",
+    "맨",
+    "▁방법이",
+    "▁오르",
+    "▁권력",
+    "▁없었",
+    "▁값",
+    "멸",
+    "▁설명했다",
+    "▁위원장",
+    "▁그녀는",
+    "째",
+    "룸",
+    "▁물건",
+    "▁편리",
+    "▁이것이",
+    "▁카메라",
+    "풀",
+    "▁여유",
+    "▁메뉴",
+    "▁외국",
+    "찌",
+    "▁민주",
+    "▁통일",
+    "▁소속",
+    "쓰",
+    "▁꾸준히",
+    "받는",
+    "▁품질",
+    "출장안마",
+    "▁사진을",
+    "▁네트워크",
+    "▁차별",
+    "▁것인가",
+    "▁자리를",
+    "▁방법은",
+    "▁차례",
+    "멘",
+    "▁않으",
+    "허",
+    "▁세대",
+    "룩",
+    "▁제출",
+    "▁긍정적",
+    "혁",
+    "화된",
+    "▁장애",
+    "▁공감",
+    "▁한국의",
+    "▁교사",
+    "▁거쳐",
+    "쇄",
+    "▁완벽한",
+    "▁안에서",
+    "▁코스",
+    "▁들고",
+    "▁갖",
+    "량이",
+    "▁이제는",
+    "▁확인할",
+    "빈",
+    "▁자격",
+    "거든요",
+    "으면서",
+    "씨는",
+    "▁깊",
+    "틴",
+    "스의",
+    "▁민족",
+    "▁누가",
+    "특",
+    "▁작용",
+    "▁민주당",
+    "윤",
+    "칙",
+    "해도",
+    "개발",
+    "▁사람들을",
+    "▁떨어지",
+    "응",
+    "▁이때",
+    "▁만들기",
+    "▁명의",
+    "▁수입",
+    "앉",
+    "▁아무리",
+    "드립니다",
+    "맥",
+    "▁뉴",
+    "▁기울",
+    "▁보이지",
+    "▁실험",
+    "▁점에서",
+    "냈",
+    "▁계획이다",
+    "▁자체가",
+    "▁않도록",
+    "▁입력",
+    "▁우리에게",
+    "은행",
+    "▁법률",
+    "년부터",
+    "▁플레이",
+    "▁세금",
+    "▁자세",
+    "늘",
+    "짜",
+    "▁히",
+    "▁영향",
+    "▁납",
+    "▁베이",
+    "▁만들고",
+    "▁물질",
+    "단체",
+    "▁문의",
+    "▁시작한",
+    "꾸",
+    "▁설립",
+    "▁불법",
+    "▁등으로",
+    "▁벗어나",
+    "▁맺",
+    "▁출발",
+    "▁영업",
+    "▁발언",
+    "▁신체",
+    "▁왔",
+    "▁심리",
+    "▁솔",
+    "▁길이",
+    "▁통과",
+    "연합",
+    "적이다",
+    "켜",
+    "듬",
+    "▁마음에",
+    "▁무엇이",
+    "스럽",
+    "▁아이디어",
+    "▁놓고",
+    "▁기술을",
+    "▁자극",
+    "짓",
+    "혔",
+    "▁않게",
+    "겼다",
+    "▁스트레스",
+    "▁업데이트",
+    "▁대책",
+    "둔",
+    "▁일반적으로",
+    "▁내용이",
+    "▁사장",
+    "▁이용하실",
+    "▁곳에서",
+    "▁범위",
+    "▁준다",
+    "함이",
+    "▁벗",
+    "튼",
+    "▁걸어",
+    "더니",
+    "▁높다",
+    "▁말하는",
+    "하였습니다",
+    "▁무엇을",
+    "게임",
+    "▁샤",
+    "▁마무리",
+    "▁그만큼",
+    "▁굉장히",
+    "엄",
+    "▁간단",
+    "별로",
+    "▁그러면",
+    "김",
+    "▁확산",
+    "▁위협",
+    "했어요",
+    "▁의식",
+    "▁근무",
+    "하기로",
+    "▁중간",
+    "▁매장",
+    "▁효",
+    "▁일이다",
+    "▁공항",
+    "▁성적",
+    "▁말고",
+    "왔",
+    "▁강의",
+    "였습니다",
+    "▁변화를",
+    "트리",
+    "▁적합",
+    "▁풀어",
+    "▁유형",
+    "인들이",
+    "▁이처럼",
+    "▁짐",
+    "▁아는",
+    "▁앉아",
+    "▁주님",
+    "였던",
+    "▁둔",
+    "▁의무",
+    "앤",
+    "▁프로그램을",
+    "▁식물",
+    "▁체계",
+    "▁덜",
+    "쉬",
+    "▁민간",
+    "벽",
+    "▁나아가",
+    "▁가능합니다",
+    "▁덕",
+    "▁태도",
+    "▁뜨",
+    "▁알아보",
+    "▁강력한",
+    "▁재판",
+    "▁상처",
+    "▁매년",
+    "▁덕분에",
+    "▁대표는",
+    "컬",
+    "▁차이가",
+    "세기",
+    "▁않을까",
+    "▁배치",
+    "▁위원",
+    "▁깨끗",
+    "낸다",
+    "톡",
+    "▁예측",
+    "▁장비",
+    "▁카지노",
+    "▁위로",
+    "▁이유가",
+    "▁한반도",
+    "▁예전",
+    "▁끌어",
+    "몰",
+    "▁들었다",
+    "▁주식",
+    "▁강조했다",
+    "▁진실",
+    "▁밑",
+    "▁선수들",
+    "몽",
+    "▁대전",
+    "▁협상",
+    "▁극복",
+    "았던",
+    "▁오늘날",
+    "▁불과",
+    "▁정확한",
+    "▁시선",
+    "▁정치적",
+    "버린",
+    "▁세상을",
+    "번째",
+    "▁남아",
+    "량을",
+    "▁성능",
+    "먼",
+    "펜",
+    "▁모르는",
+    "▁바닥",
+    "▁밝혀",
+    "▁괜찮",
+    "▁재정",
+    "▁달려",
+    "▁보였다",
+    "▁책임을",
+    "▁사용하여",
+    "▁스마트",
+    "▁개혁",
+    "킬",
+    "그룹",
+    "▁모르겠",
+    "▁중이다",
+    "▁경우도",
+    "▁사업을",
+    "▁크다",
+    "▁부부",
+    "▁좋았",
+    "▁노인",
+    "▁읽어",
+    "▁의심",
+    "대학",
+    "▁복지",
+    "▁가치를",
+    "▁유치",
+    "픽",
+    "▁버스",
+    "▁가격이",
+    "▁회사는",
+    "▁범죄",
+    "▁내놓",
+    "▁뿌리",
+    "잉",
+    "▁최저가",
+    "▁혐의",
+    "▁사람에게",
+    "▁점이",
+    "릭",
+    "▁모른다",
+    "꾼",
+    "▁그럴",
+    "▁상징",
+    "나무",
+    "▁달성",
+    "하셨",
+    "▁지배",
+    "▁작동",
+    "▁갑자기",
+    "▁어떠한",
+    "▁주민들",
+    "없",
+    "▁부여",
+    "▁단순히",
+    "▁창조",
+    "▁도로",
+    "▁관점",
+    "▁느낌이",
+    "▁이용해",
+    "▁중인",
+    "▁있음을",
+    "▁종종",
+    "▁그러한",
+    "▁군사",
+    "▁광주",
+    "▁따로",
+    "회사",
+    "▁나왔다",
+    "▁연습",
+    "▁씨는",
+    "▁연출",
+    "▁과제",
+    "▁충격",
+    "▁휴가",
+    "맛",
+    "젠",
+    "▁이론",
+    "홀",
+    "▁외교",
+    "컴",
+    "▁인류",
+    "▁봤",
+    "▁재료",
+    "었어요",
+    "값",
+    "지면서",
+    "▁상황에",
+    "▁시리즈",
+    "▁날씨",
+    "▁뽑",
+    "었을",
+    "▁종합",
+    "▁맨",
+    "▁제공하고",
+    "▁형식",
+    "▁신문",
+    "▁효과가",
+    "▁게다가",
+    "▁오픈",
+    "▁통하여",
+    "▁완전",
+    "▁움직이",
+    "▁철학",
+    "▁모여",
+    "튀",
+    "▁의혹",
+    "▁받지",
+    "냥",
+    "▁좋을",
+    "▁가서",
+    "지원",
+    "▁소프트웨어",
+    "▁집단",
+    "시키고",
+    "▁방법으로",
+    "▁호텔을",
+    "▁느",
+    "싼",
+    "▁소득",
+    "▁미술",
+    "▁보관",
+    "▁대선",
+    "▁박근혜",
+    "▁테스트",
+    "▁무선",
+    "돈",
+    "▁떨어",
+    "겐",
+    "▁아니면",
+    "▁미디어",
+    "▁경우에",
+    "▁기독교",
+    "▁질환",
+    "롱",
+    "떨",
+    "랄",
+    "▁향해",
+    "▁서울시",
+    "▁비롯한",
+    "▁않기",
+    "▁증거",
+    "▁제일",
+    "▁처음으로",
+    "▁대통령의",
+    "▁다소",
+    "▁맛있",
+    "▁기여",
+    "섰",
+    "쁜",
+    "▁다루",
+    "▁신앙",
+    "▁최대한",
+    "▁쏟아",
+    "웨이",
+    "▁만한",
+    "▁전투",
+    "▁직원들",
+    "세대",
+    "겠다고",
+    "▁힘이",
+    "썼",
+    "▁국정",
+    "거래",
+    "랍",
+    "▁우승",
+    "▁축제",
+    "▁짜",
+    "▁탓",
+    "▁발생한",
+    "동안",
+    "▁시민들",
+    "▁것인지",
+    "▁작업을",
+    "▁책은",
+    "▁발달",
+    "▁계산",
+    "▁의미를",
+    "▁사랑을",
+    "▁고급",
+    "▁확실",
+    "▁긴장",
+    "레스",
+    "▁구분",
+    "자리",
+    "덤",
+    "▁당연히",
+    "▁좋지",
+    "▁신고",
+    "▁잡아",
+    "▁드리",
+    "▁할인",
+    "▁추구",
+    "▁쉬운",
+    "▁금지",
+    "▁연속",
+    "▁하게",
+    "▁근처",
+    "▁모두가",
+    "얻",
+    "▁허용",
+    "대표",
+    "▁풍경",
+    "▁훌륭한",
+    "▁스토리",
+    "병원",
+    "▁아닌가",
+    "같은",
+    "▁기회가",
+    "▁뭔가",
+    "▁다르다",
+    "▁심지어",
+    "▁측정",
+    "▁새롭게",
+    "▁사상",
+    "▁사실상",
+    "▁아닐까",
+    "해주는",
+    "▁라인",
+    "시대",
+    "맞",
+    "▁만난",
+    "왔습니다",
+    "▁조절",
+    "▁똑같",
+    "▁지난달",
+    "▁요인",
+    "▁채용",
+    "▁생긴",
+    "▁삭제",
+    "카드",
+    "▁대출",
+    "학년",
+    "따",
+    "▁국민의",
+    "▁라며",
+    "▁마찬가지",
+    "▁비율",
+    "위원",
+    "▁겸",
+    "▁깊이",
+    "▁늘어나",
+    "푸",
+    "▁옥",
+    "▁나중에",
+    "춤",
+    "▁갑",
+    "▁관계를",
+    "▁식당",
+    "▁쓰고",
+    "▁었",
+    "▁실현",
+    "▁봉사",
+    "으려",
+    "▁유통",
+    "▁태양",
+    "▁열정",
+    "▁실력",
+    "▁꼬",
+    "▁선언",
+    "▁관계자",
+    "▁공무원",
+    "▁독자",
+    "▁멋진",
+    "써",
+    "▁말할",
+    "▁대답",
+    "픈",
+    "릅니다",
+    "▁약관",
+    "▁했지만",
+    "▁인권",
+    "▁분들이",
+    "▁통신",
+    "▁가까이",
+    "▁완료",
+    "칸",
+    "하여야",
+    "▁경쟁력",
+    "▁방지",
+    "죽",
+    "▁제목",
+    "했기",
+    "▁것만",
+    "▁당장",
+    "봤다",
+    "▁변호사",
+    "어져",
+    "꿀",
+    "구나",
+    "▁아동",
+    "▁성인",
+    "▁신규",
+    "▁뒷",
+    "▁별로",
+    "▁보고서",
+    "둘",
+    "▁휘",
+    "탑",
+    "▁먼",
+    "▁그러므로",
+    "▁지불",
+    "껏",
+    "▁띄",
+    "틱",
+    "져야",
+    "▁영양",
+    "▁통증",
+    "▁지속적으로",
+    "▁레스토랑",
+    "▁섭취",
+    "▁점은",
+    "▁멤버",
+    "▁내리",
+    "▁코드",
+    "했으며",
+    "습니까",
+    "▁뒤에",
+    "숨",
+    "문제",
+    "▁진정한",
+    "▁롯데",
+    "출장샵",
+    "▁키우",
+    "룡",
+    "▁취소",
+    "▁주말",
+    "셨습니다",
+    "▁심각한",
+    "▁알리",
+    "▁엄청난",
+    "▁익숙",
+    "▁자금",
+    "▁흔들",
+    "▁흐",
+    "▁버리",
+    "▁아니고",
+    "▁능력을",
+    "▁움직임",
+    "▁공기",
+    "▁잇",
+    "▁짧은",
+    "▁적절한",
+    "▁즉시",
+    "▁전부",
+    "▁콜",
+    "휘",
+    "받은",
+    "▁굴",
+    "▁못한다",
+    "▁올려",
+    "▁근본",
+    "▁모아",
+    "하겠다는",
+    "▁맞추",
+    "▁적극적으로",
+    "▁작년",
+    "▁식품",
+    "▁결론",
+    "▁노동자",
+    "▁나올",
+    "쇠",
+    "▁너무나",
+    "▁고양이",
+    "▁항목",
+    "▁무엇인가",
+    "스코",
+    "졌습니다",
+    "▁희",
+    "알",
+    "▁뉴욕",
+    "▁그녀의",
+    "▁위치한",
+    "▁다리",
+    "▁댓글",
+    "▁마련되어",
+    "▁버전",
+    "▁앞두고",
+    "▁그럼에도",
+    "놓고",
+    "▁눈물",
+    "프레",
+    "해야합니다",
+    "즐",
+    "▁총리",
+    "롭게",
+    "▁국민들",
+    "▁전문가들",
+    "핀",
+    "▁아직도",
+    "▁언제나",
+    "▁못할",
+    "▁통제",
+    "팩",
+    "협회",
+    "버리",
+    "▁같아",
+    "▁특수",
+    "▁자산",
+    "릇",
+    "▁나머지",
+    "든지",
+    "▁패션",
+    "▁그저",
+    "▁구원",
+    "▁데이터를",
+    "▁평생",
+    "▁여론",
+    "▁보상",
+    "룰",
+    "▁팬들",
+    "▁한국인",
+    "▁유명한",
+    "▁감동",
+    "▁장애인",
+    "▁나타났다",
+    "쥐",
+    "▁강남",
+    "▁아이템",
+    "▁제공됩니다",
+    "▁시청",
+    "▁상호",
+    "▁바란다",
+    "▁뿐이다",
+    "▁맞아",
+    "▁하락",
+    "쌍",
+    "▁동영상",
+    "▁했는데",
+    "▁인증",
+    "답니다",
+    "▁집에서",
+    "▁거예요",
+    "룬",
+    "닉",
+    "▁넘는",
+    "▁숫자",
+    "▁손님",
+    "▁투표",
+    "▁홍콩",
+    "▁출장안마",
+    "누",
+    "▁가구",
+    "▁박사",
+    "▁따뜻한",
+    "▁생성",
+    "쳤",
+    "▁그럼",
+    "꽃",
+    "▁세우",
+    "▁목록",
+    "▁무시",
+    "▁낳",
+    "▁교수는",
+    "▁셈이다",
+    "▁결제",
+    "▁의원은",
+    "▁조언",
+    "딜",
+    "▁이슈",
+    "▁바이러스",
+    "밍",
+    "▁각자",
+    "리스트",
+    "욱",
+    "▁최선을",
+    "▁블랙",
+    "빠",
+    "홍",
+    "▁알려져",
+    "닭",
+    "▁최신",
+    "▁통계",
+    "▁복음",
+    "▁선호",
+    "▁피해자",
+    "▁논리",
+    "▁애플",
+    "▁부상",
+    "륙",
+    "▁비밀",
+    "▁입장에서",
+    "▁무기",
+    "콤",
+    "▁개별",
+    "셨다",
+    "오피",
+    "▁이웃",
+    "▁숲",
+    "프트",
+    "▁해보",
+    "▁신속",
+    "▁프리",
+    "▁무리",
+    "뷰",
+    "넷",
+    "▁경기출장",
+    "졸",
+    "▁인근",
+    "▁진정",
+    "▁찾기",
+    "▁그려",
+    "흘",
+    "▁실수",
+    "▁체제",
+    "▁문자",
+    "▁드립니다",
+    "쌀",
+    "▁버튼",
+    "▁쇼",
+    "▁참조",
+    "놀",
+    "▁알려졌다",
+    "▁공사",
+    "닐",
+    "▁아빠",
+    "▁부탁",
+    "▁아기",
+    "멍",
+    "▁비슷",
+    "▁다수",
+    "▁숙소",
+    "럴",
+    "▁희생",
+    "최",
+    "▁필요합니다",
+    "▁컬러",
+    "▁서버",
+    "▁기준으로",
+    "▁읽고",
+    "▁문학",
+    "▁경기도",
+    "▁선발",
+    "▁베트남",
+    "머니",
+    "▁위반",
+    "스럽게",
+    "▁성분",
+    "하시면",
+    "▁거주",
+    "▁온도",
+    "▁부분은",
+    "▁만남",
+    "▁것보다",
+    "같이",
+    "▁대안",
+    "▁전용",
+    "▁파괴",
+    "▁동료",
+    "▁누리",
+    "하려",
+    "▁들어서",
+    "하면서도",
+    "▁담아",
+    "▁채널",
+    "▁부모님",
+    "▁하겠습니다",
+    "웅",
+    "▁불만",
+    "▁생존",
+    "▁좋겠다",
+    "켓",
+    "▁자연스럽게",
+    "▁심사",
+    "큐",
+    "▁이끌어",
+    "대회",
+    "▁역량",
+    "잎",
+    "▁그린",
+    "해졌다",
+    "▁살펴",
+    "▁전공",
+    "틈",
+    "으로써",
+    "▁들어오",
+    "▁무역",
+    "▁묵",
+    "▁경계",
+    "▁궁",
+    "괄",
+    "한테",
+    "환경",
+    "▁결합",
+    "위원장",
+    "▁흑",
+    "럭",
+    "티브",
+    "▁잘못된",
+    "▁진보",
+    "▁균형",
+    "▁규칙",
+    "통신",
+    "▁실내",
+    "▁경제적",
+    "▁없기",
+    "솔",
+    "▁좁",
+    "▁임금",
+    "▁주소",
+    "▁넣어",
+    "▁탄생",
+    "잔",
+    "▁덧붙였다",
+    "▁이전에",
+    "▁멀리",
+    "▁그곳",
+    "소리",
+    "▁분리",
+    "닿",
+    "▁손상",
+    "▁동일한",
+    "▁왼쪽",
+    "▁관광객",
+    "▁헌법",
+    "▁형태로",
+    "▁휴식",
+    "▁점이다",
+    "▁자전거",
+    "캠",
+    "▁헬",
+    "▁갖는",
+    "이에요",
+    "▁근육",
+    "▁거듭",
+    "▁자본",
+    "▁그러면서",
+    "▁발생하는",
+    "▁구현",
+    "발전",
+    "▁하자",
+    "▁청구",
+    "봤",
+    "▁공동체",
+    "방법",
+    "▁비난",
+    "▁링크",
+    "▁습관",
+    "▁마찬가지로",
+    "▁걸쳐",
+    "▁싶습니다",
+    "▁혹시",
+    "▁올림픽",
+    "▁골프",
+    "▁와서",
+    "▁오랫동안",
+    "▁분명히",
+    "▁발휘",
+    "▁않아도",
+    "잖아요",
+    "계획",
+    "납",
+    "▁재산",
+    "▁흘러",
+    "젤",
+    "▁갖춘",
+    "▁머물",
+    "▁연주",
+    "▁착용",
+    "▁보이고",
+    "엽",
+    "▁확정",
+    "친다",
+    "받고",
+    "빵",
+    "▁감각",
+    "▁로봇",
+    "▁대표적인",
+    "▁완벽",
+    "▁조용",
+    "▁창출",
+    "▁뛰어난",
+    "▁해소",
+    "▁생겨",
+    "▁나에게",
+    "▁마지막으로",
+    "업계",
+    "▁빚",
+    "▁호흡",
+    "▁표정",
+    "▁밝히",
+    "톱",
+    "▁가끔",
+    "▁필자",
+    "즘",
+    "▁통화",
+    "▁대기업",
+    "문학",
+    "▁심장",
+    "▁활발",
+    "▁봅니다",
+    "▁오른쪽",
+    "▁없지만",
+    "▁사랑하는",
+    "▁나쁜",
+    "켰",
+    "▁닫",
+    "▁판결",
+    "▁열어",
+    "▁부족한",
+    "▁자원",
+    "섞",
+    "본부",
+    "▁폭력",
+    "▁측면",
+    "▁중앙일보",
+    "▁교체",
+    "님께서",
+    "▁만들었다",
+    "▁멋",
+    "흡",
+    "버스",
+    "▁당선",
+    "▁의문",
+    "소득",
+    "릿",
+    "▁검증",
+    "하겠습니다",
+    "쇼",
+    "재단",
+    "렇",
+    "주택",
+    "▁발생할",
+    "▁이스라엘",
+    "▁응원",
+    "▁다녀",
+    "획",
+    "▁헤어",
+    "▁표준",
+    "노동",
+    "▁연장",
+    "▁것이었다",
+    "월부터",
+    "▁있으",
+    "▁명령",
+    "▁선교",
+    "▁무엇인지",
+    "▁있습니까",
+    "▁부문",
+    "▁친절",
+    "▁차단",
+    "▁간단한",
+    "엇",
+    "봐",
+    "▁흔히",
+    "▁이라며",
+    "램",
+    "▁생겼",
+    "▁추억",
+    "▁이끌",
+    "▁맞춤",
+    "영화",
+    "▁아예",
+    "▁절반",
+    "델",
+    "▁않지만",
+    "뼈",
+    "▁않았습니다",
+    "▁농업",
+    "▁구체적인",
+    "렀다",
+    "▁본질",
+    "▁일반적인",
+    "▁점차",
+    "▁조금씩",
+    "▁사유",
+    "▁이루어지",
+    "▁세워",
+    "▁건조",
+    "▁기업들",
+    "텍",
+    "▁완화",
+    "▁계정",
+    "▁보존",
+    "▁재생",
+    "▁지혜",
+    "▁오른",
+    "▁포장",
+    "공사",
+    "▁수수료",
+    "▁그러다",
+    "▁세력",
+    "▁지내",
+    "▁따르",
+    "곱",
+    "▁자신감",
+    "▁총선",
+    "▁주위",
+    "▁있음",
+    "본다",
+    "계약",
+    "▁만날",
+    "덩",
+    "삭",
+    "▁승인",
+    "▁증명",
+    "▁스파",
+    "▁빠지",
+    "▁공통",
+    "핸",
+    "▁관객",
+    "▁분류",
+    "▁색상",
+    "▁정확히",
+    "▁인재",
+    "▁덮",
+    "▁면접",
+    "▁유발",
+    "▁그렇다고",
+    "▁뛰",
+    "겹",
+    "▁관람",
+    "▁악화",
+    "렴",
+    "▁패턴",
+    "▁독특한",
+    "▁대규모",
+    "▁확신",
+    "코리아",
+    "▁법원",
+    "등록",
+    "떡",
+    "▁보면서",
+    "▁분노",
+    "▁중소기업",
+    "▁후반",
+    "▁혼란",
+    "더라",
+    "▁앨범",
+    "▁임대",
+    "▁임신",
+    "▁많습니다",
+    "▁점검",
+    "▁인력",
+    "▁약물",
+    "제품",
+    "▁솔루션",
+    "▁많지",
+    "퀴",
+    "깃",
+    "떼",
+    "▁관찰",
+    "▁강제",
+    "▁실적",
+    "▁전력",
+    "▁문제점",
+    "▁트레",
+    "▁다르게",
+    "▁장식",
+    "▁끊임없이",
+    "▁민주주의",
+    "▁논문",
+    "콜",
+    "▁목적으로",
+    "▁뜨거운",
+    "묵",
+    "▁스테",
+    "▁놀라",
+    "했으나",
+    "빗",
+    "▁알려진",
+    "▁들려",
+    "▁심각",
+    "닌",
+    "밥",
+    "윈",
+    "▁대변",
+    "▁유일한",
+    "▁자세히",
+    "▁진화",
+    "▁분야에서",
+    "굴",
+    "▁체결",
+    "▁세포",
+    "투자",
+    "▁그랬",
+    "깔",
+    "하우스",
+    "▁성령",
+    "▁페이스북",
+    "▁기쁨",
+    "▁느꼈",
+    "▁대처",
+    "▁지도자",
+    "▁오류",
+    "▁초반",
+    "▁배출",
+    "▁응용",
+    "하겠다고",
+    "▁조심",
+    "▁데뷔",
+    "▁할머니",
+    "밭",
+    "시켰다",
+    "추천",
+    "▁별도의",
+    "렵",
+    "토리",
+    "랭",
+    "빅",
+    "▁상대방",
+    "▁모니터",
+    "▁사라지",
+    "▁점수",
+    "▁마스크",
+    "▁소송",
+    "▁입학",
+    "됨",
+    "옛",
+    "▁아무런",
+    "▁동네",
+    "▁적응",
+    "▁많았",
+    "▁출판",
+    "겠죠",
+    "▁충분한",
+    "▁응답",
+    "▁구속",
+    "▁받는다",
+    "▁외에도",
+    "▁쉽고",
+    "▁하느님",
+    "짧",
+    "▁받아들이",
+    "▁느낀",
+    "릉",
+    "▁문장",
+    "뿌",
+    "▁방향으로",
+    "▁동반",
+    "▁줄어들",
+    "▁새벽",
+    "옳",
+    "콩",
+    "▁모임",
+    "▁나타난",
+    "▁좋겠",
+    "▁확실히",
+    "▁받기",
+    "▁떨어진",
+    "▁깨닫",
+    "▁예배",
+    "십니다",
+    "▁못했",
+    "▁야구",
+    "▁표면",
+    "▁구름",
+    "줬다",
+    "▁비디오",
+    "▁쇼핑",
+    "▁우수한",
+    "▁싸움",
+    "▁용기",
+    "팔",
+    "읍",
+    "햇",
+    "▁공포",
+    "엑",
+    "▁있나요",
+    "▁추세",
+    "▁평가하기",
+    "롯",
+    "▁울산",
+    "▁국회의원",
+    "야말로",
+    "▁엔진",
+    "▁군대",
+    "▁날짜",
+    "▁모집",
+    "▁웹사이트",
+    "▁충전",
+    "▁바꿔",
+    "▁도움",
+    "▁살아가는",
+    "▁로마",
+    "▁지점",
+    "▁배터리",
+    "멜",
+    "▁혁명",
+    "▁경력",
+    "▁걸리",
+    "▁아울러",
+    "▁가이드",
+    "▁리더",
+    "▁투쟁",
+    "▁하나님께서",
+    "▁만든다",
+    "▁전망이다",
+    "랙",
+    "▁아닐",
+    "▁기반으로",
+    "증권",
+    "춰",
+    "▁조작",
+    "▁혈액",
+    "▁백성",
+    "컵",
+    "▁놀라운",
+    "▁배송",
+    "밸",
+    "▁거죠",
+    "맘",
+    "▁소녀",
+    "▁많았다",
+    "랩",
+    "▁그걸",
+    "▁네이버",
+    "▁미세먼지",
+    "립니다",
+    "▁아니었다",
+    "▁상대적으로",
+    "▁치열",
+    "▁은혜",
+    "▁알려주",
+    "▁국립",
+    "듀",
+    "▁최초로",
+    "▁폭발",
+    "▁냄새",
+    "▁든다",
+    "▁김정은",
+    "▁적어도",
+    "▁사무실",
+    "마저",
+    "벼",
+    "▁침대",
+    "▁아프",
+    "▁프라",
+    "▁즐겁",
+    "▁충분",
+    "▁조각",
+    "▁플레이어",
+    "▁추정",
+    "▁환영",
+    "▁실질적",
+    "▁삼성전자",
+    "▁올바른",
+    "▁화학",
+    "둑",
+    "▁받게",
+    "▁드러내",
+    "라이트",
+    "▁거대한",
+    "▁연합뉴스",
+    "▁부품",
+    "▁지닌",
+    "▁압박",
+    "▁커뮤니티",
+    "▁의존",
+    "▁청소",
+    "▁중요합니다",
+    "▁있으므로",
+    "▁노조",
+    "▁처벌",
+    "셋",
+    "▁들여",
+    "▁아니지만",
+    "땀",
+    "▁합격",
+    "▁나쁨",
+    "▁어찌",
+    "돕",
+    "더군요",
+    "▁어려워",
+    "▁이메일",
+    "▁테이블",
+    "▁일으키",
+    "▁계절",
+    "팽",
+    "▁동작",
+    "▁바카라",
+    "▁돌려",
+    "▁음료",
+    "▁옮겨",
+    "▁집안",
+    "▁합리적",
+    "깝",
+    "▁영향력",
+    "▁종료",
+    "▁왜냐하면",
+    "▁유용한",
+    "믹",
+    "▁개인적으로",
+    "▁순위",
+    "왔던",
+    "▁집행",
+    "▁어디서",
+    "▁좋음",
+    "▁야외",
+    "▁슈퍼",
+    "▁예비",
+    "▁블록체인",
+    "▁미치는",
+    "▁영혼",
+    "▁고등학교",
+    "▁오퍼",
+    "펀",
+    "▁무조건",
+    "▁셀프",
+    "뉴스",
+    "▁수영장",
+    "갑니다",
+    "▁분양",
+    "▁비교적",
+    "▁떨어져",
+    "▁끝까지",
+    "▁것일까",
+    "▁비롯해",
+    "▁일찍",
+    "▁몇몇",
+    "스턴",
+    "▁흐르",
+    "씻",
+    "▁확률",
+    "공항",
+    "줘",
+    "▁다섯",
+    "▁투입",
+    "더라고요",
+    "▁확인함",
+    "▁측면에서",
+    "▁너희",
+    "▁어깨",
+    "팁",
+    "▁선배",
+    "▁경남",
+    "▁즐거운",
+    "쿨",
+    "▁있었는데",
+    "▁참여해주셔",
+    "펼",
+    "▁나왔",
+    "쫓",
+    "▁소중한",
+    "넘",
+    "▁매체",
+    "▁교환",
+    "▁유튜브",
+    "▁취재",
+    "▁얻는",
+    "▁있는데요",
+    "빌",
+    "▁주시기",
+    "▁아름답",
+    "▁인공지능",
+    "▁나눠",
+    "▁쿠키",
+    "끔",
+    "▁어쩌면",
+    "밟",
+    "뮤",
+    "▁변동",
+    "돋",
+    "▁즐거움",
+    "▁나섰다",
+    "▁계속해서",
+    "렸습니다",
+    "하시기",
+    "▁중요성",
+    "▁저항",
+    "▁건너",
+    "▁진입",
+    "▁깔끔",
+    "▁투명",
+    "▁담긴",
+    "시키기",
+    "묶",
+    "▁교류",
+    "▁시청자",
+    "▁연령",
+    "▁부활",
+    "▁명예",
+    "▁설득",
+    "▁나타내",
+    "주셔서",
+    "▁끝난",
+    "렁",
+    "▁활약",
+    "▁투자자",
+    "턱",
+    "▁벌써",
+    "▁운명",
+    "▁법안",
+    "▁소셜",
+    "맑",
+    "▁생물",
+    "▁블루",
+    "항공",
+    "▁스페셜",
+    "틸",
+    "드리겠습니다",
+    "▁적절",
+    "캔",
+    "▁남겨",
+    "옹",
+    "▁알아야",
+    "튜",
+    "▁얻어",
+    "▁풍부한",
+    "▁갖게",
+    "▁열리는",
+    "▁이다",
+    "▁후보자",
+    "▁몰랐",
+    "캡",
+    "흉",
+    "숭",
+    "뇌",
+    "▁대학생",
+    "포트",
+    "겼",
+    "▁영광",
+    "▁직후",
+    "▁존중",
+    "▁구성원",
+    "쉽",
+    "▁필요성",
+    "▁싫어",
+    "으므로",
+    "▁면역",
+    "▁가르치",
+    "▁있죠",
+    "▁흡수",
+    "▁카카오",
+    "▁현금",
+    "▁정확하게",
+    "▁기본적으로",
+    "▁개입",
+    "▁인정받",
+    "▁없었던",
+    "엉",
+    "▁리조트",
+    "▁담겨",
+    "▁지하철",
+    "▁일주일",
+    "곁",
+    "샵",
+    "▁먹어",
+    "긋",
+    "▁일어난",
+    "섯",
+    "▁즐기실",
+    "▁이탈리아",
+    "줘야",
+    "▁충족",
+    "▁화장품",
+    "▁드러나",
+    "않",
+    "▁들어간",
+    "얀",
+    "▁여겨",
+    "▁파티",
+    "곰",
+    "▁부작용",
+    "▁비록",
+    "하므로",
+    "봅",
+    "▁무언가",
+    "▁개봉",
+    "▁보여준다",
+    "▁강남안마",
+    "냅",
+    "▁널리",
+    "▁영웅",
+    "딘",
+    "넥",
+    "방송",
+    "▁무척",
+    "▁비행기",
+    "▁없애",
+    "▁트렌드",
+    "땅",
+    "▁며칠",
+    "▁자유롭게",
+    "▁얻은",
+    "젖",
+    "▁캐나다",
+    "개혁",
+    "▁촉구",
+    "▁클라우드",
+    "▁다이어트",
+    "▁포함되어",
+    "▁취급",
+    "▁때로는",
+    "▁취약",
+    "▁끝내",
+    "렀",
+    "▁직무",
+    "▁둘째",
+    "캐",
+    "뉴",
+    "▁얻었",
+    "▁점심",
+    "뢰",
+    "벅",
+    "▁노하우",
+    "끓",
+    "▁이외에",
+    "했는지",
+    "▁한마디",
+    "▁취임",
+    "▁아무것도",
+    "▁잃어",
+    "▁접촉",
+    "셈",
+    "윗",
+    "▁보낸",
+    "▁분쟁",
+    "▁있었지만",
+    "▁어울리",
+    "닮",
+    "쩍",
+    "능력",
+    "▁보완",
+    "▁쓰레기",
+    "연구소",
+    "▁멈추",
+    "셉",
+    "뱅",
+    "▁전세계",
+    "롬",
+    "▁민감",
+    "첩",
+    "▁돕는",
+    "▁라이브",
+    "폼",
+    "붙",
+    "붕",
+    "놈",
+    "펴",
+    "뭘",
+    "탱",
+    "뚫",
+    "눌",
+    "뚝",
+    "핵",
+    "닦",
+    "몫",
+    "싫",
+    "갱",
+    "뻐",
+    "섹",
+    "곽",
+    "샌",
+    "컸",
+    "줬",
+    "덴",
+    "몸",
+    "섬",
+    "옮",
+    "칼",
+    "뻔",
+    "낫",
+    "댄",
+    "눈",
+    "렛",
+    "띠",
+    "땐",
+    "칫",
+    "칩",
+    "쓸",
+    "찍",
+    "겉",
+    "짚",
+    "횡",
+    "멈",
+    "펙",
+    "삽",
+    "셰",
+    "굽",
+    "밤",
+    "논",
+    "쏘",
+    "붓",
+    "꼴",
+    "엠",
+    "흙",
+    "쏠",
+    "껍",
+    "눔",
+    "뭉",
+    "컷",
+    "긍",
+    "닷",
+    "픔",
+    "녔",
+    "앞",
+    "뭇",
+    "넣",
+    "팡",
+    "앗",
+    "샘",
+    "녕",
+    "낙",
+    "헤",
+    "흐",
+    "싹",
+    "찾",
+    "궤",
+    "융",
+    "잖",
+    "팝",
+    "뚱",
+    "찮",
+    "혐",
+    "놨",
+    "앓",
+    "퓨",
+    "얇",
+    "솟",
+    "핫",
+    "맵",
+    "딪",
+    "엿",
+    "줌",
+    "잭",
+    "썩",
+    "븐",
+    "핏",
+    "맙",
+    "췄",
+    "깥",
+    "둠",
+    "넬",
+    "닛",
+    "좋",
+    "뉘",
+    "륨",
+    "숍",
+    "쩌",
+    "샷",
+    "넌",
+    "덧",
+    "툴",
+    "밴",
+    "뚜",
+    "젊",
+    "즌",
+    "쭉",
+    "뇨",
+    "딱",
+    "앵",
+    "윌",
+    "펌",
+    "킷",
+    "뒀",
+    "탠",
+    "흠",
+    "잠",
+    "깨",
+    "슐",
+    "낯",
+    "륜",
+    "잦",
+    "뀐",
+    "탭",
+    "썰",
+    "휩",
+    "샤",
+    "웰",
+    "뜻",
+    "앨",
+    "뜩",
+    "휠",
+    "팬",
+    "툰",
+    "쿼",
+    "펠",
+    "큰",
+    "흰",
+    "볶",
+    "떠",
+    "랫",
+    "갇",
+    "퀘",
+    "갓",
+    "콕",
+    "쉐",
+    "얽",
+    "굿",
+    "괴",
+    "웬",
+    "횟",
+    "깁",
+    "텀",
+    "늦",
+    "못",
+    "뜬",
+    "뤄",
+    "푼",
+    "꺾",
+    "봄",
+    "왠",
+    "힘",
+    "짙",
+    "빨",
+    "옷",
+    "늬",
+    "쉴",
+    "윙",
+    "냄",
+    "뻗",
+    "쪼",
+    "뜸",
+    "딴",
+    "핍",
+    "꿈",
+    "헛",
+    "깎",
+    "옵",
+    "싶",
+    "늑",
+    "팟",
+    "탐",
+    "슴",
+    "푹",
+    "팎",
+    "끝",
+    "갚",
+    "씬",
+    "템",
+    "꽂",
+    "꼬",
+    "뱀",
+    "헐",
+    "듈",
+    "쑥",
+    "셜",
+    "켈",
+    "솜",
+    "큼",
+    "넓",
+    "뮬",
+    "또",
+    "댁",
+    "낡",
+    "꽉",
+    "힙",
+    "붐",
+    "럿",
+    "톨",
+    "펄",
+    "뤘",
+    "꼽",
+    "높",
+    "퉁",
+    "햄",
+    "툼",
+    "덥",
+    "샀",
+    "삐",
+    "궐",
+    "뜰",
+    "잘",
+    "깜",
+    "둬",
+    "뒤",
+    "뱃",
+    "훌",
+    "흔",
+    "콧",
+    "늙",
+    "젓",
+    "찔",
+    "얹",
+    "돔",
+    "홈",
+    "렘",
+    "쏙",
+    "뜯",
+    "뷔",
+    "풋",
+    "헨",
+    "캄",
+    "쏟",
+    "씹",
+    "둡",
+    "썬",
+    "엎",
+    "봇",
+    "꿨",
+    "벳",
+    "킴",
+    "엣",
+    "쉼",
+    "잣",
+    "볕",
+    "앱",
+    "듭",
+    "훔",
+    "챙",
+    "뀔",
+    "갯",
+    "덱",
+    "톰",
+    "퀄",
+    "엮",
+    "낚",
+    "볍",
+    "팍",
+    "뭔",
+    "뽐",
+    "끽",
+    "짱",
+    "찢",
+    "껴",
+    "펫",
+    "랴",
+    "똥",
+    "쓴",
+    "껑",
+    "굶",
+    "뿜",
+    "멀",
+    "벚",
+    "샴",
+    "괜",
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+    "훼",
+    "덜",
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+    "늠",
+    "왼",
+    "첼",
+    "놔",
+    "딛",
+    "홉",
+    "뱉",
+    "켄",
+    "잇",
+    "낱",
+    "췌",
+    "샐",
+    "빡",
+    "옴",
+    "눅",
+    "꾀",
+    "펑",
+    "닙",
+    "씁",
+    "킥",
+    "싣",
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+    "짖",
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+    "춥",
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+    "쩡",
+    "왈",
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+    "삿",
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+    "낍",
+    "솥",
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+    "챌",
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+    "댓",
+    "댈",
+    "쥔",
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+    "콥",
+    "폈",
+    "뽕",
+    "땡",
+    "뿍",
+    "쭈",
+    "끌",
+    "떳",
+    "찼",
+    "씀",
+    "뾰",
+    "뻑",
+    "슛",
+    "헹",
+    "늪",
+    "쎄",
+    "왓",
+    "짬",
+    "빽",
+    "옙",
+    "밑",
+    "얕",
+    "릅",
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+    "챔",
+    "텝",
+    "묻",
+    "왁",
+    "꾹",
+    "쿵",
+    "좇",
+    "킵",
+    "찻",
+    "됩",
+    "섀",
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+    "<|lb|>",
+    "<|my|>",
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+    "<|su|>",
+    "<|ASR|>",
+    "<|AED|>",
+    "<|SER|>",
+    "<|nospeech|>",
+    "<|Speech|>",
+    "<|/Speech|>",
+    "<|BGM|>",
+    "<|/BGM|>",
+    "<|Laughter|>",
+    "<|/Laughter|>",
+    "<|Applause|>",
+    "<|/Applause|>",
+    "<|HAPPY|>",
+    "<|SAD|>",
+    "<|ANGRY|>",
+    "<|NEUTRAL|>",
+    "<|FEARFUL|>",
+    "<|DISGUSTED|>",
+    "<|SURPRISED|>",
+    "<|OTHER|>",
+    "<|EMO_UNKNOWN|>",
+    "<|Cry|>",
+    "<|Sneeze|>",
+    "<|Breath|>",
+    "<|Cough|>",
+    "<|Sing|>",
+    "<|Speech_Noise|>",
+    "<|withitn|>",
+    "<|woitn|>",
+    "<|GBG|>",
+    "<|Event_UNK|>",
+    "<|SPECIAL_TOKEN_1|>",
+    "<|SPECIAL_TOKEN_2|>",
+    "<|SPECIAL_TOKEN_3|>",
+    "<|SPECIAL_TOKEN_4|>",
+    "<|SPECIAL_TOKEN_5|>",
+    "<|SPECIAL_TOKEN_6|>",
+    "<|SPECIAL_TOKEN_7|>",
+    "<|SPECIAL_TOKEN_8|>",
+    "<|SPECIAL_TOKEN_9|>",
+    "<|SPECIAL_TOKEN_10|>",
+    "<|SPECIAL_TOKEN_11|>",
+    "<|SPECIAL_TOKEN_12|>",
+    "<|SPECIAL_TOKEN_13|>",
+    "<|SPECIAL_TOKEN_14|>",
+    "<|SPECIAL_TOKEN_15|>",
+    "<|SPECIAL_TOKEN_16|>",
+    "<|SPECIAL_TOKEN_17|>",
+    "<|SPECIAL_TOKEN_18|>",
+    "<|SPECIAL_TOKEN_19|>",
+    "<|SPECIAL_TOKEN_20|>",
+    "<|SPECIAL_TOKEN_21|>",
+    "<|SPECIAL_TOKEN_22|>",
+    "<|SPECIAL_TOKEN_23|>",
+    "<|SPECIAL_TOKEN_24|>",
+    "<|SPECIAL_TOKEN_25|>",
+    "<|SPECIAL_TOKEN_26|>",
+    "<|SPECIAL_TOKEN_27|>",
+    "<|SPECIAL_TOKEN_28|>",
+    "<|SPECIAL_TOKEN_29|>",
+    "<|SPECIAL_TOKEN_30|>",
+    "<|SPECIAL_TOKEN_31|>",
+    "<|SPECIAL_TOKEN_32|>",
+    "<|SPECIAL_TOKEN_33|>",
+    "<|SPECIAL_TOKEN_34|>",
+    "<|SPECIAL_TOKEN_35|>"
+]
diff --git a/model/bert-base-chinese/.gitattributes b/model/bert-base-chinese/.gitattributes
new file mode 100644
index 0000000000000000000000000000000000000000..602b71f15d40ed68c5f96330e3f3175a76a32126
--- /dev/null
+++ b/model/bert-base-chinese/.gitattributes
@@ -0,0 +1,10 @@
+*.bin.* filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tar.gz filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+model.safetensors filter=lfs diff=lfs merge=lfs -text
diff --git a/model/bert-base-chinese/README.md b/model/bert-base-chinese/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..048fdf63b2740ccf860ad1e5b4287ccbac89869a
--- /dev/null
+++ b/model/bert-base-chinese/README.md
@@ -0,0 +1,75 @@
+---
+language: zh
+---
+
+# Bert-base-chinese
+
+## Table of Contents
+- [Model Details](#model-details)
+- [Uses](#uses)
+- [Risks, Limitations and Biases](#risks-limitations-and-biases)
+- [Training](#training)
+- [Evaluation](#evaluation)
+- [How to Get Started With the Model](#how-to-get-started-with-the-model)
+
+
+## Model Details
+
+### Model Description
+
+This model has been pre-trained for Chinese, training and random input masking has been applied independently to word pieces (as in the original BERT paper).
+
+- **Developed by:** HuggingFace team
+- **Model Type:** Fill-Mask
+- **Language(s):** Chinese
+- **License:** [More Information needed]
+- **Parent Model:** See the [BERT base uncased model](https://huggingface.co/bert-base-uncased) for more information about the BERT base model.
+
+### Model Sources
+- **Paper:** [BERT](https://arxiv.org/abs/1810.04805)
+
+## Uses
+
+#### Direct Use
+
+This model can be used for masked language modeling 
+
+
+
+## Risks, Limitations and Biases
+**CONTENT WARNING: Readers should be aware this section contains content that is disturbing, offensive, and can propagate historical and current stereotypes.**
+
+Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)).
+
+
+## Training
+
+#### Training Procedure
+* **type_vocab_size:** 2
+* **vocab_size:** 21128
+* **num_hidden_layers:** 12
+
+#### Training Data
+[More Information Needed]
+
+## Evaluation
+
+#### Results
+
+[More Information Needed]
+
+
+## How to Get Started With the Model
+```python
+from transformers import AutoTokenizer, AutoModelForMaskedLM
+
+tokenizer = AutoTokenizer.from_pretrained("bert-base-chinese")
+
+model = AutoModelForMaskedLM.from_pretrained("bert-base-chinese")
+
+```
+
+
+
+
+
diff --git a/model/bert-base-chinese/config.json b/model/bert-base-chinese/config.json
new file mode 100644
index 0000000000000000000000000000000000000000..a521dc2845bdddbe822864290c6b928396fc5ee8
--- /dev/null
+++ b/model/bert-base-chinese/config.json
@@ -0,0 +1,25 @@
+{
+  "architectures": [
+    "BertForMaskedLM"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "directionality": "bidi",
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "pooler_fc_size": 768,
+  "pooler_num_attention_heads": 12,
+  "pooler_num_fc_layers": 3,
+  "pooler_size_per_head": 128,
+  "pooler_type": "first_token_transform",
+  "type_vocab_size": 2,
+  "vocab_size": 21128
+}
diff --git a/model/bert-base-chinese/flax_model.msgpack b/model/bert-base-chinese/flax_model.msgpack
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diff --git a/model/bert-base-chinese/model.safetensors b/model/bert-base-chinese/model.safetensors
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diff --git a/model/bert-base-chinese/pytorch_model.bin b/model/bert-base-chinese/pytorch_model.bin
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diff --git a/model/bert-base-chinese/tokenizer.json b/model/bert-base-chinese/tokenizer.json
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+##🔥
+##😂
+##😎
diff --git a/model/fsmn_vad/.msc b/model/fsmn_vad/.msc
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diff --git a/model/fsmn_vad/.mv b/model/fsmn_vad/.mv
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+Revision:v2.0.4,CreatedAt:1706001004
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diff --git a/model/fsmn_vad/README.md b/model/fsmn_vad/README.md
new file mode 100644
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@@ -0,0 +1,217 @@
+---
+tasks:
+- voice-activity-detection
+domain:
+- audio
+model-type:
+- VAD model
+frameworks:
+- pytorch
+backbone:
+- fsmn
+metrics:
+- f1_score
+license: Apache License 2.0
+language: 
+- cn
+tags:
+- FunASR
+- FSMN
+- Alibaba
+- Online
+datasets:
+  train:
+  - 20,000 hour industrial Mandarin task
+  test:
+  - 20,000 hour industrial Mandarin task
+widgets:
+  - task: voice-activity-detection
+    inputs:
+      - type: audio
+        name: input
+        title: 音频
+    examples:
+      - name: 1
+        title: 示例1
+        inputs:
+          - name: input
+            data: git://example/vad_example.wav 
+    inferencespec:
+      cpu: 1 #CPU数量
+      memory: 4096
+---
+
+# FSMN-Monophone VAD 模型介绍
+
+[//]: # (FSMN-Monophone VAD 模型)
+
+## Highlight
+- 16k中文通用VAD模型：可用于检测长语音片段中有效语音的起止时间点。
+  - 基于[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)场景的使用
+  - 基于[FunASR框架](https://github.com/alibaba-damo-academy/FunASR)，可进行ASR，VAD，[中文标点](https://www.modelscope.cn/models/damo/punc_ct-transformer_zh-cn-common-vocab272727-pytorch/summary)的自由组合
+  - 基于音频数据的有效语音片段起止时间点检测
+
+## <strong>[ModelScope-FunASR](https://github.com/alibaba-damo-academy/FunASR)</strong>
+<strong>[FunASR](https://github.com/alibaba-damo-academy/FunASR)</strong>希望在语音识别方面建立学术研究和工业应用之间的桥梁。通过支持在ModelScope上发布的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，并促进语音识别生态系统的发展。
+
+[**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)
+| [**介绍文档**](https://alibaba-damo-academy.github.io/FunASR/en/index.html)
+| [**中文教程**](https://github.com/alibaba-damo-academy/FunASR/wiki#funasr%E7%94%A8%E6%88%B7%E6%89%8B%E5%86%8C)
+| [**服务部署**](https://github.com/alibaba-damo-academy/FunASR/tree/main/funasr/runtime)
+| [**模型库**](https://github.com/alibaba-damo-academy/FunASR/blob/main/docs/model_zoo/modelscope_models.md)
+| [**联系我们**](https://github.com/alibaba-damo-academy/FunASR#contact)
+
+
+## 项目介绍
+
+FSMN-Monophone VAD是达摩院语音团队提出的高效语音端点检测模型，用于检测输入音频中有效语音的起止时间点信息，并将检测出来的有效音频片段输入识别引擎进行识别，减少无效语音带来的识别错误。
+
+<p align="center">
+<img src="fig/struct.png" alt="VAD模型结构"  width="500" />
+
+FSMN-Monophone VAD模型结构如上图所示：模型结构层面，FSMN模型结构建模时可考虑上下文信息，训练和推理速度快，且时延可控；同时根据VAD模型size以及低时延的要求，对FSMN的网络结构、右看帧数进行了适配。在建模单元层面，speech信息比较丰富，仅用单类来表征学习能力有限，我们将单一speech类升级为Monophone。建模单元细分，可以避免参数平均，抽象学习能力增强，区分性更好。
+
+
+## 如何使用与训练自己的模型
+
+
+本项目提供的预训练模型是基于大数据训练的通用领域VAD模型，开发者可以基于此模型进一步利用ModelScope的微调功能或者本项目对应的Github代码仓库[FunASR](https://github.com/alibaba-damo-academy/FunASR)进一步进行模型的效果优化。
+
+### 在Notebook中开发
+
+对于有开发需求的使用者，特别推荐您使用Notebook进行离线处理。先登录ModelScope账号，点击模型页面右上角的“在Notebook中打开”按钮出现对话框，首次使用会提示您关联阿里云账号，按提示操作即可。关联账号后可进入选择启动实例界面，选择计算资源，建立实例，待实例创建完成后进入开发环境，输入api调用实例。
+
+#### 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/vad_example.wav
+  - wav文件url，例如：https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/vad_example.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("vad_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
+vad_example1  data/test/audios/vad_example1.wav
+vad_example2  data/test/audios/vad_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.voice_activity_detection,
+    model='damo/speech_fsmn_vad_zh-cn-16k-common-pytorch',
+    model_revision=None,
+)
+
+segments_result = inference_pipeline(audio_in='https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/vad_example.wav')
+print(segments_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
+segments_result = inference_pipeline(audio_in='https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/vad_example.pcm', audio_fs=16000)
+```
+
+- 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，参考示例如下：
+
+```python
+inference_pipeline = pipeline(
+    task=Tasks.voice_activity_detection,
+    model='damo/speech_fsmn_vad_zh-cn-16k-common-pytorch',
+    model_revision=None,
+    output_dir='./output_dir',
+)
+
+inference_pipeline(audio_in="wav.scp")
+```
+识别结果输出路径结构如下：
+
+```sh
+tree output_dir/
+output_dir/
+└── 1best_recog
+    └── text
+
+1 directory, 1 files
+```
+text：VAD检测语音起止时间点结果文件（单位：ms）
+
+- 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("vad_example_zh.wav")
+segments_result = inference_pipeline(audio_in=waveform)
+print(segments_result)
+```
+
+- VAD常用参数调整说明（参考：vad.yaml文件）：
+  - max_end_silence_time：尾部连续检测到多长时间静音进行尾点判停，参数范围500ms～6000ms，默认值800ms(该值过低容易出现语音提前截断的情况)。
+  - speech_noise_thres：speech的得分减去noise的得分大于此值则判断为speech，参数范围：（-1,1）
+    - 取值越趋于-1，噪音被误判定为语音的概率越大，FA越高
+    - 取值越趋于+1，语音被误判定为噪音的概率越大，Pmiss越高
+    - 通常情况下，该值会根据当前模型在长语音测试集上的效果取balance
+    
+#### 基于ModelScope进行微调
+
+待开发
+
+### 在本地机器中开发
+
+#### 基于ModelScope进行微调和推理
+
+支持基于ModelScope上数据集及私有数据集进行定制微调和推理，使用方式同Notebook中开发。
+
+#### 基于FunASR进行微调和推理
+
+FunASR框架支持魔搭社区开源的工业级的语音识别模型的training & finetuning，使得研究人员和开发者可以更加便捷的进行语音识别模型的研究和生产，目前已在Github开源：https://github.com/alibaba-damo-academy/FunASR
+
+#### FunASR框架安装
+
+- 安装FunASR和ModelScope，[详见](https://github.com/alibaba-damo-academy/FunASR/wiki)
+
+```sh
+pip3 install -U modelscope
+git clone https://github.com/alibaba/FunASR.git && cd FunASR
+pip3 install -e ./
+```nstall --editable ./
+```
+
+#### 基于FunASR进行推理
+
+接下来会以私有数据集为例，介绍如何在FunASR框架中使用VAD上进行推理。
+
+```sh
+cd egs_modelscope/vad/speech_fsmn_vad_zh-cn-16k-common/
+python infer.py
+```
+
+## 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：可以直接对长语音数据进行计算，有效语音片段的起止时间点信息（单位：ms）。
+
+## 相关论文以及引用信息
+
+```BibTeX
+@inproceedings{zhang2018deep,
+  title={Deep-FSMN for large vocabulary continuous speech recognition},
+  author={Zhang, Shiliang and Lei, Ming and Yan, Zhijie and Dai, Lirong},
+  booktitle={2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
+  pages={5869--5873},
+  year={2018},
+  organization={IEEE}
+}
+```
diff --git a/model/fsmn_vad/am.mvn b/model/fsmn_vad/am.mvn
new file mode 100644
index 0000000000000000000000000000000000000000..59f64ee6d485714bb5f9eb91842b9fa8fdb2d6f2
--- /dev/null
+++ b/model/fsmn_vad/am.mvn
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+</Nnet>
\ No newline at end of file
diff --git a/model/fsmn_vad/config.yaml b/model/fsmn_vad/config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..4664aef9579e7aeea8a6081045a84dcbd354ce3e
--- /dev/null
+++ b/model/fsmn_vad/config.yaml
@@ -0,0 +1,56 @@
+frontend: WavFrontendOnline
+frontend_conf:
+    fs: 16000
+    window: hamming
+    n_mels: 80
+    frame_length: 25
+    frame_shift: 10
+    dither: 0.0
+    lfr_m: 5
+    lfr_n: 1
+
+model: FsmnVADStreaming
+model_conf:
+    sample_rate: 16000
+    detect_mode: 1 
+    snr_mode: 0
+    max_end_silence_time: 800
+    max_start_silence_time: 3000
+    do_start_point_detection: True
+    do_end_point_detection: True
+    window_size_ms: 200
+    sil_to_speech_time_thres: 150
+    speech_to_sil_time_thres: 150
+    speech_2_noise_ratio: 1.0
+    do_extend: 1
+    lookback_time_start_point: 200
+    lookahead_time_end_point: 100
+    max_single_segment_time: 60000
+    snr_thres: -100.0
+    noise_frame_num_used_for_snr: 100
+    decibel_thres: -100.0
+    speech_noise_thres: 0.6
+    fe_prior_thres: 0.0001
+    silence_pdf_num: 1
+    sil_pdf_ids: [0]
+    speech_noise_thresh_low: -0.1
+    speech_noise_thresh_high: 0.3
+    output_frame_probs: False
+    frame_in_ms: 10
+    frame_length_ms: 25
+    
+encoder: FSMN
+encoder_conf:
+    input_dim: 400
+    input_affine_dim: 140
+    fsmn_layers: 4
+    linear_dim: 250
+    proj_dim: 128
+    lorder: 20
+    rorder: 0
+    lstride: 1
+    rstride: 0
+    output_affine_dim: 140
+    output_dim: 248
+
+
diff --git a/model/fsmn_vad/configuration.json b/model/fsmn_vad/configuration.json
new file mode 100644
index 0000000000000000000000000000000000000000..c5f8b9e15c29b9e71e6a8991e7444c8376270de7
--- /dev/null
+++ b/model/fsmn_vad/configuration.json
@@ -0,0 +1,13 @@
+{
+  "framework": "pytorch",
+  "task" : "voice-activity-detection",
+  "pipeline": {"type":"funasr-pipeline"},
+  "model": {"type" : "funasr"},
+  "file_path_metas": {
+    "init_param":"model.pt", 
+    "config":"config.yaml",
+    "frontend_conf":{"cmvn_file": "am.mvn"}},
+  "model_name_in_hub": {
+    "ms":"iic/speech_fsmn_vad_zh-cn-16k-common-pytorch", 
+    "hf":""}
+}
\ No newline at end of file
diff --git a/model/fsmn_vad/example/vad_example.wav b/model/fsmn_vad/example/vad_example.wav
new file mode 100644
index 0000000000000000000000000000000000000000..662a099f15303625769d4bb197441d9d15199c09
--- /dev/null
+++ b/model/fsmn_vad/example/vad_example.wav
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a7431f0169ef76ef630c945a1d2c3675d8c8c2df2ae4a6b16f8a88ba1bccfbbb
+size 2261722
diff --git a/model/fsmn_vad/fig/struct.png b/model/fsmn_vad/fig/struct.png
new file mode 100644
index 0000000000000000000000000000000000000000..1d102a97cddb36ecac275f7d7fcc0504503455cd
Binary files /dev/null and b/model/fsmn_vad/fig/struct.png differ
diff --git a/model/fsmn_vad/model.pt b/model/fsmn_vad/model.pt
new file mode 100644
index 0000000000000000000000000000000000000000..71f70b83cd129c16d401fef5aaf7a070753ee5db
--- /dev/null
+++ b/model/fsmn_vad/model.pt
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b3be75be477f0780277f3bae0fe489f48718f585f3a6e45d7dd1fbb1a4255fc5
+size 1721366
diff --git a/model/gaudio/am.mvn b/model/gaudio/am.mvn
new file mode 100644
index 0000000000000000000000000000000000000000..681910cd1ab6458b61474cdbf0d1ac5e810f7b0d
--- /dev/null
+++ b/model/gaudio/am.mvn
@@ -0,0 +1,8 @@
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diff --git a/model/gaudio/audio_encoder.pt b/model/gaudio/audio_encoder.pt
new file mode 100644
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--- /dev/null
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diff --git a/model/gaudio/config.json b/model/gaudio/config.json
new file mode 100644
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--- /dev/null
+++ b/model/gaudio/config.json
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+{
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+    "backbone_layer": [10],
+    "backbone_load": "/root/autodl-tmp/gaudio/model/gaudio/audio_encoder.pt",
+    "text_backbone_load": "/root/autodl-tmp/gaudio/model/gaudio/text_encoder.pt",
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+    "layer_norm_eps": 1e-05,
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+    "model_type": "grounded-dino",
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+}
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diff --git a/model/gaudio/preprocessor_config.json b/model/gaudio/preprocessor_config.json
new file mode 100644
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--- /dev/null
+++ b/model/gaudio/preprocessor_config.json
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+{
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+  "feature_extractor_type": "GroundedAudioFeatureExtractor",
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+  "frontend_config": {
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+    "filter_length_min": -1,
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+    "frame_shift": 10,
+    "fs": 32000,
+    "lfr_m": 7,
+    "lfr_n": 6,
+    "n_mels": 80,
+    "snip_edges": true,
+    "upsacle_samples": true,
+    "window": "hamming"
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+  "vad_config": {
+    "vad_pretrain_path": "/root/autodl-tmp/gaudio/model/gaudio_test/vad.pt",
+    "frontend_config": {
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+      "cmvn_file": "/root/autodl-tmp/gaudio/model/gaudio_test/am.mvn",
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+      "detect_mode": 1,
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+      "max_end_silence_time": 800,
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+      "do_start_point_detection": true,
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+      "sil_to_speech_time_thres": 150,
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+      "decibel_thres": -100.0,
+      "speech_noise_thres": 0.6,
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+      "silence_pdf_num": 1,
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+  "padding_side": "right",
+  "padding_value": 0.0,
+  "processor_class": "GroundedAudioProcessor",
+  "return_attention_mask": true
+}
diff --git a/model/gaudio/preprocessor_config.json.bak b/model/gaudio/preprocessor_config.json.bak
new file mode 100644
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--- /dev/null
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+  "feature_extractor_type": "GroundedAudioFeatureExtractor",
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+  "frontend_config": {
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+    "cmvn_file": "/root/autodl-tmp/gaudio/model/gaudio/am.mvn",
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+}
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new file mode 100644
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--- /dev/null
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\ No newline at end of file
diff --git a/model/gaudio/tokenizer_config.json b/model/gaudio/tokenizer_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..1c103f2722f84dcb7ab8feff6095b56be5fa21f3
--- /dev/null
+++ b/model/gaudio/tokenizer_config.json
@@ -0,0 +1,58 @@
+{
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+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
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+  "mask_token": "[MASK]",
+  "model_max_length": 512,
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+  "pad_token": "[PAD]",
+  "processor_class": "GroundedAudioProcessor",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}
diff --git a/model/gaudio/vad.pt b/model/gaudio/vad.pt
new file mode 100644
index 0000000000000000000000000000000000000000..71f70b83cd129c16d401fef5aaf7a070753ee5db
--- /dev/null
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+oid sha256:b3be75be477f0780277f3bae0fe489f48718f585f3a6e45d7dd1fbb1a4255fc5
+size 1721366
diff --git a/model/gaudio/vocab.txt b/model/gaudio/vocab.txt
new file mode 100644
index 0000000000000000000000000000000000000000..fb140275c155a9c7c5a3b3e0e77a9e839594a938
--- /dev/null
+++ b/model/gaudio/vocab.txt
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+}
\ No newline at end of file
diff --git a/model/gaudio_origin_checkpoint/tokenizer_config.json b/model/gaudio_origin_checkpoint/tokenizer_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..1c103f2722f84dcb7ab8feff6095b56be5fa21f3
--- /dev/null
+++ b/model/gaudio_origin_checkpoint/tokenizer_config.json
@@ -0,0 +1,58 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "101": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "102": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "103": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "never_split": null,
+  "pad_token": "[PAD]",
+  "processor_class": "GroundedAudioProcessor",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}
diff --git a/model/gaudio_origin_checkpoint/vocab.txt b/model/gaudio_origin_checkpoint/vocab.txt
new file mode 100644
index 0000000000000000000000000000000000000000..fb140275c155a9c7c5a3b3e0e77a9e839594a938
--- /dev/null
+++ b/model/gaudio_origin_checkpoint/vocab.txt
@@ -0,0 +1,30522 @@
+[PAD]
+[unused0]
+[unused1]
+[unused2]
+[unused3]
+[unused4]
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diff --git a/model/grounding-dino-tiny/README.md b/model/grounding-dino-tiny/README.md
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index 0000000000000000000000000000000000000000..c912073e1f0df1410e573d58a871eb951b376835
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+++ b/model/grounding-dino-tiny/README.md
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+---
+license: apache-2.0
+tags:
+- vision
+inference: false
+pipeline_tag: zero-shot-object-detection
+---
+
+# Grounding DINO model (tiny variant)
+
+The Grounding DINO model was proposed in [Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection](https://arxiv.org/abs/2303.05499) by Shilong Liu, Zhaoyang Zeng, Tianhe Ren, Feng Li, Hao Zhang, Jie Yang, Chunyuan Li, Jianwei Yang, Hang Su, Jun Zhu, Lei Zhang. Grounding DINO extends a closed-set object detection model with a text encoder, enabling open-set object detection. The model achieves remarkable results, such as 52.5 AP on COCO zero-shot.
+
+<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/grouding_dino_architecture.png"
+alt="drawing" width="600"/>
+
+<small> Grounding DINO overview. Taken from the <a href="https://arxiv.org/abs/2303.05499">original paper</a>. </small>
+
+## Intended uses & limitations
+
+You can use the raw model for zero-shot object detection (the task of detecting things in an image out-of-the-box without labeled data).
+
+### How to use
+
+Here's how to use the model for zero-shot object detection:
+
+```python
+import requests
+
+import torch
+from PIL import Image
+from transformers import AutoProcessor, AutoModelForZeroShotObjectDetection 
+
+model_id = "IDEA-Research/grounding-dino-tiny"
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+processor = AutoProcessor.from_pretrained(model_id)
+model = AutoModelForZeroShotObjectDetection.from_pretrained(model_id).to(device)
+
+image_url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+image = Image.open(requests.get(image_url, stream=True).raw)
+# Check for cats and remote controls
+# VERY important: text queries need to be lowercased + end with a dot
+text = "a cat. a remote control."
+
+inputs = processor(images=image, text=text, return_tensors="pt").to(device)
+with torch.no_grad():
+    outputs = model(**inputs)
+
+results = processor.post_process_grounded_object_detection(
+    outputs,
+    inputs.input_ids,
+    box_threshold=0.4,
+    text_threshold=0.3,
+    target_sizes=[image.size[::-1]]
+)
+```
+
+### BibTeX entry and citation info
+
+```bibtex
+@misc{liu2023grounding,
+      title={Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection}, 
+      author={Shilong Liu and Zhaoyang Zeng and Tianhe Ren and Feng Li and Hao Zhang and Jie Yang and Chunyuan Li and Jianwei Yang and Hang Su and Jun Zhu and Lei Zhang},
+      year={2023},
+      eprint={2303.05499},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV}
+}
+```
\ No newline at end of file
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diff --git a/model/grounding-dino-tiny/config.json b/model/grounding-dino-tiny/config.json
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+  "activation_dropout": 0.0,
+  "activation_function": "relu",
+  "architectures": [
+    "GroundingDinoForObjectDetection"
+  ],
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+  "auxiliary_loss": false,
+  "backbone": null,
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+  "decoder_bbox_embed_share": true,
+  "decoder_ffn_dim": 2048,
+  "decoder_layers": 6,
+  "decoder_n_points": 4,
+  "disable_custom_kernels": false,
+  "dropout": 0.1,
+  "embedding_init_target": true,
+  "encoder_attention_heads": 8,
+  "encoder_ffn_dim": 2048,
+  "encoder_layers": 6,
+  "encoder_n_points": 4,
+  "focal_alpha": 0.25,
+  "fusion_dropout": 0.0,
+  "fusion_droppath": 0.1,
+  "giou_cost": 2.0,
+  "giou_loss_coefficient": 2.0,
+  "init_std": 0.02,
+  "is_encoder_decoder": true,
+  "layer_norm_eps": 1e-05,
+  "max_text_len": 256,
+  "model_type": "grounding-dino",
+  "num_feature_levels": 4,
+  "num_queries": 900,
+  "position_embedding_type": "sine",
+  "positional_embedding_temperature": 20,
+  "query_dim": 4,
+  "text_config": {
+    "model_type": "bert"
+  },
+  "text_enhancer_dropout": 0.0,
+  "torch_dtype": "float32",
+  "transformers_version": "4.40.0.dev0",
+  "two_stage": true,
+  "two_stage_bbox_embed_share": false,
+  "use_pretrained_backbone": false,
+  "use_timm_backbone": false
+}
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+  "do_normalize": true,
+  "do_pad": true,
+  "do_rescale": true,
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+  "format": "coco_detection",
+  "image_mean": [
+    0.485,
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+  ],
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+    }
+  }
+}
\ No newline at end of file
diff --git a/model/grounding-dino-tiny/tokenizer_config.json b/model/grounding-dino-tiny/tokenizer_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..ed97a84add5f9b2091e756765ad3ba087a345e17
--- /dev/null
+++ b/model/grounding-dino-tiny/tokenizer_config.json
@@ -0,0 +1,56 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "101": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "102": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "103": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_lower_case": true,
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "pad_token": "[PAD]",
+  "processor_class": "GroundingDinoProcessor",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}
diff --git a/model/grounding-dino-tiny/vocab.txt b/model/grounding-dino-tiny/vocab.txt
new file mode 100644
index 0000000000000000000000000000000000000000..fb140275c155a9c7c5a3b3e0e77a9e839594a938
--- /dev/null
+++ b/model/grounding-dino-tiny/vocab.txt
@@ -0,0 +1,30522 @@
+[PAD]
+[unused0]
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