diff --git a/.gitattributes b/.gitattributes
index a6344aac8c09253b3b630fb776ae94478aa0275b..c7d9f3332a950355d5a77d85000f05e6f45435ea 100644
--- a/.gitattributes
+++ b/.gitattributes
@@ -25,7 +25,6 @@
 *.safetensors filter=lfs diff=lfs merge=lfs -text
 saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.tar.* filter=lfs diff=lfs merge=lfs -text
-*.tar filter=lfs diff=lfs merge=lfs -text
 *.tflite filter=lfs diff=lfs merge=lfs -text
 *.tgz filter=lfs diff=lfs merge=lfs -text
 *.wasm filter=lfs diff=lfs merge=lfs -text
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+Collecting datasets
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+Installing collected packages: xxhash, fsspec, dill, multiprocess, datasets
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diff --git a/README.md b/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..f3ee8aac318860b4a9538d37ee036d831a7d43df
--- /dev/null
+++ b/README.md
@@ -0,0 +1,85 @@
+---
+language:
+- zh
+license: apache-2.0
+tags:
+- whisper-event
+- generated_from_trainer
+datasets:
+- mozilla-foundation/common_voice_11_0
+metrics:
+- wer
+base_model: openai/whisper-medium
+model-index:
+- name: Whisper medium nan-tw only char
+  results:
+  - task:
+      type: automatic-speech-recognition
+      name: Automatic Speech Recognition
+    dataset:
+      name: mozilla-foundation/common_voice_11_0 nan-tw
+      type: mozilla-foundation/common_voice_11_0
+      config: nan-tw
+      split: test
+      args: nan-tw
+    metrics:
+    - type: wer
+      value: 45.2824427480916
+      name: Wer
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# Whisper medium nan-tw only char
+
+This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the mozilla-foundation/common_voice_11_0 nan-tw dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.9944
+- Wer: 45.2824
+- Cer: 45.3667
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 1e-05
+- train_batch_size: 2
+- eval_batch_size: 2
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_steps: 500
+- training_steps: 5000
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Wer     | Cer     |
+|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|
+| 0.5832        | 1.04  | 1000 | 1.0634          | 56.3053 | 56.4745 |
+| 0.1467        | 2.08  | 2000 | 1.0407          | 50.9618 | 51.0112 |
+| 0.016         | 3.13  | 3000 | 1.0226          | 46.4427 | 46.5137 |
+| 0.0001        | 5.01  | 4000 | 0.9974          | 45.4656 | 45.6082 |
+| 0.0001        | 6.05  | 5000 | 0.9944          | 45.2824 | 45.3667 |
+
+
+### Framework versions
+
+- Transformers 4.27.0.dev0
+- Pytorch 1.13.1+cu117
+- Datasets 2.8.0
+- Tokenizers 0.13.2
diff --git a/added_tokens.json b/added_tokens.json
new file mode 100644
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+}
diff --git a/all_results.json b/all_results.json
new file mode 100644
index 0000000000000000000000000000000000000000..3b0290c0bc8ca08cdc86eb0db599266fb3ca6715
--- /dev/null
+++ b/all_results.json
@@ -0,0 +1,14 @@
+{
+    "epoch": 6.05,
+    "eval_cer": 134.28571428571428,
+    "eval_loss": 2.4461984634399414,
+    "eval_model_preparation_time": 0.0121,
+    "eval_runtime": 134.5644,
+    "eval_samples_per_second": 2.229,
+    "eval_steps_per_second": 1.115,
+    "eval_wer": 87.5,
+    "train_loss": 0.3284327008752851,
+    "train_runtime": 3725.671,
+    "train_samples_per_second": 2.684,
+    "train_steps_per_second": 1.342
+}
\ No newline at end of file
diff --git a/config.json b/config.json
new file mode 100644
index 0000000000000000000000000000000000000000..fc2bbe34a6ce0f9d80afb3dbba4b270605a9145b
--- /dev/null
+++ b/config.json
@@ -0,0 +1,48 @@
+{
+  "_name_or_path": "openai/whisper-medium",
+  "activation_dropout": 0.0,
+  "activation_function": "gelu",
+  "apply_spec_augment": false,
+  "architectures": [
+    "WhisperForConditionalGeneration"
+  ],
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+  "init_std": 0.02,
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+  "mask_feature_min_masks": 0,
+  "mask_feature_prob": 0.0,
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+  "max_length": null,
+  "max_source_positions": 1500,
+  "max_target_positions": 448,
+  "median_filter_width": 7,
+  "model_type": "whisper",
+  "num_hidden_layers": 24,
+  "num_mel_bins": 80,
+  "pad_token_id": 50257,
+  "scale_embedding": false,
+  "torch_dtype": "float32",
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+  "use_cache": true,
+  "use_weighted_layer_sum": false,
+  "vocab_size": 51865
+}
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+common_voice_nan-tw_32318533.mp3,市場
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+common_voice_nan-tw_32326446.mp3,等袂赴
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+common_voice_nan-tw_32332722.mp3,鬥序碼
+common_voice_nan-tw_32332724.mp3,試看覓
diff --git a/data_com/train/common_voice_nan-tw_31381398.mp3 b/data_com/train/common_voice_nan-tw_31381398.mp3
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Binary files /dev/null and b/data_com/train/common_voice_nan-tw_32132111.mp3 differ
diff --git a/data_com/train/common_voice_nan-tw_32132112.mp3 b/data_com/train/common_voice_nan-tw_32132112.mp3
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diff --git a/data_com/train/common_voice_nan-tw_32132114.mp3 b/data_com/train/common_voice_nan-tw_32132114.mp3
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diff --git a/data_com/train/common_voice_nan-tw_32132117.mp3 b/data_com/train/common_voice_nan-tw_32132117.mp3
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Binary files /dev/null and b/data_com/train/common_voice_nan-tw_32132117.mp3 differ
diff --git a/data_com/train/common_voice_nan-tw_32132118.mp3 b/data_com/train/common_voice_nan-tw_32132118.mp3
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+common_voice_nan-tw_32314350.mp3,育飼
+common_voice_nan-tw_32314351.mp3,閣較
+common_voice_nan-tw_32314354.mp3,三言兩語 
+common_voice_nan-tw_32314369.mp3,煎包
+common_voice_nan-tw_32314391.mp3,恩主公廟
+common_voice_nan-tw_32314393.mp3,欲
+common_voice_nan-tw_32314394.mp3,著衰
+common_voice_nan-tw_32314400.mp3,菜頭鬃
+common_voice_nan-tw_32314401.mp3,相拄頭
+common_voice_nan-tw_32314403.mp3,檢體送到台北了後
+common_voice_nan-tw_32314404.mp3,參加
+common_voice_nan-tw_32314405.mp3,無錢講無話
+common_voice_nan-tw_32314407.mp3,二重埔
+common_voice_nan-tw_32314408.mp3,下痟 
+common_voice_nan-tw_32314410.mp3,方向燈
+common_voice_nan-tw_32314411.mp3,控山
+common_voice_nan-tw_32314412.mp3,刀喙 
+common_voice_nan-tw_32314413.mp3,嫁翁
+common_voice_nan-tw_32314414.mp3,害物
+common_voice_nan-tw_32314435.mp3,大家口眾 
+common_voice_nan-tw_32314436.mp3,水沖泚
+common_voice_nan-tw_32314437.mp3,曾柏元
+common_voice_nan-tw_32314460.mp3,閩東語
+common_voice_nan-tw_32314462.mp3,慣練
+common_voice_nan-tw_32314464.mp3,誠濟人
+common_voice_nan-tw_32314560.mp3,富岡
+common_voice_nan-tw_32314561.mp3,心智圖
+common_voice_nan-tw_32314562.mp3,崁耳仔帽
+common_voice_nan-tw_32314615.mp3,觀光局
+common_voice_nan-tw_32314618.mp3,一空掠兩尾 
+common_voice_nan-tw_32314654.mp3,積穗
+common_voice_nan-tw_32314655.mp3,查某演員
+common_voice_nan-tw_32314659.mp3,"少年的，你著聽老歲仔苦勸"
+common_voice_nan-tw_32318276.mp3,交接
+common_voice_nan-tw_32318278.mp3,這是
+common_voice_nan-tw_32318280.mp3,少昊
+common_voice_nan-tw_32318285.mp3,早暗愛會記得疊衫
+common_voice_nan-tw_32318286.mp3,孤鉤股
+common_voice_nan-tw_32318287.mp3,咖啡機
+common_voice_nan-tw_32318296.mp3,相趁相喊
+common_voice_nan-tw_32318297.mp3,鴨鵤
+common_voice_nan-tw_32318298.mp3,中山堂埕
+common_voice_nan-tw_32318300.mp3,北埔
+common_voice_nan-tw_32318309.mp3,西雙版納
+common_voice_nan-tw_32318321.mp3,橋仔頭糖廠
+common_voice_nan-tw_32318322.mp3,碎溶溶
+common_voice_nan-tw_32318323.mp3,古早人煎草藥仔
+common_voice_nan-tw_32318344.mp3,跩跩
+common_voice_nan-tw_32318345.mp3,雷電球
+common_voice_nan-tw_32318356.mp3,共人欺負
+common_voice_nan-tw_32318357.mp3,訂閱
+common_voice_nan-tw_32318360.mp3,無出門
+common_voice_nan-tw_32318361.mp3,五形配搭
+common_voice_nan-tw_32318369.mp3,西亞圖
+common_voice_nan-tw_32318371.mp3,實頭
+common_voice_nan-tw_32318372.mp3,赤山堡
+common_voice_nan-tw_32318374.mp3,自販機
+common_voice_nan-tw_32318375.mp3,摔大眠
+common_voice_nan-tw_32318378.mp3,朝聖地
+common_voice_nan-tw_32318385.mp3,瘦枝落葉
+common_voice_nan-tw_32318387.mp3,臆測
+common_voice_nan-tw_32318394.mp3,一文不值 
+common_voice_nan-tw_32318395.mp3,聚焦
+common_voice_nan-tw_32318398.mp3,挽菜
+common_voice_nan-tw_32318405.mp3,鹹硞硞
+common_voice_nan-tw_32318406.mp3,嘉義農林學校
+common_voice_nan-tw_32318407.mp3,膨椅予囡仔翸歹去矣
+common_voice_nan-tw_32318408.mp3,徐匯中學
+common_voice_nan-tw_32318421.mp3,大房食細房 
+common_voice_nan-tw_32318422.mp3,月娘圍箍
+common_voice_nan-tw_32318429.mp3,柴塊仔
+common_voice_nan-tw_32318430.mp3,茹藏藏
+common_voice_nan-tw_32318431.mp3,銅仙
+common_voice_nan-tw_32318432.mp3,你捌伊無？
+common_voice_nan-tw_32318450.mp3,四伨
+common_voice_nan-tw_32318453.mp3,耳桮
+common_voice_nan-tw_32318457.mp3,淡水區
+common_voice_nan-tw_32318458.mp3,番薑
+common_voice_nan-tw_32318460.mp3,北新竹
+common_voice_nan-tw_32318475.mp3,江北
+common_voice_nan-tw_32318477.mp3,袂曉衰
+common_voice_nan-tw_32318478.mp3,刜
+common_voice_nan-tw_32318491.mp3,煞了代
+common_voice_nan-tw_32318493.mp3,輾天車
+common_voice_nan-tw_32318499.mp3,拍鐵的
+common_voice_nan-tw_32318500.mp3,"這粒蚶仔袂開喙，莫食較好"
+common_voice_nan-tw_32318502.mp3,阮朋友
+common_voice_nan-tw_32318519.mp3,豆菜底的
+common_voice_nan-tw_32318522.mp3,僥揣
+common_voice_nan-tw_32318529.mp3,一清二楚 
+common_voice_nan-tw_32318530.mp3,機械學習
+common_voice_nan-tw_32318533.mp3,市場
+common_voice_nan-tw_32318546.mp3,你咧大聲啥啦
+common_voice_nan-tw_32318547.mp3,內挖仔
+common_voice_nan-tw_32318548.mp3,大肚魚仔 
+common_voice_nan-tw_32318559.mp3,命冊
+common_voice_nan-tw_32318560.mp3,"心事無人知，我鬱甲足艱苦的"
+common_voice_nan-tw_32318561.mp3,台灣查埔人
+common_voice_nan-tw_32318562.mp3,洗手膏
+common_voice_nan-tw_32318563.mp3,解鬱
+common_voice_nan-tw_32318570.mp3,養生奶
+common_voice_nan-tw_32318571.mp3,欱
+common_voice_nan-tw_32318572.mp3,予人想攏無
+common_voice_nan-tw_32318573.mp3,官田區
+common_voice_nan-tw_32318579.mp3,走馬路
+common_voice_nan-tw_32318580.mp3,干焦會 
+common_voice_nan-tw_32318593.mp3,貯水池
+common_voice_nan-tw_32318595.mp3,進前
+common_voice_nan-tw_32318596.mp3,我想欲去便所
+common_voice_nan-tw_32318605.mp3,東寧
+common_voice_nan-tw_32318606.mp3,頭家媽
+common_voice_nan-tw_32318607.mp3,倒退鱟
+common_voice_nan-tw_32318608.mp3,欶一下仔大氣
+common_voice_nan-tw_32318625.mp3,三八叮咚 
+common_voice_nan-tw_32318669.mp3,長安西路
+common_voice_nan-tw_32318672.mp3,目睭屎
+common_voice_nan-tw_32318673.mp3,向天
+common_voice_nan-tw_32318674.mp3,毛管炎
+common_voice_nan-tw_32318675.mp3,倚桌
+common_voice_nan-tw_32318676.mp3,故事
+common_voice_nan-tw_32318685.mp3,臭骨相
+common_voice_nan-tw_32325857.mp3,太空船
+common_voice_nan-tw_32325858.mp3,我佮人講話
+common_voice_nan-tw_32325859.mp3,七扣八扣 
+common_voice_nan-tw_32325908.mp3,犬
+common_voice_nan-tw_32325909.mp3,飛天掛桷
+common_voice_nan-tw_32325911.mp3,喙齒佮喙舌也會相觸著
+common_voice_nan-tw_32325959.mp3,相扯
+common_voice_nan-tw_32325960.mp3,盤查某
+common_voice_nan-tw_32325963.mp3,同苦同甜
+common_voice_nan-tw_32326000.mp3,福德正神
+common_voice_nan-tw_32326004.mp3,烏嬸黃鸝
+common_voice_nan-tw_32326021.mp3,電風柱仔
+common_voice_nan-tw_32326022.mp3,黃鸝
+common_voice_nan-tw_32326023.mp3,拳頭高高在上
+common_voice_nan-tw_32326024.mp3,底蒂
+common_voice_nan-tw_32326042.mp3,天公有帶著
+common_voice_nan-tw_32326044.mp3,暴風雪
+common_voice_nan-tw_32326045.mp3,加加
+common_voice_nan-tw_32326055.mp3,豆乾糋
+common_voice_nan-tw_32326058.mp3,麥寮
+common_voice_nan-tw_32326059.mp3,澳
+common_voice_nan-tw_32326066.mp3,騙請害餓
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+common_voice_nan-tw_32326068.mp3,伊上愛食炕肉
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+common_voice_nan-tw_32326080.mp3,源泉
+common_voice_nan-tw_32326083.mp3,林森南路車行地下道
+common_voice_nan-tw_32326084.mp3,你是家己開業抑是予人倩？
+common_voice_nan-tw_32326105.mp3,螺陽
+common_voice_nan-tw_32326106.mp3,馬槽橋
+common_voice_nan-tw_32326138.mp3,崩心
+common_voice_nan-tw_32326140.mp3,歸仁區
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+common_voice_nan-tw_32326189.mp3,雙園國中
+common_voice_nan-tw_32326206.mp3,評審老師
+common_voice_nan-tw_32326208.mp3,分局前
+common_voice_nan-tw_32326210.mp3,大直街
+common_voice_nan-tw_32326211.mp3,你袂䆀
+common_voice_nan-tw_32326231.mp3,古亭國中
+common_voice_nan-tw_32326262.mp3,講話有力頭
+common_voice_nan-tw_32326263.mp3,可持續性
+common_voice_nan-tw_32326265.mp3,大面 
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+common_voice_nan-tw_32326316.mp3,懷生國中
+common_voice_nan-tw_32326318.mp3,枝骨仔
+common_voice_nan-tw_32326353.mp3,好歹命敢是註好好的？這真歹講
+common_voice_nan-tw_32326355.mp3,走袂去
+common_voice_nan-tw_32326357.mp3,含茄
+common_voice_nan-tw_32326387.mp3,爆料
+common_voice_nan-tw_32326389.mp3,鯪鯉王
+common_voice_nan-tw_32326391.mp3,"菜蟲食菜菜跤死"
+common_voice_nan-tw_32326432.mp3,你共我記咧
+common_voice_nan-tw_32326446.mp3,等袂赴
+common_voice_nan-tw_32326447.mp3,間接稅
+common_voice_nan-tw_32326450.mp3,山豬毛鄉 
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+common_voice_nan-tw_32326463.mp3,邱厝
+common_voice_nan-tw_32326477.mp3,繃帶
+common_voice_nan-tw_32326478.mp3,龜山
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+common_voice_nan-tw_32326707.mp3,金華國中
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+common_voice_nan-tw_32326718.mp3,興寧街
+common_voice_nan-tw_32326720.mp3,大直高中
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+common_voice_nan-tw_32326778.mp3,三擺兩擺 
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+common_voice_nan-tw_32328137.mp3,永春厝
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+common_voice_nan-tw_32328142.mp3,正斡
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+common_voice_nan-tw_32328199.mp3,姨仔
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+common_voice_nan-tw_32329312.mp3,魚卵醬
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+common_voice_nan-tw_32329316.mp3,老母
+common_voice_nan-tw_32329329.mp3,瘴氣
+common_voice_nan-tw_32329330.mp3,瓦sian
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+common_voice_nan-tw_32329332.mp3,毋成人
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+common_voice_nan-tw_32329356.mp3,禿額
+common_voice_nan-tw_32329357.mp3,的確的代誌才通講
+common_voice_nan-tw_32329358.mp3,"媠，媠無十全，䆀，䆀無加圇"
+common_voice_nan-tw_32329359.mp3,一例一休 
+common_voice_nan-tw_32329365.mp3,番仔挖
+common_voice_nan-tw_32329366.mp3,唌人睏
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+common_voice_nan-tw_32329382.mp3,軟殼
+common_voice_nan-tw_32329383.mp3,袂較大
+common_voice_nan-tw_32329389.mp3,紅奶仔草
+common_voice_nan-tw_32329392.mp3,燉蹄
+common_voice_nan-tw_32329395.mp3,擬徒
+common_voice_nan-tw_32329397.mp3,屁話
+common_voice_nan-tw_32329398.mp3,環狀線
+common_voice_nan-tw_32329410.mp3,袂當算擺
+common_voice_nan-tw_32329412.mp3,韓國戲
+common_voice_nan-tw_32329413.mp3,十全大補 
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+common_voice_nan-tw_32332722.mp3,鬥序碼
+common_voice_nan-tw_32332724.mp3,試看覓
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+common_voice_nan-tw_32332736.mp3,唱機
+common_voice_nan-tw_32332737.mp3,磘廍
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+common_voice_nan-tw_32332744.mp3,豆干厝
+common_voice_nan-tw_32332751.mp3,挽瓜揫藤
+common_voice_nan-tw_32332752.mp3,"褲跤放放咧，褲才袂傷短"
+common_voice_nan-tw_32332753.mp3,龍騰
+common_voice_nan-tw_32332755.mp3,一頓冷糜抾起來囥 
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+common_voice_nan-tw_32332761.mp3,咬舌
+common_voice_nan-tw_32332767.mp3,數號
+common_voice_nan-tw_32332770.mp3,員潭溪
+common_voice_nan-tw_32332771.mp3,雲頂硬碟
+common_voice_nan-tw_32332774.mp3,集中營
+common_voice_nan-tw_32332778.mp3,粉紅色
+common_voice_nan-tw_32332779.mp3,大海 
+common_voice_nan-tw_32332781.mp3,荷蘭煎餅
+common_voice_nan-tw_32332783.mp3,咖啡樹仔
+common_voice_nan-tw_32332784.mp3,十箍 
+common_voice_nan-tw_32332791.mp3,中人 
+common_voice_nan-tw_32332792.mp3,驚天動地
+common_voice_nan-tw_32332793.mp3,石墨
+common_voice_nan-tw_32332803.mp3,芝車力
+common_voice_nan-tw_32332804.mp3,阮兜的代誌我家己會處理
+common_voice_nan-tw_32332805.mp3,塗蜂
+common_voice_nan-tw_32332806.mp3,蒜香枝
+common_voice_nan-tw_32332810.mp3,雄雄眼著
+common_voice_nan-tw_32332811.mp3,伊的工夫袂䆀
+common_voice_nan-tw_32332812.mp3,雞卵湯
+common_voice_nan-tw_32332813.mp3,光箍
+common_voice_nan-tw_32332815.mp3,鍤塗
+common_voice_nan-tw_32332816.mp3,囝孫袋
+common_voice_nan-tw_32332817.mp3,新店磅空
+common_voice_nan-tw_32332818.mp3,勻勻仔是
+common_voice_nan-tw_32332819.mp3,足久
+common_voice_nan-tw_32332820.mp3,浮洲橋
+common_voice_nan-tw_32332822.mp3,頂改
+common_voice_nan-tw_32332823.mp3,"舊年遮火燒山，樹仔攏燒了了矣"
+common_voice_nan-tw_32332824.mp3,"阿母氣甲袂講話，干焦直直捶心肝"
+common_voice_nan-tw_32332825.mp3,大頭釘 
+common_voice_nan-tw_32332826.mp3,水林
+common_voice_nan-tw_32332828.mp3,濱海公路
+common_voice_nan-tw_32332834.mp3,滋潤
+common_voice_nan-tw_32332836.mp3,大面神 
+common_voice_nan-tw_32332838.mp3,囡仔豚
+common_voice_nan-tw_32332841.mp3,辛亥生態公園
+common_voice_nan-tw_32332843.mp3,蘇鬱
+common_voice_nan-tw_32332862.mp3,"伊彼个人就是無量，袂堪得看人好"
+common_voice_nan-tw_32332863.mp3,望安國中
+common_voice_nan-tw_32332865.mp3,看啥潲
+common_voice_nan-tw_32332866.mp3,草鞋墩
+common_voice_nan-tw_32332867.mp3,岡山區
+common_voice_nan-tw_32332871.mp3,蕉城
+common_voice_nan-tw_32332877.mp3,抾予齊
+common_voice_nan-tw_32332878.mp3,頂溪
+common_voice_nan-tw_32332880.mp3,食中晝頓
+common_voice_nan-tw_32332881.mp3,"濟囝餓死爸，濟新婦逆死大家"
+common_voice_nan-tw_32332882.mp3,出日頭
+common_voice_nan-tw_32332883.mp3,天地捙畚斗
+common_voice_nan-tw_32332890.mp3,地下鐵
+common_voice_nan-tw_32332892.mp3,銅鑼
+common_voice_nan-tw_32332893.mp3,馬花糋
+common_voice_nan-tw_32332894.mp3,牽手
+common_voice_nan-tw_32332895.mp3,夯雉雞
+common_voice_nan-tw_32332896.mp3,趕緊
+common_voice_nan-tw_32332898.mp3,溜籠車
+common_voice_nan-tw_32332899.mp3,文讀
+common_voice_nan-tw_32332901.mp3,金華國小
+common_voice_nan-tw_32332902.mp3,咬岫
+common_voice_nan-tw_32332904.mp3,揤吊
+common_voice_nan-tw_32332906.mp3,福興磅空
+common_voice_nan-tw_32332907.mp3,大大細細 
+common_voice_nan-tw_32332908.mp3,土城青年住宅
+common_voice_nan-tw_32332910.mp3,颺颺飛
+common_voice_nan-tw_32332911.mp3,北絲鬮
+common_voice_nan-tw_32332913.mp3,練身間
+common_voice_nan-tw_32332916.mp3,竹仔籃
+common_voice_nan-tw_32332917.mp3,乞食揀碗箸 
+common_voice_nan-tw_32332918.mp3,鹹水
+common_voice_nan-tw_32332923.mp3,茶箍精
+common_voice_nan-tw_32332924.mp3,跳牆桶仔
+common_voice_nan-tw_32332927.mp3,滾狗湧
+common_voice_nan-tw_32332928.mp3,"索仔拍結，敨袂開矣"
+common_voice_nan-tw_32332929.mp3,充公
+common_voice_nan-tw_32332930.mp3,八仙投資人 
+common_voice_nan-tw_32332933.mp3,齊國
+common_voice_nan-tw_32332935.mp3,洗面膏
+common_voice_nan-tw_32332937.mp3,阮最近欲畢業矣
+common_voice_nan-tw_32332939.mp3,副省級
+common_voice_nan-tw_32332941.mp3,隔腹兄
+common_voice_nan-tw_32332942.mp3,滷茶卵
+common_voice_nan-tw_32332944.mp3,勇勇馬縛佇將軍柱
+common_voice_nan-tw_32332945.mp3,風颱天
+common_voice_nan-tw_32332947.mp3,"掠貓仔駛尻川，半路死"
+common_voice_nan-tw_32332948.mp3,平溪
+common_voice_nan-tw_32332949.mp3,阿婆生囝
+common_voice_nan-tw_32332952.mp3,蠓仔跤踢著
+common_voice_nan-tw_32332953.mp3,食飯坩中央
+common_voice_nan-tw_32332954.mp3,武理
+common_voice_nan-tw_32332956.mp3,朱厝崙
+common_voice_nan-tw_32332957.mp3,半路店
+common_voice_nan-tw_32332958.mp3,查某?
+common_voice_nan-tw_32332959.mp3,癩物仔
+common_voice_nan-tw_32332960.mp3,十八汴
+common_voice_nan-tw_32332961.mp3,水雞花
+common_voice_nan-tw_32332966.mp3,粕
+common_voice_nan-tw_32332968.mp3,撓鉤仔
+common_voice_nan-tw_32332976.mp3,定海
+common_voice_nan-tw_32332977.mp3,大巨蛋 
+common_voice_nan-tw_32332979.mp3,頭毛菜誠輕秤
+common_voice_nan-tw_32332980.mp3,炮艦
+common_voice_nan-tw_32332992.mp3,金華國小
+common_voice_nan-tw_32332993.mp3,浮洲橋
+common_voice_nan-tw_32332994.mp3,路墘
+common_voice_nan-tw_32332995.mp3,戇
+common_voice_nan-tw_32332996.mp3,次級市場
+common_voice_nan-tw_32332997.mp3,五叉水路
+common_voice_nan-tw_32332999.mp3,過境電商
+common_voice_nan-tw_32333000.mp3,並且
+common_voice_nan-tw_32333001.mp3,大舌口 
+common_voice_nan-tw_32333003.mp3,火刻
+common_voice_nan-tw_32333004.mp3,袂定著
+common_voice_nan-tw_32333005.mp3,空翕機
+common_voice_nan-tw_32333006.mp3,搭船
+common_voice_nan-tw_32333007.mp3,俄羅
+common_voice_nan-tw_32333008.mp3,辛勞咧咒懺無錢
+common_voice_nan-tw_32333009.mp3,延平
+common_voice_nan-tw_32333010.mp3,無驚無亂
+common_voice_nan-tw_32333013.mp3,無名氏
+common_voice_nan-tw_32333015.mp3,講風頭話
+common_voice_nan-tw_32333022.mp3,衰竭
+common_voice_nan-tw_32333025.mp3,空殼公司
+common_voice_nan-tw_32333026.mp3,崁跤
+common_voice_nan-tw_32333027.mp3,頭圍仔
+common_voice_nan-tw_32333028.mp3,隔壁國
+common_voice_nan-tw_32333037.mp3,腫豬頭爿
+common_voice_nan-tw_32333039.mp3,倯閣漚郎
+common_voice_nan-tw_32333040.mp3,攑頭三尺有神明
+common_voice_nan-tw_32333047.mp3,兩禮拜前才捌做一滿風颱
+common_voice_nan-tw_32333051.mp3,儉電減碳
+common_voice_nan-tw_32333057.mp3,構單字學
+common_voice_nan-tw_32333058.mp3,"喙呼，兩萬五"
+common_voice_nan-tw_32333060.mp3,松智路
+common_voice_nan-tw_32333061.mp3,甪職
+common_voice_nan-tw_32333063.mp3,花柱
+common_voice_nan-tw_32333771.mp3,打馬煙
+common_voice_nan-tw_32333773.mp3,賓州
+common_voice_nan-tw_32333775.mp3,硬勸
+common_voice_nan-tw_32333777.mp3,毋上正
+common_voice_nan-tw_32333778.mp3,有機化學
+common_voice_nan-tw_32333780.mp3,病媒蚊
+common_voice_nan-tw_32333781.mp3,骨骼肌
+common_voice_nan-tw_32333784.mp3,好骨骼
+common_voice_nan-tw_32333786.mp3,蟯蟯鑽
+common_voice_nan-tw_32333788.mp3,石獅
+common_voice_nan-tw_32333790.mp3,臭液話抾起來餾
+common_voice_nan-tw_32333792.mp3,敢有影
+common_voice_nan-tw_32333808.mp3,網驛
+common_voice_nan-tw_32333815.mp3,䉪錢
+common_voice_nan-tw_32333816.mp3,森
+common_voice_nan-tw_32333817.mp3,學園路
+common_voice_nan-tw_32333820.mp3,黃威廷
+common_voice_nan-tw_32333822.mp3,蔡厝
+common_voice_nan-tw_32333823.mp3,光復溪
+common_voice_nan-tw_32333824.mp3,百面
+common_voice_nan-tw_32333826.mp3,赤崁文化園區
+common_voice_nan-tw_32333828.mp3,香浴草
+common_voice_nan-tw_32333838.mp3,懵懂懵懂
+common_voice_nan-tw_32333841.mp3,大洋洲 
+common_voice_nan-tw_32333843.mp3,思覺失調症
+common_voice_nan-tw_32333844.mp3,噍吧哖
+common_voice_nan-tw_32333855.mp3,廕火
+common_voice_nan-tw_32333857.mp3,苗栗
+common_voice_nan-tw_32333858.mp3,份保
+common_voice_nan-tw_32333871.mp3,討海
+common_voice_nan-tw_32333872.mp3,竹田鄉
+common_voice_nan-tw_32333874.mp3,橋仔頭火車站
+common_voice_nan-tw_32333890.mp3,傱眾人錢
+common_voice_nan-tw_32333905.mp3,遊眠
+common_voice_nan-tw_32333909.mp3,順風勢
+common_voice_nan-tw_32333915.mp3,閩中語
+common_voice_nan-tw_32333916.mp3,軍官鵰
+common_voice_nan-tw_32333917.mp3,師大綜合大樓
+common_voice_nan-tw_32333918.mp3,後埔
+common_voice_nan-tw_32333940.mp3,齒托仔攄
+common_voice_nan-tw_32333941.mp3,花蓮市
+common_voice_nan-tw_32333942.mp3,台北驛前
+common_voice_nan-tw_32333950.mp3,菜頭
+common_voice_nan-tw_32333952.mp3,富陽生態公園
+common_voice_nan-tw_32334530.mp3,看媠
+common_voice_nan-tw_32334533.mp3,衛生福利部
+common_voice_nan-tw_32334534.mp3,廢話減講
+common_voice_nan-tw_32334545.mp3,都鑾
+common_voice_nan-tw_32334547.mp3,歸位
+common_voice_nan-tw_32334548.mp3,中央選舉委員會
+common_voice_nan-tw_32334549.mp3,石門內
+common_voice_nan-tw_32334557.mp3,正月初二
+common_voice_nan-tw_32334559.mp3,人滿四山 
+common_voice_nan-tw_32334575.mp3,新大港
+common_voice_nan-tw_32334576.mp3,關數
+common_voice_nan-tw_32334578.mp3,三兩句話 
+common_voice_nan-tw_32334615.mp3,國立臺灣師範大學和平校區
+common_voice_nan-tw_32334617.mp3,左營
+common_voice_nan-tw_32334618.mp3,無代無誌你是按怎會去惹伊？
+common_voice_nan-tw_32334619.mp3,伊下早仔去予屜仔挾著尾二指
+common_voice_nan-tw_32334693.mp3,比努悠瑪雅呢族
+common_voice_nan-tw_32334699.mp3,門扇板鬥袂峇
+common_voice_nan-tw_32334701.mp3,電池車
+common_voice_nan-tw_32334717.mp3,自輪車
+common_voice_nan-tw_32334720.mp3,杉仔
+common_voice_nan-tw_32334721.mp3,福州厝
+common_voice_nan-tw_32334737.mp3,萬刀碎割
+common_voice_nan-tw_32334738.mp3,剾洗
+common_voice_nan-tw_32334739.mp3,跋伊會落
+common_voice_nan-tw_32334740.mp3,熄去
+common_voice_nan-tw_32334747.mp3,臭頭爛耳
+common_voice_nan-tw_32334748.mp3,零確診
+common_voice_nan-tw_32334749.mp3,仙毋通
+common_voice_nan-tw_32334750.mp3,馬大安
+common_voice_nan-tw_32334761.mp3,電磁場
+common_voice_nan-tw_32334764.mp3,彰化
+common_voice_nan-tw_32334768.mp3,換速球
+common_voice_nan-tw_32334770.mp3,換飯坩
+common_voice_nan-tw_32334771.mp3,轉去矣
+common_voice_nan-tw_32334796.mp3,客人仔話
+common_voice_nan-tw_32334798.mp3,開山神社
+common_voice_nan-tw_32334799.mp3,孝孤
+common_voice_nan-tw_32334800.mp3,織女星
+common_voice_nan-tw_32334801.mp3,冰
+common_voice_nan-tw_32334809.mp3,三關兩意 
+common_voice_nan-tw_32334811.mp3,治好勢
+common_voice_nan-tw_32334813.mp3,無潲姦
+common_voice_nan-tw_32334825.mp3,基線
+common_voice_nan-tw_32334829.mp3,重耽
+common_voice_nan-tw_32334831.mp3,囡仔出癖的時陣愛予伊加歇睏
+common_voice_nan-tw_32334835.mp3,毋免講
+common_voice_nan-tw_32334855.mp3,一喙雙頭出 
+common_voice_nan-tw_32334858.mp3,藏覕
+common_voice_nan-tw_32334859.mp3,反種
+common_voice_nan-tw_32334890.mp3,大喙舌 
+common_voice_nan-tw_32334891.mp3,莫佇後壁講人的歹話
+common_voice_nan-tw_32334918.mp3,貫耳仔
+common_voice_nan-tw_32334919.mp3,托運
+common_voice_nan-tw_32334920.mp3,日月潭的景色袂䆀
+common_voice_nan-tw_32334940.mp3,石壁坑
+common_voice_nan-tw_32334941.mp3,敢有閒
+common_voice_nan-tw_32334947.mp3,一開始 
+common_voice_nan-tw_32334949.mp3,水車寮
+common_voice_nan-tw_32334951.mp3,繩
+common_voice_nan-tw_32334976.mp3,水歕冷才啉落去
+common_voice_nan-tw_32334979.mp3,溼溼仔雨
+common_voice_nan-tw_32334989.mp3,賁門
+common_voice_nan-tw_32335012.mp3,加無閒
+common_voice_nan-tw_32335014.mp3,家暴
+common_voice_nan-tw_32335015.mp3,一隻鳥仔掠在手 
+common_voice_nan-tw_32335096.mp3,非基因改造的番麥子
+common_voice_nan-tw_32335097.mp3,大政府主義 
+common_voice_nan-tw_32335100.mp3,雪霸國家公園管理處
+common_voice_nan-tw_32335115.mp3,朴子市
+common_voice_nan-tw_32335116.mp3,徛予正
+common_voice_nan-tw_32335117.mp3,孤腳戲
+common_voice_nan-tw_32335118.mp3,果子豆
+common_voice_nan-tw_32335119.mp3,為非作歹
+common_voice_nan-tw_32335125.mp3,燒薰
+common_voice_nan-tw_32335141.mp3,安平古堡
+common_voice_nan-tw_32335144.mp3,序大圖
+common_voice_nan-tw_32335145.mp3,溪仔邊
+common_voice_nan-tw_32335146.mp3,安搭
+common_voice_nan-tw_32335185.mp3,上頭上面 
+common_voice_nan-tw_32335187.mp3,隔冬
+common_voice_nan-tw_32335188.mp3,合任
+common_voice_nan-tw_32335189.mp3,店頭
+common_voice_nan-tw_32347649.mp3,枵饞
+common_voice_nan-tw_32347650.mp3,一匕一中 
+common_voice_nan-tw_32347652.mp3,編的
+common_voice_nan-tw_32347653.mp3,赤血球
+common_voice_nan-tw_32347660.mp3,平平路也會行甲跋跋倒？
+common_voice_nan-tw_32347661.mp3,落樓梯
+common_voice_nan-tw_32347662.mp3,樹奶球
+common_voice_nan-tw_32347664.mp3,重陽橋
+common_voice_nan-tw_32347665.mp3,德國簫
+common_voice_nan-tw_32347666.mp3,水汪汪流
+common_voice_nan-tw_32347667.mp3,三月 
+common_voice_nan-tw_32347670.mp3,下埤頭
+common_voice_nan-tw_32347672.mp3,毋上教
+common_voice_nan-tw_32347673.mp3,水星逆行
+common_voice_nan-tw_32347679.mp3,大葉枴仔棕 
+common_voice_nan-tw_32347681.mp3,這跡
+common_voice_nan-tw_32347682.mp3,電磁爐
+common_voice_nan-tw_32347684.mp3,身長命短
+common_voice_nan-tw_32347687.mp3,和平
+common_voice_nan-tw_32347688.mp3,後仔
+common_voice_nan-tw_32347695.mp3,石礁仔
+common_voice_nan-tw_32347698.mp3,在水
+common_voice_nan-tw_32347701.mp3,主機枋
+common_voice_nan-tw_32347703.mp3,樓仔厝
+common_voice_nan-tw_32347704.mp3,橈桮
+common_voice_nan-tw_32347705.mp3,臺東市
+common_voice_nan-tw_32347706.mp3,崁跤
+common_voice_nan-tw_32347707.mp3,過勞死
+common_voice_nan-tw_32347708.mp3,感測鐵枝仔路
+common_voice_nan-tw_32347716.mp3,小面神 
+common_voice_nan-tw_32347730.mp3,格仔膨餅
+common_voice_nan-tw_32347731.mp3,哈瑪星
+common_voice_nan-tw_32347732.mp3,菜煮甲臭火焦去
+common_voice_nan-tw_32347733.mp3,拍死人膽
+common_voice_nan-tw_32347734.mp3,錢單
+common_voice_nan-tw_32347737.mp3,十做九不成 
+common_voice_nan-tw_32347750.mp3,西門
+common_voice_nan-tw_32347751.mp3,陳柏惟
+common_voice_nan-tw_32347764.mp3,漢草好
+common_voice_nan-tw_32347765.mp3,掣一趒
+common_voice_nan-tw_32347766.mp3,干焦共伊講兩句仔
+common_voice_nan-tw_32347775.mp3,你莫佇邊仔咧插喙插舌
+common_voice_nan-tw_32347776.mp3,永春厝
+common_voice_nan-tw_32347777.mp3,阿宅
+common_voice_nan-tw_32348093.mp3,圍仔內
+common_voice_nan-tw_32348094.mp3,魔棍仔
+common_voice_nan-tw_32348095.mp3,崁蓋
+common_voice_nan-tw_32348096.mp3,"一人智，毋值兩人議 "
+common_voice_nan-tw_32348097.mp3,救命環
+common_voice_nan-tw_32348108.mp3,左營區
+common_voice_nan-tw_32348110.mp3,伊就敲電話講欲請假矣
+common_voice_nan-tw_32348112.mp3,天丼
+common_voice_nan-tw_32348122.mp3,噪耳聲
+common_voice_nan-tw_32348123.mp3,大資料 
+common_voice_nan-tw_32348124.mp3,"一兼二顧，摸蜊仔兼洗褲 "
+common_voice_nan-tw_32348126.mp3,紅杏出牆
+common_voice_nan-tw_32348180.mp3,景尾街
+common_voice_nan-tw_32348185.mp3,塗師
+common_voice_nan-tw_32348202.mp3,挫傷
+common_voice_nan-tw_32348203.mp3,鉸剪仔草
+common_voice_nan-tw_32348204.mp3,神經網路
+common_voice_nan-tw_32348206.mp3,失主
+common_voice_nan-tw_32348212.mp3,面足臭
+common_voice_nan-tw_32348213.mp3,罩雺霧
+common_voice_nan-tw_32348215.mp3,食果子拜樹頭
+common_voice_nan-tw_32348216.mp3,你有咧掠寶無
+common_voice_nan-tw_32348234.mp3,挹肥
+common_voice_nan-tw_32348235.mp3,殿風
+common_voice_nan-tw_32348236.mp3,下早市 
+common_voice_nan-tw_32348243.mp3,載工
+common_voice_nan-tw_32348244.mp3,攻殼機動隊
+common_voice_nan-tw_32348247.mp3,出巡
+common_voice_nan-tw_32348261.mp3,屘叔仔
+common_voice_nan-tw_32348264.mp3,上元 
+common_voice_nan-tw_32348279.mp3,鳥鼠糖仔
+common_voice_nan-tw_32348282.mp3,歪歪講，聖聖著
+common_voice_nan-tw_32348284.mp3,五更透早
+common_voice_nan-tw_32348298.mp3,兵馬尪仔
+common_voice_nan-tw_32348300.mp3,劍靈
+common_voice_nan-tw_32348301.mp3,吳剛
+common_voice_nan-tw_32348306.mp3,僫趁
+common_voice_nan-tw_32348310.mp3,合倚
+common_voice_nan-tw_32348315.mp3,一半擺仔 
+common_voice_nan-tw_32348323.mp3,杏鮑菇
+common_voice_nan-tw_32348324.mp3,狼狽為奸
+common_voice_nan-tw_32348325.mp3,"人食裝，佛食扛 "
+common_voice_nan-tw_32348333.mp3,大板筋 
+common_voice_nan-tw_32348334.mp3,牽水旋
+common_voice_nan-tw_32348335.mp3,"痠骨輪，疼骨節"
+common_voice_nan-tw_32348336.mp3,心肝仔狗
+common_voice_nan-tw_32348344.mp3,中正紀念公園
+common_voice_nan-tw_32348345.mp3,"餅袋仔愛封喙，餅才袂潤去"
+common_voice_nan-tw_32348346.mp3,甪官
+common_voice_nan-tw_32348347.mp3,氣暢忍
+common_voice_nan-tw_32348348.mp3,走了了
+common_voice_nan-tw_32348353.mp3,觀音媽
+common_voice_nan-tw_32348354.mp3,伊是阮祧仔內的
+common_voice_nan-tw_32348360.mp3,首爾
+common_voice_nan-tw_32348361.mp3,中正橋
+common_voice_nan-tw_32348362.mp3,襄陽路
+common_voice_nan-tw_32348370.mp3,三親六認 
+common_voice_nan-tw_32348371.mp3,誓約
+common_voice_nan-tw_32348373.mp3,甪奶
+common_voice_nan-tw_32348382.mp3,朔啡亞
+common_voice_nan-tw_32348383.mp3,魚蝦水卒
+common_voice_nan-tw_32348384.mp3,理路
+common_voice_nan-tw_32348385.mp3,學校爆發呼吸道感染事件
+common_voice_nan-tw_32348386.mp3,十四張 
+common_voice_nan-tw_32348396.mp3,儘早溝
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+common_voice_nan-tw_32348414.mp3,拖沙
+common_voice_nan-tw_32348417.mp3,充電器
+common_voice_nan-tw_32348430.mp3,攑鐵仔
+common_voice_nan-tw_32348432.mp3,司孫仔
+common_voice_nan-tw_32348435.mp3,機械人
+common_voice_nan-tw_32348440.mp3,王哥柳哥
+common_voice_nan-tw_32348443.mp3,湳田仔
+common_voice_nan-tw_32348455.mp3,花仔和尚
+common_voice_nan-tw_32348471.mp3,我無佮意伊做代誌的方式
+common_voice_nan-tw_32348472.mp3,娘?
+common_voice_nan-tw_32348473.mp3,武青
+common_voice_nan-tw_32348474.mp3,覕名
+common_voice_nan-tw_32348478.mp3,莊頂路橋
+common_voice_nan-tw_32348479.mp3,齒線
+common_voice_nan-tw_32348480.mp3,大大典典 
+common_voice_nan-tw_32348481.mp3,王拓
+common_voice_nan-tw_32348484.mp3,今仔暗
+common_voice_nan-tw_32348485.mp3,齒內治療
+common_voice_nan-tw_32348486.mp3,銅鑼灣
+common_voice_nan-tw_32348487.mp3,"毋驚天，毋驚地"
+common_voice_nan-tw_32348504.mp3,樹奶擦仔
+common_voice_nan-tw_32348506.mp3,放大縮小
+common_voice_nan-tw_32348507.mp3,嚨喉框仔
+common_voice_nan-tw_32348508.mp3,半減期
+common_voice_nan-tw_32348515.mp3,伊的思想真特別
+common_voice_nan-tw_32348529.mp3,干豆
+common_voice_nan-tw_32348530.mp3,軁山鼠
+common_voice_nan-tw_32348531.mp3,糖心
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+common_voice_nan-tw_32348540.mp3,崙背鄉
+common_voice_nan-tw_32348541.mp3,仿仔囝
+common_voice_nan-tw_32348544.mp3,東華橋
+common_voice_nan-tw_32348545.mp3,墨賊仔馬
+common_voice_nan-tw_32348546.mp3,目睭賠目睭
+common_voice_nan-tw_32348564.mp3,日南
+common_voice_nan-tw_32348567.mp3,曝衫
+common_voice_nan-tw_32348579.mp3,水雞師
+common_voice_nan-tw_32348580.mp3,而後
+common_voice_nan-tw_32348600.mp3,外省豬
+common_voice_nan-tw_32348601.mp3,虛的
+common_voice_nan-tw_32348649.mp3,崙背鄉
+common_voice_nan-tw_32348650.mp3,牛湳池
+common_voice_nan-tw_32348655.mp3,熟手
+common_voice_nan-tw_32354100.mp3,月食日
+common_voice_nan-tw_32354113.mp3,千真萬確 
+common_voice_nan-tw_32354115.mp3,"喙接喙，耳傳耳"
+common_voice_nan-tw_32354117.mp3,工業指數
+common_voice_nan-tw_32354130.mp3,豆油膏
+common_voice_nan-tw_32354133.mp3,火金棍
+common_voice_nan-tw_32354137.mp3,錢落價
+common_voice_nan-tw_32354154.mp3,迷眾頁
+common_voice_nan-tw_32354155.mp3,敢有
+common_voice_nan-tw_32354157.mp3,柴鉤
+common_voice_nan-tw_32354170.mp3,三頓綴人袂著 
+common_voice_nan-tw_32354171.mp3,東引鄉
+common_voice_nan-tw_32354173.mp3,舊營
+common_voice_nan-tw_32354174.mp3,南部人
+common_voice_nan-tw_32354180.mp3,十股
+common_voice_nan-tw_32354181.mp3,有人咧揤電鈴
+common_voice_nan-tw_32354182.mp3,統計學
+common_voice_nan-tw_32354183.mp3,上尾仔 
+common_voice_nan-tw_32354185.mp3,按律
+common_voice_nan-tw_32354186.mp3,順風捒倒牆
+common_voice_nan-tw_32354187.mp3,偏頭
+common_voice_nan-tw_32354188.mp3,斑竹相思
+common_voice_nan-tw_32354195.mp3,燒烙兄
+common_voice_nan-tw_32354200.mp3,你徛佗位
+common_voice_nan-tw_32354202.mp3,臺北海洋大學
+common_voice_nan-tw_32354209.mp3,菜頭毋知摠
+common_voice_nan-tw_32354217.mp3,"三年一閏，好歹照輪 "
+common_voice_nan-tw_32354224.mp3,大箍丁 
+common_voice_nan-tw_32354225.mp3,添
+common_voice_nan-tw_32354239.mp3,米奶
+common_voice_nan-tw_32354240.mp3,徛真徛現
+common_voice_nan-tw_32354243.mp3,下跤踏庄 
+common_voice_nan-tw_32354244.mp3,知本
+common_voice_nan-tw_32354246.mp3,三地門 
+common_voice_nan-tw_32354247.mp3,愛拚才會贏
+common_voice_nan-tw_32354248.mp3,個資
+common_voice_nan-tw_32354262.mp3,最後以九十三萬同意票通過
+common_voice_nan-tw_32354278.mp3,洋路灣
+common_voice_nan-tw_32354282.mp3,熥凊糜
+common_voice_nan-tw_32354283.mp3,有風颱才有樹葉掃
+common_voice_nan-tw_32354284.mp3,害矣
+common_voice_nan-tw_32354298.mp3,馬賽
+common_voice_nan-tw_32354299.mp3,屎尾厝
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+common_voice_nan-tw_32354302.mp3,聖神
+common_voice_nan-tw_32354323.mp3,借我
+common_voice_nan-tw_32354325.mp3,哨角隊
+common_voice_nan-tw_32354326.mp3,油骨
+common_voice_nan-tw_32354341.mp3,影一下
+common_voice_nan-tw_32354360.mp3,"禮拜欲大摒掃，毋通走出去耍"
+common_voice_nan-tw_32354361.mp3,排線
+common_voice_nan-tw_32354362.mp3,說文解字
+common_voice_nan-tw_32354371.mp3,蹺蹺板
+common_voice_nan-tw_32354383.mp3,璞石閣
+common_voice_nan-tw_32354386.mp3,畫素範
+common_voice_nan-tw_32354387.mp3,潮州街
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+common_voice_nan-tw_32354401.mp3,暖暖區
+common_voice_nan-tw_32354402.mp3,搭車
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+common_voice_nan-tw_32354416.mp3,拍鐵師傅
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+common_voice_nan-tw_32354428.mp3,倒手枴仔
+common_voice_nan-tw_32354429.mp3,停刊
+common_voice_nan-tw_32354437.mp3,真趣味
+common_voice_nan-tw_32354438.mp3,月鱧
+common_voice_nan-tw_32354439.mp3,羹飯
+common_voice_nan-tw_32354455.mp3,余姚
+common_voice_nan-tw_32354456.mp3,大元國小
+common_voice_nan-tw_32354457.mp3,趨馬溜
+common_voice_nan-tw_32354458.mp3,動力火車
+common_voice_nan-tw_32354465.mp3,仝梯
+common_voice_nan-tw_32354467.mp3,趖地者
+common_voice_nan-tw_32354468.mp3,你毋是去買物件
+common_voice_nan-tw_32354480.mp3,拍手銃
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+common_voice_nan-tw_32354631.mp3,法國麵包
+common_voice_nan-tw_32354632.mp3,離岸流
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+common_voice_nan-tw_32354647.mp3,生蚵
+common_voice_nan-tw_32354649.mp3,痟齣仔
+common_voice_nan-tw_32354655.mp3,北投
+common_voice_nan-tw_32354658.mp3,代誌大條
+common_voice_nan-tw_32354681.mp3,在天龍
+common_voice_nan-tw_32354682.mp3,放聲
+common_voice_nan-tw_32354683.mp3,內湖科技園區
+common_voice_nan-tw_32354684.mp3,內埔鄉
+common_voice_nan-tw_32354695.mp3,緣份
+common_voice_nan-tw_32354698.mp3,犀牛望月
+common_voice_nan-tw_32354699.mp3,挲目子
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+common_voice_nan-tw_32354724.mp3,頂埤
+common_voice_nan-tw_32354742.mp3,直覺
+common_voice_nan-tw_32354774.mp3,岩漿岩
+common_voice_nan-tw_32354775.mp3,九芎城 
+common_voice_nan-tw_32354776.mp3,䆀手仔枴
+common_voice_nan-tw_32354794.mp3,蜜芳烏龍
+common_voice_nan-tw_32354797.mp3,虛構
+common_voice_nan-tw_32354798.mp3,空雷無雨
+common_voice_nan-tw_32354814.mp3,矸仔頸
+common_voice_nan-tw_32354815.mp3,傱錢
+common_voice_nan-tw_32354818.mp3,澀
+common_voice_nan-tw_32354856.mp3,天星
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+common_voice_nan-tw_32354871.mp3,力帝 
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+common_voice_nan-tw_32354901.mp3,性別氣質
+common_voice_nan-tw_32354909.mp3,共肉冰起來
+common_voice_nan-tw_32354911.mp3,右昌
+common_voice_nan-tw_32354912.mp3,人造語言 
+common_voice_nan-tw_32354924.mp3,溝仔尾
+common_voice_nan-tw_32354925.mp3,若有做孝男，就會攑孝杖
+common_voice_nan-tw_32354938.mp3,掌上明珠
+common_voice_nan-tw_32354952.mp3,三遷四遷 
+common_voice_nan-tw_32354953.mp3,無空暝日
+common_voice_nan-tw_32354954.mp3,儉額
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+common_voice_nan-tw_32363599.mp3,眉筆
+common_voice_nan-tw_32363600.mp3,攏是
+common_voice_nan-tw_32363601.mp3,臺大
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+common_voice_nan-tw_32363650.mp3,軟
+common_voice_nan-tw_32363660.mp3,濁水溪
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+common_voice_nan-tw_32363667.mp3,神經線相袚
+common_voice_nan-tw_32363670.mp3,漁人
+common_voice_nan-tw_32363671.mp3,一徛一畫 
+common_voice_nan-tw_32363682.mp3,七張
+common_voice_nan-tw_32363683.mp3,塗炭仔
+common_voice_nan-tw_32363684.mp3,稀稀戽戽
+common_voice_nan-tw_32363687.mp3,尪姨厝
+common_voice_nan-tw_32363688.mp3,二十 
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+common_voice_nan-tw_32363691.mp3,上顎骨 
+common_voice_nan-tw_32363703.mp3,靴管
+common_voice_nan-tw_32363706.mp3,一關過了又一關 
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+common_voice_nan-tw_32363728.mp3,毛殺銃
+common_voice_nan-tw_32363730.mp3,見著棺柴才知哭
+common_voice_nan-tw_32363747.mp3,"伊逐工透早攏愛啉豆奶、食包仔"
+common_voice_nan-tw_32363748.mp3,弄花
+common_voice_nan-tw_32363749.mp3,在地出世
+common_voice_nan-tw_32363751.mp3,權證
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+common_voice_nan-tw_32363788.mp3,定期船
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+common_voice_nan-tw_32363790.mp3,放尿
+common_voice_nan-tw_32363792.mp3,感謝恁的支持
+common_voice_nan-tw_32363793.mp3,付諸東流
+common_voice_nan-tw_32363794.mp3,誦
+common_voice_nan-tw_32363795.mp3,一下轉到厝 
+common_voice_nan-tw_32363796.mp3,樹仔尾
+common_voice_nan-tw_32363797.mp3,我行到半路就拄著伊矣
+common_voice_nan-tw_32363799.mp3,三輪仔車 
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+common_voice_nan-tw_32363801.mp3,七美鄉
+common_voice_nan-tw_32363802.mp3,飯
+common_voice_nan-tw_32363803.mp3,傷譀啦
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+common_voice_nan-tw_32363806.mp3,民主進步黨
+common_voice_nan-tw_32363809.mp3,坑仔內
+common_voice_nan-tw_32363810.mp3,譀呱呱
+common_voice_nan-tw_32363811.mp3,伊今仔日的表現有淡薄仔失常
+common_voice_nan-tw_32363812.mp3,大角喙 
+common_voice_nan-tw_32363813.mp3,蘆洲區
+common_voice_nan-tw_32363815.mp3,你講啥
+common_voice_nan-tw_32363816.mp3,做木
+common_voice_nan-tw_32363820.mp3,撨好勢
+common_voice_nan-tw_32363822.mp3,大池國小
+common_voice_nan-tw_32363823.mp3,暢甲毋知人
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+common_voice_nan-tw_32363830.mp3,阿南坑
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+common_voice_nan-tw_32132107.mp3,秀山橋
+common_voice_nan-tw_32132108.mp3,四海遊龍
+common_voice_nan-tw_32132110.mp3,"四書熟律律，十句九句不"
+common_voice_nan-tw_32132111.mp3,市立體育場
+common_voice_nan-tw_32132112.mp3,師大
+common_voice_nan-tw_32132114.mp3,量大小
+common_voice_nan-tw_32132117.mp3,獅仔王
+common_voice_nan-tw_32132118.mp3,貓徙岫
+common_voice_nan-tw_32132120.mp3,穆斯林
+common_voice_nan-tw_32132121.mp3,拆箬
+common_voice_nan-tw_32132122.mp3,海馬精
+common_voice_nan-tw_32132125.mp3,吳興街站
+common_voice_nan-tw_32132132.mp3,愚人節
+common_voice_nan-tw_32132133.mp3,枷車藤
+common_voice_nan-tw_32132135.mp3,顛倒䆀
+common_voice_nan-tw_32132136.mp3,嘉義西堡
+common_voice_nan-tw_32132140.mp3,錢無力
+common_voice_nan-tw_32132141.mp3,袂算得實字
+common_voice_nan-tw_32132143.mp3,真珠奶茶
+common_voice_nan-tw_32132144.mp3,莫吵
+common_voice_nan-tw_32132147.mp3,興安街
+common_voice_nan-tw_32132148.mp3,兼寄
+common_voice_nan-tw_32132149.mp3,地顫
+common_voice_nan-tw_32132151.mp3,光療
+common_voice_nan-tw_32132153.mp3,延延
+common_voice_nan-tw_32132154.mp3,著癱風
+common_voice_nan-tw_32132155.mp3,裊裊
+common_voice_nan-tw_32132156.mp3,"物件用了，愛囥蹛原位"
+common_voice_nan-tw_32132158.mp3,上杭 
+common_voice_nan-tw_32132160.mp3,大暖坑
+common_voice_nan-tw_32132163.mp3,復興公園
+common_voice_nan-tw_32132165.mp3,三角鑽 
+common_voice_nan-tw_32132166.mp3,闞
+common_voice_nan-tw_32132172.mp3,縛單字元
+common_voice_nan-tw_32132174.mp3,埔心鄉
+common_voice_nan-tw_32132175.mp3,浮水沬
+common_voice_nan-tw_32132182.mp3,巴士
+common_voice_nan-tw_32132183.mp3,無夠窒喙空
+common_voice_nan-tw_32132186.mp3,酒精中毒
+common_voice_nan-tw_32132199.mp3,萬芳社區
+common_voice_nan-tw_32132200.mp3,"五時做藥，六時做石"
+common_voice_nan-tw_32132201.mp3,拋拋走節目
+common_voice_nan-tw_32132207.mp3,叫我欲按怎收山？
+common_voice_nan-tw_32132209.mp3,枕頭神較聖三界公
+common_voice_nan-tw_32132210.mp3,驚嘆號
+common_voice_nan-tw_32132211.mp3,片節
+common_voice_nan-tw_32132212.mp3,茶卵鼠
+common_voice_nan-tw_32132213.mp3,壯圍鄉
+common_voice_nan-tw_32132214.mp3,拍澹
+common_voice_nan-tw_32132217.mp3,家己做得來
+common_voice_nan-tw_32132219.mp3,仁德公園
+common_voice_nan-tw_32132220.mp3,鐵羊仔
+common_voice_nan-tw_32132221.mp3,歹囝
+common_voice_nan-tw_32132222.mp3,西國菜燕
+common_voice_nan-tw_32132224.mp3,掠大誤
+common_voice_nan-tw_32132226.mp3,綠海龜
+common_voice_nan-tw_32132228.mp3,牡喻嗙
+common_voice_nan-tw_32132229.mp3,真躼
+common_voice_nan-tw_32132232.mp3,櫻桃丸仔
+common_voice_nan-tw_32132233.mp3,長尾津仔
+common_voice_nan-tw_32132236.mp3,萬芳病院
+common_voice_nan-tw_32132241.mp3,幼
+common_voice_nan-tw_32132243.mp3,發育不良
+common_voice_nan-tw_32132245.mp3,鼎筆橋
+common_voice_nan-tw_32132247.mp3,抾箬仔
+common_voice_nan-tw_32132250.mp3,耳風
+common_voice_nan-tw_32132251.mp3,溪墘仔
+common_voice_nan-tw_32132257.mp3,團隊合作
+common_voice_nan-tw_32132259.mp3,馬走標
+common_voice_nan-tw_32132262.mp3,沙肺
+common_voice_nan-tw_32132264.mp3,鶆鴞
+common_voice_nan-tw_32132266.mp3,南北共同聯絡
+common_voice_nan-tw_32132283.mp3,鄭用錫
+common_voice_nan-tw_32132284.mp3,衛生福利部
+common_voice_nan-tw_32132286.mp3,國家防災機關的報告
+common_voice_nan-tw_32132309.mp3,䖙
+common_voice_nan-tw_32132310.mp3,關渡國中
+common_voice_nan-tw_32132311.mp3,割香
+common_voice_nan-tw_32132315.mp3,七拐八拐 
+common_voice_nan-tw_32132316.mp3,忠孝敦化
+common_voice_nan-tw_32132317.mp3,唐山屎食袂了
+common_voice_nan-tw_32132318.mp3,宇克蘭
+common_voice_nan-tw_32132325.mp3,推疊
+common_voice_nan-tw_32132326.mp3,光頭哥哥
+common_voice_nan-tw_32132339.mp3,橫山鄉
+common_voice_nan-tw_32132341.mp3,出頭出跤
+common_voice_nan-tw_32132342.mp3,鵪鶉
+common_voice_nan-tw_32132358.mp3,戰湧
+common_voice_nan-tw_32132360.mp3,公務員
+common_voice_nan-tw_32132391.mp3,雞膏比豆醬
+common_voice_nan-tw_32132392.mp3,水歕清才啉
+common_voice_nan-tw_32132487.mp3,潤餅?
+common_voice_nan-tw_32132490.mp3,訕潲
+common_voice_nan-tw_32132495.mp3,攝雨傘
+common_voice_nan-tw_32132496.mp3,台鐵美術館
+common_voice_nan-tw_32132500.mp3,菜寮
+common_voice_nan-tw_32132510.mp3,騎乘體位
+common_voice_nan-tw_32132513.mp3,金屋藏嬌
+common_voice_nan-tw_32132514.mp3,埔姜頭
+common_voice_nan-tw_32132521.mp3,董監事
+common_voice_nan-tw_32132522.mp3,關廟區
+common_voice_nan-tw_32132524.mp3,直排輪
+common_voice_nan-tw_32132555.mp3,撩布
+common_voice_nan-tw_32132556.mp3,無彩工
+common_voice_nan-tw_32132557.mp3,果子樹
+common_voice_nan-tw_32132559.mp3,掠天湳湳
+common_voice_nan-tw_32132560.mp3,食甲飽
+common_voice_nan-tw_32132561.mp3,麻豆區
+common_voice_nan-tw_32132562.mp3,杭州北路
+common_voice_nan-tw_32132568.mp3,一盡 
+common_voice_nan-tw_32132570.mp3,皮巧啾
+common_voice_nan-tw_32132577.mp3,後斗擴仔
+common_voice_nan-tw_32132582.mp3,加車股
+common_voice_nan-tw_32132584.mp3,大茴 
+common_voice_nan-tw_32132585.mp3,哆囉滿
+common_voice_nan-tw_32132586.mp3,翹喙仔
+common_voice_nan-tw_32132588.mp3,頭鬃尾仔
+common_voice_nan-tw_32132589.mp3,匯整
+common_voice_nan-tw_32132593.mp3,榮泰厝
+common_voice_nan-tw_32132595.mp3,相伨
+common_voice_nan-tw_32132596.mp3,手挲
+common_voice_nan-tw_32132597.mp3,飛機仔
+common_voice_nan-tw_32132600.mp3,使嘴食袂完
+common_voice_nan-tw_32132601.mp3,前擴
+common_voice_nan-tw_32132603.mp3,抾人个屎尾
+common_voice_nan-tw_32132604.mp3,這擺的機會是拄䢢拄䢢
+common_voice_nan-tw_32132606.mp3,數佛做粿
+common_voice_nan-tw_32132607.mp3,海墘
+common_voice_nan-tw_32132608.mp3,俄羅斯
+common_voice_nan-tw_32132609.mp3,愚戇
+common_voice_nan-tw_32132610.mp3,針蝟
+common_voice_nan-tw_32132611.mp3,電動
+common_voice_nan-tw_32132612.mp3,熟卵乳
+common_voice_nan-tw_32132613.mp3,藍絲
+common_voice_nan-tw_32132614.mp3,閃現
+common_voice_nan-tw_32132618.mp3,豆酺出芽
+common_voice_nan-tw_32132620.mp3,落崎
+common_voice_nan-tw_32132625.mp3,鵬程萬里
+common_voice_nan-tw_32132628.mp3,大蛚 
+common_voice_nan-tw_32132629.mp3,下土城
+common_voice_nan-tw_32132630.mp3,蚩尤
+common_voice_nan-tw_32132631.mp3,"人情來，人情去 "
+common_voice_nan-tw_32132632.mp3,建德
+common_voice_nan-tw_32132636.mp3,師大路
+common_voice_nan-tw_32132639.mp3,痠軟
+common_voice_nan-tw_32132640.mp3,貼秤頭
+common_voice_nan-tw_32132641.mp3,夯
+common_voice_nan-tw_32132642.mp3,伊那來那媠
+common_voice_nan-tw_32132643.mp3,跳牆
+common_voice_nan-tw_32133401.mp3,鼻空窟
+common_voice_nan-tw_32133402.mp3,彼陣
+common_voice_nan-tw_32133404.mp3,糊仔銃
+common_voice_nan-tw_32133405.mp3,舒跤
+common_voice_nan-tw_32133409.mp3,蘆洲
+common_voice_nan-tw_32133410.mp3,扴路
+common_voice_nan-tw_32136009.mp3,民族路
+common_voice_nan-tw_32136010.mp3,無頭摠
+common_voice_nan-tw_32136011.mp3,尾暗仔時
+common_voice_nan-tw_32136012.mp3,手摵茶
+common_voice_nan-tw_32136019.mp3,水遍地錦
+common_voice_nan-tw_32136020.mp3,多層電光
+common_voice_nan-tw_32136021.mp3,伸杖阪
+common_voice_nan-tw_32136022.mp3,毋知影伊的情形
+common_voice_nan-tw_32136023.mp3,傳起去
+common_voice_nan-tw_32136030.mp3,期望
+common_voice_nan-tw_32136032.mp3,台灣積體電路
+common_voice_nan-tw_32136033.mp3,遊蝶花
+common_voice_nan-tw_32136034.mp3,代理
+common_voice_nan-tw_32136035.mp3,媠面
+common_voice_nan-tw_32136036.mp3,文林北路
+common_voice_nan-tw_32136039.mp3,歹下
+common_voice_nan-tw_32136040.mp3,愛紅
+common_voice_nan-tw_32136041.mp3,望安國小
+common_voice_nan-tw_32136042.mp3,身體各樣
+common_voice_nan-tw_32136044.mp3,心適界
+common_voice_nan-tw_32136047.mp3,毋敢當
+common_voice_nan-tw_32136050.mp3,柑仔色
+common_voice_nan-tw_32136051.mp3,白石跤
+common_voice_nan-tw_32136052.mp3,坪林區
+common_voice_nan-tw_32136053.mp3,掌上壓
+common_voice_nan-tw_32136054.mp3,掣
+common_voice_nan-tw_32136055.mp3,水浡出來矣！
+common_voice_nan-tw_32136056.mp3,壁
+common_voice_nan-tw_32136057.mp3,寶山鄉
+common_voice_nan-tw_32136059.mp3,塵蜱
+common_voice_nan-tw_32136061.mp3,坩仔賴
+common_voice_nan-tw_32136062.mp3,豬血稞
+common_voice_nan-tw_32136070.mp3,鼇
+common_voice_nan-tw_32136071.mp3,沙轆
+common_voice_nan-tw_32136076.mp3,軁法律縫
+common_voice_nan-tw_32136078.mp3,不省人事 
+common_voice_nan-tw_32136112.mp3,下坪林
+common_voice_nan-tw_32136113.mp3,感冒
+common_voice_nan-tw_32136114.mp3,西園
+common_voice_nan-tw_32136115.mp3,電杉
+common_voice_nan-tw_32136117.mp3,二九暝 
+common_voice_nan-tw_32136121.mp3,你閣小等一下
+common_voice_nan-tw_32136122.mp3,落空車
+common_voice_nan-tw_32136123.mp3,大頷胿 
+common_voice_nan-tw_32136124.mp3,三頭鳥 
+common_voice_nan-tw_32136125.mp3,正港
+common_voice_nan-tw_32136126.mp3,胡蠅拑規堆
+common_voice_nan-tw_32136127.mp3,熟似
+common_voice_nan-tw_32136128.mp3,袂講蓋好
+common_voice_nan-tw_32136130.mp3,玻璃體
+common_voice_nan-tw_32136131.mp3,興順街
+common_voice_nan-tw_32136133.mp3,你哪會食甲規个面花巴哩貓？
+common_voice_nan-tw_32136134.mp3,囡仔人
+common_voice_nan-tw_32136136.mp3,燒肉粽
+common_voice_nan-tw_32136137.mp3,活蝦
+common_voice_nan-tw_32136139.mp3,下暗做伙睏 
+common_voice_nan-tw_32136140.mp3,手轎
+common_voice_nan-tw_32136142.mp3,喪屍
+common_voice_nan-tw_32136143.mp3,金跤
+common_voice_nan-tw_32136144.mp3,大大 
+common_voice_nan-tw_32136145.mp3,賴和
+common_voice_nan-tw_32136149.mp3,水源快速道路
+common_voice_nan-tw_32136150.mp3,陳柔縉
+common_voice_nan-tw_32136152.mp3,野銀村
+common_voice_nan-tw_32136153.mp3,新金山
+common_voice_nan-tw_32136154.mp3,宇志白
+common_voice_nan-tw_32136155.mp3,花雕雞
+common_voice_nan-tw_32136160.mp3,眠床枋
+common_voice_nan-tw_32136161.mp3,狂抨抨
+common_voice_nan-tw_32136167.mp3,包療
+common_voice_nan-tw_32136168.mp3,喬木
+common_voice_nan-tw_32136172.mp3,目鏡掛規爿
+common_voice_nan-tw_32136173.mp3,民族實驗國中
+common_voice_nan-tw_32136174.mp3,鐵改
+common_voice_nan-tw_32136177.mp3,頭槌
+common_voice_nan-tw_32136178.mp3,做囮
+common_voice_nan-tw_32136182.mp3,喋常
+common_voice_nan-tw_32136184.mp3,要詞
+common_voice_nan-tw_32136185.mp3,竹東鎮
+common_voice_nan-tw_32136186.mp3,送做堆
+common_voice_nan-tw_32136188.mp3,花枝丸
+common_voice_nan-tw_32136189.mp3,鄭森
+common_voice_nan-tw_32136194.mp3,總是按呢
+common_voice_nan-tw_32136195.mp3,撓耳屎
+common_voice_nan-tw_32136197.mp3,油尖旺
+common_voice_nan-tw_32136199.mp3,後生查某囝
+common_voice_nan-tw_32136200.mp3,肉感娘嬭
+common_voice_nan-tw_32136201.mp3,水面保險
+common_voice_nan-tw_32136203.mp3,移墾
+common_voice_nan-tw_32136204.mp3,霧泡
+common_voice_nan-tw_32136205.mp3,波士尼
+common_voice_nan-tw_32136206.mp3,汐科
+common_voice_nan-tw_32136208.mp3,㾀屎
+common_voice_nan-tw_32136211.mp3,金喙
+common_voice_nan-tw_32136213.mp3,意識形態
+common_voice_nan-tw_32136214.mp3,林爽文
+common_voice_nan-tw_32136216.mp3,陪伴偵訊
+common_voice_nan-tw_32136218.mp3,交冬
+common_voice_nan-tw_32136219.mp3,伊的
+common_voice_nan-tw_32136221.mp3,邵武
+common_voice_nan-tw_32136222.mp3,馬鮫鰆
+common_voice_nan-tw_32136223.mp3,國語
+common_voice_nan-tw_32136224.mp3,居比路
+common_voice_nan-tw_32136225.mp3,䢢拄䢢拄著
+common_voice_nan-tw_32136229.mp3,下港人 
+common_voice_nan-tw_32136230.mp3,三星鄉
+common_voice_nan-tw_32136232.mp3,中和
+common_voice_nan-tw_32136234.mp3,克里米亞
+common_voice_nan-tw_32136235.mp3,介壽公園
+common_voice_nan-tw_32136236.mp3,王添灯
+common_voice_nan-tw_32136239.mp3,喙口
+common_voice_nan-tw_32136240.mp3,芝蘭一堡
+common_voice_nan-tw_32136241.mp3,粉圓奶茶
+common_voice_nan-tw_32136303.mp3,鋪
+common_voice_nan-tw_32136305.mp3,無目子
+common_voice_nan-tw_32136306.mp3,興雅
+common_voice_nan-tw_32136307.mp3,布袋喙
+common_voice_nan-tw_32136308.mp3,丹鳳
+common_voice_nan-tw_32136313.mp3,飛攝機
+common_voice_nan-tw_32136314.mp3,秀山路
+common_voice_nan-tw_32136317.mp3,褪褲放屁
+common_voice_nan-tw_32136318.mp3,麻糍角
+common_voice_nan-tw_32136319.mp3,跋桮
+common_voice_nan-tw_32136322.mp3,魚狗
+common_voice_nan-tw_32136324.mp3,匙仔糖
+common_voice_nan-tw_32136329.mp3,古莊號誌
+common_voice_nan-tw_32136330.mp3,阻卻違法事由
+common_voice_nan-tw_32136331.mp3,水轉跤
+common_voice_nan-tw_32136332.mp3,拄才
+common_voice_nan-tw_32136334.mp3,食匏無留種
+common_voice_nan-tw_32136336.mp3,我毋知發生啥物代誌
+common_voice_nan-tw_32136337.mp3,四角城
+common_voice_nan-tw_32136338.mp3,巫醫
+common_voice_nan-tw_32136339.mp3,天花亂墜
+common_voice_nan-tw_32136340.mp3,歹喙
+common_voice_nan-tw_32136341.mp3,哀爸哭娘嬭
+common_voice_nan-tw_32136342.mp3,藥頭仔排骨
+common_voice_nan-tw_32136344.mp3,網誌
+common_voice_nan-tw_32136345.mp3,景山溪
+common_voice_nan-tw_32136346.mp3,遂昌
+common_voice_nan-tw_32136347.mp3,特殊教育
+common_voice_nan-tw_32136348.mp3,死纏爛糊
+common_voice_nan-tw_32136350.mp3,三枋橋
+common_voice_nan-tw_32136353.mp3,粗坑
+common_voice_nan-tw_32136356.mp3,青埔
+common_voice_nan-tw_32136358.mp3,某囝寄人飼
+common_voice_nan-tw_32136363.mp3,拭仔糊
+common_voice_nan-tw_32136364.mp3,期尾
+common_voice_nan-tw_32136366.mp3,郭柏川紀念館
+common_voice_nan-tw_32136369.mp3,埤仔墘
+common_voice_nan-tw_32136370.mp3,起起落落
+common_voice_nan-tw_32136372.mp3,臺胞證
+common_voice_nan-tw_32136373.mp3,臺南市美術館二館
+common_voice_nan-tw_32136374.mp3,牛頭仔
+common_voice_nan-tw_32136375.mp3,巧機仔
+common_voice_nan-tw_32136376.mp3,頭燒耳熱
+common_voice_nan-tw_32136378.mp3,萬盛
+common_voice_nan-tw_32136379.mp3,大直
+common_voice_nan-tw_32136380.mp3,就
+common_voice_nan-tw_32136382.mp3,果汁機
+common_voice_nan-tw_32136383.mp3,著來呢！
+common_voice_nan-tw_32136385.mp3,症候群
+common_voice_nan-tw_32136386.mp3,小人國 
+common_voice_nan-tw_32136389.mp3,志學
+common_voice_nan-tw_32136475.mp3,起凊凹
+common_voice_nan-tw_32136479.mp3,車埕
+common_voice_nan-tw_32136701.mp3,鹽埔鄉
+common_voice_nan-tw_32136704.mp3,死罪
+common_voice_nan-tw_32136706.mp3,辛苦囉！歇睏一下
+common_voice_nan-tw_32136707.mp3,斷半紗縒
+common_voice_nan-tw_32136709.mp3,梓官區
+common_voice_nan-tw_32136710.mp3,將近
+common_voice_nan-tw_32136714.mp3,樟湖
+common_voice_nan-tw_32136715.mp3,綿綿仔
+common_voice_nan-tw_32136716.mp3,兩翁仔某
+common_voice_nan-tw_32136725.mp3,松隆路
+common_voice_nan-tw_32136726.mp3,坐船
+common_voice_nan-tw_32136729.mp3,慈
+common_voice_nan-tw_32136730.mp3,鑠奅
+common_voice_nan-tw_32136732.mp3,苗栗工藝園區
+common_voice_nan-tw_32136733.mp3,雙連
+common_voice_nan-tw_32136734.mp3,宏牙利
+common_voice_nan-tw_32136740.mp3,蠓婆
+common_voice_nan-tw_32136741.mp3,至善公園
+common_voice_nan-tw_32136742.mp3,"卌九日烏，出大日頭"
+common_voice_nan-tw_32136745.mp3,大腦 
+common_voice_nan-tw_32136746.mp3,食電
+common_voice_nan-tw_32136747.mp3,佗一工
+common_voice_nan-tw_32136748.mp3,抾水池
+common_voice_nan-tw_32136749.mp3,三跤仔 
+common_voice_nan-tw_32136750.mp3,尖山仔
+common_voice_nan-tw_32136751.mp3,滋養層
+common_voice_nan-tw_32136752.mp3,彈力質
+common_voice_nan-tw_32136754.mp3,史港
+common_voice_nan-tw_32136755.mp3,意在言外
+common_voice_nan-tw_32136759.mp3,愣目
+common_voice_nan-tw_32136761.mp3,趨樓仔
+common_voice_nan-tw_32136763.mp3,關聖帝君
+common_voice_nan-tw_32136767.mp3,宜
+common_voice_nan-tw_32136768.mp3,咖啡豆仔
+common_voice_nan-tw_32136775.mp3,婦科
+common_voice_nan-tw_32136776.mp3,内角
+common_voice_nan-tw_32136779.mp3,大開 
+common_voice_nan-tw_32136785.mp3,烏鬼仔洞
+common_voice_nan-tw_32136789.mp3,繭仔
+common_voice_nan-tw_32136796.mp3,月娘
+common_voice_nan-tw_32136799.mp3,永春橋
+common_voice_nan-tw_32136802.mp3,小可大代誌 
+common_voice_nan-tw_32136804.mp3,揬破盲點
+common_voice_nan-tw_32136806.mp3,臺灣大學
+common_voice_nan-tw_32136807.mp3,妙蛤仔花
+common_voice_nan-tw_32136808.mp3,看了有落目
+common_voice_nan-tw_32136809.mp3,排隊排甲若長蛇陣
+common_voice_nan-tw_32136810.mp3,粉雞胿
+common_voice_nan-tw_32136812.mp3,相刣
+common_voice_nan-tw_32136813.mp3,秀洲
+common_voice_nan-tw_32136815.mp3,菜椒
+common_voice_nan-tw_32136818.mp3,法則
+common_voice_nan-tw_32136820.mp3,屎仔空
+common_voice_nan-tw_32136823.mp3,澹洗
+common_voice_nan-tw_32136824.mp3,公企中心
+common_voice_nan-tw_32136825.mp3,批水披
+common_voice_nan-tw_32136830.mp3,實習醫師
+common_voice_nan-tw_32136832.mp3,受著社會讀報習慣的改變
+common_voice_nan-tw_32136835.mp3,欲若毋
+common_voice_nan-tw_32136836.mp3,刣文
+common_voice_nan-tw_32136837.mp3,伊第一擺煮的菜閣袂歹食
+common_voice_nan-tw_32136838.mp3,覺醒青年
+common_voice_nan-tw_32136839.mp3,蚵炱
+common_voice_nan-tw_32136840.mp3,封裝
+common_voice_nan-tw_32136842.mp3,"花開滿天芳，結子才驚人"
+common_voice_nan-tw_32136843.mp3,楓香道
+common_voice_nan-tw_32136846.mp3,風傳
+common_voice_nan-tw_32136847.mp3,魚櫥仔
+common_voice_nan-tw_32136848.mp3,白鐵仔
+common_voice_nan-tw_32136850.mp3,有空無榫
+common_voice_nan-tw_32136852.mp3,籤仔
+common_voice_nan-tw_32136854.mp3,地契
+common_voice_nan-tw_32136855.mp3,桌頂耍奕
+common_voice_nan-tw_32136856.mp3,魑魅
+common_voice_nan-tw_32136857.mp3,肉片
+common_voice_nan-tw_32136859.mp3,生殖器官
+common_voice_nan-tw_32136860.mp3,曝日
+common_voice_nan-tw_32136861.mp3,閘斷
+common_voice_nan-tw_32136864.mp3,跙湧
+common_voice_nan-tw_32136865.mp3,劉
+common_voice_nan-tw_32136866.mp3,時到時擔當
+common_voice_nan-tw_32136868.mp3,倒手
+common_voice_nan-tw_32136869.mp3,囥歲
+common_voice_nan-tw_32136870.mp3,阿母惜
+common_voice_nan-tw_32136871.mp3,田尾鄉
+common_voice_nan-tw_32136877.mp3,小吹 
+common_voice_nan-tw_32136878.mp3,"你毋通掛意，我會趕緊轉來"
+common_voice_nan-tw_32136879.mp3,頂個月
+common_voice_nan-tw_32136880.mp3,風櫃國小
+common_voice_nan-tw_32136881.mp3,客家話
+common_voice_nan-tw_32136882.mp3,海
+common_voice_nan-tw_32136883.mp3,張蠓仔燈
+common_voice_nan-tw_32136884.mp3,無閒
+common_voice_nan-tw_32136886.mp3,逐家講笑爾爾
+common_voice_nan-tw_32136887.mp3,飛魚卵
+common_voice_nan-tw_32136890.mp3,這世人
+common_voice_nan-tw_32136891.mp3,光尾草
+common_voice_nan-tw_32136892.mp3,出海
+common_voice_nan-tw_32136893.mp3,逗點
+common_voice_nan-tw_32136895.mp3,搭連
+common_voice_nan-tw_32136896.mp3,流鼻流血
+common_voice_nan-tw_32136899.mp3,你毋通共序大人按呢應喙應舌
+common_voice_nan-tw_32136902.mp3,原碼
+common_voice_nan-tw_32136904.mp3,茈雞毋捌鳥
+common_voice_nan-tw_32136905.mp3,緬甸
+common_voice_nan-tw_32136906.mp3,和平區
+common_voice_nan-tw_32136907.mp3,內蒙古自治區
+common_voice_nan-tw_32136908.mp3,人共伊歹
+common_voice_nan-tw_32136914.mp3,餅麩
+common_voice_nan-tw_32136915.mp3,鼻骨
+common_voice_nan-tw_32136918.mp3,起家厝
+common_voice_nan-tw_32136919.mp3,巨蟹
+common_voice_nan-tw_32136921.mp3,台話文
+common_voice_nan-tw_32136922.mp3,洗頭水
+common_voice_nan-tw_32136923.mp3,牛奶粉錢
+common_voice_nan-tw_32136927.mp3,袂現示
+common_voice_nan-tw_32136929.mp3,不使鬼 
+common_voice_nan-tw_32136930.mp3,方陣
+common_voice_nan-tw_32136931.mp3,你欲啉咖啡抑是欲啉茶？
+common_voice_nan-tw_32136932.mp3,為著感謝讀者的支持
+common_voice_nan-tw_32136933.mp3,白花仔心
+common_voice_nan-tw_32136935.mp3,雞未啼，狗未吠
+common_voice_nan-tw_32136936.mp3,囡仔精
+common_voice_nan-tw_32136937.mp3,水按圳行
+common_voice_nan-tw_32136944.mp3,耳空彈
+common_voice_nan-tw_32136945.mp3,老老茈茈
+common_voice_nan-tw_32136946.mp3,細本奶仔草
+common_voice_nan-tw_32136948.mp3,一句講都袂曉話 
+common_voice_nan-tw_32136957.mp3,外交縛陣
+common_voice_nan-tw_32136959.mp3,愛媛縣
+common_voice_nan-tw_32136960.mp3,三分球 
+common_voice_nan-tw_32136962.mp3,膠質
+common_voice_nan-tw_32136963.mp3,撤勇
+common_voice_nan-tw_32136965.mp3,傷過份矣
+common_voice_nan-tw_32136967.mp3,啥物命，食甲竹塹餅
+common_voice_nan-tw_32136968.mp3,烏頭鵠仔
+common_voice_nan-tw_32136969.mp3,日本時代
+common_voice_nan-tw_32136972.mp3,探險隊
+common_voice_nan-tw_32136973.mp3,聿
+common_voice_nan-tw_32136976.mp3,姑將無奈
+common_voice_nan-tw_32136977.mp3,糙米飯
+common_voice_nan-tw_32136979.mp3,媽祖宮起毋著面，痟的出袂盡
+common_voice_nan-tw_32136980.mp3,伸勼帶
+common_voice_nan-tw_32136981.mp3,支那
+common_voice_nan-tw_32136982.mp3,秀才秀才騎馬弄弄來
+common_voice_nan-tw_32136983.mp3,辜振甫
+common_voice_nan-tw_32136985.mp3,汗溼
+common_voice_nan-tw_32136986.mp3,警察大--的
+common_voice_nan-tw_32136988.mp3,辛勞
+common_voice_nan-tw_32136990.mp3,"你是真正毋知，抑是假影毋知？"
+common_voice_nan-tw_32136991.mp3,潮州街
+common_voice_nan-tw_32136992.mp3,菜頭粿
+common_voice_nan-tw_32136993.mp3,浦江
+common_voice_nan-tw_32136995.mp3,上店子
+common_voice_nan-tw_32136996.mp3,體認
+common_voice_nan-tw_32136997.mp3,全部攏看無
+common_voice_nan-tw_32137001.mp3,掣斷腦筋
+common_voice_nan-tw_32137002.mp3,一點一揬 
+common_voice_nan-tw_32137299.mp3,雨掰仔
+common_voice_nan-tw_32137301.mp3,食菜的
+common_voice_nan-tw_32137303.mp3,麥芽
+common_voice_nan-tw_32137442.mp3,無時無候
+common_voice_nan-tw_32137443.mp3,錠
+common_voice_nan-tw_32137445.mp3,正爿踅
+common_voice_nan-tw_32137449.mp3,錢筒
+common_voice_nan-tw_32137450.mp3,沼平
+common_voice_nan-tw_32137457.mp3,大腿頭 
+common_voice_nan-tw_32137459.mp3,搶頭香
+common_voice_nan-tw_32137460.mp3,帶原
+common_voice_nan-tw_32137461.mp3,仝字姓
+common_voice_nan-tw_32137462.mp3,趁墟
+common_voice_nan-tw_32137463.mp3,解文說字
+common_voice_nan-tw_32137465.mp3,梧棲區
+common_voice_nan-tw_32137476.mp3,在你去
+common_voice_nan-tw_32137478.mp3,妝娗
+common_voice_nan-tw_32137479.mp3,湳仔溝
+common_voice_nan-tw_32137480.mp3,"缺喙食米粉，看現現"
+common_voice_nan-tw_32137482.mp3,牛埔
+common_voice_nan-tw_32137483.mp3,尾溜火
+common_voice_nan-tw_32137484.mp3,加氣惱的
+common_voice_nan-tw_32137485.mp3,好鬥陣
+common_voice_nan-tw_32137486.mp3,水杉道
+common_voice_nan-tw_32137493.mp3,騃錢
+common_voice_nan-tw_32137494.mp3,石門
+common_voice_nan-tw_32137495.mp3,邊仔墘人
+common_voice_nan-tw_32137496.mp3,趨蹌
+common_voice_nan-tw_32137497.mp3,結嚾結黨
+common_voice_nan-tw_32137498.mp3,龜仔
+common_voice_nan-tw_32137499.mp3,使性地
+common_voice_nan-tw_32137501.mp3,股份有限公司
+common_voice_nan-tw_32137508.mp3,真正是
+common_voice_nan-tw_32137509.mp3,內圍
+common_voice_nan-tw_32137510.mp3,代替金
+common_voice_nan-tw_32137511.mp3,關節疼
+common_voice_nan-tw_32137512.mp3,番麥筍
+common_voice_nan-tw_32137519.mp3,桌奕
+common_voice_nan-tw_32137520.mp3,拄拄仔好
+common_voice_nan-tw_32137521.mp3,好入喙
+common_voice_nan-tw_32137524.mp3,衡陽路
+common_voice_nan-tw_32137527.mp3,屏東縣
+common_voice_nan-tw_32137538.mp3,文藝青年
+common_voice_nan-tw_32137540.mp3,戒急用忍
+common_voice_nan-tw_32137541.mp3,日出申時
+common_voice_nan-tw_32137547.mp3,磺港橋
+common_voice_nan-tw_32137549.mp3,創治
+common_voice_nan-tw_32137550.mp3,八斗仔
+common_voice_nan-tw_32137552.mp3,麵衫
+common_voice_nan-tw_32137553.mp3,位元幣
+common_voice_nan-tw_32137554.mp3,鳥仔毛球
+common_voice_nan-tw_32137556.mp3,民族東路
+common_voice_nan-tw_32137566.mp3,氛圍
+common_voice_nan-tw_32137568.mp3,相同
+common_voice_nan-tw_32137570.mp3,重型機車
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+common_voice_nan-tw_32137590.mp3,療
+common_voice_nan-tw_32137591.mp3,變無輦
+common_voice_nan-tw_32137605.mp3,迷眾
+common_voice_nan-tw_32137607.mp3,牡丹社事件
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+common_voice_nan-tw_32137615.mp3,楠仔坑加工區
+common_voice_nan-tw_32137643.mp3,閣寫講過身了後
+common_voice_nan-tw_32137644.mp3,淡海新市鎮
+common_voice_nan-tw_32137646.mp3,自我介紹
+common_voice_nan-tw_32137647.mp3,豬跤包
+common_voice_nan-tw_32137648.mp3,避孕
+common_voice_nan-tw_32140981.mp3,陽字
+common_voice_nan-tw_32140982.mp3,聬儱
+common_voice_nan-tw_32140983.mp3,攏總
+common_voice_nan-tw_32140984.mp3,死鰗鰡袂轉變
+common_voice_nan-tw_32140986.mp3,卸肩
+common_voice_nan-tw_32140987.mp3,拳頭拇收佇手䘼內
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+common_voice_nan-tw_32141003.mp3,肉醬
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+common_voice_nan-tw_32141014.mp3,遏制
+common_voice_nan-tw_32141016.mp3,肉麻
+common_voice_nan-tw_32141018.mp3,去髮
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+common_voice_nan-tw_32141037.mp3,店仔街
+common_voice_nan-tw_32141038.mp3,"兩人兩樣心，無錢通買針"
+common_voice_nan-tw_32141051.mp3,一人講一話 
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+common_voice_nan-tw_32141062.mp3,豆膎
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+common_voice_nan-tw_32141073.mp3,一空一隙 
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+common_voice_nan-tw_32141394.mp3,搤水
+common_voice_nan-tw_32141395.mp3,你欲去佗
+common_voice_nan-tw_32141396.mp3,查水表
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+common_voice_nan-tw_32141414.mp3,漢羅
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+common_voice_nan-tw_32146649.mp3,竹子腳
+common_voice_nan-tw_32146650.mp3,手段
+common_voice_nan-tw_32146651.mp3,想袂到你竟然會曉歕鼓吹
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+common_voice_nan-tw_32146674.mp3,你愛準備六塊飯碗才有夠
+common_voice_nan-tw_32146675.mp3,台江文化中心
+common_voice_nan-tw_32146677.mp3,"人比人，氣死人 "
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+common_voice_nan-tw_32146679.mp3,伊真好鬥陣
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+common_voice_nan-tw_32146717.mp3,出境
+common_voice_nan-tw_32146719.mp3,三十張犁 
+common_voice_nan-tw_32146723.mp3,若是電話亭
+common_voice_nan-tw_32146726.mp3,兩个人的感情真好
+common_voice_nan-tw_32146735.mp3,施琅
+common_voice_nan-tw_32146736.mp3,水龜
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+common_voice_nan-tw_32146739.mp3,"三个人，六个目 "
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+common_voice_nan-tw_32146747.mp3,相扯一下無偌濟
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+common_voice_nan-tw_32146779.mp3,喙鬚剾刀
+common_voice_nan-tw_32146783.mp3,你按呢傷譀啦！
+common_voice_nan-tw_32146784.mp3,馬明潭
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+common_voice_nan-tw_32146786.mp3,後壁寮
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+common_voice_nan-tw_32146798.mp3,枵鬼
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+common_voice_nan-tw_32146805.mp3,新創的
+common_voice_nan-tw_32146806.mp3,寄錢
+common_voice_nan-tw_32146807.mp3,特
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+common_voice_nan-tw_32146818.mp3,選擇性放袂記
+common_voice_nan-tw_32146819.mp3,藍金黃
+common_voice_nan-tw_32146820.mp3,倚--過-來
+common_voice_nan-tw_32146828.mp3,牛頂頭
+common_voice_nan-tw_32146829.mp3,獨角獸
+common_voice_nan-tw_32146839.mp3,尪姨順話尾
+common_voice_nan-tw_32146841.mp3,博愛路
+common_voice_nan-tw_32146848.mp3,永和區
+common_voice_nan-tw_32146849.mp3,十四份
+common_voice_nan-tw_32146851.mp3,三角娘仔 
+common_voice_nan-tw_32146852.mp3,今年大西洋頭一粒
+common_voice_nan-tw_32146857.mp3,雙溪口
+common_voice_nan-tw_32146858.mp3,只要改過就好矣
+common_voice_nan-tw_32146859.mp3,心腹腸肚
+common_voice_nan-tw_32146863.mp3,細粒山
+common_voice_nan-tw_32146864.mp3,糖含丸仔
+common_voice_nan-tw_32146866.mp3,雷精靈
+common_voice_nan-tw_32146872.mp3,奶
+common_voice_nan-tw_32146874.mp3,金錢仔
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+common_voice_nan-tw_32146876.mp3,衛生箸
+common_voice_nan-tw_32146880.mp3,開刀房
+common_voice_nan-tw_32146881.mp3,地動尾
+common_voice_nan-tw_32146883.mp3,痟狗舂墓壙
+common_voice_nan-tw_32146887.mp3,牽手的
+common_voice_nan-tw_32146889.mp3,釘紙機
+common_voice_nan-tw_32146890.mp3,你真無效
+common_voice_nan-tw_32146897.mp3,經貿園區
+common_voice_nan-tw_32146901.mp3,著獎
+common_voice_nan-tw_32146905.mp3,破膽蛇
+common_voice_nan-tw_32146906.mp3,七張
+common_voice_nan-tw_32146908.mp3,點點龜仔
+common_voice_nan-tw_32147005.mp3,存範欲大抐一下
+common_voice_nan-tw_32147006.mp3,蠓仔電
+common_voice_nan-tw_32147007.mp3,南屯區
+common_voice_nan-tw_32147008.mp3,酸辣湯
+common_voice_nan-tw_32147009.mp3,欲哭欲啼
+common_voice_nan-tw_32147015.mp3,千刀萬割 
+common_voice_nan-tw_32147018.mp3,人奶 
+common_voice_nan-tw_32147019.mp3,花鰍
+common_voice_nan-tw_32147024.mp3,予人請
+common_voice_nan-tw_32147026.mp3,東區商圈
+common_voice_nan-tw_32147035.mp3,剃平頭
+common_voice_nan-tw_32147036.mp3,阿里山鄉
+common_voice_nan-tw_32147038.mp3,一空掠雙隻 
+common_voice_nan-tw_32147039.mp3,昆明街
+common_voice_nan-tw_32147042.mp3,功課
+common_voice_nan-tw_32147043.mp3,五柳居
+common_voice_nan-tw_32147045.mp3,磕
+common_voice_nan-tw_32147046.mp3,白雪公主
+common_voice_nan-tw_32147054.mp3,友禿伯
+common_voice_nan-tw_32147055.mp3,敢有法度
+common_voice_nan-tw_32147058.mp3,大溝 
+common_voice_nan-tw_32147059.mp3,夢想
+common_voice_nan-tw_32147060.mp3,牽夢怪
+common_voice_nan-tw_32147061.mp3,較規氣
+common_voice_nan-tw_32148118.mp3,彼頭
+common_voice_nan-tw_32148119.mp3,連鞭就來
+common_voice_nan-tw_32148120.mp3,民族西路
+common_voice_nan-tw_32148138.mp3,凱達格蘭大道
+common_voice_nan-tw_32148140.mp3,迷魂水雞
+common_voice_nan-tw_32148141.mp3,哪有可能
+common_voice_nan-tw_32148143.mp3,牽親引戚
+common_voice_nan-tw_32153216.mp3,毋知好歹
+common_voice_nan-tw_32153217.mp3,薰衣草
+common_voice_nan-tw_32153218.mp3,邑
+common_voice_nan-tw_32153222.mp3,鐵甲龍
+common_voice_nan-tw_32153223.mp3,坦敧棍
+common_voice_nan-tw_32153224.mp3,狗母秮
+common_voice_nan-tw_32153235.mp3,手尾力
+common_voice_nan-tw_32153236.mp3,豬血粿
+common_voice_nan-tw_32153237.mp3,是頓
+common_voice_nan-tw_32153238.mp3,趨崎
+common_voice_nan-tw_32153239.mp3,阿泉坑
+common_voice_nan-tw_32153241.mp3,牛眠山
+common_voice_nan-tw_32153242.mp3,得定
+common_voice_nan-tw_32153243.mp3,大莊仔
+common_voice_nan-tw_32153244.mp3,正箍踅
+common_voice_nan-tw_32153245.mp3,一匕中 
+common_voice_nan-tw_32153256.mp3,你咧講啥潲
+common_voice_nan-tw_32153258.mp3,著毒
+common_voice_nan-tw_32153259.mp3,水務局
+common_voice_nan-tw_32153261.mp3,食奶動物
+common_voice_nan-tw_32153268.mp3,公家債
+common_voice_nan-tw_32153269.mp3,龜壁灣
+common_voice_nan-tw_32153270.mp3,匼桮
+common_voice_nan-tw_32153501.mp3,形成層
+common_voice_nan-tw_32153502.mp3,瑪家鄉
+common_voice_nan-tw_32153504.mp3,北新橋
+common_voice_nan-tw_32153581.mp3,柴犬
+common_voice_nan-tw_32153584.mp3,赤柱
+common_voice_nan-tw_32153585.mp3,烏有去矣
+common_voice_nan-tw_32158677.mp3,日頭猛
+common_voice_nan-tw_32158678.mp3,歐票
+common_voice_nan-tw_32158679.mp3,代誌就按呢進行
+common_voice_nan-tw_32158680.mp3,糖仔攕
+common_voice_nan-tw_32158684.mp3,喙瀾水
+common_voice_nan-tw_32158685.mp3,往診
+common_voice_nan-tw_32158687.mp3,重利
+common_voice_nan-tw_32158688.mp3,"求平安就好，毋通閣想欲添福壽"
+common_voice_nan-tw_32158689.mp3,較贏
+common_voice_nan-tw_32158690.mp3,烏珠
+common_voice_nan-tw_32158691.mp3,安溪
+common_voice_nan-tw_32158693.mp3,隨行科技
+common_voice_nan-tw_32158694.mp3,甜素
+common_voice_nan-tw_32158695.mp3,起磅
+common_voice_nan-tw_32158700.mp3,反天
+common_voice_nan-tw_32158701.mp3,新市
+common_voice_nan-tw_32158703.mp3,做狗毋認路食屎
+common_voice_nan-tw_32158704.mp3,鳥仔籠
+common_voice_nan-tw_32158706.mp3,冉
+common_voice_nan-tw_32158709.mp3,竹南鎮
+common_voice_nan-tw_32158710.mp3,中興新村
+common_voice_nan-tw_32158711.mp3,才袂食虧
+common_voice_nan-tw_32158712.mp3,蜂王奶
+common_voice_nan-tw_32158713.mp3,番仔墩
+common_voice_nan-tw_32158714.mp3,喙邊
+common_voice_nan-tw_32158715.mp3,客話
+common_voice_nan-tw_32158716.mp3,莫閣擋路
+common_voice_nan-tw_32158719.mp3,波霸奶茶
+common_voice_nan-tw_32158720.mp3,涵空龜
+common_voice_nan-tw_32158721.mp3,袂忍心
+common_voice_nan-tw_32158722.mp3,坑仔口
+common_voice_nan-tw_32158723.mp3,前氣接袂著後氣
+common_voice_nan-tw_32158724.mp3,真無聊
+common_voice_nan-tw_32158726.mp3,揣求
+common_voice_nan-tw_32158728.mp3,袋鼠龍
+common_voice_nan-tw_32158735.mp3,零星肉
+common_voice_nan-tw_32158736.mp3,蛤仔泅
+common_voice_nan-tw_32158741.mp3,中山女中
+common_voice_nan-tw_32158829.mp3,分母
+common_voice_nan-tw_32158831.mp3,霧社
+common_voice_nan-tw_32158832.mp3,無濫無濫
+common_voice_nan-tw_32158850.mp3,橫柯
+common_voice_nan-tw_32158851.mp3,遏米糕
+common_voice_nan-tw_32158852.mp3,大經橋
+common_voice_nan-tw_32158853.mp3,異性戀
+common_voice_nan-tw_32158862.mp3,公家辦公室
+common_voice_nan-tw_32158870.mp3,莊重
+common_voice_nan-tw_32158872.mp3,大暑 
+common_voice_nan-tw_32158873.mp3,釋字
+common_voice_nan-tw_32158874.mp3,懷疑主義
+common_voice_nan-tw_32158875.mp3,塗豆醬
+common_voice_nan-tw_32158877.mp3,福州
+common_voice_nan-tw_32158878.mp3,無底代
+common_voice_nan-tw_32158880.mp3,蛇空透鳥鼠仔岫
+common_voice_nan-tw_32158881.mp3,先生無佇館，學生起海反
+common_voice_nan-tw_32158882.mp3,海面溫度當咧熱烙所致
+common_voice_nan-tw_32158886.mp3,錫器
+common_voice_nan-tw_32158888.mp3,無親像款
+common_voice_nan-tw_32158891.mp3,下晡時仔 
+common_voice_nan-tw_32158893.mp3,八珍 
+common_voice_nan-tw_32158897.mp3,保鮮膜
+common_voice_nan-tw_32158901.mp3,吵班
+common_voice_nan-tw_32158902.mp3,北區
+common_voice_nan-tw_32158903.mp3,安定區
+common_voice_nan-tw_32158904.mp3,我拄啉過
+common_voice_nan-tw_32158905.mp3,茶心卵
+common_voice_nan-tw_32158907.mp3,碗糕粿
+common_voice_nan-tw_32158908.mp3,疊仔
+common_voice_nan-tw_32158909.mp3,瀰
+common_voice_nan-tw_32158910.mp3,百百空
+common_voice_nan-tw_32158911.mp3,一耙食到冬尾 
+common_voice_nan-tw_32158912.mp3,迴龍
+common_voice_nan-tw_32158914.mp3,北新國小
+common_voice_nan-tw_32158915.mp3,研烌
+common_voice_nan-tw_32158917.mp3,支理
+common_voice_nan-tw_32158918.mp3,御
+common_voice_nan-tw_32158919.mp3,白目球
+common_voice_nan-tw_32158920.mp3,費氣費觸
+common_voice_nan-tw_32158922.mp3,量
+common_voice_nan-tw_32158923.mp3,稻香路
+common_voice_nan-tw_32158924.mp3,"賣雜細，搖玲瑯鼓"
+common_voice_nan-tw_32158925.mp3,月事文章
+common_voice_nan-tw_32158927.mp3,動輪車
+common_voice_nan-tw_32158933.mp3,疊柴磚仔
+common_voice_nan-tw_32158934.mp3,臭頭和尚，做無好功德
+common_voice_nan-tw_32158935.mp3,哈馬星
+common_voice_nan-tw_32158937.mp3,鹹鰱魚
+common_voice_nan-tw_32158939.mp3,大漢嬰 
+common_voice_nan-tw_32158942.mp3,樹霜
+common_voice_nan-tw_32158943.mp3,食迵海
+common_voice_nan-tw_32158944.mp3,愣路
+common_voice_nan-tw_32158945.mp3,新埔
+common_voice_nan-tw_32158946.mp3,按呢款
+common_voice_nan-tw_32158949.mp3,騙食
+common_voice_nan-tw_32158950.mp3,巴大蛾仔
+common_voice_nan-tw_32158952.mp3,溫惜
+common_voice_nan-tw_32158953.mp3,看袂做
+common_voice_nan-tw_32158955.mp3,走逝
+common_voice_nan-tw_32158956.mp3,濱海義山
+common_voice_nan-tw_32158957.mp3,新竹出產的貢丸真有名
+common_voice_nan-tw_32158959.mp3,塗
+common_voice_nan-tw_32158962.mp3,衲襪
+common_voice_nan-tw_32158964.mp3,佮便的
+common_voice_nan-tw_32158965.mp3,食餐廳
+common_voice_nan-tw_32158966.mp3,日語
+common_voice_nan-tw_32158967.mp3,我沒咧驚
+common_voice_nan-tw_32158968.mp3,今仔日
+common_voice_nan-tw_32158969.mp3,大包細包 
+common_voice_nan-tw_32158970.mp3,蔫去
+common_voice_nan-tw_32158971.mp3,呔會
+common_voice_nan-tw_32158972.mp3,淡文湖
+common_voice_nan-tw_32158975.mp3,狗咬毋吼
+common_voice_nan-tw_32158976.mp3,臺中市
+common_voice_nan-tw_32158977.mp3,德化
+common_voice_nan-tw_32159231.mp3,人來來去去 
+common_voice_nan-tw_32159232.mp3,清水區
+common_voice_nan-tw_32159233.mp3,鄭南榕
+common_voice_nan-tw_32159237.mp3,死大箍
+common_voice_nan-tw_32159238.mp3,建國路
+common_voice_nan-tw_32159240.mp3,血糖低
+common_voice_nan-tw_32159242.mp3,卵塔
+common_voice_nan-tw_32159243.mp3,頭殼楞
+common_voice_nan-tw_32159245.mp3,劍脊
+common_voice_nan-tw_32159705.mp3,白仁反甲後擴去
+common_voice_nan-tw_32159706.mp3,霧
+common_voice_nan-tw_32159708.mp3,豬油粩
+common_voice_nan-tw_32159709.mp3,頭汴坑溪
+common_voice_nan-tw_32159716.mp3,皮皮
+common_voice_nan-tw_32159717.mp3,高樹鄉
+common_voice_nan-tw_32159719.mp3,去老還童
+common_voice_nan-tw_32159760.mp3,大魚大肉 
+common_voice_nan-tw_32159764.mp3,子貢 
+common_voice_nan-tw_32159771.mp3,南湖大橋
+common_voice_nan-tw_32159772.mp3,一時風駛一時船 
+common_voice_nan-tw_32159773.mp3,南方澳仔
+common_voice_nan-tw_32159774.mp3,拐馬跤
+common_voice_nan-tw_32159780.mp3,愛阿蘭
+common_voice_nan-tw_32159781.mp3,拆食落腹
+common_voice_nan-tw_32159783.mp3,紹興北街
+common_voice_nan-tw_32159785.mp3,人濟代誌著濟 
+common_voice_nan-tw_32159786.mp3,相伴關係
+common_voice_nan-tw_32159788.mp3,含水量
+common_voice_nan-tw_32159789.mp3,這路尾
+common_voice_nan-tw_32159930.mp3,當場往生
+common_voice_nan-tw_32159932.mp3,漲水
+common_voice_nan-tw_32159933.mp3,昆陽實驗室
+common_voice_nan-tw_32159934.mp3,灌木叢
+common_voice_nan-tw_32159938.mp3,齊頭水
+common_voice_nan-tw_32159943.mp3,歹鬥陣
+common_voice_nan-tw_32159944.mp3,燙麵線
+common_voice_nan-tw_32167369.mp3,布里斯托
+common_voice_nan-tw_32167370.mp3,有風毋通駛盡帆
+common_voice_nan-tw_32167371.mp3,雨抿仔
+common_voice_nan-tw_32167373.mp3,卸磨刣牛
+common_voice_nan-tw_32167379.mp3,柴龜
+common_voice_nan-tw_32167380.mp3,日曝
+common_voice_nan-tw_32167382.mp3,這條歌你會曉唱無？
+common_voice_nan-tw_32167383.mp3,當鋪
+common_voice_nan-tw_32167396.mp3,"嚨喉疼、吐等症頭"
+common_voice_nan-tw_32167397.mp3,泉源路
+common_voice_nan-tw_32167398.mp3,焐卡
+common_voice_nan-tw_32167418.mp3,你嘛較拜託的
+common_voice_nan-tw_32167420.mp3,看過
+common_voice_nan-tw_32167431.mp3,硬絲
+common_voice_nan-tw_32167433.mp3,雞指
+common_voice_nan-tw_32167435.mp3,葉菊蘭
+common_voice_nan-tw_32167445.mp3,夯刀探病牛
+common_voice_nan-tw_32167447.mp3,禁氣
+common_voice_nan-tw_32167472.mp3,滿洲國
+common_voice_nan-tw_32167473.mp3,雙跤踏雙船
+common_voice_nan-tw_32167474.mp3,出經入典
+common_voice_nan-tw_32167475.mp3,超紅燈
+common_voice_nan-tw_32167483.mp3,卵煎餅
+common_voice_nan-tw_32167485.mp3,青年公園
+common_voice_nan-tw_32167486.mp3,開泰
+common_voice_nan-tw_32167487.mp3,淡水河
+common_voice_nan-tw_32167502.mp3,牢腹
+common_voice_nan-tw_32167503.mp3,馬鈴薯
+common_voice_nan-tw_32167504.mp3,沈葆楨
+common_voice_nan-tw_32167505.mp3,新北市政府
+common_voice_nan-tw_32167506.mp3,新界
+common_voice_nan-tw_32167615.mp3,頂埤
+common_voice_nan-tw_32167618.mp3,髮僧
+common_voice_nan-tw_32167619.mp3,麵粉粿
+common_voice_nan-tw_32167627.mp3,中正國中
+common_voice_nan-tw_32167628.mp3,過田仔
+common_voice_nan-tw_32167632.mp3,番薯糋
+common_voice_nan-tw_32167634.mp3,疫情指揮中心
+common_voice_nan-tw_32167639.mp3,如此如此
+common_voice_nan-tw_32167640.mp3,七股臺灣鹽博物館 
+common_voice_nan-tw_32167641.mp3,鯪鯉鼠
+common_voice_nan-tw_32167642.mp3,嫌東嫌西
+common_voice_nan-tw_32167643.mp3,囂俳
+common_voice_nan-tw_32167645.mp3,毋講是
+common_voice_nan-tw_32167646.mp3,雙鉤股
+common_voice_nan-tw_32167647.mp3,烰豬排丼
+common_voice_nan-tw_32167649.mp3,你這馬咧做啥物穡頭？
+common_voice_nan-tw_32167650.mp3,林森南路
+common_voice_nan-tw_32167660.mp3,戰爭
+common_voice_nan-tw_32167661.mp3,桑穗
+common_voice_nan-tw_32167662.mp3,淺沬
+common_voice_nan-tw_32167663.mp3,解密
+common_voice_nan-tw_32167664.mp3,爛性
+common_voice_nan-tw_32167675.mp3,水轉仔
+common_voice_nan-tw_32167676.mp3,和藹
+common_voice_nan-tw_32167688.mp3,檸檬紅茶
+common_voice_nan-tw_32167689.mp3,做心酸的
+common_voice_nan-tw_32167708.mp3,燃滾水
+common_voice_nan-tw_32167838.mp3,牛隱窟仔
+common_voice_nan-tw_32167840.mp3,較等咧
+common_voice_nan-tw_32167841.mp3,烏日區
+common_voice_nan-tw_32167842.mp3,周瑜
+common_voice_nan-tw_32167843.mp3,後紐
+common_voice_nan-tw_32167870.mp3,小巨蛋
+common_voice_nan-tw_32167873.mp3,腌蜚
+common_voice_nan-tw_32167874.mp3,倒轉問家己
+common_voice_nan-tw_32167886.mp3,打瀾
+common_voice_nan-tw_32167887.mp3,周怡怡
+common_voice_nan-tw_32167889.mp3,斷熱過程
+common_voice_nan-tw_32167892.mp3,桃源街
+common_voice_nan-tw_32167893.mp3,明德橋
+common_voice_nan-tw_32167894.mp3,尻川空
+common_voice_nan-tw_32167895.mp3,親事講袂成
+common_voice_nan-tw_32168376.mp3,"仙人拍鼓有時錯，跤步踏差啥人無"
+common_voice_nan-tw_32168377.mp3,停車格仔
+common_voice_nan-tw_32168378.mp3,辛亥磅空
+common_voice_nan-tw_32168380.mp3,我聽無
+common_voice_nan-tw_32168381.mp3,豆乳雞
+common_voice_nan-tw_32168382.mp3,檳榔路
+common_voice_nan-tw_32168383.mp3,鄧不利多
+common_voice_nan-tw_32168468.mp3,牛公
+common_voice_nan-tw_32168469.mp3,大同公園
+common_voice_nan-tw_32168478.mp3,伊對食這項真甘開
+common_voice_nan-tw_32168479.mp3,共你辦好勢矣
+common_voice_nan-tw_32168482.mp3,一日不見如三秋 
+common_voice_nan-tw_32168488.mp3,冰地
+common_voice_nan-tw_32168489.mp3,規陣
+common_voice_nan-tw_32168491.mp3,濟新婦逆死大家
+common_voice_nan-tw_32168492.mp3,松勇路
+common_voice_nan-tw_32168494.mp3,天落甘露
+common_voice_nan-tw_32168495.mp3,消防局
+common_voice_nan-tw_32168496.mp3,阿母
+common_voice_nan-tw_32168497.mp3,花花草草
+common_voice_nan-tw_32168499.mp3,恆常性
+common_voice_nan-tw_32168514.mp3,十扳仔 
+common_voice_nan-tw_32168515.mp3,免細膩
+common_voice_nan-tw_32168516.mp3,三和國中
+common_voice_nan-tw_32168524.mp3,小澳 
+common_voice_nan-tw_32168525.mp3,港仔墘
+common_voice_nan-tw_32168526.mp3,柳州街
+common_voice_nan-tw_32168527.mp3,精神病院
+common_voice_nan-tw_32168544.mp3,新婦仔相
+common_voice_nan-tw_32168546.mp3,文盛公園
+common_voice_nan-tw_32168557.mp3,忠孝國中
+common_voice_nan-tw_32168558.mp3,杏仁露
+common_voice_nan-tw_32168560.mp3,下半晡仔 
+common_voice_nan-tw_32168561.mp3,西打
+common_voice_nan-tw_32168563.mp3,國姓
+common_voice_nan-tw_32168565.mp3,時錶仔
+common_voice_nan-tw_32168567.mp3,食屎
+common_voice_nan-tw_32168569.mp3,數碼雞
+common_voice_nan-tw_32168570.mp3,欲烘啥物
+common_voice_nan-tw_32168572.mp3,踮沬艇
+common_voice_nan-tw_32168575.mp3,悠風號
+common_voice_nan-tw_32168576.mp3,大滾水 
+common_voice_nan-tw_32168584.mp3,咱這月日的生產目標（Lán tsit gue̍h-ji̍t ê sing-sán bo̍k-piau
+common_voice_nan-tw_32168588.mp3,幾點
+common_voice_nan-tw_32168591.mp3,麋鹿
+common_voice_nan-tw_32168592.mp3,諷刺
+common_voice_nan-tw_32168593.mp3,山仔後
+common_voice_nan-tw_32168595.mp3,雞那霜
+common_voice_nan-tw_32168597.mp3,新莊區
+common_voice_nan-tw_32168598.mp3,梨山國中小
+common_voice_nan-tw_32168599.mp3,斷層
+common_voice_nan-tw_32168600.mp3,地牛相觸
+common_voice_nan-tw_32168601.mp3,外山跤
+common_voice_nan-tw_32168606.mp3,梗枋
+common_voice_nan-tw_32168614.mp3,天焦燥
+common_voice_nan-tw_32168624.mp3,迎神明
+common_voice_nan-tw_32168625.mp3,舊坡
+common_voice_nan-tw_32168628.mp3,生時日月
+common_voice_nan-tw_32168635.mp3,機車店
+common_voice_nan-tw_32168636.mp3,小所見，大所怪 
+common_voice_nan-tw_32168637.mp3,注音符號
+common_voice_nan-tw_32168638.mp3,荏懶
+common_voice_nan-tw_32168639.mp3,頂溪洲
+common_voice_nan-tw_32168650.mp3,趨壘
+common_voice_nan-tw_32168652.mp3,相拍輾挵膏
+common_voice_nan-tw_32168662.mp3,電秤
+common_voice_nan-tw_32168664.mp3,陳奕迅
+common_voice_nan-tw_32168665.mp3,貴興路
+common_voice_nan-tw_32168681.mp3,摘菜
+common_voice_nan-tw_32168683.mp3,刣雞用牛刀
+common_voice_nan-tw_32168693.mp3,上廁所 
+common_voice_nan-tw_32168694.mp3,十字路
+common_voice_nan-tw_32168704.mp3,夢泄
+common_voice_nan-tw_32168705.mp3,毋成猴
+common_voice_nan-tw_32168706.mp3,出恭
+common_voice_nan-tw_32168707.mp3,害溜溜
+common_voice_nan-tw_32168708.mp3,禽胸
+common_voice_nan-tw_32168714.mp3,拍狗毋出門个天
+common_voice_nan-tw_32168716.mp3,真歹勢
+common_voice_nan-tw_32168717.mp3,赦餉
+common_voice_nan-tw_32168720.mp3,薌城
+common_voice_nan-tw_32168721.mp3,三峽國中
+common_voice_nan-tw_32168722.mp3,便若是
+common_voice_nan-tw_32168723.mp3,茄苳跤
+common_voice_nan-tw_32168755.mp3,開山王
+common_voice_nan-tw_32168757.mp3,這个所在真閃風
+common_voice_nan-tw_32168758.mp3,電腦斷層
+common_voice_nan-tw_32168767.mp3,暗崁
+common_voice_nan-tw_32168769.mp3,徛壁
+common_voice_nan-tw_32168771.mp3,報你焐
+common_voice_nan-tw_32168776.mp3,思源街
+common_voice_nan-tw_32168777.mp3,牽挽
+common_voice_nan-tw_32168778.mp3,古魯碩古
+common_voice_nan-tw_32168784.mp3,剾皮
+common_voice_nan-tw_32168786.mp3,無人緣兼臭乞食羶
+common_voice_nan-tw_32168787.mp3,蘭雅
+common_voice_nan-tw_32168802.mp3,人工拍種 
+common_voice_nan-tw_32168803.mp3,躼跤仔
+common_voice_nan-tw_32168805.mp3,杯仔雞卵糕
+common_voice_nan-tw_32168823.mp3,洋面保險
+common_voice_nan-tw_32168825.mp3,搖搖擺擺
+common_voice_nan-tw_32168826.mp3,樹林仔
+common_voice_nan-tw_32168834.mp3,氣氛
+common_voice_nan-tw_32168835.mp3,減省
+common_voice_nan-tw_32168836.mp3,倒
+common_voice_nan-tw_32168837.mp3,臭喙
+common_voice_nan-tw_32168838.mp3,桌仔予蟲蛀一空
+common_voice_nan-tw_32170939.mp3,這尾魚仔煎了有赤
+common_voice_nan-tw_32170941.mp3,造橋鄉
+common_voice_nan-tw_32170943.mp3,白骨龍
+common_voice_nan-tw_32170944.mp3,呵咾甲會徛起來
+common_voice_nan-tw_32170946.mp3,分類械鬥
+common_voice_nan-tw_32170953.mp3,果子皮
+common_voice_nan-tw_32170955.mp3,候眠
+common_voice_nan-tw_32170956.mp3,掠肉
+common_voice_nan-tw_32171003.mp3,摔胸
+common_voice_nan-tw_32171004.mp3,出癖仔
+common_voice_nan-tw_32171006.mp3,柯橋
+common_voice_nan-tw_32171007.mp3,你鬼我閻羅
+common_voice_nan-tw_32171009.mp3,畏懶
+common_voice_nan-tw_32171011.mp3,國校仔
+common_voice_nan-tw_32172309.mp3,硩予碎
+common_voice_nan-tw_32172310.mp3,番仔火
+common_voice_nan-tw_32172313.mp3,六甲區
+common_voice_nan-tw_32172317.mp3,魔術師
+common_voice_nan-tw_32172319.mp3,水梘頭
+common_voice_nan-tw_32172388.mp3,毛兒仔
+common_voice_nan-tw_32172389.mp3,精粹
+common_voice_nan-tw_32172391.mp3,四送个
+common_voice_nan-tw_32172392.mp3,伊真正是鐵齒銅牙槽
+common_voice_nan-tw_32177325.mp3,化學工程
+common_voice_nan-tw_32177326.mp3,用風筒來灌風
+common_voice_nan-tw_32177328.mp3,伴景
+common_voice_nan-tw_32177329.mp3,海肚
+common_voice_nan-tw_32177337.mp3,批水phiu
+common_voice_nan-tw_32177338.mp3,鴨雄
+common_voice_nan-tw_32177339.mp3,名
+common_voice_nan-tw_32177340.mp3,燭鼓
+common_voice_nan-tw_32177341.mp3,舊廍
+common_voice_nan-tw_32177352.mp3,教育程度
+common_voice_nan-tw_32177353.mp3,永安區
+common_voice_nan-tw_32177354.mp3,毋管東西廿七八
+common_voice_nan-tw_32177361.mp3,螢橋國小
+common_voice_nan-tw_32177362.mp3,內寮
+common_voice_nan-tw_32177363.mp3,"伊共我拍，我用手共伊掰開"
+common_voice_nan-tw_32177371.mp3,"講三色話，食四面風"
+common_voice_nan-tw_32177372.mp3,燒桌
+common_voice_nan-tw_32177373.mp3,龜顧
+common_voice_nan-tw_32177381.mp3,龍磐
+common_voice_nan-tw_32177383.mp3,清氣溜溜
+common_voice_nan-tw_32177385.mp3,水雞
+common_voice_nan-tw_32177394.mp3,南化區
+common_voice_nan-tw_32177395.mp3,頭份市
+common_voice_nan-tw_32177397.mp3,揹鳳金欲去共人看風水
+common_voice_nan-tw_32177398.mp3,有好毋知惜寶
+common_voice_nan-tw_32177403.mp3,頭鬃型
+common_voice_nan-tw_32177404.mp3,大埔街
+common_voice_nan-tw_32177405.mp3,歹星入境
+common_voice_nan-tw_32177410.mp3,搝空鐘仔
+common_voice_nan-tw_32177411.mp3,崁頂
+common_voice_nan-tw_32177412.mp3,濃縮咖啡
+common_voice_nan-tw_32177414.mp3,大湳坑
+common_voice_nan-tw_32177423.mp3,風潮
+common_voice_nan-tw_32177430.mp3,發開
+common_voice_nan-tw_32177431.mp3,滿洲
+common_voice_nan-tw_32177432.mp3,刺襪
+common_voice_nan-tw_32177455.mp3,真好
+common_voice_nan-tw_32177457.mp3,"跤手遮緊，隨予你搩幾若塊"
+common_voice_nan-tw_32177473.mp3,紅寶貓
+common_voice_nan-tw_32177474.mp3,占買加
+common_voice_nan-tw_32177475.mp3,前鎮區
+common_voice_nan-tw_32177478.mp3,佛洛伊德
+common_voice_nan-tw_32177481.mp3,地球科學
+common_voice_nan-tw_32177501.mp3,信義商圈
+common_voice_nan-tw_32177504.mp3,磨鏡的
+common_voice_nan-tw_32177505.mp3,鱸鰻根仔
+common_voice_nan-tw_32177508.mp3,獨角蟲
+common_voice_nan-tw_32177510.mp3,跤頭趺下
+common_voice_nan-tw_32177523.mp3,拍棒球
+common_voice_nan-tw_32177524.mp3,關鍵
+common_voice_nan-tw_32177525.mp3,石城仔
+common_voice_nan-tw_32177526.mp3,眾人事僫辦
+common_voice_nan-tw_32182012.mp3,剪蟲
+common_voice_nan-tw_32182014.mp3,天使投資者
+common_voice_nan-tw_32182015.mp3,山懸水牛大 
+common_voice_nan-tw_32183780.mp3,我頂伊的缺
+common_voice_nan-tw_32183783.mp3,戲棚跤著有彼號人
+common_voice_nan-tw_32183786.mp3,這層代誌咱頂過已經講過矣！
+common_voice_nan-tw_32183787.mp3,烏溪
+common_voice_nan-tw_32183788.mp3,菜頭long
+common_voice_nan-tw_32183789.mp3,耙形
+common_voice_nan-tw_32183790.mp3,捽
+common_voice_nan-tw_32183791.mp3,火戛
+common_voice_nan-tw_32183794.mp3,隘寮
+common_voice_nan-tw_32183796.mp3,竹坑口
+common_voice_nan-tw_32183797.mp3,金閃閃
+common_voice_nan-tw_32183799.mp3,小鬼仔毋捌見過大把金 
+common_voice_nan-tw_32183801.mp3,人面冊 
+common_voice_nan-tw_32183803.mp3,害我十二生相變透透嘛變無撚
+common_voice_nan-tw_32183805.mp3,操舟
+common_voice_nan-tw_32183808.mp3,莿桐鄉
+common_voice_nan-tw_32183809.mp3,淹大水
+common_voice_nan-tw_32183810.mp3,人造話語 
+common_voice_nan-tw_32183813.mp3,誠貴
+common_voice_nan-tw_32183814.mp3,"我做工課做甲烏暗眩，足艱苦的"
+common_voice_nan-tw_32183817.mp3,坐中堵船
+common_voice_nan-tw_32183819.mp3,加用
+common_voice_nan-tw_32183820.mp3,旁邊
+common_voice_nan-tw_32183821.mp3,山頭鹿角尾 
+common_voice_nan-tw_32183822.mp3,牢咧
+common_voice_nan-tw_32183823.mp3,做木的
+common_voice_nan-tw_32183826.mp3,紅磡灣
+common_voice_nan-tw_32183828.mp3,頭頁
+common_voice_nan-tw_32183830.mp3,弓仔跤 
+common_voice_nan-tw_32183832.mp3,新廍邊
+common_voice_nan-tw_32183834.mp3,堆山堆海
+common_voice_nan-tw_32183836.mp3,無愛閣再
+common_voice_nan-tw_32183837.mp3,光華新村
+common_voice_nan-tw_32183839.mp3,耍戲
+common_voice_nan-tw_32183841.mp3,免疫系統
+common_voice_nan-tw_32183843.mp3,大粒汗細粒汗 
+common_voice_nan-tw_32183844.mp3,硞硞吠
+common_voice_nan-tw_32183845.mp3,長流口
+common_voice_nan-tw_32183848.mp3,欲行無
+common_voice_nan-tw_32183850.mp3,濟濟人運動
+common_voice_nan-tw_32183851.mp3,浸水式教學
+common_voice_nan-tw_32183852.mp3,喙水
+common_voice_nan-tw_32183853.mp3,我的口座猶閣有存款
+common_voice_nan-tw_32183855.mp3,信義杭州路口
+common_voice_nan-tw_32183856.mp3,尪仔
+common_voice_nan-tw_32183857.mp3,漉糊仔糜
+common_voice_nan-tw_32183858.mp3,這件代誌咱有需要參詳一下！
+common_voice_nan-tw_32183859.mp3,盤整
+common_voice_nan-tw_32183864.mp3,額單字
+common_voice_nan-tw_32183867.mp3,透光目鏡
+common_voice_nan-tw_32183871.mp3,講話無照理路
+common_voice_nan-tw_32206258.mp3,行行出狀元
+common_voice_nan-tw_32206259.mp3,想倒轉來
+common_voice_nan-tw_32206263.mp3,龜鹿二仙膠
+common_voice_nan-tw_32295564.mp3,大學口
+common_voice_nan-tw_32295576.mp3,食著羹
+common_voice_nan-tw_32295577.mp3,散赤
+common_voice_nan-tw_32295586.mp3,十二點摃十二下 
+common_voice_nan-tw_32295588.mp3,土城區
+common_voice_nan-tw_32295589.mp3,新宿
+common_voice_nan-tw_32295591.mp3,文魯彬
+common_voice_nan-tw_32295592.mp3,安和敦化路口
+common_voice_nan-tw_32295600.mp3,假鬼六怪
+common_voice_nan-tw_32295601.mp3,法式
+common_voice_nan-tw_32295603.mp3,戮璇
+common_voice_nan-tw_32295606.mp3,樹奶拭仔
+common_voice_nan-tw_32295607.mp3,埔源
+common_voice_nan-tw_32295608.mp3,耕耘
+common_voice_nan-tw_32295610.mp3,犁頭戴鼎
+common_voice_nan-tw_32295611.mp3,赤甲若瀝金
+common_voice_nan-tw_32295613.mp3,指甲剪仔
+common_voice_nan-tw_32295618.mp3,環南市場
+common_voice_nan-tw_32295619.mp3,菜花
+common_voice_nan-tw_32295620.mp3,蠓仔水
+common_voice_nan-tw_32295623.mp3,"一日討海，三日曝網 "
+common_voice_nan-tw_32295624.mp3,南州鄉
+common_voice_nan-tw_32295625.mp3,椅脊骿
+common_voice_nan-tw_32295626.mp3,日落申
+common_voice_nan-tw_32295630.mp3,日頭暗
+common_voice_nan-tw_32295632.mp3,麵包超人
+common_voice_nan-tw_32295634.mp3,鹿草
+common_voice_nan-tw_32295636.mp3,棕花水雞
+common_voice_nan-tw_32295638.mp3,登山鞋
+common_voice_nan-tw_32295645.mp3,講話袂畏寒
+common_voice_nan-tw_32295648.mp3,人面 
+common_voice_nan-tw_32295658.mp3,桃花心木道
+common_voice_nan-tw_32295659.mp3,皮材
+common_voice_nan-tw_32295906.mp3,旗山溪
+common_voice_nan-tw_32295910.mp3,鼎崁鼎
+common_voice_nan-tw_32295989.mp3,大坵園
+common_voice_nan-tw_32295992.mp3,標頭
+common_voice_nan-tw_32295994.mp3,楊梅
+common_voice_nan-tw_32295995.mp3,唄
+common_voice_nan-tw_32295996.mp3,塔志克
+common_voice_nan-tw_32295997.mp3,舊囉囉
+common_voice_nan-tw_32295998.mp3,全你的意見了了
+common_voice_nan-tw_32296000.mp3,死目毋願瞌
+common_voice_nan-tw_32296001.mp3,"門樓雖破，更鼓原在"
+common_voice_nan-tw_32296002.mp3,冰箱
+common_voice_nan-tw_32296013.mp3,生瓜
+common_voice_nan-tw_32296014.mp3,深坑
+common_voice_nan-tw_32296019.mp3,物流
+common_voice_nan-tw_32296020.mp3,魔女
+common_voice_nan-tw_32296021.mp3,"一言既出，駟馬難追 "
+common_voice_nan-tw_32296023.mp3,臭腥龜仔
+common_voice_nan-tw_32296029.mp3,主權爭議
+common_voice_nan-tw_32296030.mp3,公民投票
+common_voice_nan-tw_32296033.mp3,文華高中
+common_voice_nan-tw_32296035.mp3,呻食人
+common_voice_nan-tw_32296037.mp3,捲心白仔
+common_voice_nan-tw_32296038.mp3,咖哩咖咾
+common_voice_nan-tw_32296040.mp3,龍門國中
+common_voice_nan-tw_32296041.mp3,壓縮機
+common_voice_nan-tw_32296043.mp3,兇
+common_voice_nan-tw_32296044.mp3,放雨白
+common_voice_nan-tw_32296045.mp3,旗津區
+common_voice_nan-tw_32296046.mp3,鴟鴞批身
+common_voice_nan-tw_32296047.mp3,食藥仔愛配滾水
+common_voice_nan-tw_32297458.mp3,軟腦膜
+common_voice_nan-tw_32297460.mp3,阿香去買的香真芳
+common_voice_nan-tw_32297476.mp3,呵咾甲會觸舌
+common_voice_nan-tw_32297477.mp3,含鞭
+common_voice_nan-tw_32297478.mp3,徛哨
+common_voice_nan-tw_32297479.mp3,胸坎實實
+common_voice_nan-tw_32297851.mp3,通北街
+common_voice_nan-tw_32297852.mp3,大林鎮
+common_voice_nan-tw_32297988.mp3,十分佔九分九 
+common_voice_nan-tw_32297989.mp3,人地生疏 
+common_voice_nan-tw_32297992.mp3,"揀也揀，揀著一个賣龍眼"
+common_voice_nan-tw_32297998.mp3,頭城
+common_voice_nan-tw_32297999.mp3,真忝
+common_voice_nan-tw_32298000.mp3,你這改的成績有淡薄仔退步
+common_voice_nan-tw_32298002.mp3,新興
+common_voice_nan-tw_32298133.mp3,牢鼎
+common_voice_nan-tw_32298134.mp3,無影話
+common_voice_nan-tw_32298136.mp3,應米
+common_voice_nan-tw_32298143.mp3,老倒退
+common_voice_nan-tw_32298146.mp3,反向
+common_voice_nan-tw_32298150.mp3,活字
+common_voice_nan-tw_32298151.mp3,舟山路
+common_voice_nan-tw_32298156.mp3,耳口邊話
+common_voice_nan-tw_32298158.mp3,彰化銀行
+common_voice_nan-tw_32298163.mp3,曝焦
+common_voice_nan-tw_32298164.mp3,侵門踏戶
+common_voice_nan-tw_32298165.mp3,竹蜂
+common_voice_nan-tw_32298166.mp3,下大靜脈 
+common_voice_nan-tw_32298182.mp3,較是
+common_voice_nan-tw_32298184.mp3,規家伙仔
+common_voice_nan-tw_32298185.mp3,我的錢袋仔拍交落矣
+common_voice_nan-tw_32298186.mp3,柴較冷灶
+common_voice_nan-tw_32298210.mp3,術仔囝
+common_voice_nan-tw_32298213.mp3,伊是咧拍拳賣膏藥的
+common_voice_nan-tw_32302972.mp3,桃仔色
+common_voice_nan-tw_32302973.mp3,峇里島
+common_voice_nan-tw_32302982.mp3,掩門
+common_voice_nan-tw_32302984.mp3,景隆街
+common_voice_nan-tw_32302985.mp3,伸課球
+common_voice_nan-tw_32302986.mp3,刺鼠菇
+common_voice_nan-tw_32302988.mp3,著狂雨
+common_voice_nan-tw_32302989.mp3,性地
+common_voice_nan-tw_32302990.mp3,花東縱谷
+common_voice_nan-tw_32302991.mp3,南區
+common_voice_nan-tw_32302992.mp3,摩薩德
+common_voice_nan-tw_32302993.mp3,北勢溪
+common_voice_nan-tw_32302994.mp3,痟齣無大簿
+common_voice_nan-tw_32302996.mp3,儑目袂曉看三色
+common_voice_nan-tw_32302997.mp3,麟光
+common_voice_nan-tw_32302998.mp3,時機誠䆀
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+common_voice_nan-tw_38072749.mp3,泅水箍仔
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+common_voice_nan-tw_38072767.mp3,癡情有毒
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+common_voice_nan-tw_38072823.mp3,通氣貼布
+common_voice_nan-tw_38072824.mp3,上代先 
+common_voice_nan-tw_38072825.mp3,梳妝打扮
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+common_voice_nan-tw_38072835.mp3,散翸翸
+common_voice_nan-tw_38072836.mp3,笑笑仔哭
+common_voice_nan-tw_38072844.mp3,行健機
+common_voice_nan-tw_38072846.mp3,目袋
+common_voice_nan-tw_38072859.mp3,罪刑法定
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+common_voice_nan-tw_38072872.mp3,透濫的
+common_voice_nan-tw_38072882.mp3,袂曉見笑
+common_voice_nan-tw_38072883.mp3,逆乖
+common_voice_nan-tw_38072884.mp3,"三工傷趕啦，交袂出來"
+common_voice_nan-tw_38072886.mp3,四湖鄉
+common_voice_nan-tw_38072888.mp3,失格
+common_voice_nan-tw_38072892.mp3,統媒
+common_voice_nan-tw_38072893.mp3,毋過意
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+common_voice_nan-tw_38072910.mp3,莿仔埒
+common_voice_nan-tw_38072911.mp3,港闊水深
+common_voice_nan-tw_38072912.mp3,南方公園
+common_voice_nan-tw_38072922.mp3,世界第一等
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+common_voice_nan-tw_38072930.mp3,輸入法
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+common_voice_nan-tw_38072975.mp3,藍牙滑鼠
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+common_voice_nan-tw_38072986.mp3,潭子
+common_voice_nan-tw_38072994.mp3,毛管蟲
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+common_voice_nan-tw_38073012.mp3,譁譁叫
+common_voice_nan-tw_38073022.mp3,你若欲半䖙倒看電視
+common_voice_nan-tw_38073024.mp3,請逐家恬恬聽我講
+common_voice_nan-tw_38073029.mp3,做句
+common_voice_nan-tw_38073043.mp3,頂半晡
+common_voice_nan-tw_38073055.mp3,下城 
+common_voice_nan-tw_38073056.mp3,風範
+common_voice_nan-tw_38073063.mp3,食補較贏食苦
+common_voice_nan-tw_38073077.mp3,講三十三天外
+common_voice_nan-tw_38073078.mp3,烏骨雞的市價比肉雞仔較好
+common_voice_nan-tw_38073083.mp3,人情較薄信紙 
+common_voice_nan-tw_38073087.mp3,手機仔電動
+common_voice_nan-tw_38073090.mp3,簡化
+common_voice_nan-tw_38073091.mp3,鹿頸
+common_voice_nan-tw_38073096.mp3,下昏時 
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+common_voice_nan-tw_38073101.mp3,"毋驚虎有三支喙，只驚人有兩片心"
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+common_voice_nan-tw_38073116.mp3,是咧發爐
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+common_voice_nan-tw_38073171.mp3,冤家變親家
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+common_voice_nan-tw_38075360.mp3,三甲
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+common_voice_nan-tw_38076684.mp3,潭仔墘
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+common_voice_nan-tw_38076695.mp3,揻針
+common_voice_nan-tw_38076699.mp3,警官學校
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+common_voice_nan-tw_38076724.mp3,金面
+common_voice_nan-tw_38076725.mp3,煎餃
+common_voice_nan-tw_38076729.mp3,便椅
+common_voice_nan-tw_38076733.mp3,阿明
+common_voice_nan-tw_38076737.mp3,薄紙
+common_voice_nan-tw_38076738.mp3,光?
+common_voice_nan-tw_38076742.mp3,三層埔
+common_voice_nan-tw_38076743.mp3,驚險
+common_voice_nan-tw_38076747.mp3,荷蘭豬
+common_voice_nan-tw_38076753.mp3,相捏尻川
+common_voice_nan-tw_38076756.mp3,紲倩
+common_voice_nan-tw_38076760.mp3,顯影
+common_voice_nan-tw_38076766.mp3,外埔
+common_voice_nan-tw_38076767.mp3,擋皮仔
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+common_voice_nan-tw_38076772.mp3,假現實
+common_voice_nan-tw_38076776.mp3,鑽筆
+common_voice_nan-tw_38076780.mp3,索取
+common_voice_nan-tw_38076788.mp3,苦工
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+common_voice_nan-tw_38076807.mp3,毋成丁
+common_voice_nan-tw_38076814.mp3,"伊做生理了錢，改途去食人的頭路"
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+common_voice_nan-tw_38076831.mp3,書經
+common_voice_nan-tw_38076835.mp3,頂埔
+common_voice_nan-tw_38076837.mp3,煮暗頓
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+common_voice_nan-tw_38076851.mp3,你按呢放捒某囝敢著？
+common_voice_nan-tw_38076853.mp3,歪頭菜
+common_voice_nan-tw_38076860.mp3,"伊毋但毋驚，閣敢佮人㧣"
+common_voice_nan-tw_38076861.mp3,爍
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+common_voice_nan-tw_38076875.mp3,還在
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+common_voice_nan-tw_38076899.mp3,關渡
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+common_voice_nan-tw_38076952.mp3,鐵支
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+common_voice_nan-tw_38076997.mp3,真自在
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+common_voice_nan-tw_38077016.mp3,可以
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+common_voice_nan-tw_38084974.mp3,大抐一場 
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+common_voice_nan-tw_38085243.mp3,"釘是釘，鉚是鉚"
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+common_voice_nan-tw_38085284.mp3,"講來講去，總講一句，是你毋著"
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+common_voice_nan-tw_38085344.mp3,興德路
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+common_voice_nan-tw_38086051.mp3,黃喙鶯仔
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+common_voice_nan-tw_38086137.mp3,行路行壁邊，做事較大天
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+common_voice_nan-tw_38086167.mp3,有兜有捎
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+common_voice_nan-tw_38086266.mp3,冷水坑
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+common_voice_nan-tw_38086272.mp3,轉載
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+common_voice_nan-tw_38086405.mp3,落格
+common_voice_nan-tw_38086410.mp3,見笑袂死，慣勢著好
+common_voice_nan-tw_38086436.mp3,光華國小
+common_voice_nan-tw_38086447.mp3,猴囡仔
+common_voice_nan-tw_38086448.mp3,"也好氣，也好笑 "
+common_voice_nan-tw_38086449.mp3,團買
+common_voice_nan-tw_38086453.mp3,詔安
+common_voice_nan-tw_38086465.mp3,藥頭仔
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+common_voice_nan-tw_38086486.mp3,蟮尪仔
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+common_voice_nan-tw_38086489.mp3,望春風
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+common_voice_nan-tw_38086493.mp3,第一等
+common_voice_nan-tw_38086501.mp3,會通
+common_voice_nan-tw_38086508.mp3,熬糖
+common_voice_nan-tw_38086529.mp3,復興橋
+common_voice_nan-tw_38086532.mp3,敗機
+common_voice_nan-tw_38086536.mp3,嬰仔愛坐奶母車
+common_voice_nan-tw_38086565.mp3,臺南市兒童科學館
+common_voice_nan-tw_38086583.mp3,一尾魚 
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+common_voice_nan-tw_38086596.mp3,牛約
+common_voice_nan-tw_38086597.mp3,三文魚 
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+common_voice_nan-tw_38088400.mp3,箍樂?
+common_voice_nan-tw_38088407.mp3,簿記員
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+common_voice_nan-tw_38097270.mp3,大好額 
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+common_voice_nan-tw_38165587.mp3,線跡
+common_voice_nan-tw_38165626.mp3,議量
+common_voice_nan-tw_38165667.mp3,國風
+common_voice_nan-tw_38165699.mp3,棺柴頭
+common_voice_nan-tw_38177703.mp3,海上的人
+common_voice_nan-tw_38177723.mp3,斷糧
+common_voice_nan-tw_38177731.mp3,漚客
+common_voice_nan-tw_38177790.mp3,華安
+common_voice_nan-tw_38177792.mp3,六尾紅
+common_voice_nan-tw_38179520.mp3,愛而蘭
\ No newline at end of file
diff --git a/eval_results.json b/eval_results.json
new file mode 100644
index 0000000000000000000000000000000000000000..7f75ee257a43f799fbe4c676606045d4f5ade4c8
--- /dev/null
+++ b/eval_results.json
@@ -0,0 +1,9 @@
+{
+    "eval_cer": 134.28571428571428,
+    "eval_loss": 2.4461984634399414,
+    "eval_model_preparation_time": 0.0121,
+    "eval_runtime": 134.5644,
+    "eval_samples_per_second": 2.229,
+    "eval_steps_per_second": 1.115,
+    "eval_wer": 87.5
+}
\ No newline at end of file
diff --git a/generation_config.json b/generation_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..6a9f9f1c0895f8a20f22bde887e64ddf6eb70e74
--- /dev/null
+++ b/generation_config.json
@@ -0,0 +1,224 @@
+{
+  "begin_suppress_tokens": [
+    220,
+    50257
+  ],
+  "bos_token_id": 50257,
+  "decoder_start_token_id": 50258,
+  "eos_token_id": 50257,
+  "forced_decoder_ids": [
+    [
+      1,
+      null
+    ],
+    [
+      2,
+      50359
+    ],
+    [
+      3,
+      50363
+    ]
+  ],
+  "is_multilingual": true,
+  "lang_to_id": {
+    "<|af|>": 50327,
+    "<|am|>": 50334,
+    "<|ar|>": 50272,
+    "<|as|>": 50350,
+    "<|az|>": 50304,
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+    "<|cy|>": 50297,
+    "<|da|>": 50285,
+    "<|de|>": 50261,
+    "<|el|>": 50281,
+    "<|en|>": 50259,
+    "<|es|>": 50262,
+    "<|et|>": 50307,
+    "<|eu|>": 50310,
+    "<|fa|>": 50300,
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+    "<|fo|>": 50338,
+    "<|fr|>": 50265,
+    "<|gl|>": 50319,
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+    "<|haw|>": 50352,
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+    "<|he|>": 50279,
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+    "<|km|>": 50323,
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+    "<|ms|>": 50282,
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+    "<|ru|>": 50263,
+    "<|sa|>": 50344,
+    "<|sd|>": 50332,
+    "<|si|>": 50322,
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+    "<|sl|>": 50305,
+    "<|sn|>": 50324,
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+    "<|sw|>": 50318,
+    "<|ta|>": 50287,
+    "<|te|>": 50299,
+    "<|tg|>": 50331,
+    "<|th|>": 50289,
+    "<|tk|>": 50341,
+    "<|tl|>": 50348,
+    "<|tr|>": 50268,
+    "<|tt|>": 50351,
+    "<|uk|>": 50280,
+    "<|ur|>": 50290,
+    "<|uz|>": 50337,
+    "<|vi|>": 50278,
+    "<|yi|>": 50335,
+    "<|yo|>": 50325,
+    "<|zh|>": 50260
+  },
+  "max_initial_timestamp_index": 1,
+  "max_length": 448,
+  "no_timestamps_token_id": 50363,
+  "pad_token_id": 50257,
+  "suppress_tokens": [
+    1,
+    2,
+    7,
+    8,
+    9,
+    10,
+    14,
+    25,
+    26,
+    27,
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+    50254,
+    50258,
+    50358,
+    50359,
+    50360,
+    50361,
+    50362
+  ],
+  "task_to_id": {
+    "transcribe": 50359,
+    "translate": 50358
+  },
+  "transformers_version": "4.27.0.dev0"
+}
diff --git a/label.txt b/label.txt
new file mode 100644
index 0000000000000000000000000000000000000000..f0d67e12e89c1d936c3652f9c885bb80f16b408f
--- /dev/null
+++ b/label.txt
@@ -0,0 +1 @@
+label_str=['新 庄 仔 ', '剪 仔 ', '袂 煞 ', '艋 舺 大 道 ', '我 伨 你 ', '心 肝 較 大 牛 肺 ', '北 環 路 ', '孔 廟 文 化 園 區 ', '低 黐 麵 粉 ', '家 宿 ', '目 睭 瞌 瞌 講 白 賊 ', '客 運 的 ', '太 陽 能 ', '掩 來 差 去 ', '漢 堡 ', '林 濁 水 ', '兵 庫 縣 ', '育 飼 ', '閣 較 ', '三 言 兩 語 ', '煎 包 ', '恩 主 公 廟 ', '欲 ', '著 衰 ', '菜 頭 鬃 ', '相 拄 頭 ', '檢 體 送 到 台 北 了 後 ', '參 加 ', '無 錢 講 無 話 ', '二 重 埔 ', '下 痟 ', '方 向 燈 ', '控 山 ', '刀 喙 ', '嫁 翁 ', '害 物 ', '大 家 口 眾 ', '水 沖 泚 ', '曾 柏 元 ', '閩 東 語 ', '慣 練 ', '誠 濟 人 ', '富 岡 ', '心 智 圖 ', '崁 耳 仔 帽 ', '觀 光 局 ', '一 空 掠 兩 尾 ', '積 穗 ', '查 某 演 員 ', '少 年 的 你 著 聽 老 歲 仔 苦 勸 ', '交 接 ', '這 是 ', '少 昊 ', '早 暗 愛 會 記 得 疊 衫 ', '孤 鉤 股 ', '咖 啡 機 ', '相 趁 相 喊 ', '鴨 鵤 ', '中 山 堂 埕 ', '北 埔 ', '西 雙 版 納 ', '橋 仔 頭 糖 廠 ', '碎 溶 溶 ', '古 早 人 煎 草 藥 仔 ', '跩 跩 ', '雷 電 球 ', '共 人 欺 負 ', '訂 閱 ', '無 出 門 ', '五 形 配 搭 ', '西 亞 圖 ', '實 頭 ', '赤 山 堡 ', '自 販 機 ', '摔 大 眠 ', '朝 聖 地 ', '瘦 枝 落 葉 ', '臆 測 ', '一 文 不 值 ', '聚 焦 ', '挽 菜 ', '鹹 硞 硞 ', '嘉 義 農 林 學 校 ', '膨 椅 予 囡 仔 翸 歹 去 矣 ', '徐 匯 中 學 ', '大 房 食 細 房 ', '月 娘 圍 箍 ', '柴 塊 仔 ', '茹 藏 藏 ', '銅 仙 ', '你 捌 伊 無 ', '四 伨 ', '耳 桮 ', '淡 水 區 ', '番 薑 ', '北 新 竹 ', '江 北 ', '袂 曉 衰 ', '刜 ', '煞 了 代 ', '輾 天 車 ', '拍 鐵 的 ', '這 粒 蚶 仔 袂 開 喙 莫 食 較 好 ', '阮 朋 友 ', '豆 菜 底 的 ', '僥 揣 ', '一 清 二 楚 ', '機 械 學 習 ', '市 場 ', '你 咧 大 聲 啥 啦 ', '內 挖 仔 ', '大 肚 魚 仔 ', '命 冊 ', '心 事 無 人 知 我 鬱 甲 足 艱 苦 的 ', '台 灣 查 埔 人 ', '洗 手 膏 ', '解 鬱 ', '養 生 奶 ', '欱 ', '予 人 想 攏 無 ', '官 田 區 ', '走 馬 路 ', '干 焦 會 ', '貯 水 池 ', '進 前 ', '我 想 欲 去 便 所 ', '東 寧 ', '頭 家 媽 ', '倒 退 鱟 ', '欶 一 下 仔 大 氣 ', '三 八 叮 咚 ', '長 安 西 路 ', '目 睭 屎 ', '向 天 ', '毛 管 炎 ', '倚 桌 ', '故 事 ', '臭 骨 相 ', '太 空 船 ', '我 佮 人 講 話 ', '七 扣 八 扣 ', '犬 ', '飛 天 掛 桷 ', '喙 齒 佮 喙 舌 也 會 相 觸 著 ', '相 扯 ', '盤 查 某 ', '同 苦 同 甜 ', '福 德 正 神 ', '烏 嬸 黃 鸝 ', '電 風 柱 仔 ', '黃 鸝 ', '拳 頭 高 高 在 上 ', '底 蒂 ', '天 公 有 帶 著 ', '暴 風 雪 ', '加 加 ', '豆 乾 糋 ', '麥 寮 ', '澳 ', '騙 請 害 餓 ', '多 林 ', '伊 上 愛 食 炕 肉 ', '濱 海 沙 崙 ', '源 泉 ', '林 森 南 路 車 行 地 下 道 ', '你 是 家 己 開 業 抑 是 予 人 倩 ', '螺 陽 ', '馬 槽 橋 ', '崩 心 ', '歸 仁 區 ', '臺 灣 閩 南 語 常 用 詞 辭 典 ', '冊 苴 仔 ', '彼 隻 貓 仔 的 爪 仔 真 利 ', '精 髓 ', '雙 園 國 中 ', '評 審 老 師 ', '分 局 前 ', '大 直 街 ', '你 袂 䆀 ', '古 亭 國 中 ', '講 話 有 力 頭 ', '可 持 續 性 ', '大 面 ', '南 庄 鄉 ', '懷 生 國 中 ', '枝 骨 仔 ', '好 歹 命 敢 是 註 好 好 的 這 真 歹 講 ', '走 袂 去 ', '含 茄 ', '爆 料 ', '鯪 鯉 王 ', '菜 蟲 食 菜 菜 跤 死 ', '你 共 我 記 咧 ', '等 袂 赴 ', '間 接 稅 ', '山 豬 毛 鄉 ', '必 箍 銀 ', '邱 厝 ', '繃 帶 ', '龜 山 ', '闔 雺 ', '準 煞 ', '碎 糊 糊 ', '蔥 仔 餅 ', '尿 蜚 ', '我 佮 伊 無 親 ', '媽 當 奴 ', '城 內 ', '焐 冰 ', '恆 光 橋 ', '景 尾 ', '烏 白 沐 ', '霸 凌 ', '臭 鼬 ', '站 贏 頭 ', '寶 斗 厝 ', '派 大 星 ', '山 仙 楂 ', '阿 拉 伯 ', '許 世 楷 ', '顏 智 ', '師 翁 ', '金 華 國 中 ', '獨 乙 ', '北 京 語 ', '袂 堪 得 煩 ', '興 寧 街 ', '大 直 高 中 ', '損 ', '吊 樹 猴 ', '彰 化 縣 ', '塗 庫 莊 ', '搬 電 影 ', '哈 韓 ', '三 擺 兩 擺 ', '搤 塗 ', '永 春 厝 ', '橫 山 ', '河 上 洲 ', '正 斡 ', '紅 頭 嬰 仔 ', '誠 濟 ', '食 甲 九 十 外 歲 矣 閣 誠 勇 健 ', '烏 焦 蛇 ', '猶 未 傀 儡 囉 ', '硩 傷 ', '情 單 字 ', '小 坵 村 ', '身 軀 ', '散 甲 割 喉 無 血 ', '灰 窯 坑 ', '打 鼓 山 ', '密 號 ', '中 央 警 察 大 學 ', '阮 小 弟 的 風 吹 牢 佇 樹 仔 頂 ', '目 加 溜 ', '果 子 蠅 ', '王 育 德 紀 念 館 ', '姨 仔 ', '愛 綴 倯 ', '驚 死 閣 欲 看 目 連 ', '草 橄 仔 ', '雷 公 摃 死 ', '棕 蓑 猫 ', '魚 卵 醬 ', '熱 水 爐 ', '老 母 ', '瘴 氣 ', '瓦 sian', '太 麻 里 鄉 ', '毋 成 人 ', '對 牛 彈 琴 ', '較 加 嘛 會 感 冒 ', '禿 額 ', '的 確 的 代 誌 才 通 講 ', '媠 媠 無 十 全 䆀 䆀 無 加 圇 ', '一 例 一 休 ', '番 仔 挖 ', '唌 人 睏 ', '搦 屎 搦 尿 ', '大 旗 尾 ', '深 層 學 習 ', '軟 殼 ', '袂 較 大 ', '紅 奶 仔 草 ', '燉 蹄 ', '擬 徒 ', '屁 話 ', '環 狀 線 ', '袂 當 算 擺 ', '韓 國 戲 ', '十 全 大 補 ', '壽 具 ', '洗 衫 粉 ', '弄 井 ', '好 市 草 ', '峨 眉 街 ', '講 著 ', '鬥 序 碼 ', '試 看 覓 ']
\ No newline at end of file
diff --git a/local_data_run_speech_recognition_seq2seq_streaming.py b/local_data_run_speech_recognition_seq2seq_streaming.py
new file mode 100644
index 0000000000000000000000000000000000000000..08ffdb0fb53f13a368d813360442ab07f2927fa7
--- /dev/null
+++ b/local_data_run_speech_recognition_seq2seq_streaming.py
@@ -0,0 +1,620 @@
+#!/usr/bin/env python
+# coding=utf-8
+# Copyright 2022 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+Fine-tuning the library models for sequence to sequence speech recognition
+with 🤗 Datasets' streaming mode.
+"""
+# You can also adapt this script for your own sequence to sequence speech
+# recognition task. Pointers for this are left as comments.
+import ryNormText
+import logging
+import os
+import re
+import string
+import sys
+from dataclasses import dataclass, field
+from typing import Any, Dict, List, Optional, Union
+
+import datasets
+import torch
+from datasets import IterableDatasetDict, interleave_datasets, load_dataset
+from torch.utils.data import IterableDataset
+
+import evaluate
+import transformers
+from transformers import (
+    AutoConfig,
+    AutoFeatureExtractor,
+    AutoModelForSpeechSeq2Seq,
+    AutoProcessor,
+    AutoTokenizer,
+    HfArgumentParser,
+    Seq2SeqTrainer,
+    Seq2SeqTrainingArguments,
+    TrainerCallback,
+    set_seed,
+)
+from transformers.trainer_pt_utils import IterableDatasetShard
+from transformers.trainer_utils import get_last_checkpoint, is_main_process
+from transformers.utils import check_min_version, send_example_telemetry
+from transformers.utils.versions import require_version
+
+# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
+check_min_version("4.25.0.dev0")
+
+require_version("datasets>=1.18.2", "To fix: pip install -r examples/pytorch/speech-recognition/requirements.txt")
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class ModelArguments:
+    """
+    Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
+    """
+
+    model_name_or_path: str = field(
+        metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}
+    )
+    config_name: Optional[str] = field(
+        default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
+    )
+    tokenizer_name: Optional[str] = field(
+        default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
+    )
+    feature_extractor_name: Optional[str] = field(
+        default=None, metadata={"help": "feature extractor name or path if not the same as model_name"}
+    )
+    cache_dir: Optional[str] = field(
+        default=None,
+        metadata={"help": "Where to store the pretrained models downloaded from huggingface.co"},
+    )
+    use_fast_tokenizer: bool = field(
+        default=True,
+        metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."},
+    )
+    model_revision: str = field(
+        default="main",
+        metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."},
+    )
+    use_auth_token: bool = field(
+        default=False,
+        metadata={
+            "help": (
+                "Will use the token generated when running `huggingface-cli login` (necessary to use this script "
+                "with private models)."
+            )
+        },
+    )
+    freeze_feature_encoder: bool = field(
+        default=True, metadata={"help": "Whether to freeze the feature encoder layers of the model."}
+    )
+    freeze_encoder: bool = field(
+        default=False, metadata={"help": "Whether to freeze the entire encoder of the seq2seq model."}
+    )
+    forced_decoder_ids: List[List[int]] = field(
+        default=None,
+        metadata={
+            "help": (
+                "A list of pairs of integers which indicates a mapping from generation indices to token indices "
+                "that will be forced before sampling. For example, [[0, 123]] means the first generated token "
+                "will always be a token of index 123."
+            )
+        },
+    )
+    suppress_tokens: List[int] = field(
+        default=None, metadata={"help": "A list of tokens that will be suppressed at generation."}
+    )
+    model_index_name: str = field(default=None, metadata={"help": "Pretty name for the model card."})
+
+
+@dataclass
+class DataTrainingArguments:
+    """
+    Arguments pertaining to what data we are going to input our model for training and eval.
+    """
+
+    dataset_name: str = field(
+        default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."}
+    )
+    dataset_config_name: Optional[str] = field(
+        default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}
+    )
+    text_column: Optional[str] = field(
+        default=None,
+        metadata={"help": "The name of the column in the datasets containing the full texts (for summarization)."},
+    )
+    max_train_samples: Optional[int] = field(
+        default=None,
+        metadata={
+            "help": (
+                "For debugging purposes or quicker training, truncate the number of training examples to this "
+                "value if set."
+            )
+        },
+    )
+    max_eval_samples: Optional[int] = field(
+        default=None,
+        metadata={
+            "help": (
+                "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
+                "value if set."
+            )
+        },
+    )
+    audio_column_name: str = field(
+        default="audio",
+        metadata={"help": "The name of the dataset column containing the audio data. Defaults to 'audio'"},
+    )
+    text_column_name: str = field(
+        default="text",
+        metadata={"help": "The name of the dataset column containing the text data. Defaults to 'text'"},
+    )
+    max_duration_in_seconds: float = field(
+        default=20.0,
+        metadata={
+            "help": (
+                "Truncate audio files that are longer than `max_duration_in_seconds` seconds to"
+                " 'max_duration_in_seconds`"
+            )
+        },
+    )
+    min_duration_in_seconds: float = field(
+        default=0.0, metadata={"help": "Filter audio files that are shorter than `min_duration_in_seconds` seconds"}
+    )
+    train_split_name: str = field(
+        default="train",
+        metadata={
+            "help": "The name of the training data set split to use (via the datasets library). Defaults to 'train'"
+        },
+    )
+    eval_split_name: str = field(
+        default="test",
+        metadata={
+            "help": "The name of the training data set split to use (via the datasets library). Defaults to 'train'"
+        },
+    )
+    do_lower_case: bool = field(
+        default=False,
+        metadata={"help": "Whether the target text should be lower cased."},
+    )
+    do_remove_punctuation: bool = field(
+        default=False,
+        metadata={"help": "Whether the target text should be striped of punctuation."},
+    )
+    do_normalize_eval: bool = field(
+        default=True,
+        metadata={"help": "Whether to normalise the references and predictions in the eval WER calculation."},
+    )
+    language: str = field(
+        default=None,
+        metadata={
+            "help": (
+                "Language for multilingual fine-tuning. This argument should be set for multilingual fine-tuning "
+                "only. For English speech recognition, it should be set to `None`."
+            )
+        },
+    )
+    task: str = field(
+        default="transcribe",
+        metadata={"help": "Task, either `transcribe` for speech recognition or `translate` for speech translation."},
+    )
+    shuffle_buffer_size: Optional[int] = field(
+        default=500,
+        metadata={
+            "help": (
+                "The number of streamed examples to download before shuffling them. The large the buffer, "
+                "the closer it is to real offline shuffling."
+            )
+        },
+    )
+
+
+@dataclass
+class DataCollatorSpeechSeq2SeqWithPadding:
+    """
+    Data collator that will dynamically pad the inputs received.
+    Args:
+        processor ([`WhisperProcessor`])
+            The processor used for processing the data.
+        decoder_start_token_id (`int`)
+            The begin-of-sentence of the decoder.
+    """
+
+    processor: Any
+    decoder_start_token_id: int
+
+    def __call__(self, features: List[Dict[str, Union[List[int], torch.Tensor]]]) -> Dict[str, torch.Tensor]:
+        # split inputs and labels since they have to be of different lengths and need
+        # different padding methods
+        model_input_name = self.processor.model_input_names[0]
+        input_features = [{model_input_name: feature[model_input_name]} for feature in features]
+        label_features = [{"input_ids": feature["labels"]} for feature in features]
+
+        batch = self.processor.feature_extractor.pad(input_features, return_tensors="pt")
+
+        labels_batch = self.processor.tokenizer.pad(label_features, return_tensors="pt")
+
+        # replace padding with -100 to ignore loss correctly
+        labels = labels_batch["input_ids"].masked_fill(labels_batch.attention_mask.ne(1), -100)
+
+        # if bos token is appended in previous tokenization step,
+        # cut bos token here as it's append later anyways
+        if (labels[:, 0] == self.decoder_start_token_id).all().cpu().item():
+            labels = labels[:, 1:]
+
+        batch["labels"] = labels
+
+        return batch
+
+
+def load_streaming_dataset(dataset_name, dataset_config_name, split="train", **kwargs):
+    """
+    Utility function to load a dataset in streaming mode. For datasets with multiple splits,
+    each split is loaded individually and then splits combined by taking alternating examples from
+    each (interleaving).
+    """
+    # 移除 use_auth_token 參數
+    if 'use_auth_token' in kwargs:
+        del kwargs['use_auth_token']
+    if "+" in split:
+        # load multiple splits separated by the `+` symbol with streaming mode
+        dataset_splits = [
+            load_dataset(dataset_name, dataset_config_name, split=split_name, streaming=True, **kwargs)
+            for split_name in split.split("+")
+        ]
+        # interleave multiple splits to form one dataset
+        interleaved_dataset = interleave_datasets(dataset_splits)
+        return interleaved_dataset
+    else:
+        # load a single split *with* streaming mode
+        dataset = load_dataset(dataset_name, dataset_config_name, data_dir="data", split=split, streaming=True, **kwargs)
+        return dataset
+
+
+def main():
+    # 1. Parse input arguments
+    # See all possible arguments in src/transformers/training_args.py
+    # or by passing the --help flag to this script.
+    # We now keep distinct sets of args, for a cleaner separation of concerns.
+    parser = HfArgumentParser((ModelArguments, DataTrainingArguments, Seq2SeqTrainingArguments))
+
+    if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
+        # If we pass only one argument to the script and it's the path to a json file,
+        # let's parse it to get our arguments.
+        model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
+    else:
+        model_args, data_args, training_args = parser.parse_args_into_dataclasses()
+
+    # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
+    # information sent is the one passed as arguments along with your Python/PyTorch versions.
+    send_example_telemetry("run_speech_recognition_seq2seq_streaming", model_args, data_args)
+
+    # 2. Setup logging
+    logging.basicConfig(
+        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
+        datefmt="%m/%d/%Y %H:%M:%S",
+        handlers=[logging.StreamHandler(sys.stdout)],
+    )
+    log_level = training_args.get_process_log_level()
+    logger.setLevel(log_level)
+    datasets.utils.logging.set_verbosity(log_level)
+    transformers.utils.logging.set_verbosity(log_level)
+    transformers.utils.logging.enable_default_handler()
+    transformers.utils.logging.enable_explicit_format()
+
+    logger.setLevel(logging.INFO if is_main_process(training_args.local_rank) else logging.WARN)
+
+    # Log on each process the small summary:
+    logger.warning(
+        f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
+        f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}"
+    )
+    logger.info(f"Training/evaluation parameters {training_args}")
+
+    # Set the verbosity to info of the Transformers logger (on main process only):
+    if is_main_process(training_args.local_rank):
+        transformers.utils.logging.set_verbosity_info()
+    logger.info("Training/evaluation parameters %s", training_args)
+
+    # 3. Detecting last checkpoint and eventually continue from last checkpoint
+    last_checkpoint = None
+    if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
+        last_checkpoint = get_last_checkpoint(training_args.output_dir)
+        if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:
+            raise ValueError(
+                f"Output directory ({training_args.output_dir}) already exists and is not empty. "
+                "Use --overwrite_output_dir to overcome."
+            )
+        elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
+            logger.info(
+                f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
+                "the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
+            )
+
+    # Set seed before initializing model.
+    set_seed(training_args.seed)
+
+    # 4. Load dataset
+    raw_datasets = IterableDatasetDict()
+
+    if training_args.do_train:
+        raw_datasets["train"] = load_streaming_dataset(
+            data_args.dataset_name,
+            data_args.dataset_config_name,
+            split=data_args.train_split_name,
+            use_auth_token=True if model_args.use_auth_token else None,
+        )
+
+    if training_args.do_eval:
+        raw_datasets["eval"] = load_streaming_dataset(
+            data_args.dataset_name,
+            data_args.dataset_config_name,
+            split=data_args.eval_split_name,
+            use_auth_token=True if model_args.use_auth_token else None,
+        )
+
+    raw_datasets_features = list(next(iter(raw_datasets.values())).features.keys())
+
+    if data_args.audio_column_name not in raw_datasets_features:
+        raise ValueError(
+            f"--audio_column_name '{data_args.audio_column_name}' not found in dataset '{data_args.dataset_name}'. "
+            "Make sure to set `--audio_column_name` to the correct audio column - one of "
+            f"{', '.join(raw_datasets_features)}."
+        )
+
+    if data_args.text_column_name not in raw_datasets_features:
+        raise ValueError(
+            f"--text_column_name {data_args.text_column_name} not found in dataset '{data_args.dataset_name}'. "
+            "Make sure to set `--text_column_name` to the correct text column - one of "
+            f"{', '.join(raw_datasets_features)}."
+        )
+
+    # 5. Load pretrained model, tokenizer, and feature extractor
+    #
+    # Distributed training:
+    # The .from_pretrained methods guarantee that only one local process can concurrently
+    config = AutoConfig.from_pretrained(
+        model_args.config_name if model_args.config_name else model_args.model_name_or_path,
+        cache_dir=model_args.cache_dir,
+        revision=model_args.model_revision,
+        use_auth_token=True if model_args.use_auth_token else None,
+    )
+
+    config.update({"forced_decoder_ids": model_args.forced_decoder_ids, "suppress_tokens": model_args.suppress_tokens})
+
+    feature_extractor = AutoFeatureExtractor.from_pretrained(
+        model_args.feature_extractor_name if model_args.feature_extractor_name else model_args.model_name_or_path,
+        cache_dir=model_args.cache_dir,
+        revision=model_args.model_revision,
+        use_auth_token=True if model_args.use_auth_token else None,
+    )
+    tokenizer = AutoTokenizer.from_pretrained(
+        model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
+        cache_dir=model_args.cache_dir,
+        use_fast=model_args.use_fast_tokenizer,
+        revision=model_args.model_revision,
+        use_auth_token=True if model_args.use_auth_token else None,
+    )
+    model = AutoModelForSpeechSeq2Seq.from_pretrained(
+        model_args.model_name_or_path,
+        config=config,
+        cache_dir=model_args.cache_dir,
+        revision=model_args.model_revision,
+        use_auth_token=True if model_args.use_auth_token else None,
+    )
+
+    if model.config.decoder_start_token_id is None:
+        raise ValueError("Make sure that `config.decoder_start_token_id` is correctly defined")
+
+    if model_args.freeze_feature_encoder:
+        model.freeze_feature_encoder()
+
+    if model_args.freeze_encoder:
+        model.freeze_encoder()
+        model.model.encoder.gradient_checkpointing = False
+
+    if data_args.language is not None:
+        # We only need to set the task id when the language is specified (i.e. in a multilingual setting)
+        tokenizer.set_prefix_tokens(language=data_args.language, task=data_args.task)
+
+    # 6. Resample speech dataset if necessary
+    dataset_sampling_rate = next(iter(raw_datasets.values())).features[data_args.audio_column_name].sampling_rate
+    if dataset_sampling_rate != feature_extractor.sampling_rate:
+        raw_datasets = raw_datasets.cast_column(
+            data_args.audio_column_name, datasets.features.Audio(sampling_rate=feature_extractor.sampling_rate)
+        )
+
+    # 7. Preprocessing the datasets.
+    # We need to read the audio files as arrays and tokenize the targets.
+    max_input_length = data_args.max_duration_in_seconds * feature_extractor.sampling_rate
+    min_input_length = data_args.min_duration_in_seconds * feature_extractor.sampling_rate
+    audio_column_name = data_args.audio_column_name
+    text_column_name = data_args.text_column_name
+    model_input_name = feature_extractor.model_input_names[0]
+    do_lower_case = data_args.do_lower_case
+    do_remove_punctuation = data_args.do_remove_punctuation
+
+    punctuation_to_remove = string.punctuation.replace("'", "")  # don't remove apostrophes
+    punctuation_to_remove_regex = f"[{''.join(punctuation_to_remove)}]"
+
+    if data_args.max_train_samples is not None:
+        raw_datasets["train"] = raw_datasets["train"].take(data_args.max_train_samples)
+
+    if data_args.max_eval_samples is not None:
+        raw_datasets["eval"] = raw_datasets["eval"].take(data_args.max_eval_samples)
+
+    def prepare_dataset(batch):
+        # process audio
+        sample = batch[audio_column_name]
+        inputs = feature_extractor(sample["array"], sampling_rate=sample["sampling_rate"])
+        # process audio length
+        batch[model_input_name] = inputs.get(model_input_name)[0]
+        batch["input_length"] = len(sample["array"])
+
+        # process targets (only char no rome)
+        input_str = batch[text_column_name].lower().split('（')[0] if do_lower_case else batch[text_column_name].split('（')[0]
+        
+        if do_remove_punctuation:
+            input_str = re.sub(punctuation_to_remove_regex, " ", input_str).strip()
+            input_str = re.sub("\s\s+", " ", input_str)
+        batch["labels"] = tokenizer(input_str).input_ids
+        return batch
+
+    with training_args.main_process_first(desc="dataset map pre-processing"):
+        vectorized_datasets = raw_datasets.map(
+            prepare_dataset,
+            remove_columns=raw_datasets_features,
+        ).with_format("torch")
+
+        if training_args.do_train:
+            vectorized_datasets["train"] = vectorized_datasets["train"].shuffle(
+                buffer_size=data_args.shuffle_buffer_size,
+                seed=training_args.seed,
+            )
+
+    # filter training data that is shorter than min_input_length or longer than
+    # max_input_length
+    def is_audio_in_length_range(length):
+        return min_input_length < length < max_input_length
+
+    if training_args.do_train:
+        vectorized_datasets["train"] = vectorized_datasets["train"].filter(
+            is_audio_in_length_range,
+            input_columns=["input_length"],
+        )
+
+    # 8. Load Metric
+    cer_metric = evaluate.load("cer")
+    wer_metric = evaluate.load("wer")
+    do_normalize_eval = data_args.do_normalize_eval
+
+    def compute_metrics(pred):
+        pred_ids = pred.predictions
+
+        pred.label_ids[pred.label_ids == -100] = tokenizer.pad_token_id
+
+        pred_str = tokenizer.batch_decode(pred_ids, skip_special_tokens=True, normalize=do_normalize_eval)
+        # we do not want to group tokens when computing the metrics
+        label_str = tokenizer.batch_decode(pred.label_ids, skip_special_tokens=True, normalize=do_normalize_eval)
+        cer = 100 * cer_metric.compute(predictions=pred_str, references=label_str)
+        pred_str = [ryNormText.separ_char_word(x) for x in pred_str]
+        label_str = [ryNormText.separ_char_word(x) for x in label_str]
+        p = open('pred.txt','w')
+        p.write(f'{pred_str=}')
+        p.close()
+        p = open('label.txt','w')
+        p.write(f'{label_str=}')
+        p.close()
+        wer = 100 * wer_metric.compute(predictions=pred_str, references=label_str)
+        return {"wer": wer, "cer": cer}
+
+    # 9. Create a single speech processor
+    if is_main_process(training_args.local_rank):
+        # save feature extractor, tokenizer and config
+        feature_extractor.save_pretrained(training_args.output_dir)
+        tokenizer.save_pretrained(training_args.output_dir)
+        config.save_pretrained(training_args.output_dir)
+
+    processor = AutoProcessor.from_pretrained(training_args.output_dir)
+
+    # 10. Define data collator
+    data_collator = DataCollatorSpeechSeq2SeqWithPadding(
+        processor=processor,
+        decoder_start_token_id=model.config.decoder_start_token_id,
+    )
+
+    # 11. Configure Trainer
+    # Trainer callback to reinitialise and reshuffle the streamable datasets at the beginning of each epoch
+    class ShuffleCallback(TrainerCallback):
+        def on_epoch_begin(self, args, state, control, train_dataloader, **kwargs):
+            if isinstance(train_dataloader.dataset, IterableDatasetShard):
+                pass  # set_epoch() is handled by the Trainer
+            elif isinstance(train_dataloader.dataset, IterableDataset):
+                train_dataloader.dataset.set_epoch(train_dataloader.dataset._epoch + 1)
+
+    # Initialize Trainer
+    trainer = Seq2SeqTrainer(
+        model=model,
+        args=training_args,
+        train_dataset=vectorized_datasets["train"] if training_args.do_train else None,
+        eval_dataset=vectorized_datasets["eval"] if training_args.do_eval else None,
+        tokenizer=feature_extractor,
+        data_collator=data_collator,
+        compute_metrics=compute_metrics if training_args.predict_with_generate else None,
+        callbacks=[ShuffleCallback()],
+    )
+
+    # 12. Training
+    if training_args.do_train:
+        checkpoint = None
+        if training_args.resume_from_checkpoint is not None:
+            checkpoint = training_args.resume_from_checkpoint
+        elif last_checkpoint is not None:
+            checkpoint = last_checkpoint
+        train_result = trainer.train(resume_from_checkpoint=checkpoint)
+        trainer.save_model()  # Saves the feature extractor too for easy upload
+
+        metrics = train_result.metrics
+        if data_args.max_train_samples:
+            metrics["train_samples"] = data_args.max_train_samples
+        trainer.log_metrics("train", metrics)
+        trainer.save_metrics("train", metrics)
+        trainer.save_state()
+
+    # 13. Evaluation
+    results = {}
+    if training_args.do_eval:
+        logger.info("*** Evaluate ***")
+        metrics = trainer.evaluate(
+
+            metric_key_prefix="eval",
+            max_length=training_args.generation_max_length,
+            num_beams=training_args.generation_num_beams,
+        )
+        if data_args.max_eval_samples:
+            metrics["eval_samples"] = data_args.max_eval_samples
+
+        trainer.log_metrics("eval", metrics)
+        trainer.save_metrics("eval", metrics)
+
+    # 14. Write Training Stats
+    kwargs = {
+        "finetuned_from": model_args.model_name_or_path,
+        "tasks": "automatic-speech-recognition",
+        "tags": "whisper-event",
+    }
+    if data_args.dataset_name is not None:
+        kwargs["dataset_tags"] = data_args.dataset_name
+        if data_args.dataset_config_name is not None:
+            kwargs["dataset"] = f"{data_args.dataset_name} {data_args.dataset_config_name}"
+        else:
+            kwargs["dataset"] = data_args.dataset_name
+        if "common_voice" in data_args.dataset_name:
+            kwargs["language"] = data_args.dataset_config_name
+        if model_args.model_index_name is not None:
+            kwargs["model_name"] = model_args.model_index_name
+
+    if training_args.push_to_hub:
+        trainer.push_to_hub(**kwargs)
+    else:
+        trainer.create_model_card(**kwargs)
+
+    return results
+
+
+if __name__ == "__main__":
+    main()
diff --git a/local_data_run_speech_recognition_seq2seq_streaming_first_evaluate.py b/local_data_run_speech_recognition_seq2seq_streaming_first_evaluate.py
new file mode 100644
index 0000000000000000000000000000000000000000..763572812b6ef769c0009da11162e7b9b8499873
--- /dev/null
+++ b/local_data_run_speech_recognition_seq2seq_streaming_first_evaluate.py
@@ -0,0 +1,635 @@
+#!/usr/bin/env python
+# coding=utf-8
+# Copyright 2022 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+Fine-tuning the library models for sequence to sequence speech recognition
+with 🤗 Datasets' streaming mode.
+"""
+# You can also adapt this script for your own sequence to sequence speech
+# recognition task. Pointers for this are left as comments.
+import ryNormText
+import logging
+import os
+import re
+import string
+import sys
+from dataclasses import dataclass, field
+from typing import Any, Dict, List, Optional, Union
+
+import datasets
+import torch
+from datasets import IterableDatasetDict, interleave_datasets, load_dataset
+from torch.utils.data import IterableDataset
+
+import evaluate
+import transformers
+from transformers import (
+    AutoConfig,
+    AutoFeatureExtractor,
+    AutoModelForSpeechSeq2Seq,
+    AutoProcessor,
+    AutoTokenizer,
+    HfArgumentParser,
+    Seq2SeqTrainer,
+    Seq2SeqTrainingArguments,
+    TrainerCallback,
+    set_seed,
+)
+from transformers.trainer_pt_utils import IterableDatasetShard
+from transformers.trainer_utils import get_last_checkpoint, is_main_process
+from transformers.utils import check_min_version, send_example_telemetry
+from transformers.utils.versions import require_version
+
+# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
+check_min_version("4.25.0.dev0")
+
+require_version("datasets>=1.18.2", "To fix: pip install -r examples/pytorch/speech-recognition/requirements.txt")
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class ModelArguments:
+    """
+    Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
+    """
+
+    model_name_or_path: str = field(
+        metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}
+    )
+    config_name: Optional[str] = field(
+        default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
+    )
+    tokenizer_name: Optional[str] = field(
+        default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
+    )
+    feature_extractor_name: Optional[str] = field(
+        default=None, metadata={"help": "feature extractor name or path if not the same as model_name"}
+    )
+    cache_dir: Optional[str] = field(
+        default=None,
+        metadata={"help": "Where to store the pretrained models downloaded from huggingface.co"},
+    )
+    use_fast_tokenizer: bool = field(
+        default=True,
+        metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."},
+    )
+    model_revision: str = field(
+        default="main",
+        metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."},
+    )
+    use_auth_token: bool = field(
+        default=False,
+        metadata={
+            "help": (
+                "Will use the token generated when running `huggingface-cli login` (necessary to use this script "
+                "with private models)."
+            )
+        },
+    )
+    freeze_feature_encoder: bool = field(
+        default=True, metadata={"help": "Whether to freeze the feature encoder layers of the model."}
+    )
+    freeze_encoder: bool = field(
+        default=False, metadata={"help": "Whether to freeze the entire encoder of the seq2seq model."}
+    )
+    forced_decoder_ids: List[List[int]] = field(
+        default=None,
+        metadata={
+            "help": (
+                "A list of pairs of integers which indicates a mapping from generation indices to token indices "
+                "that will be forced before sampling. For example, [[0, 123]] means the first generated token "
+                "will always be a token of index 123."
+            )
+        },
+    )
+    suppress_tokens: List[int] = field(
+        default=None, metadata={"help": "A list of tokens that will be suppressed at generation."}
+    )
+    model_index_name: str = field(default=None, metadata={"help": "Pretty name for the model card."})
+
+
+@dataclass
+class DataTrainingArguments:
+    """
+    Arguments pertaining to what data we are going to input our model for training and eval.
+    """
+
+    dataset_name: str = field(
+        default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."}
+    )
+    dataset_config_name: Optional[str] = field(
+        default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}
+    )
+    text_column: Optional[str] = field(
+        default=None,
+        metadata={"help": "The name of the column in the datasets containing the full texts (for summarization)."},
+    )
+    max_train_samples: Optional[int] = field(
+        default=None,
+        metadata={
+            "help": (
+                "For debugging purposes or quicker training, truncate the number of training examples to this "
+                "value if set."
+            )
+        },
+    )
+    max_eval_samples: Optional[int] = field(
+        default=None,
+        metadata={
+            "help": (
+                "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
+                "value if set."
+            )
+        },
+    )
+    audio_column_name: str = field(
+        default="audio",
+        metadata={"help": "The name of the dataset column containing the audio data. Defaults to 'audio'"},
+    )
+    text_column_name: str = field(
+        default="text",
+        metadata={"help": "The name of the dataset column containing the text data. Defaults to 'text'"},
+    )
+    max_duration_in_seconds: float = field(
+        default=20.0,
+        metadata={
+            "help": (
+                "Truncate audio files that are longer than `max_duration_in_seconds` seconds to"
+                " 'max_duration_in_seconds`"
+            )
+        },
+    )
+    min_duration_in_seconds: float = field(
+        default=0.0, metadata={"help": "Filter audio files that are shorter than `min_duration_in_seconds` seconds"}
+    )
+    train_split_name: str = field(
+        default="train",
+        metadata={
+            "help": "The name of the training data set split to use (via the datasets library). Defaults to 'train'"
+        },
+    )
+    eval_split_name: str = field(
+        default="test",
+        metadata={
+            "help": "The name of the training data set split to use (via the datasets library). Defaults to 'train'"
+        },
+    )
+    do_lower_case: bool = field(
+        default=False,
+        metadata={"help": "Whether the target text should be lower cased."},
+    )
+    do_remove_punctuation: bool = field(
+        default=False,
+        metadata={"help": "Whether the target text should be striped of punctuation."},
+    )
+    do_normalize_eval: bool = field(
+        default=True,
+        metadata={"help": "Whether to normalise the references and predictions in the eval WER calculation."},
+    )
+    language: str = field(
+        default=None,
+        metadata={
+            "help": (
+                "Language for multilingual fine-tuning. This argument should be set for multilingual fine-tuning "
+                "only. For English speech recognition, it should be set to `None`."
+            )
+        },
+    )
+    task: str = field(
+        default="transcribe",
+        metadata={"help": "Task, either `transcribe` for speech recognition or `translate` for speech translation."},
+    )
+    shuffle_buffer_size: Optional[int] = field(
+        default=500,
+        metadata={
+            "help": (
+                "The number of streamed examples to download before shuffling them. The large the buffer, "
+                "the closer it is to real offline shuffling."
+            )
+        },
+    )
+
+
+@dataclass
+class DataCollatorSpeechSeq2SeqWithPadding:
+    """
+    Data collator that will dynamically pad the inputs received.
+    Args:
+        processor ([`WhisperProcessor`])
+            The processor used for processing the data.
+        decoder_start_token_id (`int`)
+            The begin-of-sentence of the decoder.
+    """
+
+    processor: Any
+    decoder_start_token_id: int
+
+    def __call__(self, features: List[Dict[str, Union[List[int], torch.Tensor]]]) -> Dict[str, torch.Tensor]:
+        # split inputs and labels since they have to be of different lengths and need
+        # different padding methods
+        model_input_name = self.processor.model_input_names[0]
+        input_features = [{model_input_name: feature[model_input_name]} for feature in features]
+        label_features = [{"input_ids": feature["labels"]} for feature in features]
+
+        batch = self.processor.feature_extractor.pad(input_features, return_tensors="pt")
+
+        labels_batch = self.processor.tokenizer.pad(label_features, return_tensors="pt")
+
+        # replace padding with -100 to ignore loss correctly
+        labels = labels_batch["input_ids"].masked_fill(labels_batch.attention_mask.ne(1), -100)
+
+        # if bos token is appended in previous tokenization step,
+        # cut bos token here as it's append later anyways
+        if (labels[:, 0] == self.decoder_start_token_id).all().cpu().item():
+            labels = labels[:, 1:]
+
+        batch["labels"] = labels
+
+        return batch
+
+
+def load_streaming_dataset(dataset_name, dataset_config_name, split="train", **kwargs):
+    """
+    Utility function to load a dataset in streaming mode. For datasets with multiple splits,
+    each split is loaded individually and then splits combined by taking alternating examples from
+    each (interleaving).
+    """
+    # 移除 use_auth_token 參數
+    if 'use_auth_token' in kwargs:
+        del kwargs['use_auth_token']
+    if "+" in split:
+        # load multiple splits separated by the `+` symbol with streaming mode
+        dataset_splits = [
+            load_dataset(dataset_name, dataset_config_name, split=split_name, streaming=True, **kwargs)
+            for split_name in split.split("+")
+        ]
+        # interleave multiple splits to form one dataset
+        interleaved_dataset = interleave_datasets(dataset_splits)
+        return interleaved_dataset
+    else:
+        # load a single split *with* streaming mode
+        dataset = load_dataset(dataset_name, dataset_config_name, data_dir="data_com", split=split, streaming=True, **kwargs)
+        return dataset
+
+
+def main():
+    # 1. Parse input arguments
+    # See all possible arguments in src/transformers/training_args.py
+    # or by passing the --help flag to this script.
+    # We now keep distinct sets of args, for a cleaner separation of concerns.
+    parser = HfArgumentParser((ModelArguments, DataTrainingArguments, Seq2SeqTrainingArguments))
+
+    if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
+        # If we pass only one argument to the script and it's the path to a json file,
+        # let's parse it to get our arguments.
+        model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
+    else:
+        model_args, data_args, training_args = parser.parse_args_into_dataclasses()
+
+    # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
+    # information sent is the one passed as arguments along with your Python/PyTorch versions.
+    send_example_telemetry("run_speech_recognition_seq2seq_streaming", model_args, data_args)
+
+    # 2. Setup logging
+    logging.basicConfig(
+        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
+        datefmt="%m/%d/%Y %H:%M:%S",
+        handlers=[logging.StreamHandler(sys.stdout)],
+    )
+    log_level = training_args.get_process_log_level()
+    logger.setLevel(log_level)
+    datasets.utils.logging.set_verbosity(log_level)
+    transformers.utils.logging.set_verbosity(log_level)
+    transformers.utils.logging.enable_default_handler()
+    transformers.utils.logging.enable_explicit_format()
+
+    logger.setLevel(logging.INFO if is_main_process(training_args.local_rank) else logging.WARN)
+
+    # Log on each process the small summary:
+    logger.warning(
+        f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
+        f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}"
+    )
+    logger.info(f"Training/evaluation parameters {training_args}")
+
+    # Set the verbosity to info of the Transformers logger (on main process only):
+    if is_main_process(training_args.local_rank):
+        transformers.utils.logging.set_verbosity_info()
+    logger.info("Training/evaluation parameters %s", training_args)
+
+    # 3. Detecting last checkpoint and eventually continue from last checkpoint
+    last_checkpoint = None
+    if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
+        last_checkpoint = get_last_checkpoint(training_args.output_dir)
+        if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:
+            raise ValueError(
+                f"Output directory ({training_args.output_dir}) already exists and is not empty. "
+                "Use --overwrite_output_dir to overcome."
+            )
+        elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
+            logger.info(
+                f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
+                "the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
+            )
+
+    # Set seed before initializing model.
+    set_seed(training_args.seed)
+
+    # 4. Load dataset
+    raw_datasets = IterableDatasetDict()
+
+    if training_args.do_train:
+        raw_datasets["train"] = load_streaming_dataset(
+            data_args.dataset_name,
+            data_args.dataset_config_name,
+            split=data_args.train_split_name,
+            use_auth_token=True if model_args.use_auth_token else None,
+        )
+
+    if training_args.do_eval:
+        raw_datasets["eval"] = load_streaming_dataset(
+            data_args.dataset_name,
+            data_args.dataset_config_name,
+            split=data_args.eval_split_name,
+            use_auth_token=True if model_args.use_auth_token else None,
+        )
+
+    raw_datasets_features = list(next(iter(raw_datasets.values())).features.keys())
+
+    if data_args.audio_column_name not in raw_datasets_features:
+        raise ValueError(
+            f"--audio_column_name '{data_args.audio_column_name}' not found in dataset '{data_args.dataset_name}'. "
+            "Make sure to set `--audio_column_name` to the correct audio column - one of "
+            f"{', '.join(raw_datasets_features)}."
+        )
+
+    if data_args.text_column_name not in raw_datasets_features:
+        raise ValueError(
+            f"--text_column_name {data_args.text_column_name} not found in dataset '{data_args.dataset_name}'. "
+            "Make sure to set `--text_column_name` to the correct text column - one of "
+            f"{', '.join(raw_datasets_features)}."
+        )
+
+    # 5. Load pretrained model, tokenizer, and feature extractor
+    #
+    # Distributed training:
+    # The .from_pretrained methods guarantee that only one local process can concurrently
+    config = AutoConfig.from_pretrained(
+        model_args.config_name if model_args.config_name else model_args.model_name_or_path,
+        cache_dir=model_args.cache_dir,
+        revision=model_args.model_revision,
+        use_auth_token=True if model_args.use_auth_token else None,
+    )
+
+    config.update({"forced_decoder_ids": model_args.forced_decoder_ids, "suppress_tokens": model_args.suppress_tokens})
+
+    feature_extractor = AutoFeatureExtractor.from_pretrained(
+        model_args.feature_extractor_name if model_args.feature_extractor_name else model_args.model_name_or_path,
+        cache_dir=model_args.cache_dir,
+        revision=model_args.model_revision,
+        use_auth_token=True if model_args.use_auth_token else None,
+    )
+    tokenizer = AutoTokenizer.from_pretrained(
+        model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
+        cache_dir=model_args.cache_dir,
+        use_fast=model_args.use_fast_tokenizer,
+        revision=model_args.model_revision,
+        use_auth_token=True if model_args.use_auth_token else None,
+    )
+    model = AutoModelForSpeechSeq2Seq.from_pretrained(
+        model_args.model_name_or_path,
+        config=config,
+        cache_dir=model_args.cache_dir,
+        revision=model_args.model_revision,
+        use_auth_token=True if model_args.use_auth_token else None,
+    )
+
+    if model.config.decoder_start_token_id is None:
+        raise ValueError("Make sure that `config.decoder_start_token_id` is correctly defined")
+
+    if model_args.freeze_feature_encoder:
+        model.freeze_feature_encoder()
+
+    if model_args.freeze_encoder:
+        model.freeze_encoder()
+        model.model.encoder.gradient_checkpointing = False
+
+    if data_args.language is not None:
+        # We only need to set the task id when the language is specified (i.e. in a multilingual setting)
+        tokenizer.set_prefix_tokens(language=data_args.language, task=data_args.task)
+
+    # 6. Resample speech dataset if necessary
+    dataset_sampling_rate = next(iter(raw_datasets.values())).features[data_args.audio_column_name].sampling_rate
+    if dataset_sampling_rate != feature_extractor.sampling_rate:
+        raw_datasets = raw_datasets.cast_column(
+            data_args.audio_column_name, datasets.features.Audio(sampling_rate=feature_extractor.sampling_rate)
+        )
+
+    # 7. Preprocessing the datasets.
+    # We need to read the audio files as arrays and tokenize the targets.
+    max_input_length = data_args.max_duration_in_seconds * feature_extractor.sampling_rate
+    min_input_length = data_args.min_duration_in_seconds * feature_extractor.sampling_rate
+    audio_column_name = data_args.audio_column_name
+    text_column_name = data_args.text_column_name
+    model_input_name = feature_extractor.model_input_names[0]
+    do_lower_case = data_args.do_lower_case
+    do_remove_punctuation = data_args.do_remove_punctuation
+
+    punctuation_to_remove = string.punctuation.replace("'", "")  # don't remove apostrophes
+    punctuation_to_remove_regex = f"[{''.join(punctuation_to_remove)}]"
+
+    if data_args.max_train_samples is not None:
+        raw_datasets["train"] = raw_datasets["train"].take(data_args.max_train_samples)
+
+    if data_args.max_eval_samples is not None:
+        raw_datasets["eval"] = raw_datasets["eval"].take(data_args.max_eval_samples)
+
+    def prepare_dataset(batch):
+        # process audio
+        sample = batch[audio_column_name]
+        inputs = feature_extractor(sample["array"], sampling_rate=sample["sampling_rate"])
+        # process audio length
+        batch[model_input_name] = inputs.get(model_input_name)[0]
+        batch["input_length"] = len(sample["array"])
+
+        # process targets (only char no rome)
+        input_str = batch[text_column_name].lower().split('（')[0] if do_lower_case else batch[text_column_name].split('（')[0]
+        
+        if do_remove_punctuation:
+            input_str = re.sub(punctuation_to_remove_regex, " ", input_str).strip()
+            input_str = re.sub("\s\s+", " ", input_str)
+        batch["labels"] = tokenizer(input_str).input_ids
+        return batch
+
+    with training_args.main_process_first(desc="dataset map pre-processing"):
+        vectorized_datasets = raw_datasets.map(
+            prepare_dataset,
+            remove_columns=raw_datasets_features,
+        ).with_format("torch")
+
+        if training_args.do_train:
+            vectorized_datasets["train"] = vectorized_datasets["train"].shuffle(
+                buffer_size=data_args.shuffle_buffer_size,
+                seed=training_args.seed,
+            )
+
+    # filter training data that is shorter than min_input_length or longer than
+    # max_input_length
+    def is_audio_in_length_range(length):
+        return min_input_length < length < max_input_length
+
+    if training_args.do_train:
+        vectorized_datasets["train"] = vectorized_datasets["train"].filter(
+            is_audio_in_length_range,
+            input_columns=["input_length"],
+        )
+
+    # 8. Load Metric
+    cer_metric = evaluate.load("cer")
+    wer_metric = evaluate.load("wer")
+    do_normalize_eval = data_args.do_normalize_eval
+
+    def compute_metrics(pred):
+        pred_ids = pred.predictions
+
+        pred.label_ids[pred.label_ids == -100] = tokenizer.pad_token_id
+
+        pred_str = tokenizer.batch_decode(pred_ids, skip_special_tokens=True, normalize=do_normalize_eval)
+        # we do not want to group tokens when computing the metrics
+        label_str = tokenizer.batch_decode(pred.label_ids, skip_special_tokens=True, normalize=do_normalize_eval)
+        cer = 100 * cer_metric.compute(predictions=pred_str, references=label_str)
+        pred_str = [ryNormText.separ_char_word(x) for x in pred_str]
+        label_str = [ryNormText.separ_char_word(x) for x in label_str]
+        p = open('pred.txt','w')
+        p.write(f'{pred_str=}')
+        p.close()
+        p = open('label.txt','w')
+        p.write(f'{label_str=}')
+        p.close()
+        wer = 100 * wer_metric.compute(predictions=pred_str, references=label_str)
+        return {"wer": wer, "cer": cer}
+
+    # 9. Create a single speech processor
+    if is_main_process(training_args.local_rank):
+        # save feature extractor, tokenizer and config
+        feature_extractor.save_pretrained(training_args.output_dir)
+        tokenizer.save_pretrained(training_args.output_dir)
+        config.save_pretrained(training_args.output_dir)
+
+    processor = AutoProcessor.from_pretrained(training_args.output_dir)
+
+    # 10. Define data collator
+    data_collator = DataCollatorSpeechSeq2SeqWithPadding(
+        processor=processor,
+        decoder_start_token_id=model.config.decoder_start_token_id,
+    )
+
+    # 11. Configure Trainer
+    # Trainer callback to reinitialise and reshuffle the streamable datasets at the beginning of each epoch
+    class ShuffleCallback(TrainerCallback):
+        def on_epoch_begin(self, args, state, control, train_dataloader, **kwargs):
+            if isinstance(train_dataloader.dataset, IterableDatasetShard):
+                pass  # set_epoch() is handled by the Trainer
+            elif isinstance(train_dataloader.dataset, IterableDataset):
+                train_dataloader.dataset.set_epoch(train_dataloader.dataset._epoch + 1)
+
+    # Initialize Trainer
+    trainer = Seq2SeqTrainer(
+        model=model,
+        args=training_args,
+        train_dataset=vectorized_datasets["train"] if training_args.do_train else None,
+        eval_dataset=vectorized_datasets["eval"] if training_args.do_eval else None,
+        tokenizer=feature_extractor,
+        data_collator=data_collator,
+        compute_metrics=compute_metrics if training_args.predict_with_generate else None,
+        callbacks=[ShuffleCallback()],
+    )
+
+    # 12. Training
+    if training_args.do_train:
+        checkpoint = None
+        if training_args.resume_from_checkpoint is not None:
+            checkpoint = training_args.resume_from_checkpoint
+        elif last_checkpoint is not None:
+            checkpoint = last_checkpoint
+    
+        # Evaluate before training starts
+        if training_args.do_eval:
+            logger.info("*** Evaluate before training ***")
+            metrics = trainer.evaluate(
+                metric_key_prefix="eval",
+                max_length=training_args.generation_max_length,
+                num_beams=training_args.generation_num_beams,
+            )
+            if data_args.max_eval_samples:
+                metrics["eval_samples"] = data_args.max_eval_samples
+    
+            trainer.log_metrics("eval", metrics)
+            trainer.save_metrics("eval", metrics)
+    
+        train_result = trainer.train(resume_from_checkpoint=checkpoint)
+        trainer.save_model()  # Saves the feature extractor too for easy upload
+    
+        metrics = train_result.metrics
+        if data_args.max_train_samples:
+            metrics["train_samples"] = data_args.max_train_samples
+        trainer.log_metrics("train", metrics)
+        trainer.save_metrics("train", metrics)
+        trainer.save_state()
+
+    # 13. Evaluation
+    results = {}
+    if training_args.do_eval:
+        logger.info("*** Evaluate ***")
+        metrics = trainer.evaluate(
+
+            metric_key_prefix="eval",
+            max_length=training_args.generation_max_length,
+            num_beams=training_args.generation_num_beams,
+        )
+        if data_args.max_eval_samples:
+            metrics["eval_samples"] = data_args.max_eval_samples
+
+        trainer.log_metrics("eval", metrics)
+        trainer.save_metrics("eval", metrics)
+
+    # 14. Write Training Stats
+    kwargs = {
+        "finetuned_from": model_args.model_name_or_path,
+        "tasks": "automatic-speech-recognition",
+        "tags": "whisper-event",
+    }
+    if data_args.dataset_name is not None:
+        kwargs["dataset_tags"] = data_args.dataset_name
+        if data_args.dataset_config_name is not None:
+            kwargs["dataset"] = f"{data_args.dataset_name} {data_args.dataset_config_name}"
+        else:
+            kwargs["dataset"] = data_args.dataset_name
+        if "common_voice" in data_args.dataset_name:
+            kwargs["language"] = data_args.dataset_config_name
+        if model_args.model_index_name is not None:
+            kwargs["model_name"] = model_args.model_index_name
+
+    if training_args.push_to_hub:
+        trainer.push_to_hub(**kwargs)
+    else:
+        trainer.create_model_card(**kwargs)
+
+    return results
+
+
+if __name__ == "__main__":
+    main()
diff --git a/merges.txt b/merges.txt
new file mode 100644
index 0000000000000000000000000000000000000000..6038932a2a1f09a66991b1c2adae0d14066fa29e
--- /dev/null
+++ b/merges.txt
@@ -0,0 +1,50001 @@
+#version: 0.2
+Ġ t
+Ġ a
+Ġt h
+i n
+e r
+Ġ w
+Ġ s
+o u
+Ġth e
+r e
+o n
+a t
+e n
+Ġ c
+i t
+i s
+Ġ b
+n d
+Ġ d
+Ġ m
+Ġ h
+Ġ o
+in g
+e s
+Ġ p
+Ġt o
+a n
+Ġ f
+o r
+l l
+Ġ I
+Ġ l
+Ġ y
+a r
+Ġ g
+Ġy ou
+e d
+Ġa nd
+Ġ in
+Ġo f
+a s
+Ġ n
+o m
+i c
+Ġth at
+u s
+e t
+v e
+a l
+o w
+l e
+Ġ is
+Ġ e
+Ġ it
+o t
+' s
+Ġb e
+i on
+Ġ T
+Ġw h
+Ġ A
+en t
+Ġ S
+Ġ re
+a y
+Ġw e
+Ġ on
+er e
+Ġh a
+u t
+a c
+i d
+i g
+o s
+k e
+v er
+i m
+Ġ Ð
+ĠT h
+a m
+a ll
+Ġf or
+e l
+c h
+r o
+Ġth is
+Ġs t
+Ġ W
+Ġ u
+a d
+ou t
+i r
+l d
+c t
+Ġ k
+i f
+Ġg o
+. .
+Ð ¾
+it h
+l y
+h t
+q u
+Ġ -
+Ġd o
+Ġ j
+Ġha ve
+Ġ B
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diff --git a/model.safetensors b/model.safetensors
new file mode 100644
index 0000000000000000000000000000000000000000..60fb280037c973da6d48a9fdbb164f664887bf53
--- /dev/null
+++ b/model.safetensors
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:93b27f283c1dc9a73cd221799e1e072145880641a52e8a2279ae3d24c220ab54
+size 3055544304
diff --git a/normalizer.json b/normalizer.json
new file mode 100644
index 0000000000000000000000000000000000000000..dd6ae819ad738ac1a546e9f9282ef325c33b9ea0
--- /dev/null
+++ b/normalizer.json
@@ -0,0 +1,1742 @@
+{
+  "accessorise": "accessorize",
+  "accessorised": "accessorized",
+  "accessorises": "accessorizes",
+  "accessorising": "accessorizing",
+  "acclimatisation": "acclimatization",
+  "acclimatise": "acclimatize",
+  "acclimatised": "acclimatized",
+  "acclimatises": "acclimatizes",
+  "acclimatising": "acclimatizing",
+  "accoutrements": "accouterments",
+  "aeon": "eon",
+  "aeons": "eons",
+  "aerogramme": "aerogram",
+  "aerogrammes": "aerograms",
+  "aeroplane": "airplane",
+  "aeroplanes": "airplanes",
+  "aesthete": "esthete",
+  "aesthetes": "esthetes",
+  "aesthetic": "esthetic",
+  "aesthetically": "esthetically",
+  "aesthetics": "esthetics",
+  "aetiology": "etiology",
+  "ageing": "aging",
+  "aggrandisement": "aggrandizement",
+  "agonise": "agonize",
+  "agonised": "agonized",
+  "agonises": "agonizes",
+  "agonising": "agonizing",
+  "agonisingly": "agonizingly",
+  "almanack": "almanac",
+  "almanacks": "almanacs",
+  "aluminium": "aluminum",
+  "amortisable": "amortizable",
+  "amortisation": "amortization",
+  "amortisations": "amortizations",
+  "amortise": "amortize",
+  "amortised": "amortized",
+  "amortises": "amortizes",
+  "amortising": "amortizing",
+  "amphitheatre": "amphitheater",
+  "amphitheatres": "amphitheaters",
+  "anaemia": "anemia",
+  "anaemic": "anemic",
+  "anaesthesia": "anesthesia",
+  "anaesthetic": "anesthetic",
+  "anaesthetics": "anesthetics",
+  "anaesthetise": "anesthetize",
+  "anaesthetised": "anesthetized",
+  "anaesthetises": "anesthetizes",
+  "anaesthetising": "anesthetizing",
+  "anaesthetist": "anesthetist",
+  "anaesthetists": "anesthetists",
+  "anaesthetize": "anesthetize",
+  "anaesthetized": "anesthetized",
+  "anaesthetizes": "anesthetizes",
+  "anaesthetizing": "anesthetizing",
+  "analogue": "analog",
+  "analogues": "analogs",
+  "analyse": "analyze",
+  "analysed": "analyzed",
+  "analyses": "analyzes",
+  "analysing": "analyzing",
+  "anglicise": "anglicize",
+  "anglicised": "anglicized",
+  "anglicises": "anglicizes",
+  "anglicising": "anglicizing",
+  "annualised": "annualized",
+  "antagonise": "antagonize",
+  "antagonised": "antagonized",
+  "antagonises": "antagonizes",
+  "antagonising": "antagonizing",
+  "apologise": "apologize",
+  "apologised": "apologized",
+  "apologises": "apologizes",
+  "apologising": "apologizing",
+  "appal": "appall",
+  "appals": "appalls",
+  "appetiser": "appetizer",
+  "appetisers": "appetizers",
+  "appetising": "appetizing",
+  "appetisingly": "appetizingly",
+  "arbour": "arbor",
+  "arbours": "arbors",
+  "archaeologically": "archeologically",
+  "archaeologist": "archeologist",
+  "archaeologists": "archeologists",
+  "archaeology": "archeology</span>",
+  "archeological": "archaeological",
+  "ardour": "ardor",
+  "armour": "armor",
+  "armoured": "armored",
+  "armourer": "armorer",
+  "armourers": "armorers",
+  "armouries": "armories",
+  "armoury": "armory",
+  "artefact": "artifact",
+  "artefacts": "artifacts",
+  "authorise": "authorize",
+  "authorised": "authorized",
+  "authorises": "authorizes",
+  "authorising": "authorizing",
+  "axe": "ax",
+  "backpedalled": "backpedaled",
+  "backpedalling": "backpedaling",
+  "bannister": "banister",
+  "bannisters": "banisters",
+  "baptise": "baptize",
+  "baptised": "baptized",
+  "baptises": "baptizes",
+  "baptising": "baptizing",
+  "bastardise": "bastardize",
+  "bastardised": "bastardized",
+  "bastardises": "bastardizes",
+  "bastardising": "bastardizing",
+  "battleax": "battleaxe",
+  "baulk": "balk",
+  "baulked": "balked",
+  "baulking": "balking",
+  "baulks": "balks",
+  "bedevilled": "bedeviled",
+  "bedevilling": "bedeviling",
+  "behaviour": "behavior",
+  "behavioural": "behavioral",
+  "behaviourism": "behaviorism",
+  "behaviourist": "behaviorist",
+  "behaviourists": "behaviorists",
+  "behaviours": "behaviors",
+  "behove": "behoove",
+  "behoved": "behooved",
+  "behoves": "behooves",
+  "bejewelled": "bejeweled",
+  "belabour": "belabor",
+  "belaboured": "belabored",
+  "belabouring": "belaboring",
+  "belabours": "belabors",
+  "bevelled": "beveled",
+  "bevvies": "bevies",
+  "bevvy": "bevy",
+  "biassed": "biased",
+  "biassing": "biasing",
+  "bingeing": "binging",
+  "bougainvillaea": "bougainvillea",
+  "bougainvillaeas": "bougainvilleas",
+  "bowdlerise": "bowdlerize",
+  "bowdlerised": "bowdlerized",
+  "bowdlerises": "bowdlerizes",
+  "bowdlerising": "bowdlerizing",
+  "breathalyse": "breathalyze",
+  "breathalysed": "breathalyzed",
+  "breathalyser": "breathalyzer",
+  "breathalysers": "breathalyzers",
+  "breathalyses": "breathalyzes",
+  "breathalysing": "breathalyzing",
+  "brutalise": "brutalize",
+  "brutalised": "brutalized",
+  "brutalises": "brutalizes",
+  "brutalising": "brutalizing",
+  "busses": "buses",
+  "bussing": "busing",
+  "caesarean": "cesarean",
+  "caesareans": "cesareans",
+  "calibre": "caliber",
+  "calibres": "calibers",
+  "calliper": "caliper",
+  "callipers": "calipers",
+  "callisthenics": "calisthenics",
+  "canalise": "canalize",
+  "canalised": "canalized",
+  "canalises": "canalizes",
+  "canalising": "canalizing",
+  "cancelation": "cancellation",
+  "cancelations": "cancellations",
+  "cancelled": "canceled",
+  "cancelling": "canceling",
+  "candour": "candor",
+  "cannibalise": "cannibalize",
+  "cannibalised": "cannibalized",
+  "cannibalises": "cannibalizes",
+  "cannibalising": "cannibalizing",
+  "canonise": "canonize",
+  "canonised": "canonized",
+  "canonises": "canonizes",
+  "canonising": "canonizing",
+  "capitalise": "capitalize",
+  "capitalised": "capitalized",
+  "capitalises": "capitalizes",
+  "capitalising": "capitalizing",
+  "caramelise": "caramelize",
+  "caramelised": "caramelized",
+  "caramelises": "caramelizes",
+  "caramelising": "caramelizing",
+  "carbonise": "carbonize",
+  "carbonised": "carbonized",
+  "carbonises": "carbonizes",
+  "carbonising": "carbonizing",
+  "carolled": "caroled",
+  "carolling": "caroling",
+  "catalogue": "catalog",
+  "catalogued": "cataloged",
+  "catalogues": "catalogs",
+  "cataloguing": "cataloging",
+  "catalyse": "catalyze",
+  "catalysed": "catalyzed",
+  "catalyses": "catalyzes",
+  "catalysing": "catalyzing",
+  "categorise": "categorize",
+  "categorised": "categorized",
+  "categorises": "categorizes",
+  "categorising": "categorizing",
+  "cauterise": "cauterize",
+  "cauterised": "cauterized",
+  "cauterises": "cauterizes",
+  "cauterising": "cauterizing",
+  "cavilled": "caviled",
+  "cavilling": "caviling",
+  "centigramme": "centigram",
+  "centigrammes": "centigrams",
+  "centilitre": "centiliter",
+  "centilitres": "centiliters",
+  "centimetre": "centimeter",
+  "centimetres": "centimeters",
+  "centralise": "centralize",
+  "centralised": "centralized",
+  "centralises": "centralizes",
+  "centralising": "centralizing",
+  "centre": "center",
+  "centred": "centered",
+  "centrefold": "centerfold",
+  "centrefolds": "centerfolds",
+  "centrepiece": "centerpiece",
+  "centrepieces": "centerpieces",
+  "centres": "centers",
+  "channelled": "channeled",
+  "channelling": "channeling",
+  "characterise": "characterize",
+  "characterised": "characterized",
+  "characterises": "characterizes",
+  "characterising": "characterizing",
+  "cheque": "check",
+  "chequebook": "checkbook",
+  "chequebooks": "checkbooks",
+  "chequered": "checkered",
+  "cheques": "checks",
+  "chilli": "chili",
+  "chimaera": "chimera",
+  "chimaeras": "chimeras",
+  "chiselled": "chiseled",
+  "chiselling": "chiseling",
+  "circularise": "circularize",
+  "circularised": "circularized",
+  "circularises": "circularizes",
+  "circularising": "circularizing",
+  "civilise": "civilize",
+  "civilised": "civilized",
+  "civilises": "civilizes",
+  "civilising": "civilizing",
+  "clamour": "clamor",
+  "clamoured": "clamored",
+  "clamouring": "clamoring",
+  "clamours": "clamors",
+  "clangour": "clangor",
+  "clarinettist": "clarinetist",
+  "clarinettists": "clarinetists",
+  "collectivise": "collectivize",
+  "collectivised": "collectivized",
+  "collectivises": "collectivizes",
+  "collectivising": "collectivizing",
+  "colonisation": "colonization",
+  "colonise": "colonize",
+  "colonised": "colonized",
+  "coloniser": "colonizer",
+  "colonisers": "colonizers",
+  "colonises": "colonizes",
+  "colonising": "colonizing",
+  "colour": "color",
+  "colourant": "colorant",
+  "colourants": "colorants",
+  "coloured": "colored",
+  "coloureds": "coloreds",
+  "colourful": "colorful",
+  "colourfully": "colorfully",
+  "colouring": "coloring",
+  "colourize": "colorize",
+  "colourized": "colorized",
+  "colourizes": "colorizes",
+  "colourizing": "colorizing",
+  "colourless": "colorless",
+  "colours": "colors",
+  "commercialise": "commercialize",
+  "commercialised": "commercialized",
+  "commercialises": "commercializes",
+  "commercialising": "commercializing",
+  "compartmentalise": "compartmentalize",
+  "compartmentalised": "compartmentalized",
+  "compartmentalises": "compartmentalizes",
+  "compartmentalising": "compartmentalizing",
+  "computerise": "computerize",
+  "computerised": "computerized",
+  "computerises": "computerizes",
+  "computerising": "computerizing",
+  "conceptualise": "conceptualize",
+  "conceptualised": "conceptualized",
+  "conceptualises": "conceptualizes",
+  "conceptualising": "conceptualizing",
+  "connexion": "connection",
+  "connexions": "connections",
+  "contextualise": "contextualize",
+  "contextualised": "contextualized",
+  "contextualises": "contextualizes",
+  "contextualising": "contextualizing",
+  "cosier": "cozier",
+  "cosies": "cozies",
+  "cosiest": "coziest",
+  "cosily": "cozily",
+  "cosiness": "coziness",
+  "cosy": "cozy",
+  "councillor": "councilor",
+  "councillors": "councilors",
+  "counselled": "counseled",
+  "counselling": "counseling",
+  "counsellor": "counselor",
+  "counsellors": "counselors",
+  "crenelated": "crenellated",
+  "criminalise": "criminalize",
+  "criminalised": "criminalized",
+  "criminalises": "criminalizes",
+  "criminalising": "criminalizing",
+  "criticise": "criticize",
+  "criticised": "criticized",
+  "criticises": "criticizes",
+  "criticising": "criticizing",
+  "crueller": "crueler",
+  "cruellest": "cruelest",
+  "crystallisation": "crystallization",
+  "crystallise": "crystallize",
+  "crystallised": "crystallized",
+  "crystallises": "crystallizes",
+  "crystallising": "crystallizing",
+  "cudgelled": "cudgeled",
+  "cudgelling": "cudgeling",
+  "customise": "customize",
+  "customised": "customized",
+  "customises": "customizes",
+  "customising": "customizing",
+  "cypher": "cipher",
+  "cyphers": "ciphers",
+  "decentralisation": "decentralization",
+  "decentralise": "decentralize",
+  "decentralised": "decentralized",
+  "decentralises": "decentralizes",
+  "decentralising": "decentralizing",
+  "decriminalisation": "decriminalization",
+  "decriminalise": "decriminalize",
+  "decriminalised": "decriminalized",
+  "decriminalises": "decriminalizes",
+  "decriminalising": "decriminalizing",
+  "defence": "defense",
+  "defenceless": "defenseless",
+  "defences": "defenses",
+  "dehumanisation": "dehumanization",
+  "dehumanise": "dehumanize",
+  "dehumanised": "dehumanized",
+  "dehumanises": "dehumanizes",
+  "dehumanising": "dehumanizing",
+  "demeanour": "demeanor",
+  "demilitarisation": "demilitarization",
+  "demilitarise": "demilitarize",
+  "demilitarised": "demilitarized",
+  "demilitarises": "demilitarizes",
+  "demilitarising": "demilitarizing",
+  "demobilisation": "demobilization",
+  "demobilise": "demobilize",
+  "demobilised": "demobilized",
+  "demobilises": "demobilizes",
+  "demobilising": "demobilizing",
+  "democratisation": "democratization",
+  "democratise": "democratize",
+  "democratised": "democratized",
+  "democratises": "democratizes",
+  "democratising": "democratizing",
+  "demonise": "demonize",
+  "demonised": "demonized",
+  "demonises": "demonizes",
+  "demonising": "demonizing",
+  "demoralisation": "demoralization",
+  "demoralise": "demoralize",
+  "demoralised": "demoralized",
+  "demoralises": "demoralizes",
+  "demoralising": "demoralizing",
+  "denationalisation": "denationalization",
+  "denationalise": "denationalize",
+  "denationalised": "denationalized",
+  "denationalises": "denationalizes",
+  "denationalising": "denationalizing",
+  "deodorise": "deodorize",
+  "deodorised": "deodorized",
+  "deodorises": "deodorizes",
+  "deodorising": "deodorizing",
+  "depersonalise": "depersonalize",
+  "depersonalised": "depersonalized",
+  "depersonalises": "depersonalizes",
+  "depersonalising": "depersonalizing",
+  "deputise": "deputize",
+  "deputised": "deputized",
+  "deputises": "deputizes",
+  "deputising": "deputizing",
+  "desensitisation": "desensitization",
+  "desensitise": "desensitize",
+  "desensitised": "desensitized",
+  "desensitises": "desensitizes",
+  "desensitising": "desensitizing",
+  "destabilisation": "destabilization",
+  "destabilise": "destabilize",
+  "destabilised": "destabilized",
+  "destabilises": "destabilizes",
+  "destabilising": "destabilizing",
+  "dialled": "dialed",
+  "dialling": "dialing",
+  "dialogue": "dialog",
+  "dialogues": "dialogs",
+  "diarrhoea": "diarrhea",
+  "digitise": "digitize",
+  "digitised": "digitized",
+  "digitises": "digitizes",
+  "digitising": "digitizing",
+  "disc": "disk",
+  "discolour": "discolor",
+  "discoloured": "discolored",
+  "discolouring": "discoloring",
+  "discolours": "discolors",
+  "discs": "disks",
+  "disembowelled": "disemboweled",
+  "disembowelling": "disemboweling",
+  "disfavour": "disfavor",
+  "dishevelled": "disheveled",
+  "dishonour": "dishonor",
+  "dishonourable": "dishonorable",
+  "dishonourably": "dishonorably",
+  "dishonoured": "dishonored",
+  "dishonouring": "dishonoring",
+  "dishonours": "dishonors",
+  "disorganisation": "disorganization",
+  "disorganised": "disorganized",
+  "distil": "distill",
+  "distils": "distills",
+  "dramatisation": "dramatization",
+  "dramatisations": "dramatizations",
+  "dramatise": "dramatize",
+  "dramatised": "dramatized",
+  "dramatises": "dramatizes",
+  "dramatising": "dramatizing",
+  "draught": "draft",
+  "draughtboard": "draftboard",
+  "draughtboards": "draftboards",
+  "draughtier": "draftier",
+  "draughtiest": "draftiest",
+  "draughts": "drafts",
+  "draughtsman": "draftsman",
+  "draughtsmanship": "draftsmanship",
+  "draughtsmen": "draftsmen",
+  "draughtswoman": "draftswoman",
+  "draughtswomen": "draftswomen",
+  "draughty": "drafty",
+  "drivelled": "driveled",
+  "drivelling": "driveling",
+  "duelled": "dueled",
+  "duelling": "dueling",
+  "economise": "economize",
+  "economised": "economized",
+  "economises": "economizes",
+  "economising": "economizing",
+  "editorialise": "editorialize",
+  "editorialised": "editorialized",
+  "editorialises": "editorializes",
+  "editorialising": "editorializing",
+  "edoema": "edema",
+  "empathise": "empathize",
+  "empathised": "empathized",
+  "empathises": "empathizes",
+  "empathising": "empathizing",
+  "emphasise": "emphasize",
+  "emphasised": "emphasized",
+  "emphasises": "emphasizes",
+  "emphasising": "emphasizing",
+  "enamelled": "enameled",
+  "enamelling": "enameling",
+  "enamoured": "enamored",
+  "encyclopaedia": "encyclopedia",
+  "encyclopaedias": "encyclopedias",
+  "encyclopaedic": "encyclopedic",
+  "endeavour": "endeavor",
+  "endeavoured": "endeavored",
+  "endeavouring": "endeavoring",
+  "endeavours": "endeavors",
+  "energise": "energize",
+  "energised": "energized",
+  "energises": "energizes",
+  "energising": "energizing",
+  "enrol": "enroll",
+  "enrols": "enrolls",
+  "enthral": "enthrall",
+  "enthrals": "enthralls",
+  "epaulette": "epaulet",
+  "epaulettes": "epaulets",
+  "epicentre": "epicenter",
+  "epicentres": "epicenters",
+  "epilogue": "epilog",
+  "epilogues": "epilogs",
+  "epitomise": "epitomize",
+  "epitomised": "epitomized",
+  "epitomises": "epitomizes",
+  "epitomising": "epitomizing",
+  "equalisation": "equalization",
+  "equalise": "equalize",
+  "equalised": "equalized",
+  "equaliser": "equalizer",
+  "equalisers": "equalizers",
+  "equalises": "equalizes",
+  "equalising": "equalizing",
+  "eulogise": "eulogize",
+  "eulogised": "eulogized",
+  "eulogises": "eulogizes",
+  "eulogising": "eulogizing",
+  "evangelise": "evangelize",
+  "evangelised": "evangelized",
+  "evangelises": "evangelizes",
+  "evangelising": "evangelizing",
+  "exorcise": "exorcize",
+  "exorcised": "exorcized",
+  "exorcises": "exorcizes",
+  "exorcising": "exorcizing",
+  "extemporisation": "extemporization",
+  "extemporise": "extemporize",
+  "extemporised": "extemporized",
+  "extemporises": "extemporizes",
+  "extemporising": "extemporizing",
+  "externalisation": "externalization",
+  "externalisations": "externalizations",
+  "externalise": "externalize",
+  "externalised": "externalized",
+  "externalises": "externalizes",
+  "externalising": "externalizing",
+  "factorise": "factorize",
+  "factorised": "factorized",
+  "factorises": "factorizes",
+  "factorising": "factorizing",
+  "faecal": "fecal",
+  "faeces": "feces",
+  "familiarisation": "familiarization",
+  "familiarise": "familiarize",
+  "familiarised": "familiarized",
+  "familiarises": "familiarizes",
+  "familiarising": "familiarizing",
+  "fantasise": "fantasize",
+  "fantasised": "fantasized",
+  "fantasises": "fantasizes",
+  "fantasising": "fantasizing",
+  "favour": "favor",
+  "favourable": "favorable",
+  "favourably": "favorably",
+  "favoured": "favored",
+  "favouring": "favoring",
+  "favourite": "favorite",
+  "favourites": "favorites",
+  "favouritism": "favoritism",
+  "favours": "favors",
+  "feminise": "feminize",
+  "feminised": "feminized",
+  "feminises": "feminizes",
+  "feminising": "feminizing",
+  "fertilisation": "fertilization",
+  "fertilise": "fertilize",
+  "fertilised": "fertilized",
+  "fertiliser": "fertilizer",
+  "fertilisers": "fertilizers",
+  "fertilises": "fertilizes",
+  "fertilising": "fertilizing",
+  "fervour": "fervor",
+  "fibre": "fiber",
+  "fibreglass": "fiberglass",
+  "fibres": "fibers",
+  "fictionalisation": "fictionalization",
+  "fictionalisations": "fictionalizations",
+  "fictionalise": "fictionalize",
+  "fictionalised": "fictionalized",
+  "fictionalises": "fictionalizes",
+  "fictionalising": "fictionalizing",
+  "fillet": "filet",
+  "filleted": "fileted",
+  "filleting": "fileting",
+  "fillets": "filets",
+  "finalisation": "finalization",
+  "finalise": "finalize",
+  "finalised": "finalized",
+  "finalises": "finalizes",
+  "finalising": "finalizing",
+  "flautist": "flutist",
+  "flautists": "flutists",
+  "flavour": "flavor",
+  "flavoured": "flavored",
+  "flavouring": "flavoring",
+  "flavourings": "flavorings",
+  "flavourless": "flavorless",
+  "flavours": "flavors",
+  "flavoursome": "flavorsome",
+  "flyer / flier": "flier / flyer",
+  "foetal": "fetal",
+  "foetid": "fetid",
+  "foetus": "fetus",
+  "foetuses": "fetuses",
+  "formalisation": "formalization",
+  "formalise": "formalize",
+  "formalised": "formalized",
+  "formalises": "formalizes",
+  "formalising": "formalizing",
+  "fossilisation": "fossilization",
+  "fossilise": "fossilize",
+  "fossilised": "fossilized",
+  "fossilises": "fossilizes",
+  "fossilising": "fossilizing",
+  "fraternisation": "fraternization",
+  "fraternise": "fraternize",
+  "fraternised": "fraternized",
+  "fraternises": "fraternizes",
+  "fraternising": "fraternizing",
+  "fulfil": "fulfill",
+  "fulfilment": "fulfillment",
+  "fulfils": "fulfills",
+  "funnelled": "funneled",
+  "funnelling": "funneling",
+  "gage": "gauge",
+  "gaged": "gauged",
+  "gages": "gauges",
+  "gaging": "gauging",
+  "galvanise": "galvanize",
+  "galvanised": "galvanized",
+  "galvanises": "galvanizes",
+  "galvanising": "galvanizing",
+  "gambolled": "gamboled",
+  "gambolling": "gamboling",
+  "gaol": "jail",
+  "gaolbird": "jailbird",
+  "gaolbirds": "jailbirds",
+  "gaolbreak": "jailbreak",
+  "gaolbreaks": "jailbreaks",
+  "gaoled": "jailed",
+  "gaoler": "jailer",
+  "gaolers": "jailers",
+  "gaoling": "jailing",
+  "gaols": "jails",
+  "gasses": "gases",
+  "generalisation": "generalization",
+  "generalisations": "generalizations",
+  "generalise": "generalize",
+  "generalised": "generalized",
+  "generalises": "generalizes",
+  "generalising": "generalizing",
+  "ghettoise": "ghettoize",
+  "ghettoised": "ghettoized",
+  "ghettoises": "ghettoizes",
+  "ghettoising": "ghettoizing",
+  "gipsies": "gypsies",
+  "glamor": "glamour",
+  "glamorise": "glamorize",
+  "glamorised": "glamorized",
+  "glamorises": "glamorizes",
+  "glamorising": "glamorizing",
+  "globalisation": "globalization",
+  "globalise": "globalize",
+  "globalised": "globalized",
+  "globalises": "globalizes",
+  "globalising": "globalizing",
+  "glueing": "gluing",
+  "goitre": "goiter",
+  "goitres": "goiters",
+  "gonorrhoea": "gonorrhea",
+  "gramme": "gram",
+  "grammes": "grams",
+  "gravelled": "graveled",
+  "grey": "gray",
+  "greyed": "grayed",
+  "greying": "graying",
+  "greyish": "grayish",
+  "greyness": "grayness",
+  "greys": "grays",
+  "grovelled": "groveled",
+  "grovelling": "groveling",
+  "groyne": "groin",
+  "groynes": "groins",
+  "gruelling": "grueling",
+  "gruellingly": "gruelingly",
+  "gryphon": "griffin",
+  "gryphons": "griffins",
+  "gynaecological": "gynecological",
+  "gynaecologist": "gynecologist",
+  "gynaecologists": "gynecologists",
+  "gynaecology": "gynecology",
+  "haematological": "hematological",
+  "haematologist": "hematologist",
+  "haematologists": "hematologists",
+  "haematology": "hematology",
+  "haemoglobin": "hemoglobin",
+  "haemophilia": "hemophilia",
+  "haemophiliac": "hemophiliac",
+  "haemophiliacs": "hemophiliacs",
+  "haemorrhage": "hemorrhage",
+  "haemorrhaged": "hemorrhaged",
+  "haemorrhages": "hemorrhages",
+  "haemorrhaging": "hemorrhaging",
+  "haemorrhoids": "hemorrhoids",
+  "harbour": "harbor",
+  "harboured": "harbored",
+  "harbouring": "harboring",
+  "harbours": "harbors",
+  "harmonisation": "harmonization",
+  "harmonise": "harmonize",
+  "harmonised": "harmonized",
+  "harmonises": "harmonizes",
+  "harmonising": "harmonizing",
+  "homoeopath": "homeopath",
+  "homoeopathic": "homeopathic",
+  "homoeopaths": "homeopaths",
+  "homoeopathy": "homeopathy",
+  "homogenise": "homogenize",
+  "homogenised": "homogenized",
+  "homogenises": "homogenizes",
+  "homogenising": "homogenizing",
+  "honour": "honor",
+  "honourable": "honorable",
+  "honourably": "honorably",
+  "honoured": "honored",
+  "honouring": "honoring",
+  "honours": "honors",
+  "hospitalisation": "hospitalization",
+  "hospitalise": "hospitalize",
+  "hospitalised": "hospitalized",
+  "hospitalises": "hospitalizes",
+  "hospitalising": "hospitalizing",
+  "humanise": "humanize",
+  "humanised": "humanized",
+  "humanises": "humanizes",
+  "humanising": "humanizing",
+  "humour": "humor",
+  "humoured": "humored",
+  "humouring": "humoring",
+  "humourless": "humorless",
+  "humours": "humors",
+  "hybridise": "hybridize",
+  "hybridised": "hybridized",
+  "hybridises": "hybridizes",
+  "hybridising": "hybridizing",
+  "hypnotise": "hypnotize",
+  "hypnotised": "hypnotized",
+  "hypnotises": "hypnotizes",
+  "hypnotising": "hypnotizing",
+  "hypothesise": "hypothesize",
+  "hypothesised": "hypothesized",
+  "hypothesises": "hypothesizes",
+  "hypothesising": "hypothesizing",
+  "idealisation": "idealization",
+  "idealise": "idealize",
+  "idealised": "idealized",
+  "idealises": "idealizes",
+  "idealising": "idealizing",
+  "idolise": "idolize",
+  "idolised": "idolized",
+  "idolises": "idolizes",
+  "idolising": "idolizing",
+  "immobilisation": "immobilization",
+  "immobilise": "immobilize",
+  "immobilised": "immobilized",
+  "immobiliser": "immobilizer",
+  "immobilisers": "immobilizers",
+  "immobilises": "immobilizes",
+  "immobilising": "immobilizing",
+  "immortalise": "immortalize",
+  "immortalised": "immortalized",
+  "immortalises": "immortalizes",
+  "immortalising": "immortalizing",
+  "immunisation": "immunization",
+  "immunise": "immunize",
+  "immunised": "immunized",
+  "immunises": "immunizes",
+  "immunising": "immunizing",
+  "impanelled": "impaneled",
+  "impanelling": "impaneling",
+  "imperilled": "imperiled",
+  "imperilling": "imperiling",
+  "individualise": "individualize",
+  "individualised": "individualized",
+  "individualises": "individualizes",
+  "individualising": "individualizing",
+  "industrialise": "industrialize",
+  "industrialised": "industrialized",
+  "industrialises": "industrializes",
+  "industrialising": "industrializing",
+  "inflexion": "inflection",
+  "inflexions": "inflections",
+  "initialise": "initialize",
+  "initialised": "initialized",
+  "initialises": "initializes",
+  "initialising": "initializing",
+  "initialled": "initialed",
+  "initialling": "initialing",
+  "instal": "install",
+  "instalment": "installment",
+  "instalments": "installments",
+  "instals": "installs",
+  "instil": "instill",
+  "instils": "instills",
+  "institutionalisation": "institutionalization",
+  "institutionalise": "institutionalize",
+  "institutionalised": "institutionalized",
+  "institutionalises": "institutionalizes",
+  "institutionalising": "institutionalizing",
+  "intellectualise": "intellectualize",
+  "intellectualised": "intellectualized",
+  "intellectualises": "intellectualizes",
+  "intellectualising": "intellectualizing",
+  "internalisation": "internalization",
+  "internalise": "internalize",
+  "internalised": "internalized",
+  "internalises": "internalizes",
+  "internalising": "internalizing",
+  "internationalisation": "internationalization",
+  "internationalise": "internationalize",
+  "internationalised": "internationalized",
+  "internationalises": "internationalizes",
+  "internationalising": "internationalizing",
+  "ionisation": "ionization",
+  "ionise": "ionize",
+  "ionised": "ionized",
+  "ioniser": "ionizer",
+  "ionisers": "ionizers",
+  "ionises": "ionizes",
+  "ionising": "ionizing",
+  "italicise": "italicize",
+  "italicised": "italicized",
+  "italicises": "italicizes",
+  "italicising": "italicizing",
+  "itemise": "itemize",
+  "itemised": "itemized",
+  "itemises": "itemizes",
+  "itemising": "itemizing",
+  "jeopardise": "jeopardize",
+  "jeopardised": "jeopardized",
+  "jeopardises": "jeopardizes",
+  "jeopardising": "jeopardizing",
+  "jewelled": "jeweled",
+  "jeweller": "jeweler",
+  "jewellers": "jewelers",
+  "jewellery": "jewelry",
+  "judgement": "judgment",
+  "kilogramme": "kilogram",
+  "kilogrammes": "kilograms",
+  "kilometre": "kilometer",
+  "kilometres": "kilometers",
+  "labelled": "labeled",
+  "labelling": "labeling",
+  "labour": "labor",
+  "laboured": "labored",
+  "labourer": "laborer",
+  "labourers": "laborers",
+  "labouring": "laboring",
+  "labours": "labors",
+  "lacklustre": "lackluster",
+  "legalisation": "legalization",
+  "legalise": "legalize",
+  "legalised": "legalized",
+  "legalises": "legalizes",
+  "legalising": "legalizing",
+  "legitimise": "legitimize",
+  "legitimised": "legitimized",
+  "legitimises": "legitimizes",
+  "legitimising": "legitimizing",
+  "leukaemia": "leukemia",
+  "levelled": "leveled",
+  "leveller": "leveler",
+  "levellers": "levelers",
+  "levelling": "leveling",
+  "libelled": "libeled",
+  "libelling": "libeling",
+  "libellous": "libelous",
+  "liberalisation": "liberalization",
+  "liberalise": "liberalize",
+  "liberalised": "liberalized",
+  "liberalises": "liberalizes",
+  "liberalising": "liberalizing",
+  "licence": "license",
+  "licenced": "licensed",
+  "licences": "licenses",
+  "licencing": "licensing",
+  "likeable": "likable",
+  "lionisation": "lionization",
+  "lionise": "lionize",
+  "lionised": "lionized",
+  "lionises": "lionizes",
+  "lionising": "lionizing",
+  "liquidise": "liquidize",
+  "liquidised": "liquidized",
+  "liquidiser": "liquidizer",
+  "liquidisers": "liquidizers",
+  "liquidises": "liquidizes",
+  "liquidising": "liquidizing",
+  "litre": "liter",
+  "litres": "liters",
+  "localise": "localize",
+  "localised": "localized",
+  "localises": "localizes",
+  "localising": "localizing",
+  "louvre": "louver",
+  "louvred": "louvered",
+  "louvres": "louvers",
+  "lustre": "luster",
+  "magnetise": "magnetize",
+  "magnetised": "magnetized",
+  "magnetises": "magnetizes",
+  "magnetising": "magnetizing",
+  "manoeuvrability": "maneuverability",
+  "manoeuvrable": "maneuverable",
+  "manoeuvre": "maneuver",
+  "manoeuvred": "maneuvered",
+  "manoeuvres": "maneuvers",
+  "manoeuvring": "maneuvering",
+  "manoeuvrings": "maneuverings",
+  "marginalisation": "marginalization",
+  "marginalise": "marginalize",
+  "marginalised": "marginalized",
+  "marginalises": "marginalizes",
+  "marginalising": "marginalizing",
+  "marshalled": "marshaled",
+  "marshalling": "marshaling",
+  "marvelled": "marveled",
+  "marvelling": "marveling",
+  "marvellous": "marvelous",
+  "marvellously": "marvelously",
+  "materialisation": "materialization",
+  "materialise": "materialize",
+  "materialised": "materialized",
+  "materialises": "materializes",
+  "materialising": "materializing",
+  "maximisation": "maximization",
+  "maximise": "maximize",
+  "maximised": "maximized",
+  "maximises": "maximizes",
+  "maximising": "maximizing",
+  "meagre": "meager",
+  "mechanisation": "mechanization",
+  "mechanise": "mechanize",
+  "mechanised": "mechanized",
+  "mechanises": "mechanizes",
+  "mechanising": "mechanizing",
+  "mediaeval": "medieval",
+  "memorialise": "memorialize",
+  "memorialised": "memorialized",
+  "memorialises": "memorializes",
+  "memorialising": "memorializing",
+  "memorise": "memorize",
+  "memorised": "memorized",
+  "memorises": "memorizes",
+  "memorising": "memorizing",
+  "mesmerise": "mesmerize",
+  "mesmerised": "mesmerized",
+  "mesmerises": "mesmerizes",
+  "mesmerising": "mesmerizing",
+  "metabolise": "metabolize",
+  "metabolised": "metabolized",
+  "metabolises": "metabolizes",
+  "metabolising": "metabolizing",
+  "metre": "meter",
+  "metres": "meters",
+  "mhm": "hmm",
+  "micrometre": "micrometer",
+  "micrometres": "micrometers",
+  "militarise": "militarize",
+  "militarised": "militarized",
+  "militarises": "militarizes",
+  "militarising": "militarizing",
+  "milligramme": "milligram",
+  "milligrammes": "milligrams",
+  "millilitre": "milliliter",
+  "millilitres": "milliliters",
+  "millimetre": "millimeter",
+  "millimetres": "millimeters",
+  "miniaturisation": "miniaturization",
+  "miniaturise": "miniaturize",
+  "miniaturised": "miniaturized",
+  "miniaturises": "miniaturizes",
+  "miniaturising": "miniaturizing",
+  "minibusses": "minibuses",
+  "minimise": "minimize",
+  "minimised": "minimized",
+  "minimises": "minimizes",
+  "minimising": "minimizing",
+  "misbehaviour": "misbehavior",
+  "misdemeanour": "misdemeanor",
+  "misdemeanours": "misdemeanors",
+  "misspelt": "misspelled",
+  "mitre": "miter",
+  "mitres": "miters",
+  "mm": "hmm",
+  "mmm": "hmm",
+  "mobilisation": "mobilization",
+  "mobilise": "mobilize",
+  "mobilised": "mobilized",
+  "mobilises": "mobilizes",
+  "mobilising": "mobilizing",
+  "modelled": "modeled",
+  "modeller": "modeler",
+  "modellers": "modelers",
+  "modelling": "modeling",
+  "modernise": "modernize",
+  "modernised": "modernized",
+  "modernises": "modernizes",
+  "modernising": "modernizing",
+  "moisturise": "moisturize",
+  "moisturised": "moisturized",
+  "moisturiser": "moisturizer",
+  "moisturisers": "moisturizers",
+  "moisturises": "moisturizes",
+  "moisturising": "moisturizing",
+  "monologue": "monolog",
+  "monologues": "monologs",
+  "monopolisation": "monopolization",
+  "monopolise": "monopolize",
+  "monopolised": "monopolized",
+  "monopolises": "monopolizes",
+  "monopolising": "monopolizing",
+  "moralise": "moralize",
+  "moralised": "moralized",
+  "moralises": "moralizes",
+  "moralising": "moralizing",
+  "motorised": "motorized",
+  "mould": "mold",
+  "moulded": "molded",
+  "moulder": "molder",
+  "mouldered": "moldered",
+  "mouldering": "moldering",
+  "moulders": "molders",
+  "mouldier": "moldier",
+  "mouldiest": "moldiest",
+  "moulding": "molding",
+  "mouldings": "moldings",
+  "moulds": "molds",
+  "mouldy": "moldy",
+  "moult": "molt",
+  "moulted": "molted",
+  "moulting": "molting",
+  "moults": "molts",
+  "moustache": "mustache",
+  "moustached": "mustached",
+  "moustaches": "mustaches",
+  "moustachioed": "mustachioed",
+  "multicoloured": "multicolored",
+  "nationalisation": "nationalization",
+  "nationalisations": "nationalizations",
+  "nationalise": "nationalize",
+  "nationalised": "nationalized",
+  "nationalises": "nationalizes",
+  "nationalising": "nationalizing",
+  "naturalisation": "naturalization",
+  "naturalise": "naturalize",
+  "naturalised": "naturalized",
+  "naturalises": "naturalizes",
+  "naturalising": "naturalizing",
+  "neighbour": "neighbor",
+  "neighbourhood": "neighborhood",
+  "neighbourhoods": "neighborhoods",
+  "neighbouring": "neighboring",
+  "neighbourliness": "neighborliness",
+  "neighbourly": "neighborly",
+  "neighbours": "neighbors",
+  "neutralisation": "neutralization",
+  "neutralise": "neutralize",
+  "neutralised": "neutralized",
+  "neutralises": "neutralizes",
+  "neutralising": "neutralizing",
+  "normalisation": "normalization",
+  "normalise": "normalize",
+  "normalised": "normalized",
+  "normalises": "normalizes",
+  "normalising": "normalizing",
+  "odour": "odor",
+  "odourless": "odorless",
+  "odours": "odors",
+  "oesophagus": "esophagus",
+  "oesophaguses": "esophaguses",
+  "oestrogen": "estrogen",
+  "offence": "offense",
+  "offences": "offenses",
+  "omelette": "omelet",
+  "omelettes": "omelets",
+  "optimise": "optimize",
+  "optimised": "optimized",
+  "optimises": "optimizes",
+  "optimising": "optimizing",
+  "organisation": "organization",
+  "organisational": "organizational",
+  "organisations": "organizations",
+  "organise": "organize",
+  "organised": "organized",
+  "organiser": "organizer",
+  "organisers": "organizers",
+  "organises": "organizes",
+  "organising": "organizing",
+  "orthopaedic": "orthopedic",
+  "orthopaedics": "orthopedics",
+  "ostracise": "ostracize",
+  "ostracised": "ostracized",
+  "ostracises": "ostracizes",
+  "ostracising": "ostracizing",
+  "outmanoeuvre": "outmaneuver",
+  "outmanoeuvred": "outmaneuvered",
+  "outmanoeuvres": "outmaneuvers",
+  "outmanoeuvring": "outmaneuvering",
+  "overemphasise": "overemphasize",
+  "overemphasised": "overemphasized",
+  "overemphasises": "overemphasizes",
+  "overemphasising": "overemphasizing",
+  "oxidisation": "oxidization",
+  "oxidise": "oxidize",
+  "oxidised": "oxidized",
+  "oxidises": "oxidizes",
+  "oxidising": "oxidizing",
+  "paederast": "pederast",
+  "paederasts": "pederasts",
+  "paediatric": "pediatric",
+  "paediatrician": "pediatrician",
+  "paediatricians": "pediatricians",
+  "paediatrics": "pediatrics",
+  "paedophile": "pedophile",
+  "paedophiles": "pedophiles",
+  "paedophilia": "pedophilia",
+  "palaeolithic": "paleolithic",
+  "palaeontologist": "paleontologist",
+  "palaeontologists": "paleontologists",
+  "palaeontology": "paleontology",
+  "panelled": "paneled",
+  "panelling": "paneling",
+  "panellist": "panelist",
+  "panellists": "panelists",
+  "paralyse": "paralyze",
+  "paralysed": "paralyzed",
+  "paralyses": "paralyzes",
+  "paralysing": "paralyzing",
+  "parcelled": "parceled",
+  "parcelling": "parceling",
+  "parlour": "parlor",
+  "parlours": "parlors",
+  "particularise": "particularize",
+  "particularised": "particularized",
+  "particularises": "particularizes",
+  "particularising": "particularizing",
+  "passivisation": "passivization",
+  "passivise": "passivize",
+  "passivised": "passivized",
+  "passivises": "passivizes",
+  "passivising": "passivizing",
+  "pasteurisation": "pasteurization",
+  "pasteurise": "pasteurize",
+  "pasteurised": "pasteurized",
+  "pasteurises": "pasteurizes",
+  "pasteurising": "pasteurizing",
+  "patronise": "patronize",
+  "patronised": "patronized",
+  "patronises": "patronizes",
+  "patronising": "patronizing",
+  "patronisingly": "patronizingly",
+  "pedalled": "pedaled",
+  "pedalling": "pedaling",
+  "pedestrianisation": "pedestrianization",
+  "pedestrianise": "pedestrianize",
+  "pedestrianised": "pedestrianized",
+  "pedestrianises": "pedestrianizes",
+  "pedestrianising": "pedestrianizing",
+  "penalise": "penalize",
+  "penalised": "penalized",
+  "penalises": "penalizes",
+  "penalising": "penalizing",
+  "pencilled": "penciled",
+  "pencilling": "penciling",
+  "personalise": "personalize",
+  "personalised": "personalized",
+  "personalises": "personalizes",
+  "personalising": "personalizing",
+  "pharmacopoeia": "pharmacopeia",
+  "pharmacopoeias": "pharmacopeias",
+  "philosophise": "philosophize",
+  "philosophised": "philosophized",
+  "philosophises": "philosophizes",
+  "philosophising": "philosophizing",
+  "philtre": "filter",
+  "philtres": "filters",
+  "phoney": "phony",
+  "plagiarise": "plagiarize",
+  "plagiarised": "plagiarized",
+  "plagiarises": "plagiarizes",
+  "plagiarising": "plagiarizing",
+  "plough": "plow",
+  "ploughed": "plowed",
+  "ploughing": "plowing",
+  "ploughman": "plowman",
+  "ploughmen": "plowmen",
+  "ploughs": "plows",
+  "ploughshare": "plowshare",
+  "ploughshares": "plowshares",
+  "polarisation": "polarization",
+  "polarise": "polarize",
+  "polarised": "polarized",
+  "polarises": "polarizes",
+  "polarising": "polarizing",
+  "politicisation": "politicization",
+  "politicise": "politicize",
+  "politicised": "politicized",
+  "politicises": "politicizes",
+  "politicising": "politicizing",
+  "popularisation": "popularization",
+  "popularise": "popularize",
+  "popularised": "popularized",
+  "popularises": "popularizes",
+  "popularising": "popularizing",
+  "pouffe": "pouf",
+  "pouffes": "poufs",
+  "practise": "practice",
+  "practised": "practiced",
+  "practises": "practices",
+  "practising": "practicing",
+  "praesidium": "presidium",
+  "praesidiums": "presidiums",
+  "pressurisation": "pressurization",
+  "pressurise": "pressurize",
+  "pressurised": "pressurized",
+  "pressurises": "pressurizes",
+  "pressurising": "pressurizing",
+  "pretence": "pretense",
+  "pretences": "pretenses",
+  "primaeval": "primeval",
+  "prioritisation": "prioritization",
+  "prioritise": "prioritize",
+  "prioritised": "prioritized",
+  "prioritises": "prioritizes",
+  "prioritising": "prioritizing",
+  "privatisation": "privatization",
+  "privatisations": "privatizations",
+  "privatise": "privatize",
+  "privatised": "privatized",
+  "privatises": "privatizes",
+  "privatising": "privatizing",
+  "professionalisation": "professionalization",
+  "professionalise": "professionalize",
+  "professionalised": "professionalized",
+  "professionalises": "professionalizes",
+  "professionalising": "professionalizing",
+  "programme": "program",
+  "programmes": "programs",
+  "prologue": "prolog",
+  "prologues": "prologs",
+  "propagandise": "propagandize",
+  "propagandised": "propagandized",
+  "propagandises": "propagandizes",
+  "propagandising": "propagandizing",
+  "proselytise": "proselytize",
+  "proselytised": "proselytized",
+  "proselytiser": "proselytizer",
+  "proselytisers": "proselytizers",
+  "proselytises": "proselytizes",
+  "proselytising": "proselytizing",
+  "psychoanalyse": "psychoanalyze",
+  "psychoanalysed": "psychoanalyzed",
+  "psychoanalyses": "psychoanalyzes",
+  "psychoanalysing": "psychoanalyzing",
+  "publicise": "publicize",
+  "publicised": "publicized",
+  "publicises": "publicizes",
+  "publicising": "publicizing",
+  "pulverisation": "pulverization",
+  "pulverise": "pulverize",
+  "pulverised": "pulverized",
+  "pulverises": "pulverizes",
+  "pulverising": "pulverizing",
+  "pummelled": "pummel",
+  "pummelling": "pummeled",
+  "pyjama": "pajama",
+  "pyjamas": "pajamas",
+  "pzazz": "pizzazz",
+  "quarrelled": "quarreled",
+  "quarrelling": "quarreling",
+  "radicalise": "radicalize",
+  "radicalised": "radicalized",
+  "radicalises": "radicalizes",
+  "radicalising": "radicalizing",
+  "rancour": "rancor",
+  "randomise": "randomize",
+  "randomised": "randomized",
+  "randomises": "randomizes",
+  "randomising": "randomizing",
+  "rationalisation": "rationalization",
+  "rationalisations": "rationalizations",
+  "rationalise": "rationalize",
+  "rationalised": "rationalized",
+  "rationalises": "rationalizes",
+  "rationalising": "rationalizing",
+  "ravelled": "raveled",
+  "ravelling": "raveling",
+  "realisable": "realizable",
+  "realisation": "realization",
+  "realisations": "realizations",
+  "realise": "realize",
+  "realised": "realized",
+  "realises": "realizes",
+  "realising": "realizing",
+  "recognisable": "recognizable",
+  "recognisably": "recognizably",
+  "recognisance": "recognizance",
+  "recognise": "recognize",
+  "recognised": "recognized",
+  "recognises": "recognizes",
+  "recognising": "recognizing",
+  "reconnoitre": "reconnoiter",
+  "reconnoitred": "reconnoitered",
+  "reconnoitres": "reconnoiters",
+  "reconnoitring": "reconnoitering",
+  "refuelled": "refueled",
+  "refuelling": "refueling",
+  "regularisation": "regularization",
+  "regularise": "regularize",
+  "regularised": "regularized",
+  "regularises": "regularizes",
+  "regularising": "regularizing",
+  "remodelled": "remodeled",
+  "remodelling": "remodeling",
+  "remould": "remold",
+  "remoulded": "remolded",
+  "remoulding": "remolding",
+  "remoulds": "remolds",
+  "reorganisation": "reorganization",
+  "reorganisations": "reorganizations",
+  "reorganise": "reorganize",
+  "reorganised": "reorganized",
+  "reorganises": "reorganizes",
+  "reorganising": "reorganizing",
+  "revelled": "reveled",
+  "reveller": "reveler",
+  "revellers": "revelers",
+  "revelling": "reveling",
+  "revitalise": "revitalize",
+  "revitalised": "revitalized",
+  "revitalises": "revitalizes",
+  "revitalising": "revitalizing",
+  "revolutionise": "revolutionize",
+  "revolutionised": "revolutionized",
+  "revolutionises": "revolutionizes",
+  "revolutionising": "revolutionizing",
+  "rhapsodise": "rhapsodize",
+  "rhapsodised": "rhapsodized",
+  "rhapsodises": "rhapsodizes",
+  "rhapsodising": "rhapsodizing",
+  "rigour": "rigor",
+  "rigours": "rigors",
+  "ritualised": "ritualized",
+  "rivalled": "rivaled",
+  "rivalling": "rivaling",
+  "romanticise": "romanticize",
+  "romanticised": "romanticized",
+  "romanticises": "romanticizes",
+  "romanticising": "romanticizing",
+  "rumour": "rumor",
+  "rumoured": "rumored",
+  "rumours": "rumors",
+  "sabre": "saber",
+  "sabres": "sabers",
+  "saltpetre": "saltpeter",
+  "sanitise": "sanitize",
+  "sanitised": "sanitized",
+  "sanitises": "sanitizes",
+  "sanitising": "sanitizing",
+  "satirise": "satirize",
+  "satirised": "satirized",
+  "satirises": "satirizes",
+  "satirising": "satirizing",
+  "saviour": "savior",
+  "saviours": "saviors",
+  "savour": "savor",
+  "savoured": "savored",
+  "savouries": "savories",
+  "savouring": "savoring",
+  "savours": "savors",
+  "savoury": "savory",
+  "scandalise": "scandalize",
+  "scandalised": "scandalized",
+  "scandalises": "scandalizes",
+  "scandalising": "scandalizing",
+  "sceptic": "skeptic",
+  "sceptical": "skeptical",
+  "sceptically": "skeptically",
+  "scepticism": "skepticism",
+  "sceptics": "skeptics",
+  "sceptre": "scepter",
+  "sceptres": "scepters",
+  "scrutinise": "scrutinize",
+  "scrutinised": "scrutinized",
+  "scrutinises": "scrutinizes",
+  "scrutinising": "scrutinizing",
+  "secularisation": "secularization",
+  "secularise": "secularize",
+  "secularised": "secularized",
+  "secularises": "secularizes",
+  "secularising": "secularizing",
+  "sensationalise": "sensationalize",
+  "sensationalised": "sensationalized",
+  "sensationalises": "sensationalizes",
+  "sensationalising": "sensationalizing",
+  "sensitise": "sensitize",
+  "sensitised": "sensitized",
+  "sensitises": "sensitizes",
+  "sensitising": "sensitizing",
+  "sentimentalise": "sentimentalize",
+  "sentimentalised": "sentimentalized",
+  "sentimentalises": "sentimentalizes",
+  "sentimentalising": "sentimentalizing",
+  "sepulchre": "sepulcher",
+  "sepulchres": "sepulchers",
+  "serialisation": "serialization",
+  "serialisations": "serializations",
+  "serialise": "serialize",
+  "serialised": "serialized",
+  "serialises": "serializes",
+  "serialising": "serializing",
+  "sermonise": "sermonize",
+  "sermonised": "sermonized",
+  "sermonises": "sermonizes",
+  "sermonising": "sermonizing",
+  "sheikh": "sheik",
+  "shovelled": "shoveled",
+  "shovelling": "shoveling",
+  "shrivelled": "shriveled",
+  "shrivelling": "shriveling",
+  "signalise": "signalize",
+  "signalised": "signalized",
+  "signalises": "signalizes",
+  "signalising": "signalizing",
+  "signalled": "signaled",
+  "signalling": "signaling",
+  "smoulder": "smolder",
+  "smouldered": "smoldered",
+  "smouldering": "smoldering",
+  "smoulders": "smolders",
+  "snivelled": "sniveled",
+  "snivelling": "sniveling",
+  "snorkelled": "snorkeled",
+  "snorkelling": "snorkeling",
+  "snowplough": "snowplow",
+  "snowploughs": "snowplow",
+  "socialisation": "socialization",
+  "socialise": "socialize",
+  "socialised": "socialized",
+  "socialises": "socializes",
+  "socialising": "socializing",
+  "sodomise": "sodomize",
+  "sodomised": "sodomized",
+  "sodomises": "sodomizes",
+  "sodomising": "sodomizing",
+  "solemnise": "solemnize",
+  "solemnised": "solemnized",
+  "solemnises": "solemnizes",
+  "solemnising": "solemnizing",
+  "sombre": "somber",
+  "specialisation": "specialization",
+  "specialisations": "specializations",
+  "specialise": "specialize",
+  "specialised": "specialized",
+  "specialises": "specializes",
+  "specialising": "specializing",
+  "spectre": "specter",
+  "spectres": "specters",
+  "spiralled": "spiraled",
+  "spiralling": "spiraling",
+  "splendour": "splendor",
+  "splendours": "splendors",
+  "squirrelled": "squirreled",
+  "squirrelling": "squirreling",
+  "stabilisation": "stabilization",
+  "stabilise": "stabilize",
+  "stabilised": "stabilized",
+  "stabiliser": "stabilizer",
+  "stabilisers": "stabilizers",
+  "stabilises": "stabilizes",
+  "stabilising": "stabilizing",
+  "standardisation": "standardization",
+  "standardise": "standardize",
+  "standardised": "standardized",
+  "standardises": "standardizes",
+  "standardising": "standardizing",
+  "stencilled": "stenciled",
+  "stencilling": "stenciling",
+  "sterilisation": "sterilization",
+  "sterilisations": "sterilizations",
+  "sterilise": "sterilize",
+  "sterilised": "sterilized",
+  "steriliser": "sterilizer",
+  "sterilisers": "sterilizers",
+  "sterilises": "sterilizes",
+  "sterilising": "sterilizing",
+  "stigmatisation": "stigmatization",
+  "stigmatise": "stigmatize",
+  "stigmatised": "stigmatized",
+  "stigmatises": "stigmatizes",
+  "stigmatising": "stigmatizing",
+  "storey": "story",
+  "storeys": "stories",
+  "subsidisation": "subsidization",
+  "subsidise": "subsidize",
+  "subsidised": "subsidized",
+  "subsidiser": "subsidizer",
+  "subsidisers": "subsidizers",
+  "subsidises": "subsidizes",
+  "subsidising": "subsidizing",
+  "succour": "succor",
+  "succoured": "succored",
+  "succouring": "succoring",
+  "succours": "succors",
+  "sulphate": "sulfate",
+  "sulphates": "sulfates",
+  "sulphide": "sulfide",
+  "sulphides": "sulfides",
+  "sulphur": "sulfur",
+  "sulphurous": "sulfurous",
+  "summarise": "summarize",
+  "summarised": "summarized",
+  "summarises": "summarizes",
+  "summarising": "summarizing",
+  "swivelled": "swiveled",
+  "swivelling": "swiveling",
+  "symbolise": "symbolize",
+  "symbolised": "symbolized",
+  "symbolises": "symbolizes",
+  "symbolising": "symbolizing",
+  "sympathise": "sympathize",
+  "sympathised": "sympathized",
+  "sympathiser": "sympathizer",
+  "sympathisers": "sympathizers",
+  "sympathises": "sympathizes",
+  "sympathising": "sympathizing",
+  "synchronisation": "synchronization",
+  "synchronise": "synchronize",
+  "synchronised": "synchronized",
+  "synchronises": "synchronizes",
+  "synchronising": "synchronizing",
+  "synthesise": "synthesize",
+  "synthesised": "synthesized",
+  "synthesiser": "synthesizer",
+  "synthesisers": "synthesizers",
+  "synthesises": "synthesizes",
+  "synthesising": "synthesizing",
+  "syphon": "siphon",
+  "syphoned": "siphoned",
+  "syphoning": "siphoning",
+  "syphons": "siphons",
+  "systematisation": "systematization",
+  "systematise": "systematize",
+  "systematised": "systematized",
+  "systematises": "systematizes",
+  "systematising": "systematizing",
+  "tantalise": "tantalize",
+  "tantalised": "tantalized",
+  "tantalises": "tantalizes",
+  "tantalising": "tantalizing",
+  "tantalisingly": "tantalizingly",
+  "tasselled": "tasseled",
+  "technicolour": "technicolor",
+  "temporise": "temporize",
+  "temporised": "temporized",
+  "temporises": "temporizes",
+  "temporising": "temporizing",
+  "tenderise": "tenderize",
+  "tenderised": "tenderized",
+  "tenderises": "tenderizes",
+  "tenderising": "tenderizing",
+  "terrorise": "terrorize",
+  "terrorised": "terrorized",
+  "terrorises": "terrorizes",
+  "terrorising": "terrorizing",
+  "theatre": "theater",
+  "theatregoer": "theatergoer",
+  "theatregoers": "theatergoers",
+  "theatres": "theaters",
+  "theorise": "theorize",
+  "theorised": "theorized",
+  "theorises": "theorizes",
+  "theorising": "theorizing",
+  "tonne": "ton",
+  "tonnes": "tons",
+  "towelled": "toweled",
+  "towelling": "toweling",
+  "toxaemia": "toxemia",
+  "tranquillise": "tranquilize",
+  "tranquillised": "tranquilized",
+  "tranquilliser": "tranquilizer",
+  "tranquillisers": "tranquilizers",
+  "tranquillises": "tranquilizes",
+  "tranquillising": "tranquilizing",
+  "tranquillity": "tranquility",
+  "tranquillize": "tranquilize",
+  "tranquillized": "tranquilized",
+  "tranquillizer": "tranquilizer",
+  "tranquillizers": "tranquilizers",
+  "tranquillizes": "tranquilizes",
+  "tranquillizing": "tranquilizing",
+  "tranquilly": "tranquility",
+  "transistorised": "transistorized",
+  "traumatise": "traumatize",
+  "traumatised": "traumatized",
+  "traumatises": "traumatizes",
+  "traumatising": "traumatizing",
+  "travelled": "traveled",
+  "traveller": "traveler",
+  "travellers": "travelers",
+  "travelling": "traveling",
+  "travelog": "travelogue",
+  "travelogs": "travelogues",
+  "trialled": "trialed",
+  "trialling": "trialing",
+  "tricolour": "tricolor",
+  "tricolours": "tricolors",
+  "trivialise": "trivialize",
+  "trivialised": "trivialized",
+  "trivialises": "trivializes",
+  "trivialising": "trivializing",
+  "tumour": "tumor",
+  "tumours": "tumors",
+  "tunnelled": "tunneled",
+  "tunnelling": "tunneling",
+  "tyrannise": "tyrannize",
+  "tyrannised": "tyrannized",
+  "tyrannises": "tyrannizes",
+  "tyrannising": "tyrannizing",
+  "tyre": "tire",
+  "tyres": "tires",
+  "unauthorised": "unauthorized",
+  "uncivilised": "uncivilized",
+  "underutilised": "underutilized",
+  "unequalled": "unequaled",
+  "unfavourable": "unfavorable",
+  "unfavourably": "unfavorably",
+  "unionisation": "unionization",
+  "unionise": "unionize",
+  "unionised": "unionized",
+  "unionises": "unionizes",
+  "unionising": "unionizing",
+  "unorganised": "unorganized",
+  "unravelled": "unraveled",
+  "unravelling": "unraveling",
+  "unrecognisable": "unrecognizable",
+  "unrecognised": "unrecognized",
+  "unrivalled": "unrivaled",
+  "unsavoury": "unsavory",
+  "untrammelled": "untrammeled",
+  "urbanisation": "urbanization",
+  "urbanise": "urbanize",
+  "urbanised": "urbanized",
+  "urbanises": "urbanizes",
+  "urbanising": "urbanizing",
+  "utilisable": "utilizable",
+  "utilisation": "utilization",
+  "utilise": "utilize",
+  "utilised": "utilized",
+  "utilises": "utilizes",
+  "utilising": "utilizing",
+  "valour": "valor",
+  "vandalise": "vandalize",
+  "vandalised": "vandalized",
+  "vandalises": "vandalizes",
+  "vandalising": "vandalizing",
+  "vaporisation": "vaporization",
+  "vaporise": "vaporize",
+  "vaporised": "vaporized",
+  "vaporises": "vaporizes",
+  "vaporising": "vaporizing",
+  "vapour": "vapor",
+  "vapours": "vapors",
+  "verbalise": "verbalize",
+  "verbalised": "verbalized",
+  "verbalises": "verbalizes",
+  "verbalising": "verbalizing",
+  "victimisation": "victimization",
+  "victimise": "victimize",
+  "victimised": "victimized",
+  "victimises": "victimizes",
+  "victimising": "victimizing",
+  "videodisc": "videodisk",
+  "videodiscs": "videodisks",
+  "vigour": "vigor",
+  "visualisation": "visualization",
+  "visualisations": "visualizations",
+  "visualise": "visualize",
+  "visualised": "visualized",
+  "visualises": "visualizes",
+  "visualising": "visualizing",
+  "vocalisation": "vocalization",
+  "vocalisations": "vocalizations",
+  "vocalise": "vocalize",
+  "vocalised": "vocalized",
+  "vocalises": "vocalizes",
+  "vocalising": "vocalizing",
+  "vulcanised": "vulcanized",
+  "vulgarisation": "vulgarization",
+  "vulgarise": "vulgarize",
+  "vulgarised": "vulgarized",
+  "vulgarises": "vulgarizes",
+  "vulgarising": "vulgarizing",
+  "waggon": "wagon",
+  "waggons": "wagons",
+  "watercolour": "watercolor",
+  "watercolours": "watercolors",
+  "weaselled": "weaseled",
+  "weaselling": "weaseling",
+  "westernisation": "westernization",
+  "westernise": "westernize",
+  "westernised": "westernized",
+  "westernises": "westernizes",
+  "westernising": "westernizing",
+  "womanise": "womanize",
+  "womanised": "womanized",
+  "womaniser": "womanizer",
+  "womanisers": "womanizers",
+  "womanises": "womanizes",
+  "womanising": "womanizing",
+  "woollen": "woolen",
+  "woollens": "woolens",
+  "woollies": "woolies",
+  "woolly": "wooly",
+  "worshipped": "worshiped",
+  "worshipper": "worshiper",
+  "worshipping": "worshiping",
+  "yodelled": "yodeled",
+  "yodelling": "yodeling",
+  "yoghourt": "yogurt",
+  "yoghourts": "yogurts",
+  "yoghurt": "yogurt",
+  "yoghurts": "yogurts"
+}
diff --git a/pred.txt b/pred.txt
new file mode 100644
index 0000000000000000000000000000000000000000..2fc67baa9a0b417de3e63e1e43924aed685209fc
--- /dev/null
+++ b/pred.txt
@@ -0,0 +1 @@
+pred_str=['新 莊 仔 ', '煎 仔 ', '白 色 ', '盲 價 大 道 ', '我 伨 你 ', '心 肝 較 大 牛 氣 ', '北 權 路 ', '孔 廟 文 化 園 區 ', '芥 池 麵 粉 ', '加 速 ', '目 睭 磕 磕 講 白 癮 ', '客 運 的 ', '太 陽 能 ', '掩 來 差 氣 ', '漢 堡 ', '林 作 水 ', '免 庫 權 ', '藥 廠 ', '閣 跤 ', '三 言 兩 語 ', '煎 包 ', '文 主 公 廟 ', '欲 ', '著 西 ', '菜 頭 莊 ', '相 拄 頭 ', '鹹 體 送 到 臺 北 了 後 ', '參 加 ', '無 錢 講 無 話 ', '二 重 埔 ', '哈 銷 ', '豐 興 汀 ', '礁 山 ', '桌 碎 ', '過 湧 ', '海 面 ', '大 雞 口 眾 ', '水 沖 池 ', '前 壁 園 ', '面 當 義 ', '灌 鰮 ', '誠 濟 人 ', '富 岡 ', '心 智 圖 ', '看 魚 無 ', '觀 光 局 ', '一 空 掠 兩 尾 ', '水 ', '十 五 延 元 ', '少 年 呢 你 著 聽 老 花 仔 苦 囝 ', '溝 接 ', '這 世 ', '笑 號 ', '早 暗 愛 會 記 你 拆 山 ', '孤 交 古 ', '咖 啡 機 ', '相 趁 相 喊 ', '阿 角 ', '中 山 湯 池 ', '北 埔 ', '西 雙 板 納 ', '橋 頭 糖 廠 ', '細 溶 溶 ', '古 早 人 煎 草 藥 仔 ', '跩 跩 ', '雷 電 球 ', '共 人 池 夫 ', '靈 活 ', '無 出 門 ', '五 行 配 搭 ', '西 亞 圖 ', '濟 頭 ', '赤 山 寶 ', '自 焚 池 ', '聖 大 面 ', '著 性 地 ', '山 機 落 葉 ', '逆 觸 ', '一 文 不 值 ', '自 焦 ', '免 菜 ', '鹹 硞 硞 ', '嘉 義 農 林 學 校 ', '碰 意 予 囡 仔 分 歹 氣 矣 ', '匙 匯 中 學 ', '大 盤 食 細 盤 ', '過 年 維 庫 ', '柴 底 仔 ', '如 沖 沖 ', '東 線 ', '你 把 伊 無 ', '四 伨 ', '亦 飛 ', '淡 水 區 ', '番 薑 ', '北 新 竹 ', '東 北 ', '袂 曉 衰 ', '糊 ', '煞 了 臺 ', '輪 天 車 ', '拍 鐵 的 ', '這 粒 蛤 仔 袂 開 喙 莽 食 較 好 ', '源 平 湧 ', '豆 菜 底 ', '囂 碎 ', '一 清 二 掣 ', '機 械 學 習 ', '汐 場 ', '你 咧 大 聲 啥 啦 ', '內 湖 仔 ', '大 肚 魚 仔 ', '命 切 ', '心 思 無 人 知 我 有 加 足 工 庫 的 ', '臺 灣 十 部 人 ', '西 邱 溝 ', '解 物 ', '陽 生 年 ', '福 ', '予 人 想 攏 無 ', '官 田 區 ', '走 馬 路 ', '甘 若 蝦 ', '底 水 池 ', '浸 泉 ', '我 想 欲 去 便 所 ', '東 寧 ', '頭 家 媽 ', '倒 退 後 ', '數 日 呢 矣 大 氣 ', '三 八 叮 咚 ', '中 安 西 路 ', '目 睭 細 ', '向 天 ', '無 光 炎 ', '我 就 ', '故 事 ', '草 骨 上 ', '太 空 船 ', '我 共 人 講 話 ', '七 口 八 口 ', '見 ', '飛 天 掛 鉴 ', '碎 氣 加 碎 池 也 會 相 觸 著 ', '相 廁 ', '盤 十 五 ', '東 庫 東 田 ', '福 德 正 神 ', '烏 金 黃 鯉 ', '電 風 橋 仔 ', '風 雷 ', '軍 頭 哥 哥 在 襄 ', '底 池 ', '天 公 有 大 著 ', '暴 風 雪 ', '過 街 ', '豆 乾 糋 ', '袂 聊 ', '鱷 ', '騙 赤 海 鵰 ', '桌 林 ', '伊 上 愛 食 控 碼 ', '濱 海 山 鰮 ', '源 泉 ', '林 森 南 路 車 行 大 學 道 ', '你 是 家 己 開 業 亦 是 予 人 請 ', '禮 陽 ', '馬 座 橋 ', '芳 心 ', '貴 仁 區 ', '臺 灣 馬 南 義 襄 陽 師 師 電 ', '切 池 仔 ', '去 見 貓 仔 的 鳥 仔 真 來 ', '精 細 ', '雙 園 國 中 ', '評 審 老 師 ', '分 局 前 ', '大 直 街 ', '你 袂 ', '古 亭 國 中 ', '公 園 有 力 頭 ', '可 持 續 性 ', '大 面 ', '南 層 鄉 ', '懷 生 國 中 ', '巨 骨 仔 ', '好 歹 名 敢 是 做 好 好 的 這 真 歹 講 ', '走 袂 志 ', '鑑 橋 ', '薄 寮 ', '勞 里 王 ', '菜 蟲 食 菜 菜 跤 死 ', '你 共 我 記 咧 ', '等 袂 赴 ', '簡 接 碎 ', '山 豬 無 兄 ', '必 箍 銀 ', '溪 厝 ', '冰 大 ', '牛 山 ', '踏 門 ', '尊 紮 ', '水 箍 箍 ', '藏 仔 餅 ', '藥 廢 ', '我 介 意 無 親 ', '媽 當 奴 ', '生 來 ', '務 兵 ', '星 光 橋 ', '經 濟 ', '烏 白 目 ', '霸 凌 ', '草 藥 ', '誠 明 頭 ', '寶 斗 厝 ', '拍 大 車 ', '山 仙 紮 ', '阿 拉 伯 ', '箍 細 塊 ', '顏 智 ', '思 王 ', '金 華 國 中 ', '獨 乙 ', '伯 爭 義 ', '袂 堪 得 煩 ', '興 寧 街 ', '臺 德 國 中 ', '算 ', '著 手 溝 ', '彰 化 縣 ', '塗 庫 莊 ', '無 電 影 ', '客 汗 ', '三 擺 兩 擺 ', '厚 圖 ', '永 川 厝 ', '懷 山 ', '和 尚 洲 ', '正 月 ', '烏 頭 鞭 仔 ', '誠 濟 ', '食 到 九 十 外 歲 矣 閣 誠 勇 驚 ', '烏 焦 蛇 ', '要 袂 加 咧 路 ', '地 上 ', '真 單 字 ', '相 箍 村 ', '新 區 ', '散 到 割 喉 無 血 ', '匯 陽 溪 ', '打 箍 山 ', '密 號 ', '中 央 警 察 大 學 ', '管 相 豬 的 風 吹 著 佇 樹 仔 頂 ', '目 加 溜 ', '雞 池 神 ', '王 逆 德 紀 念 館 ', '伊 亞 ', '愛 著 鬆 ', '行 死 閣 會 看 無 連 ', '草 干 目 ', '雷 光 公 司 ', '張 水 暴 ', '魚 卵 醬 ', '熱 水 爐 ', '老 母 ', '縱 氣 ', '額 線 ', '泰 馬 里 鄉 ', '毋 成 人 ', '隊 牛 大 前 ', '客 家 也 會 感 冒 ', '吐 血 ', '的 確 的 代 誌 才 通 講 ', '媠 媠 無 十 重 買 買 無 加 農 ', '一 袋 一 收 ', '番 仔 鰮 ', '啥 人 看 ', '六 西 六 州 ', '大 支 尾 ', '深 層 學 習 ', '軀 礐 ', '袂 較 大 ', '紅 奶 仔 草 ', '燉 蹄 ', '擬 徒 ', '匯 圖 ', '權 重 線 ', '袂 當 算 擺 ', '韓 國 戲 ', '十 全 大 埔 ', '壽 具 ', '西 山 分 ', '龍 舍 ', '好 似 草 ', '糯 米 芽 ', '光 著 ', '倒 數 尾 ', '似 看 賣 ']
\ No newline at end of file
diff --git a/preprocessor_config.json b/preprocessor_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..91876762a536a746d268353c5cba57286e76b058
--- /dev/null
+++ b/preprocessor_config.json
@@ -0,0 +1,14 @@
+{
+  "chunk_length": 30,
+  "feature_extractor_type": "WhisperFeatureExtractor",
+  "feature_size": 80,
+  "hop_length": 160,
+  "n_fft": 400,
+  "n_samples": 480000,
+  "nb_max_frames": 3000,
+  "padding_side": "right",
+  "padding_value": 0.0,
+  "processor_class": "WhisperProcessor",
+  "return_attention_mask": false,
+  "sampling_rate": 16000
+}
diff --git a/pytorch_model.bin b/pytorch_model.bin
new file mode 100644
index 0000000000000000000000000000000000000000..26310520bdfa1090fea185eba6170bf94bd12954
--- /dev/null
+++ b/pytorch_model.bin
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1b342399c8b0cd943f4d47e8fe025ee74c94323c03953e46a6c9fcf73d1e42d7
+size 135
diff --git a/run copy.sh b/run copy.sh
new file mode 100644
index 0000000000000000000000000000000000000000..06683e4c2978ef0ffbbdf216d2ac47fe5a699ede
--- /dev/null
+++ b/run copy.sh	
@@ -0,0 +1,34 @@
+python3 run_speech_recognition_seq2seq_streaming.py \
+--model_name_or_path="openai/whisper-medium" \
+--dataset_name="mozilla-foundation/common_voice_11_0" \
+--dataset_config_name="nan-tw" \
+--language="chinese" \
+--train_split_name="train" \
+--eval_split_name="test" \
+--model_index_name="Whisper medium nan-tw" \
+--max_steps="5000" \
+--output_dir="./" \
+--per_device_train_batch_size="2" \
+--per_device_eval_batch_size="2" \
+--logging_steps="25" \
+--learning_rate="1e-5" \
+--warmup_steps="500" \
+--evaluation_strategy="steps" \
+--eval_steps="1000" \
+--save_strategy="steps" \
+--save_steps="1000" \
+--generation_max_length="225" \
+--length_column_name="input_length" \
+--max_duration_in_seconds="30" \
+--text_column_name="sentence" \
+--freeze_feature_encoder="False" \
+--report_to="tensorboard" \
+--gradient_checkpointing \
+--fp16 \
+--overwrite_output_dir \
+--do_train \
+--do_eval \
+--predict_with_generate \
+--do_normalize_eval \
+--use_auth_token \
+--optim="adamw_bnb_8bit" 
diff --git a/run.sh b/run.sh
new file mode 100644
index 0000000000000000000000000000000000000000..1fb8ffaf1e536e00c724089200f9856d7231cb5f
--- /dev/null
+++ b/run.sh
@@ -0,0 +1,35 @@
+python3 local_data_run_speech_recognition_seq2seq_streaming_first_evaluate.py \
+--model_name_or_path="openai/whisper-medium" \
+--dataset_name="audiofolder" \
+--dataset_config_name="nan-tw" \
+--language="chinese" \
+--train_split_name="train" \
+--eval_split_name="test" \
+--model_index_name="Whisper medium nan-tw common voice" \
+--max_steps="5000" \
+--output_dir="./" \
+--per_device_train_batch_size="2" \
+--per_device_eval_batch_size="2" \
+--logging_steps="25" \
+--learning_rate="1e-5" \
+--warmup_steps="500" \
+--evaluation_strategy="steps" \
+--eval_steps="1000" \
+--save_strategy="steps" \
+--save_steps="1000" \
+--generation_max_length="225" \
+--length_column_name="input_length" \
+--max_duration_in_seconds="30" \
+--text_column_name="sentence" \
+--freeze_feature_encoder="False" \
+--report_to="tensorboard" \
+--gradient_checkpointing \
+--fp16 \
+--overwrite_output_dir \
+--do_train \
+--do_eval \
+--predict_with_generate \
+--do_normalize_eval \
+--use_auth_token \
+--push_to_hub="True" \
+--optim="adamw_bnb_8bit" 
diff --git a/run_speech_recognition_seq2seq_streaming.py b/run_speech_recognition_seq2seq_streaming.py
new file mode 100644
index 0000000000000000000000000000000000000000..b51a8f44759289fd1230d818a9dd5797a97b85d2
--- /dev/null
+++ b/run_speech_recognition_seq2seq_streaming.py
@@ -0,0 +1,617 @@
+#!/usr/bin/env python
+# coding=utf-8
+# Copyright 2022 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+Fine-tuning the library models for sequence to sequence speech recognition
+with 🤗 Datasets' streaming mode.
+"""
+# You can also adapt this script for your own sequence to sequence speech
+# recognition task. Pointers for this are left as comments.
+import ryNormText
+import logging
+import os
+import re
+import string
+import sys
+from dataclasses import dataclass, field
+from typing import Any, Dict, List, Optional, Union
+
+import datasets
+import torch
+from datasets import IterableDatasetDict, interleave_datasets, load_dataset
+from torch.utils.data import IterableDataset
+
+import evaluate
+import transformers
+from transformers import (
+    AutoConfig,
+    AutoFeatureExtractor,
+    AutoModelForSpeechSeq2Seq,
+    AutoProcessor,
+    AutoTokenizer,
+    HfArgumentParser,
+    Seq2SeqTrainer,
+    Seq2SeqTrainingArguments,
+    TrainerCallback,
+    set_seed,
+)
+from transformers.trainer_pt_utils import IterableDatasetShard
+from transformers.trainer_utils import get_last_checkpoint, is_main_process
+from transformers.utils import check_min_version, send_example_telemetry
+from transformers.utils.versions import require_version
+
+# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
+check_min_version("4.25.0.dev0")
+
+require_version("datasets>=1.18.2", "To fix: pip install -r examples/pytorch/speech-recognition/requirements.txt")
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class ModelArguments:
+    """
+    Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
+    """
+
+    model_name_or_path: str = field(
+        metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}
+    )
+    config_name: Optional[str] = field(
+        default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
+    )
+    tokenizer_name: Optional[str] = field(
+        default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
+    )
+    feature_extractor_name: Optional[str] = field(
+        default=None, metadata={"help": "feature extractor name or path if not the same as model_name"}
+    )
+    cache_dir: Optional[str] = field(
+        default=None,
+        metadata={"help": "Where to store the pretrained models downloaded from huggingface.co"},
+    )
+    use_fast_tokenizer: bool = field(
+        default=True,
+        metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."},
+    )
+    model_revision: str = field(
+        default="main",
+        metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."},
+    )
+    use_auth_token: bool = field(
+        default=False,
+        metadata={
+            "help": (
+                "Will use the token generated when running `huggingface-cli login` (necessary to use this script "
+                "with private models)."
+            )
+        },
+    )
+    freeze_feature_encoder: bool = field(
+        default=True, metadata={"help": "Whether to freeze the feature encoder layers of the model."}
+    )
+    freeze_encoder: bool = field(
+        default=False, metadata={"help": "Whether to freeze the entire encoder of the seq2seq model."}
+    )
+    forced_decoder_ids: List[List[int]] = field(
+        default=None,
+        metadata={
+            "help": (
+                "A list of pairs of integers which indicates a mapping from generation indices to token indices "
+                "that will be forced before sampling. For example, [[0, 123]] means the first generated token "
+                "will always be a token of index 123."
+            )
+        },
+    )
+    suppress_tokens: List[int] = field(
+        default=None, metadata={"help": "A list of tokens that will be suppressed at generation."}
+    )
+    model_index_name: str = field(default=None, metadata={"help": "Pretty name for the model card."})
+
+
+@dataclass
+class DataTrainingArguments:
+    """
+    Arguments pertaining to what data we are going to input our model for training and eval.
+    """
+
+    dataset_name: str = field(
+        default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."}
+    )
+    dataset_config_name: Optional[str] = field(
+        default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}
+    )
+    text_column: Optional[str] = field(
+        default=None,
+        metadata={"help": "The name of the column in the datasets containing the full texts (for summarization)."},
+    )
+    max_train_samples: Optional[int] = field(
+        default=None,
+        metadata={
+            "help": (
+                "For debugging purposes or quicker training, truncate the number of training examples to this "
+                "value if set."
+            )
+        },
+    )
+    max_eval_samples: Optional[int] = field(
+        default=None,
+        metadata={
+            "help": (
+                "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
+                "value if set."
+            )
+        },
+    )
+    audio_column_name: str = field(
+        default="audio",
+        metadata={"help": "The name of the dataset column containing the audio data. Defaults to 'audio'"},
+    )
+    text_column_name: str = field(
+        default="text",
+        metadata={"help": "The name of the dataset column containing the text data. Defaults to 'text'"},
+    )
+    max_duration_in_seconds: float = field(
+        default=20.0,
+        metadata={
+            "help": (
+                "Truncate audio files that are longer than `max_duration_in_seconds` seconds to"
+                " 'max_duration_in_seconds`"
+            )
+        },
+    )
+    min_duration_in_seconds: float = field(
+        default=0.0, metadata={"help": "Filter audio files that are shorter than `min_duration_in_seconds` seconds"}
+    )
+    train_split_name: str = field(
+        default="train",
+        metadata={
+            "help": "The name of the training data set split to use (via the datasets library). Defaults to 'train'"
+        },
+    )
+    eval_split_name: str = field(
+        default="test",
+        metadata={
+            "help": "The name of the training data set split to use (via the datasets library). Defaults to 'train'"
+        },
+    )
+    do_lower_case: bool = field(
+        default=False,
+        metadata={"help": "Whether the target text should be lower cased."},
+    )
+    do_remove_punctuation: bool = field(
+        default=False,
+        metadata={"help": "Whether the target text should be striped of punctuation."},
+    )
+    do_normalize_eval: bool = field(
+        default=True,
+        metadata={"help": "Whether to normalise the references and predictions in the eval WER calculation."},
+    )
+    language: str = field(
+        default=None,
+        metadata={
+            "help": (
+                "Language for multilingual fine-tuning. This argument should be set for multilingual fine-tuning "
+                "only. For English speech recognition, it should be set to `None`."
+            )
+        },
+    )
+    task: str = field(
+        default="transcribe",
+        metadata={"help": "Task, either `transcribe` for speech recognition or `translate` for speech translation."},
+    )
+    shuffle_buffer_size: Optional[int] = field(
+        default=500,
+        metadata={
+            "help": (
+                "The number of streamed examples to download before shuffling them. The large the buffer, "
+                "the closer it is to real offline shuffling."
+            )
+        },
+    )
+
+
+@dataclass
+class DataCollatorSpeechSeq2SeqWithPadding:
+    """
+    Data collator that will dynamically pad the inputs received.
+    Args:
+        processor ([`WhisperProcessor`])
+            The processor used for processing the data.
+        decoder_start_token_id (`int`)
+            The begin-of-sentence of the decoder.
+    """
+
+    processor: Any
+    decoder_start_token_id: int
+
+    def __call__(self, features: List[Dict[str, Union[List[int], torch.Tensor]]]) -> Dict[str, torch.Tensor]:
+        # split inputs and labels since they have to be of different lengths and need
+        # different padding methods
+        model_input_name = self.processor.model_input_names[0]
+        input_features = [{model_input_name: feature[model_input_name]} for feature in features]
+        label_features = [{"input_ids": feature["labels"]} for feature in features]
+
+        batch = self.processor.feature_extractor.pad(input_features, return_tensors="pt")
+
+        labels_batch = self.processor.tokenizer.pad(label_features, return_tensors="pt")
+
+        # replace padding with -100 to ignore loss correctly
+        labels = labels_batch["input_ids"].masked_fill(labels_batch.attention_mask.ne(1), -100)
+
+        # if bos token is appended in previous tokenization step,
+        # cut bos token here as it's append later anyways
+        if (labels[:, 0] == self.decoder_start_token_id).all().cpu().item():
+            labels = labels[:, 1:]
+
+        batch["labels"] = labels
+
+        return batch
+
+
+def load_streaming_dataset(dataset_name, dataset_config_name, split="train", **kwargs):
+    """
+    Utility function to load a dataset in streaming mode. For datasets with multiple splits,
+    each split is loaded individually and then splits combined by taking alternating examples from
+    each (interleaving).
+    """
+    if "+" in split:
+        # load multiple splits separated by the `+` symbol with streaming mode
+        dataset_splits = [
+            load_dataset(dataset_name, dataset_config_name, split=split_name, streaming=True, **kwargs)
+            for split_name in split.split("+")
+        ]
+        # interleave multiple splits to form one dataset
+        interleaved_dataset = interleave_datasets(dataset_splits)
+        return interleaved_dataset
+    else:
+        # load a single split *with* streaming mode
+        dataset = load_dataset(dataset_name, dataset_config_name, split=split, streaming=True, **kwargs)
+        return dataset
+
+
+def main():
+    # 1. Parse input arguments
+    # See all possible arguments in src/transformers/training_args.py
+    # or by passing the --help flag to this script.
+    # We now keep distinct sets of args, for a cleaner separation of concerns.
+    parser = HfArgumentParser((ModelArguments, DataTrainingArguments, Seq2SeqTrainingArguments))
+
+    if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
+        # If we pass only one argument to the script and it's the path to a json file,
+        # let's parse it to get our arguments.
+        model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
+    else:
+        model_args, data_args, training_args = parser.parse_args_into_dataclasses()
+
+    # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
+    # information sent is the one passed as arguments along with your Python/PyTorch versions.
+    send_example_telemetry("run_speech_recognition_seq2seq_streaming", model_args, data_args)
+
+    # 2. Setup logging
+    logging.basicConfig(
+        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
+        datefmt="%m/%d/%Y %H:%M:%S",
+        handlers=[logging.StreamHandler(sys.stdout)],
+    )
+    log_level = training_args.get_process_log_level()
+    logger.setLevel(log_level)
+    datasets.utils.logging.set_verbosity(log_level)
+    transformers.utils.logging.set_verbosity(log_level)
+    transformers.utils.logging.enable_default_handler()
+    transformers.utils.logging.enable_explicit_format()
+
+    logger.setLevel(logging.INFO if is_main_process(training_args.local_rank) else logging.WARN)
+
+    # Log on each process the small summary:
+    logger.warning(
+        f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
+        f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}"
+    )
+    logger.info(f"Training/evaluation parameters {training_args}")
+
+    # Set the verbosity to info of the Transformers logger (on main process only):
+    if is_main_process(training_args.local_rank):
+        transformers.utils.logging.set_verbosity_info()
+    logger.info("Training/evaluation parameters %s", training_args)
+
+    # 3. Detecting last checkpoint and eventually continue from last checkpoint
+    last_checkpoint = None
+    if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
+        last_checkpoint = get_last_checkpoint(training_args.output_dir)
+        if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:
+            raise ValueError(
+                f"Output directory ({training_args.output_dir}) already exists and is not empty. "
+                "Use --overwrite_output_dir to overcome."
+            )
+        elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
+            logger.info(
+                f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
+                "the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
+            )
+
+    # Set seed before initializing model.
+    set_seed(training_args.seed)
+
+    # 4. Load dataset
+    raw_datasets = IterableDatasetDict()
+
+    if training_args.do_train:
+        raw_datasets["train"] = load_streaming_dataset(
+            data_args.dataset_name,
+            data_args.dataset_config_name,
+            split=data_args.train_split_name,
+            use_auth_token=True if model_args.use_auth_token else None,
+        )
+
+    if training_args.do_eval:
+        raw_datasets["eval"] = load_streaming_dataset(
+            data_args.dataset_name,
+            data_args.dataset_config_name,
+            split=data_args.eval_split_name,
+            use_auth_token=True if model_args.use_auth_token else None,
+        )
+
+    raw_datasets_features = list(next(iter(raw_datasets.values())).features.keys())
+
+    if data_args.audio_column_name not in raw_datasets_features:
+        raise ValueError(
+            f"--audio_column_name '{data_args.audio_column_name}' not found in dataset '{data_args.dataset_name}'. "
+            "Make sure to set `--audio_column_name` to the correct audio column - one of "
+            f"{', '.join(raw_datasets_features)}."
+        )
+
+    if data_args.text_column_name not in raw_datasets_features:
+        raise ValueError(
+            f"--text_column_name {data_args.text_column_name} not found in dataset '{data_args.dataset_name}'. "
+            "Make sure to set `--text_column_name` to the correct text column - one of "
+            f"{', '.join(raw_datasets_features)}."
+        )
+
+    # 5. Load pretrained model, tokenizer, and feature extractor
+    #
+    # Distributed training:
+    # The .from_pretrained methods guarantee that only one local process can concurrently
+    config = AutoConfig.from_pretrained(
+        model_args.config_name if model_args.config_name else model_args.model_name_or_path,
+        cache_dir=model_args.cache_dir,
+        revision=model_args.model_revision,
+        use_auth_token=True if model_args.use_auth_token else None,
+    )
+
+    config.update({"forced_decoder_ids": model_args.forced_decoder_ids, "suppress_tokens": model_args.suppress_tokens})
+
+    feature_extractor = AutoFeatureExtractor.from_pretrained(
+        model_args.feature_extractor_name if model_args.feature_extractor_name else model_args.model_name_or_path,
+        cache_dir=model_args.cache_dir,
+        revision=model_args.model_revision,
+        use_auth_token=True if model_args.use_auth_token else None,
+    )
+    tokenizer = AutoTokenizer.from_pretrained(
+        model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
+        cache_dir=model_args.cache_dir,
+        use_fast=model_args.use_fast_tokenizer,
+        revision=model_args.model_revision,
+        use_auth_token=True if model_args.use_auth_token else None,
+    )
+    model = AutoModelForSpeechSeq2Seq.from_pretrained(
+        model_args.model_name_or_path,
+        config=config,
+        cache_dir=model_args.cache_dir,
+        revision=model_args.model_revision,
+        use_auth_token=True if model_args.use_auth_token else None,
+    )
+
+    if model.config.decoder_start_token_id is None:
+        raise ValueError("Make sure that `config.decoder_start_token_id` is correctly defined")
+
+    if model_args.freeze_feature_encoder:
+        model.freeze_feature_encoder()
+
+    if model_args.freeze_encoder:
+        model.freeze_encoder()
+        model.model.encoder.gradient_checkpointing = False
+
+    if data_args.language is not None:
+        # We only need to set the task id when the language is specified (i.e. in a multilingual setting)
+        tokenizer.set_prefix_tokens(language=data_args.language, task=data_args.task)
+
+    # 6. Resample speech dataset if necessary
+    dataset_sampling_rate = next(iter(raw_datasets.values())).features[data_args.audio_column_name].sampling_rate
+    if dataset_sampling_rate != feature_extractor.sampling_rate:
+        raw_datasets = raw_datasets.cast_column(
+            data_args.audio_column_name, datasets.features.Audio(sampling_rate=feature_extractor.sampling_rate)
+        )
+
+    # 7. Preprocessing the datasets.
+    # We need to read the audio files as arrays and tokenize the targets.
+    max_input_length = data_args.max_duration_in_seconds * feature_extractor.sampling_rate
+    min_input_length = data_args.min_duration_in_seconds * feature_extractor.sampling_rate
+    audio_column_name = data_args.audio_column_name
+    text_column_name = data_args.text_column_name
+    model_input_name = feature_extractor.model_input_names[0]
+    do_lower_case = data_args.do_lower_case
+    do_remove_punctuation = data_args.do_remove_punctuation
+
+    punctuation_to_remove = string.punctuation.replace("'", "")  # don't remove apostrophes
+    punctuation_to_remove_regex = f"[{''.join(punctuation_to_remove)}]"
+
+    if data_args.max_train_samples is not None:
+        raw_datasets["train"] = raw_datasets["train"].take(data_args.max_train_samples)
+
+    if data_args.max_eval_samples is not None:
+        raw_datasets["eval"] = raw_datasets["eval"].take(data_args.max_eval_samples)
+
+    def prepare_dataset(batch):
+        # process audio
+        sample = batch[audio_column_name]
+        inputs = feature_extractor(sample["array"], sampling_rate=sample["sampling_rate"])
+        # process audio length
+        batch[model_input_name] = inputs.get(model_input_name)[0]
+        batch["input_length"] = len(sample["array"])
+
+        # process targets (only char no rome)
+        input_str = batch[text_column_name].lower().split('（')[0] if do_lower_case else batch[text_column_name].split('（')[0]
+        
+        if do_remove_punctuation:
+            input_str = re.sub(punctuation_to_remove_regex, " ", input_str).strip()
+            input_str = re.sub("\s\s+", " ", input_str)
+        batch["labels"] = tokenizer(input_str).input_ids
+        return batch
+
+    with training_args.main_process_first(desc="dataset map pre-processing"):
+        vectorized_datasets = raw_datasets.map(
+            prepare_dataset,
+            remove_columns=raw_datasets_features,
+        ).with_format("torch")
+
+        if training_args.do_train:
+            vectorized_datasets["train"] = vectorized_datasets["train"].shuffle(
+                buffer_size=data_args.shuffle_buffer_size,
+                seed=training_args.seed,
+            )
+
+    # filter training data that is shorter than min_input_length or longer than
+    # max_input_length
+    def is_audio_in_length_range(length):
+        return min_input_length < length < max_input_length
+
+    if training_args.do_train:
+        vectorized_datasets["train"] = vectorized_datasets["train"].filter(
+            is_audio_in_length_range,
+            input_columns=["input_length"],
+        )
+
+    # 8. Load Metric
+    cer_metric = evaluate.load("cer")
+    wer_metric = evaluate.load("wer")
+    do_normalize_eval = data_args.do_normalize_eval
+
+    def compute_metrics(pred):
+        pred_ids = pred.predictions
+
+        pred.label_ids[pred.label_ids == -100] = tokenizer.pad_token_id
+
+        pred_str = tokenizer.batch_decode(pred_ids, skip_special_tokens=True, normalize=do_normalize_eval)
+        # we do not want to group tokens when computing the metrics
+        label_str = tokenizer.batch_decode(pred.label_ids, skip_special_tokens=True, normalize=do_normalize_eval)
+        cer = 100 * cer_metric.compute(predictions=pred_str, references=label_str)
+        pred_str = [ryNormText.separ_char_word(x) for x in pred_str]
+        label_str = [ryNormText.separ_char_word(x) for x in label_str]
+        p = open('pred.txt','w')
+        p.write(f'{pred_str=}')
+        p.close()
+        p = open('label.txt','w')
+        p.write(f'{label_str=}')
+        p.close()
+        wer = 100 * wer_metric.compute(predictions=pred_str, references=label_str)
+        return {"wer": wer, "cer": cer}
+
+    # 9. Create a single speech processor
+    if is_main_process(training_args.local_rank):
+        # save feature extractor, tokenizer and config
+        feature_extractor.save_pretrained(training_args.output_dir)
+        tokenizer.save_pretrained(training_args.output_dir)
+        config.save_pretrained(training_args.output_dir)
+
+    processor = AutoProcessor.from_pretrained(training_args.output_dir)
+
+    # 10. Define data collator
+    data_collator = DataCollatorSpeechSeq2SeqWithPadding(
+        processor=processor,
+        decoder_start_token_id=model.config.decoder_start_token_id,
+    )
+
+    # 11. Configure Trainer
+    # Trainer callback to reinitialise and reshuffle the streamable datasets at the beginning of each epoch
+    class ShuffleCallback(TrainerCallback):
+        def on_epoch_begin(self, args, state, control, train_dataloader, **kwargs):
+            if isinstance(train_dataloader.dataset, IterableDatasetShard):
+                pass  # set_epoch() is handled by the Trainer
+            elif isinstance(train_dataloader.dataset, IterableDataset):
+                train_dataloader.dataset.set_epoch(train_dataloader.dataset._epoch + 1)
+
+    # Initialize Trainer
+    trainer = Seq2SeqTrainer(
+        model=model,
+        args=training_args,
+        train_dataset=vectorized_datasets["train"] if training_args.do_train else None,
+        eval_dataset=vectorized_datasets["eval"] if training_args.do_eval else None,
+        tokenizer=feature_extractor,
+        data_collator=data_collator,
+        compute_metrics=compute_metrics if training_args.predict_with_generate else None,
+        callbacks=[ShuffleCallback()],
+    )
+
+    # 12. Training
+    if training_args.do_train:
+        checkpoint = None
+        if training_args.resume_from_checkpoint is not None:
+            checkpoint = training_args.resume_from_checkpoint
+        elif last_checkpoint is not None:
+            checkpoint = last_checkpoint
+        train_result = trainer.train(resume_from_checkpoint=checkpoint)
+        trainer.save_model()  # Saves the feature extractor too for easy upload
+
+        metrics = train_result.metrics
+        if data_args.max_train_samples:
+            metrics["train_samples"] = data_args.max_train_samples
+        trainer.log_metrics("train", metrics)
+        trainer.save_metrics("train", metrics)
+        trainer.save_state()
+
+    # 13. Evaluation
+    results = {}
+    if training_args.do_eval:
+        logger.info("*** Evaluate ***")
+        metrics = trainer.evaluate(
+
+            metric_key_prefix="eval",
+            max_length=training_args.generation_max_length,
+            num_beams=training_args.generation_num_beams,
+        )
+        if data_args.max_eval_samples:
+            metrics["eval_samples"] = data_args.max_eval_samples
+
+        trainer.log_metrics("eval", metrics)
+        trainer.save_metrics("eval", metrics)
+
+    # 14. Write Training Stats
+    kwargs = {
+        "finetuned_from": model_args.model_name_or_path,
+        "tasks": "automatic-speech-recognition",
+        "tags": "whisper-event",
+    }
+    if data_args.dataset_name is not None:
+        kwargs["dataset_tags"] = data_args.dataset_name
+        if data_args.dataset_config_name is not None:
+            kwargs["dataset"] = f"{data_args.dataset_name} {data_args.dataset_config_name}"
+        else:
+            kwargs["dataset"] = data_args.dataset_name
+        if "common_voice" in data_args.dataset_name:
+            kwargs["language"] = data_args.dataset_config_name
+        if model_args.model_index_name is not None:
+            kwargs["model_name"] = model_args.model_index_name
+
+    if training_args.push_to_hub:
+        trainer.push_to_hub(**kwargs)
+    else:
+        trainer.create_model_card(**kwargs)
+
+    return results
+
+
+if __name__ == "__main__":
+    main()
diff --git a/runs/May27_19-41-29_DESKTOP-EVSD799/1685187714.55857/events.out.tfevents.1685187714.DESKTOP-EVSD799.25803.1 b/runs/May27_19-41-29_DESKTOP-EVSD799/1685187714.55857/events.out.tfevents.1685187714.DESKTOP-EVSD799.25803.1
new file mode 100644
index 0000000000000000000000000000000000000000..66100a68961ef076f6546f47a2294f4730eb8788
--- /dev/null
+++ b/runs/May27_19-41-29_DESKTOP-EVSD799/1685187714.55857/events.out.tfevents.1685187714.DESKTOP-EVSD799.25803.1
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b8e273f15bb28107c6019ee0e2bed78203992ee066322efd5080ecb07a220ccc
+size 129
diff --git a/runs/May27_19-41-29_DESKTOP-EVSD799/events.out.tfevents.1685187714.DESKTOP-EVSD799.25803.0 b/runs/May27_19-41-29_DESKTOP-EVSD799/events.out.tfevents.1685187714.DESKTOP-EVSD799.25803.0
new file mode 100644
index 0000000000000000000000000000000000000000..bc9c38efe81777d62c59e70ee82b472ed696e9d3
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+++ b/runs/May27_19-41-29_DESKTOP-EVSD799/events.out.tfevents.1685187714.DESKTOP-EVSD799.25803.0
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:272e91d4a7b8b74d6ca6acf2fecda6c4c9cbde85869d4e4322e11b77ee333f8c
+size 130
diff --git a/runs/May27_19-41-29_DESKTOP-EVSD799/events.out.tfevents.1685191671.DESKTOP-EVSD799.25803.2 b/runs/May27_19-41-29_DESKTOP-EVSD799/events.out.tfevents.1685191671.DESKTOP-EVSD799.25803.2
new file mode 100644
index 0000000000000000000000000000000000000000..206fbcaf72f00fb95afc25dcef59c95028147e2d
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@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f997e82251581473f084079d2ebf0a7b276b51541184fd23195993c61671fcf9
+size 128
diff --git a/runs/Nov29_01-15-14_19115951741f/events.out.tfevents.1732843151.19115951741f.2594.0 b/runs/Nov29_01-15-14_19115951741f/events.out.tfevents.1732843151.19115951741f.2594.0
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index 0000000000000000000000000000000000000000..02070ada56cd4c5f0c3000c213615f6538d4597b
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+++ b/runs/Nov29_01-15-14_19115951741f/events.out.tfevents.1732843151.19115951741f.2594.0
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d82c041e25b4b4424b8a829adf3dfdcd151b372d2d52f546a7064f4e7f6f1068
+size 17772
diff --git a/ryNormText.py b/ryNormText.py
new file mode 100644
index 0000000000000000000000000000000000000000..a6490f2e05f05d4a1db86427a6af9aaf5e2bb6ee
--- /dev/null
+++ b/ryNormText.py
@@ -0,0 +1,81 @@
+import string, re, opencc
+
+
+全型2半型= str.maketrans(
+    '　０１２３４５６７８９'
+    'ａｂｃｄｅｆｇｈｉｊｋｌｍｎｏｐｑｒｓｔｕｖｗｘｙｚ'
+    'ＡＢＣＤＥＦＧＨＩＪＫＬＭＮＯＰＱＲＳＴＵＶＷＸＹＺ'
+    '！゛＃＄％＆（）＊＋、ー。／：；〈＝〉？＠［］＾＿‘｛｜｝～',
+    ' 0123456789'
+    'abcdefghijklmnopqrstuvwxyz'
+    'ABCDEFGHIJKLMNOPQRSTUVWXYZ'
+    '!"#$%&()*+,-./:;<=>?@[]^_`{|}~'
+    )
+
+def 把怪字修進unicode(xStr):
+    xStr= re.sub('\uf5c3','𪜶', xStr)
+    return xStr
+
+def ryNormText(s):
+    """
+    <<<None>>> ==> 刪除
+    標點 ==> 空白
+    連續空白 ==> 1個空白
+    簡繁
+    """
+
+    punc1= string.punctuation  # 這是為英文
+    punc1
+    punc2= '。，﹐、！？：：；『』「」…,\n' # 這是為中文，尚未完備！！
+
+    punc= f"[{punc1}{punc2}]"  ## 這是 regular expression 的 pattern
+
+    ## <<<None>>> ==> 刪除
+    s= re.sub('<<<None>>>','',s)
+
+    # 標點 ==> 空白
+    s= re.sub(punc,' ',s)
+    
+
+    # 連續空白 ==> 1個空白
+    s= re.sub('[ ]+',' ',s)
+    
+    
+    # 空白 ==> 刪除
+    s= re.sub(' ','',s)
+    
+    s= 把怪字修進unicode(s)
+
+    # 簡繁
+    s= opencc.OpenCC('s2tw').convert(s)
+    
+
+    return s
+
+import unicodedata
+import re
+
+
+def separ_char_word(inputString= '我是呂仁園 Renyuan Lyu'):
+
+    inputString= 把怪字修進unicode(inputString)
+
+    y= ''
+    for x in inputString:
+        y += x
+        try:
+            un= unicodedata.name(x)
+            if un.startswith('CJK'):
+                y += ' '
+            else:
+                pass
+        except Exception as ex:
+            y = ' '+y+' '
+            print(f'ryErr:(def 中英分開:){ex= }\t【{x= }】\t{inputString= }')
+
+    y= re.sub('[ ]+',' ', y) #連續空白只保留1個空白
+    return y
+
+#q= 中英分開('大家好 da jia hao 我是呂仁園 I am Renyuan Lyu')
+
+#print(q)
\ No newline at end of file
diff --git a/special_tokens_map.json b/special_tokens_map.json
new file mode 100644
index 0000000000000000000000000000000000000000..bf69932dca4b3719b59fdd8f6cc1978109509f6c
--- /dev/null
+++ b/special_tokens_map.json
@@ -0,0 +1,139 @@
+{
+  "additional_special_tokens": [
+    "<|endoftext|>",
+    "<|startoftranscript|>",
+    "<|en|>",
+    "<|zh|>",
+    "<|de|>",
+    "<|es|>",
+    "<|ru|>",
+    "<|ko|>",
+    "<|fr|>",
+    "<|ja|>",
+    "<|pt|>",
+    "<|tr|>",
+    "<|pl|>",
+    "<|ca|>",
+    "<|nl|>",
+    "<|ar|>",
+    "<|sv|>",
+    "<|it|>",
+    "<|id|>",
+    "<|hi|>",
+    "<|fi|>",
+    "<|vi|>",
+    "<|he|>",
+    "<|uk|>",
+    "<|el|>",
+    "<|ms|>",
+    "<|cs|>",
+    "<|ro|>",
+    "<|da|>",
+    "<|hu|>",
+    "<|ta|>",
+    "<|no|>",
+    "<|th|>",
+    "<|ur|>",
+    "<|hr|>",
+    "<|bg|>",
+    "<|lt|>",
+    "<|la|>",
+    "<|mi|>",
+    "<|ml|>",
+    "<|cy|>",
+    "<|sk|>",
+    "<|te|>",
+    "<|fa|>",
+    "<|lv|>",
+    "<|bn|>",
+    "<|sr|>",
+    "<|az|>",
+    "<|sl|>",
+    "<|kn|>",
+    "<|et|>",
+    "<|mk|>",
+    "<|br|>",
+    "<|eu|>",
+    "<|is|>",
+    "<|hy|>",
+    "<|ne|>",
+    "<|mn|>",
+    "<|bs|>",
+    "<|kk|>",
+    "<|sq|>",
+    "<|sw|>",
+    "<|gl|>",
+    "<|mr|>",
+    "<|pa|>",
+    "<|si|>",
+    "<|km|>",
+    "<|sn|>",
+    "<|yo|>",
+    "<|so|>",
+    "<|af|>",
+    "<|oc|>",
+    "<|ka|>",
+    "<|be|>",
+    "<|tg|>",
+    "<|sd|>",
+    "<|gu|>",
+    "<|am|>",
+    "<|yi|>",
+    "<|lo|>",
+    "<|uz|>",
+    "<|fo|>",
+    "<|ht|>",
+    "<|ps|>",
+    "<|tk|>",
+    "<|nn|>",
+    "<|mt|>",
+    "<|sa|>",
+    "<|lb|>",
+    "<|my|>",
+    "<|bo|>",
+    "<|tl|>",
+    "<|mg|>",
+    "<|as|>",
+    "<|tt|>",
+    "<|haw|>",
+    "<|ln|>",
+    "<|ha|>",
+    "<|ba|>",
+    "<|jw|>",
+    "<|su|>",
+    "<|translate|>",
+    "<|transcribe|>",
+    "<|startoflm|>",
+    "<|startofprev|>",
+    "<|nocaptions|>",
+    "<|notimestamps|>"
+  ],
+  "bos_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}
diff --git a/tokenizer.json b/tokenizer.json
new file mode 100644
index 0000000000000000000000000000000000000000..513a5b03131d55339b86f759dc96112e07e7cb62
--- /dev/null
+++ b/tokenizer.json
@@ -0,0 +1,264896 @@
+{
+  "version": "1.0",
+  "truncation": null,
+  "padding": null,
+  "added_tokens": [
+    {
+      "id": 50257,
+      "content": "<|endoftext|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    {
+      "id": 50258,
+      "content": "<|startoftranscript|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    {
+      "id": 50259,
+      "content": "<|en|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    {
+      "id": 50260,
+      "content": "<|zh|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    {
+      "id": 50261,
+      "content": "<|de|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    {
+      "id": 50262,
+      "content": "<|es|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    {
+      "id": 50263,
+      "content": "<|ru|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    {
+      "id": 50264,
+      "content": "<|ko|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    {
+      "id": 50265,
+      "content": "<|fr|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    {
+      "id": 50266,
+      "content": "<|ja|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    {
+      "id": 50267,
+      "content": "<|pt|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    {
+      "id": 50268,
+      "content": "<|tr|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    {
+      "id": 50269,
+      "content": "<|pl|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    {
+      "id": 50270,
+      "content": "<|ca|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    {
+      "id": 50271,
+      "content": "<|nl|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    {
+      "id": 50272,
+      "content": "<|ar|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    {
+      "id": 50273,
+      "content": "<|sv|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    {
+      "id": 50274,
+      "content": "<|it|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    {
+      "id": 50275,
+      "content": "<|id|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    {
+      "id": 50276,
+      "content": "<|hi|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    {
+      "id": 50277,
+      "content": "<|fi|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    {
+      "id": 50278,
+      "content": "<|vi|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    {
+      "id": 50279,
+      "content": "<|he|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    {
+      "id": 50280,
+      "content": "<|uk|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    {
+      "id": 50281,
+      "content": "<|el|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    {
+      "id": 50282,
+      "content": "<|ms|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    {
+      "id": 50283,
+      "content": "<|cs|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    {
+      "id": 50284,
+      "content": "<|ro|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    {
+      "id": 50285,
+      "content": "<|da|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    {
+      "id": 50286,
+      "content": "<|hu|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    {
+      "id": 50287,
+      "content": "<|ta|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    {
+      "id": 50288,
+      "content": "<|no|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    {
+      "id": 50289,
+      "content": "<|th|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
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+      [
+        "v",
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+      [
+        "mer",
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+      [
+        "M",
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+      [
+        "Ġcast",
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+      [
+        "п",
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+      [
+        "ĠÑħоÑĤ",
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+      ],
+      [
+        "Ġн",
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+        "Ġпоб",
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+      [
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+        "Ġmin",
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+        "Ġcompar",
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+      [
+        "car",
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+      [
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+        "Ġsjäl",
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+        "Ġd",
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+        "æĿ",
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+        "ĠN",
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+      [
+        "ĠV",
+        "ay"
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+      [
+        "ST",
+        "ALK"
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+        "ак",
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+        "av",
+        "at"
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+        "çł",
+        "´"
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+        "Ġpun",
+        "kt"
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+        "Ġpad",
+        "ding"
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+      [
+        "ĠT",
+        "empl"
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+      [
+        "Ġe",
+        "je"
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+        "Ġa",
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+      [
+        "Ġcolonial",
+        "ism"
+      ],
+      [
+        "ĠBl",
+        "iss"
+      ],
+      [
+        "str",
+        "ings"
+      ],
+      [
+        "Ġreun",
+        "ited"
+      ],
+      [
+        "ĠPs",
+        "aki"
+      ],
+      [
+        "w",
+        "ach"
+      ],
+      [
+        "Ġcl",
+        "iffs"
+      ],
+      [
+        "ĠFal",
+        "se"
+      ],
+      [
+        "ä",
+        "g"
+      ],
+      [
+        "p",
+        "ipe"
+      ],
+      [
+        "Ġwh",
+        "opping"
+      ],
+      [
+        "Ġmering",
+        "ue"
+      ],
+      [
+        "Ġb",
+        "ung"
+      ],
+      [
+        "indust",
+        "rie"
+      ],
+      [
+        "Ġle",
+        "che"
+      ],
+      [
+        "ĠL",
+        "oy"
+      ],
+      [
+        "Ġdr",
+        "ie"
+      ],
+      [
+        "Ġpass",
+        "at"
+      ],
+      [
+        "Ġo",
+        "leh"
+      ],
+      [
+        "Ġcé",
+        "u"
+      ],
+      [
+        "ĠGab",
+        "rie"
+      ],
+      [
+        "Ġreef",
+        "s"
+      ],
+      [
+        "Ġbom",
+        "bers"
+      ],
+      [
+        "Ġepisód",
+        "io"
+      ],
+      [
+        "ĠR",
+        "ug"
+      ],
+      [
+        "ĠPr",
+        "ose"
+      ],
+      [
+        "on",
+        "os"
+      ],
+      [
+        "Ġob",
+        "ese"
+      ],
+      [
+        "Ġgo",
+        "og"
+      ],
+      [
+        "Ġpi",
+        "ace"
+      ],
+      [
+        "flan",
+        "zen"
+      ],
+      [
+        "éĴ",
+        "Ł"
+      ],
+      [
+        "Ġfl",
+        "aps"
+      ],
+      [
+        "ĠAl",
+        "to"
+      ],
+      [
+        "é£Ł",
+        "ãģ¹"
+      ],
+      [
+        "F",
+        "in"
+      ],
+      [
+        "Ġres",
+        "ize"
+      ],
+      [
+        "ê·¸ë",
+        "ŀ¨"
+      ],
+      [
+        "è²",
+        "»"
+      ],
+      [
+        "N",
+        "athan"
+      ],
+      [
+        "ŀĪë",
+        "ł¤"
+      ],
+      [
+        "ĠÑĤ",
+        "ай"
+      ],
+      [
+        "ĠNF",
+        "T"
+      ],
+      [
+        "Ġsne",
+        "eze"
+      ],
+      [
+        "Ġshr",
+        "oud"
+      ],
+      [
+        "i",
+        "é"
+      ],
+      [
+        "Ġver",
+        "amente"
+      ],
+      [
+        "Ġcas",
+        "cade"
+      ],
+      [
+        "ĠO",
+        "ok"
+      ],
+      [
+        "ìĹĨ",
+        "ìĿ´"
+      ],
+      [
+        "Ġinf",
+        "used"
+      ],
+      [
+        "f",
+        "ps"
+      ],
+      [
+        "cent",
+        "er"
+      ],
+      [
+        "Ġgrapp",
+        "ling"
+      ],
+      [
+        "ĠWohn",
+        "ung"
+      ],
+      [
+        "ĠT",
+        "umb"
+      ],
+      [
+        "ĠIm",
+        "ma"
+      ],
+      [
+        "ĠDu",
+        "ygusal"
+      ],
+      [
+        "ен",
+        "ÑĤи"
+      ],
+      [
+        "Ġstewards",
+        "hip"
+      ],
+      [
+        "Ġhar",
+        "p"
+      ],
+      [
+        "Ġendors",
+        "ed"
+      ],
+      [
+        "ıl",
+        "an"
+      ],
+      [
+        "Ġод",
+        "ним"
+      ],
+      [
+        "Ġcompet",
+        "ency"
+      ],
+      [
+        "Ġb",
+        "ert"
+      ],
+      [
+        "ĠT",
+        "ales"
+      ],
+      [
+        "Ġr",
+        "he"
+      ],
+      [
+        "Ġoh",
+        "h"
+      ],
+      [
+        "Ġê°Ħ",
+        "ëĭ¨"
+      ],
+      [
+        "Ġm",
+        "RNA"
+      ],
+      [
+        "Ġgang",
+        "ster"
+      ],
+      [
+        "ĠRun",
+        "ner"
+      ],
+      [
+        "ен",
+        "нÑĭм"
+      ],
+      [
+        "ph",
+        "oria"
+      ],
+      [
+        "ĠwÅĤaÅĽci",
+        "wie"
+      ],
+      [
+        "Ġqu",
+        "arto"
+      ],
+      [
+        "Ġorgan",
+        "ise"
+      ],
+      [
+        "ĠV",
+        "et"
+      ],
+      [
+        "P",
+        "ad"
+      ],
+      [
+        "ĠÙħ",
+        "Ø«"
+      ],
+      [
+        "Ġst",
+        "inks"
+      ],
+      [
+        "ĠD",
+        "ul"
+      ],
+      [
+        "u",
+        "em"
+      ],
+      [
+        "is",
+        "iej"
+      ],
+      [
+        "T",
+        "op"
+      ],
+      [
+        "Ġt",
+        "ussen"
+      ],
+      [
+        "ĠE",
+        "fendimiz"
+      ],
+      [
+        "ĠB",
+        "oule"
+      ],
+      [
+        "ĠSlo",
+        "ven"
+      ],
+      [
+        "ĠL",
+        "ö"
+      ],
+      [
+        "Ñij",
+        "з"
+      ],
+      [
+        "ÑĢи",
+        "п"
+      ],
+      [
+        "ca",
+        "ve"
+      ],
+      [
+        "Ġbo",
+        "î"
+      ],
+      [
+        "Ġapolog",
+        "ise"
+      ],
+      [
+        "ĠMar",
+        "ly"
+      ],
+      [
+        "ĠEx",
+        "port"
+      ],
+      [
+        "ĠCait",
+        "lin"
+      ],
+      [
+        "Ġtav",
+        "alla"
+      ],
+      [
+        "Ġent",
+        "ails"
+      ],
+      [
+        "Ġbr",
+        "om"
+      ],
+      [
+        "ĠCop",
+        "enh"
+      ],
+      [
+        "Ġwal",
+        "nut"
+      ],
+      [
+        "Ġins",
+        "ists"
+      ],
+      [
+        "Ġcu",
+        "á»Ļc"
+      ],
+      [
+        "ĠQ",
+        "uit"
+      ],
+      [
+        "ĠDev",
+        "ice"
+      ],
+      [
+        "×Ĵ",
+        "×Ŀ"
+      ],
+      [
+        "ĠD",
+        "OT"
+      ],
+      [
+        "Ġveloc",
+        "idad"
+      ],
+      [
+        "L",
+        "IE"
+      ],
+      [
+        "C",
+        "ool"
+      ],
+      [
+        "Ġsan",
+        "itation"
+      ],
+      [
+        "Ġol",
+        "ho"
+      ],
+      [
+        "ĠE",
+        "B"
+      ],
+      [
+        "ĠíĻķ",
+        "ìĭ¤íŀĪ"
+      ],
+      [
+        "ĠÐľ",
+        "иÑħ"
+      ],
+      [
+        "Ġz",
+        "uk"
+      ],
+      [
+        "Ġsurn",
+        "ame"
+      ],
+      [
+        "ĠSch",
+        "uld"
+      ],
+      [
+        "ru",
+        "ff"
+      ],
+      [
+        "c",
+        "ultural"
+      ],
+      [
+        "ĠÑģÑĤ",
+        "олÑĮко"
+      ],
+      [
+        "æĻļ",
+        "ä¸Ĭ"
+      ],
+      [
+        "Įë",
+        "į°"
+      ],
+      [
+        "Ġtor",
+        "to"
+      ],
+      [
+        "Ġback",
+        "ups"
+      ],
+      [
+        "ÑĢи",
+        "й"
+      ],
+      [
+        "re",
+        "lax"
+      ],
+      [
+        "Ġsyner",
+        "gy"
+      ],
+      [
+        "Ġbuff",
+        "s"
+      ],
+      [
+        "Ġap",
+        "o"
+      ],
+      [
+        "ĠWell",
+        "ness"
+      ],
+      [
+        "round",
+        "ed"
+      ],
+      [
+        "Ġunivers",
+        "es"
+      ],
+      [
+        "Ġf",
+        "era"
+      ],
+      [
+        "Ġstand",
+        "by"
+      ],
+      [
+        "ĠSil",
+        "va"
+      ],
+      [
+        "ĠJ",
+        "I"
+      ],
+      [
+        "ens",
+        "ored"
+      ],
+      [
+        "ĠìĹĨ",
+        "ëĭ¤"
+      ],
+      [
+        "ĠÐIJ",
+        "в"
+      ],
+      [
+        "ĠоÑĤ",
+        "дел"
+      ],
+      [
+        "Ġf",
+        "ø"
+      ],
+      [
+        "ĠRoc",
+        "kef"
+      ],
+      [
+        "ĠComp",
+        "ass"
+      ],
+      [
+        "ĠBe",
+        "ars"
+      ],
+      [
+        "Ġ",
+        "ä¸įè¦ģ"
+      ],
+      [
+        "T",
+        "urn"
+      ],
+      [
+        "Ġth",
+        "á»±c"
+      ],
+      [
+        "Ġposs",
+        "ibile"
+      ],
+      [
+        "Ġest",
+        "em"
+      ],
+      [
+        "ĠCroat",
+        "ia"
+      ],
+      [
+        "Ġtät",
+        "ä"
+      ],
+      [
+        "ĠC",
+        "AL"
+      ],
+      [
+        "à¹Ģà¸",
+        "ŀ"
+      ],
+      [
+        "ĠÑģÑĤ",
+        "ÑĢаÑħ"
+      ],
+      [
+        "Ġsal",
+        "ts"
+      ],
+      [
+        "Ġminimal",
+        "ist"
+      ],
+      [
+        "Ġincorpor",
+        "ates"
+      ],
+      [
+        "ĠÙĨÛģ",
+        "ÛĮÚº"
+      ],
+      [
+        "aca",
+        "o"
+      ],
+      [
+        "Ġsl",
+        "ammed"
+      ],
+      [
+        "Ġcam",
+        "a"
+      ],
+      [
+        "T",
+        "ext"
+      ],
+      [
+        "!!!!",
+        "!!"
+      ],
+      [
+        "Ġalc",
+        "anz"
+      ],
+      [
+        "é",
+        "ma"
+      ],
+      [
+        "Ġinc",
+        "ense"
+      ],
+      [
+        "Ġhard",
+        "en"
+      ],
+      [
+        "Ġgrant",
+        "ing"
+      ],
+      [
+        "ĠN",
+        "ai"
+      ],
+      [
+        "ĠFir",
+        "ma"
+      ],
+      [
+        "Ġhyp",
+        "oc"
+      ],
+      [
+        "j",
+        "ob"
+      ],
+      [
+        "ĠR",
+        "H"
+      ],
+      [
+        "z",
+        "ur"
+      ],
+      [
+        "ил",
+        "Ñı"
+      ],
+      [
+        "ĠÅ",
+        "º"
+      ],
+      [
+        "Ġda",
+        "res"
+      ],
+      [
+        "an",
+        "h"
+      ],
+      [
+        "Ġë§Į",
+        "íģ¼"
+      ],
+      [
+        "Ġcuest",
+        "ión"
+      ],
+      [
+        "ĠL",
+        "ima"
+      ],
+      [
+        "æĻ",
+        "¯"
+      ],
+      [
+        "Ġass",
+        "unto"
+      ],
+      [
+        "ĠIP",
+        "O"
+      ],
+      [
+        "ĠBeng",
+        "al"
+      ],
+      [
+        "ĠB",
+        "ier"
+      ],
+      [
+        "Ġpsy",
+        "che"
+      ],
+      [
+        "Ġacqu",
+        "ainted"
+      ],
+      [
+        "ĠG",
+        "ün"
+      ],
+      [
+        "оз",
+        "и"
+      ],
+      [
+        "ÅĽci",
+        "Äħ"
+      ],
+      [
+        "A",
+        "G"
+      ],
+      [
+        "Ġmalf",
+        "unction"
+      ],
+      [
+        "Ġastero",
+        "ids"
+      ],
+      [
+        "ire",
+        "z"
+      ],
+      [
+        "am",
+        "orph"
+      ],
+      [
+        "ĠÑģоÑĤ",
+        "ÑĢÑĥд"
+      ],
+      [
+        "Ġfresh",
+        "water"
+      ],
+      [
+        "Ġar",
+        "ran"
+      ],
+      [
+        "ĠпÑĢ",
+        "Ñĭ"
+      ],
+      [
+        "н",
+        "ог"
+      ],
+      [
+        "Ġdiab",
+        "etic"
+      ],
+      [
+        "ĠÙĤ",
+        "اÙĦ"
+      ],
+      [
+        "Ġopp",
+        "ress"
+      ],
+      [
+        "Ġcapacit",
+        "ance"
+      ],
+      [
+        "perform",
+        "ance"
+      ],
+      [
+        "cr",
+        "ates"
+      ],
+      [
+        "Ġapost",
+        "le"
+      ],
+      [
+        "ĠJ",
+        "EN"
+      ],
+      [
+        "OU",
+        "LD"
+      ],
+      [
+        "Int",
+        "ro"
+      ],
+      [
+        "Ġstall",
+        "s"
+      ],
+      [
+        "ĠAB",
+        "OUT"
+      ],
+      [
+        "ctic",
+        "amente"
+      ],
+      [
+        "Ġdil",
+        "igent"
+      ],
+      [
+        "Ġmanif",
+        "ests"
+      ],
+      [
+        "ĠPak",
+        "istani"
+      ],
+      [
+        "Ġ(",
+        "'"
+      ],
+      [
+        "åľ",
+        "º"
+      ]
+    ]
+  }
+}
\ No newline at end of file
diff --git a/tokenizer_config.json b/tokenizer_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..3ae24e088bb83a2aaa76a3bf6e3f1cef13e78b53
--- /dev/null
+++ b/tokenizer_config.json
@@ -0,0 +1,12990 @@
+{
+  "add_bos_token": false,
+  "add_prefix_space": false,
+  "added_tokens_decoder": {
+    "50257": {
+      "content": "<|endoftext|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50258": {
+      "content": "<|startoftranscript|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50259": {
+      "content": "<|en|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50260": {
+      "content": "<|zh|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50261": {
+      "content": "<|de|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50262": {
+      "content": "<|es|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50263": {
+      "content": "<|ru|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50264": {
+      "content": "<|ko|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50265": {
+      "content": "<|fr|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50266": {
+      "content": "<|ja|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50267": {
+      "content": "<|pt|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50268": {
+      "content": "<|tr|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50269": {
+      "content": "<|pl|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50270": {
+      "content": "<|ca|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50271": {
+      "content": "<|nl|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50272": {
+      "content": "<|ar|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50273": {
+      "content": "<|sv|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50274": {
+      "content": "<|it|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50275": {
+      "content": "<|id|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50276": {
+      "content": "<|hi|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50277": {
+      "content": "<|fi|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50278": {
+      "content": "<|vi|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50279": {
+      "content": "<|he|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50280": {
+      "content": "<|uk|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50281": {
+      "content": "<|el|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50282": {
+      "content": "<|ms|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50283": {
+      "content": "<|cs|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50284": {
+      "content": "<|ro|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
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