jonatasgrosman commited on
Commit
8f51404
1 Parent(s): 0f57d88

update model

Browse files
Files changed (4) hide show
  1. README.md +16 -14
  2. config.json +1 -1
  3. pytorch_model.bin +2 -2
  4. vocab.json +1 -1
README.md CHANGED
@@ -24,15 +24,15 @@ model-index:
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  metrics:
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  - name: Test WER
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  type: wer
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- value: 93.35
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  - name: Test CER
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  type: cer
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- value: 29.24
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  ---
32
 
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  # Wav2Vec2-Large-XLSR-53-Japanese
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- Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Japanese using the [Common Voice](https://huggingface.co/datasets/common_voice) and [CSS10](https://github.com/Kyubyong/css10).
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  When using this model, make sure that your speech input is sampled at 16kHz.
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  The script used for training can be found here: https://github.com/jonatasgrosman/wav2vec2-sprint
@@ -81,11 +81,11 @@ for i, predicted_sentence in enumerate(predicted_sentences):
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  | Reference | Prediction |
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  | ------------- | ------------- |
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- | 祖母は、おおむね機嫌よく、サイコロをころがしている。 | 都ぼは重い記念よくさいこところがしている |
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- | 財布をなくしたので、交番へ行きます。 | 財布王なクしたので、交番へへ行きます |
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- | 飲み屋のおやじ、旅館の主人、医者をはじめ、交際のある人にきいてまわったら、みんな、私より収入が多いはずなのに、税金は安い。 | ノみアのやじ、旅館の筋時に、医者を初め、交際なる人に聞いて廻ったら、みんな、私しより周入が多い弾ず脱に、制金は安すい |
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- | 新しい靴をはいて出かけます。 | 新しに靴をはいてかけます |
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- | このためプラズマ中のイオンや電子の持つ平均運動エネルギーを温度で表現することがある | このため、プラズマ中の医本や、電手のもつ平均運動をエネルギーを穏<unk>で、表現することがある |
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  ## Evaluation
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@@ -102,9 +102,11 @@ LANG_ID = "ja"
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  MODEL_ID = "jonatasgrosman/wav2vec2-large-xlsr-53-japanese"
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  DEVICE = "cuda"
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- CHARS_TO_IGNORE = [",", "?", "¿", ".", "!", "¡", ";", ":", '""', "%", '"', "�", "ʿ", "·", "჻", "~", "՞",
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  "؟", "،", "।", "॥", "«", "»", "„", "“", "”", "「", "」", "‘", "’", "《", "》", "(", ")", "[", "]",
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- "=", "`", "_", "+", "<", ">", "…", "–", "°", "´", "ʾ", "‹", "›", "©", "®", "—", "→", "。"]
 
 
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  test_dataset = load_dataset("common_voice", LANG_ID, split="test")
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@@ -152,10 +154,10 @@ print(f"CER: {cer.compute(predictions=predictions, references=references, chunk_
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  **Test Result**:
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- In the table below I report the Word Error Rate (WER) and the Character Error Rate (CER) of the model. I ran the evaluation script described above on other models as well (on 2021-04-21). Note that the table below may show different results from those already reported, this may have been caused due to some specificity of the other evaluation scripts used.
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  | Model | WER | CER |
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  | ------------- | ------------- | ------------- |
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- | vumichien/wav2vec2-large-xlsr-japanese | 1108.86% | **25.92%** |
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- | jonatasgrosman/wav2vec2-large-xlsr-53-japanese | **93.35%** | 29.24% |
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- | qqhann/w2v_hf_jsut_xlsr53 | 1012.18% | 69.46% |
 
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  metrics:
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  - name: Test WER
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  type: wer
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+ value: 79.58
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  - name: Test CER
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  type: cer
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+ value: 21.95
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  ---
32
 
33
  # Wav2Vec2-Large-XLSR-53-Japanese
34
 
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+ Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Japanese using the [Common Voice](https://huggingface.co/datasets/common_voice), [CSS10](https://github.com/Kyubyong/css10) and [JSUT](https://sites.google.com/site/shinnosuketakamichi/publication/jsut).
36
  When using this model, make sure that your speech input is sampled at 16kHz.
37
 
38
  The script used for training can be found here: https://github.com/jonatasgrosman/wav2vec2-sprint
 
81
 
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  | Reference | Prediction |
83
  | ------------- | ------------- |
84
+ | 祖母は、おおむね機嫌よく、サイコロをころがしている。 | 外ぼは重に機れをくさいことがしている |
85
+ | 財布をなくしたので、交番へ行きます。 | 財布をな靴館ので交番でへ行きます |
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+ | 飲み屋のおやじ、旅館の主人、医者をはじめ、交際のある人にきいてまわったら、みんな、私より収入が多いはずなのに、税金は安い。 | ノ屋のお親旅館の主時に医者を初め交際のある人に聞いて回ったらみんな私より収入がおいはなうに生近は安い |
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+ | 新しい靴をはいて出かけます。 | わしに靴をはいて出かけます |
88
+ | このためプラズマ中のイオンや電子の持つ平均運動エネルギーを温度で表現することがある | このためプラズマ中のイオンや電子の持つ平均運動エネルギーを温度で表現することがある |
89
 
90
  ## Evaluation
91
 
 
102
  MODEL_ID = "jonatasgrosman/wav2vec2-large-xlsr-53-japanese"
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  DEVICE = "cuda"
104
 
105
+ CHARS_TO_IGNORE = [",", "?", "¿", ".", "!", "¡", ";", ";", ":", '""', "%", '"', "�", "ʿ", "·", "჻", "~", "՞",
106
  "؟", "،", "।", "॥", "«", "»", "„", "“", "”", "「", "」", "‘", "’", "《", "》", "(", ")", "[", "]",
107
+ "{", "}", "=", "`", "_", "+", "<", ">", "…", "–", "°", "´", "ʾ", "‹", "›", "©", "®", "—", "→", "。",
108
+ "、", "﹂", "﹁", "‧", "~", "﹏", ",", "{", "}", "(", ")", "[", "]", "【", "】", "‥", "〽",
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+ "『", "』", "〝", "〟", "⟨", "⟩", "〜", ":", "!", "?", "♪", "؛", "/", "\\", "º", "−", "^", "'", "ʻ", "ˆ"]
110
 
111
  test_dataset = load_dataset("common_voice", LANG_ID, split="test")
112
 
 
154
 
155
  **Test Result**:
156
 
157
+ In the table below I report the Word Error Rate (WER) and the Character Error Rate (CER) of the model. I ran the evaluation script described above on other models as well (on 2021-05-10). Note that the table below may show different results from those already reported, this may have been caused due to some specificity of the other evaluation scripts used.
158
 
159
  | Model | WER | CER |
160
  | ------------- | ------------- | ------------- |
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+ | jonatasgrosman/wav2vec2-large-xlsr-53-japanese | **79.58%** | **21.95%** |
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+ | vumichien/wav2vec2-large-xlsr-japanese | 1108.86% | 23.40% |
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+ | qqhann/w2v_hf_jsut_xlsr53 | 1012.18% | 70.77% |
config.json CHANGED
@@ -72,5 +72,5 @@
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  "num_hidden_layers": 24,
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  "pad_token_id": 0,
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  "transformers_version": "4.5.0.dev0",
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- "vocab_size": 1767
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  }
 
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  "num_hidden_layers": 24,
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  "pad_token_id": 0,
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  "transformers_version": "4.5.0.dev0",
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+ "vocab_size": 1582
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  }
pytorch_model.bin CHANGED
@@ -1,3 +1,3 @@
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- size 1269178519
 
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vocab.json CHANGED
@@ -1 +1 @@
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- {"<pad>": 0, "<s>": 1, "</s>": 2, "<unk>": 3, "|": 4, "然": 5, "政": 6, "美": 7, "脅": 8, "痕": 9, "胸": 10, "ほ": 11, "陳": 12, "拾": 13, "被": 14, "暮": 15, "腮": 16, "潰": 17, "群": 18, "難": 19, "承": 20, "技": 21, "歴": 22, "済": 23, "棚": 24, "埋": 25, "刻": 26, "差": 27, "訳": 28, "写": 29, "の": 30, "飯": 31, "ざ": 32, "忍": 33, "不": 34, "燥": 35, "倹": 36, "レ": 37, "積": 38, "察": 39, "教": 40, "防": 41, "成": 42, "季": 43, "退": 44, "締": 45, "礼": 46, "肯": 47, "哀": 48, "繕": 49, "寺": 50, "ガ": 51, "餓": 52, "類": 53, "置": 54, "役": 55, "侮": 56, "迷": 57, "概": 58, "ぽ": 59, "狩": 60, "庇": 61, "味": 62, "勢": 63, "冴": 64, "衛": 65, "六": 66, "名": 67, "寒": 68, "要": 69, "迂": 70, "濯": 71, "選": 72, "卓": 73, "所": 74, "扱": 75, "牛": 76, "状": 77, "旋": 78, "底": 79, "べ": 80, "堅": 81, "銀": 82, "蒼": 83, "布": 84, "ャ": 85, "幅": 86, "兄": 87, "袋": 88, "翌": 89, "宇": 90, "婢": 91, "席": 92, "設": 93, "答": 94, "恰": 95, "暖": 96, "筋": 97, "緒": 98, "払": 99, "息": 100, "齢": 101, "震": 102, "族": 103, "那": 104, "僕": 105, "由": 106, "莨": 107, "派": 108, "徐": 109, "疼": 110, "賛": 111, "翳": 112, "弁": 113, "が": 114, "挨": 115, "筒": 116, "継": 117, "暴": 118, "景": 119, "却": 120, "堤": 121, "遣": 122, "性": 123, "這": 124, "犬": 125, "ぞ": 126, "業": 127, "訓": 128, "こ": 129, "私": 130, "約": 131, "ち": 132, "暑": 133, "粧": 134, "官": 135, "引": 136, "呂": 137, "マ": 138, "飾": 139, "を": 140, "骨": 141, "棒": 142, "頁": 143, "子": 144, "豊": 145, "戸": 146, "改": 147, "告": 148, "谷": 149, "袍": 150, "棉": 151, "習": 152, "塩": 153, "罪": 154, "内": 155, "堀": 156, "比": 157, "腑": 158, "己": 159, "屑": 160, "魚": 161, "寓": 162, "樹": 163, "動": 164, "呑": 165, "板": 166, "批": 167, "拍": 168, "幾": 169, "爺": 170, "弓": 171, "離": 172, "缶": 173, "コ": 174, "甜": 175, "洗": 176, "刊": 177, "し": 178, "簾": 179, "毛": 180, "瀬": 181, "省": 182, "漁": 183, "郊": 184, "敗": 185, "娘": 186, "欧": 187, "転": 188, "諸": 189, "般": 190, "慕": 191, "膜": 192, "断": 193, "癖": 194, "怒": 195, "因": 196, "肺": 197, "満": 198, "巣": 199, "ス": 200, "扇": 201, "掠": 202, "番": 203, "袖": 204, "啓": 205, "襟": 206, "温": 207, "籠": 208, "止": 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1099, "突": 1100, "窓": 1101, "窟": 1102, "窮": 1103, "立": 1104, "竜": 1105, "章": 1106, "端": 1107, "競": 1108, "竹": 1109, "笑": 1110, "符": 1111, "第": 1112, "筆": 1113, "等": 1114, "筋": 1115, "筒": 1116, "答": 1117, "策": 1118, "箋": 1119, "算": 1120, "管": 1121, "箱": 1122, "節": 1123, "範": 1124, "築": 1125, "篤": 1126, "簡": 1127, "籍": 1128, "籠": 1129, "米": 1130, "粉": 1131, "精": 1132, "糖": 1133, "糞": 1134, "約": 1135, "納": 1136, "紙": 1137, "級": 1138, "素": 1139, "紫": 1140, "細": 1141, "終": 1142, "組": 1143, "経": 1144, "結": 1145, "絡": 1146, "給": 1147, "統": 1148, "絵": 1149, "絶": 1150, "継": 1151, "続": 1152, "綱": 1153, "綺": 1154, "綿": 1155, "緊": 1156, "総": 1157, "���": 1158, "緒": 1159, "線": 1160, "締": 1161, "編": 1162, "緩": 1163, "練": 1164, "縁": 1165, "縮": 1166, "繋": 1167, "織": 1168, "繰": 1169, "纏": 1170, "罪": 1171, "置": 1172, "罹": 1173, "美": 1174, "群": 1175, "義": 1176, "羽": 1177, "習": 1178, "翻": 1179, "翼": 1180, "老": 1181, "考": 1182, "者": 1183, "耳": 1184, "聖": 1185, "聞": 1186, "聯": 1187, "聴": 1188, "職": 1189, "肉": 1190, "肝": 1191, "肥": 1192, "肩": 1193, "肯": 1194, "育": 1195, "胆": 1196, "背": 1197, "胸": 1198, "能": 1199, "脅": 1200, "脇": 1201, "脈": 1202, "脚": 1203, "脱": 1204, "脳": 1205, "腐": 1206, "腕": 1207, "腰": 1208, "腹": 1209, "膜": 1210, "膝": 1211, "膨": 1212, "膳": 1213, "臓": 1214, "臥": 1215, "自": 1216, "臭": 1217, "至": 1218, "致": 1219, "興": 1220, "舎": 1221, "舗": 1222, "舞": 1223, "航": 1224, "般": 1225, "船": 1226, "艦": 1227, "良": 1228, "色": 1229, "芝": 1230, "花": 1231, "芸": 1232, "芽": 1233, "若": 1234, "苦": 1235, "英": 1236, "茶": 1237, "草": 1238, "荒": 1239, "荷": 1240, "菌": 1241, "菜": 1242, "華": 1243, "落": 1244, "葉": 1245, "著": 1246, "葬": 1247, "蒼": 1248, "蔑": 1249, "蔭": 1250, "蔵": 1251, "薄": 1252, "薩": 1253, "薬": 1254, "藤": 1255, "藩": 1256, "虐": 1257, "虚": 1258, "虫": 1259, "蚕": 1260, "蛇": 1261, "融": 1262, "血": 1263, "衆": 1264, "行": 1265, "術": 1266, "街": 1267, "衝": 1268, "衣": 1269, "表": 1270, "袋": 1271, "袍": 1272, "袖": 1273, "被": 1274, "袴": 1275, "裁": 1276, "裂": 1277, "装": 1278, "裏": 1279, "裕": 1280, "補": 1281, "裡": 1282, "裸": 1283, "製": 1284, "襟": 1285, "襲": 1286, "西": 1287, "要": 1288, "覆": 1289, "見": 1290, "規": 1291, "視": 1292, "覚": 1293, "覧": 1294, "親": 1295, "観": 1296, "角": 1297, "解": 1298, "触": 1299, "言": 1300, "計": 1301, "訊": 1302, "訓": 1303, "記": 1304, "訪": 1305, "設": 1306, "許": 1307, "訳": 1308, "訴": 1309, "診": 1310, "証": 1311, "評": 1312, "詞": 1313, "試": 1314, "詩": 1315, "詫": 1316, "詰": 1317, "話": 1318, "詳": 1319, "誇": 1320, "認": 1321, "誘": 1322, "語": 1323, "誠": 1324, "誤": 1325, "説": 1326, "読": 1327, "誰": 1328, "課": 1329, "調": 1330, "談": 1331, "請": 1332, "論": 1333, "諸": 1334, "諾": 1335, "謝": 1336, "識": 1337, "警": 1338, "議": 1339, "譲": 1340, "護": 1341, "讃": 1342, "谷": 1343, "豚": 1344, "象": 1345, "負": 1346, "財": 1347, "貢": 1348, "貧": 1349, "貨": 1350, "販": 1351, "貫": 1352, "責": 1353, "貯": 1354, "貰": 1355, "貴": 1356, "買": 1357, "貸": 1358, "費": 1359, "賃": 1360, "資": 1361, "賑": 1362, "賛": 1363, "賞": 1364, "賢": 1365, "質": 1366, "贅": 1367, "贈": 1368, "赤": 1369, "走": 1370, "赴": 1371, "起": 1372, "越": 1373, "趣": 1374, "足": 1375, "距": 1376, "跡": 1377, "路": 1378, "跳": 1379, "踏": 1380, "躇": 1381, "躊": 1382, "身": 1383, "車": 1384, "軍": 1385, "軒": 1386, "転": 1387, "軽": 1388, "較": 1389, "載": 1390, "輝": 1391, "輩": 1392, "輪": 1393, "辛": 1394, "辞": 1395, "農": 1396, "辺": 1397, "込": 1398, "迎": 1399, "近": 1400, "返": 1401, "迫": 1402, "迷": 1403, "追": 1404, "退": 1405, "送": 1406, "逃": 1407, "逆": 1408, "透": 1409, "途": 1410, "通": 1411, "速": 1412, "造": 1413, "連": 1414, "週": 1415, "進": 1416, "遂": 1417, "遅": 1418, "遊": 1419, "運": 1420, "遍": 1421, "過": 1422, "道": 1423, "達": 1424, "違": 1425, "遠": 1426, "遣": 1427, "適": 1428, "遮": 1429, "選": 1430, "遺": 1431, "避": 1432, "那": 1433, "邦": 1434, "邪": 1435, "部": 1436, "都": 1437, "配": 1438, "酒": 1439, "酔": 1440, "酢": 1441, "酪": 1442, "酷": 1443, "酸": 1444, "釈": 1445, "里": 1446, "重": 1447, "野": 1448, "量": 1449, "金": 1450, "釣": 1451, "鈴": 1452, "鉄": 1453, "鉢": 1454, "銃": 1455, "銘": 1456, "銭": 1457, "鋭": 1458, "鋼": 1459, "錠": 1460, "錯": 1461, "録": 1462, "鎖": 1463, "鏡": 1464, "鑑": 1465, "長": 1466, "門": 1467, "閃": 1468, "閉": 1469, "開": 1470, "閑": 1471, "間": 1472, "関": 1473, "闇": 1474, "闘": 1475, "阪": 1476, "防": 1477, "附": 1478, "降": 1479, "限": 1480, "院": 1481, "除": 1482, "陥": 1483, "陸": 1484, "険": 1485, "陽": 1486, "隅": 1487, "隊": 1488, "階": 1489, "随": 1490, "隔": 1491, "際": 1492, "障": 1493, "隠": 1494, "隣": 1495, "雄": 1496, "集": 1497, "雑": 1498, "離": 1499, "難": 1500, "雨": 1501, "雲": 1502, "雷": 1503, "電": 1504, "震": 1505, "霊": 1506, "露": 1507, "青": 1508, "静": 1509, "非": 1510, "面": 1511, "革": 1512, "靴": 1513, "音": 1514, "響": 1515, "頁": 1516, "頂": 1517, "頃": 1518, "順": 1519, "預": 1520, "頓": 1521, "領": 1522, "頭": 1523, "頼": 1524, "題": 1525, "額": 1526, "顔": 1527, "願": 1528, "類": 1529, "顧": 1530, "風": 1531, "飛": 1532, "食": 1533, "飯": 1534, "飲": 1535, "飽": 1536, "飾": 1537, "養": 1538, "館": 1539, "首": 1540, "香": 1541, "馬": 1542, "馳": 1543, "駄": 1544, "駅": 1545, "駒": 1546, "騎": 1547, "騒": 1548, "験": 1549, "驚": 1550, "骨": 1551, "高": 1552, "髪": 1553, "魂": 1554, "魅": 1555, "魔": 1556, "魚": 1557, "鮮": 1558, "鳥": 1559, "鳴": 1560, "鶏": 1561, "鹿": 1562, "麗": 1563, "麦": 1564, "麭": 1565, "麺": 1566, "黄": 1567, "黒": 1568, "黙": 1569, "鼻": 1570, "齢": 1571, "0": 1572, "1": 1573, "2": 1574, "3": 1575, "4": 1576, "5": 1577, "6": 1578, "7": 1579, "8": 1580, "9": 1581}