Saving train state of step 1000

.gitignore

@@ -0,0 +1 @@
+wandb

added_tokens.json

@@ -0,0 +1,1609 @@
+{
+  "<|0.00|>": 50364,
+  "<|0.02|>": 50365,
+  "<|0.04|>": 50366,
+  "<|0.06|>": 50367,
+  "<|0.08|>": 50368,
+  "<|0.10|>": 50369,
+  "<|0.12|>": 50370,
+  "<|0.14|>": 50371,
+  "<|0.16|>": 50372,
+  "<|0.18|>": 50373,
+  "<|0.20|>": 50374,
+  "<|0.22|>": 50375,
+  "<|0.24|>": 50376,
+  "<|0.26|>": 50377,
+  "<|0.28|>": 50378,
+  "<|0.30|>": 50379,
+  "<|0.32|>": 50380,
+  "<|0.34|>": 50381,
+  "<|0.36|>": 50382,
+  "<|0.38|>": 50383,
+  "<|0.40|>": 50384,
+  "<|0.42|>": 50385,
+  "<|0.44|>": 50386,
+  "<|0.46|>": 50387,
+  "<|0.48|>": 50388,
+  "<|0.50|>": 50389,
+  "<|0.52|>": 50390,
+  "<|0.54|>": 50391,
+  "<|0.56|>": 50392,
+  "<|0.58|>": 50393,
+  "<|0.60|>": 50394,
+  "<|0.62|>": 50395,
+  "<|0.64|>": 50396,
+  "<|0.66|>": 50397,
+  "<|0.68|>": 50398,
+  "<|0.70|>": 50399,
+  "<|0.72|>": 50400,
+  "<|0.74|>": 50401,
+  "<|0.76|>": 50402,
+  "<|0.78|>": 50403,
+  "<|0.80|>": 50404,
+  "<|0.82|>": 50405,
+  "<|0.84|>": 50406,
+  "<|0.86|>": 50407,
+  "<|0.88|>": 50408,
+  "<|0.90|>": 50409,
+  "<|0.92|>": 50410,
+  "<|0.94|>": 50411,
+  "<|0.96|>": 50412,
+  "<|0.98|>": 50413,
+  "<|1.00|>": 50414,
+  "<|1.02|>": 50415,
+  "<|1.04|>": 50416,
+  "<|1.06|>": 50417,
+  "<|1.08|>": 50418,
+  "<|1.10|>": 50419,
+  "<|1.12|>": 50420,
+  "<|1.14|>": 50421,
+  "<|1.16|>": 50422,
+  "<|1.18|>": 50423,
+  "<|1.20|>": 50424,
+  "<|1.22|>": 50425,
+  "<|1.24|>": 50426,
+  "<|1.26|>": 50427,
+  "<|1.28|>": 50428,
+  "<|1.30|>": 50429,
+  "<|1.32|>": 50430,
+  "<|1.34|>": 50431,
+  "<|1.36|>": 50432,
+  "<|1.38|>": 50433,
+  "<|1.40|>": 50434,
+  "<|1.42|>": 50435,
+  "<|1.44|>": 50436,
+  "<|1.46|>": 50437,
+  "<|1.48|>": 50438,
+  "<|1.50|>": 50439,
+  "<|1.52|>": 50440,
+  "<|1.54|>": 50441,
+  "<|1.56|>": 50442,
+  "<|1.58|>": 50443,
+  "<|1.60|>": 50444,
+  "<|1.62|>": 50445,
+  "<|1.64|>": 50446,
+  "<|1.66|>": 50447,
+  "<|1.68|>": 50448,
+  "<|1.70|>": 50449,
+  "<|1.72|>": 50450,
+  "<|1.74|>": 50451,
+  "<|1.76|>": 50452,
+  "<|1.78|>": 50453,
+  "<|1.80|>": 50454,
+  "<|1.82|>": 50455,
+  "<|1.84|>": 50456,
+  "<|1.86|>": 50457,
+  "<|1.88|>": 50458,
+  "<|1.90|>": 50459,
+  "<|1.92|>": 50460,
+  "<|1.94|>": 50461,
+  "<|1.96|>": 50462,
+  "<|1.98|>": 50463,
+  "<|10.00|>": 50864,
+  "<|10.02|>": 50865,
+  "<|10.04|>": 50866,
+  "<|10.06|>": 50867,
+  "<|10.08|>": 50868,
+  "<|10.10|>": 50869,
+  "<|10.12|>": 50870,
+  "<|10.14|>": 50871,
+  "<|10.16|>": 50872,
+  "<|10.18|>": 50873,
+  "<|10.20|>": 50874,
+  "<|10.22|>": 50875,
+  "<|10.24|>": 50876,
+  "<|10.26|>": 50877,
+  "<|10.28|>": 50878,
+  "<|10.30|>": 50879,
+  "<|10.32|>": 50880,
+  "<|10.34|>": 50881,
+  "<|10.36|>": 50882,
+  "<|10.38|>": 50883,
+  "<|10.40|>": 50884,
+  "<|10.42|>": 50885,
+  "<|10.44|>": 50886,
+  "<|10.46|>": 50887,
+  "<|10.48|>": 50888,
+  "<|10.50|>": 50889,
+  "<|10.52|>": 50890,
+  "<|10.54|>": 50891,
+  "<|10.56|>": 50892,
+  "<|10.58|>": 50893,
+  "<|10.60|>": 50894,
+  "<|10.62|>": 50895,
+  "<|10.64|>": 50896,
+  "<|10.66|>": 50897,
+  "<|10.68|>": 50898,
+  "<|10.70|>": 50899,
+  "<|10.72|>": 50900,
+  "<|10.74|>": 50901,
+  "<|10.76|>": 50902,
+  "<|10.78|>": 50903,
+  "<|10.80|>": 50904,
+  "<|10.82|>": 50905,
+  "<|10.84|>": 50906,
+  "<|10.86|>": 50907,
+  "<|10.88|>": 50908,
+  "<|10.90|>": 50909,
+  "<|10.92|>": 50910,
+  "<|10.94|>": 50911,
+  "<|10.96|>": 50912,
+  "<|10.98|>": 50913,
+  "<|11.00|>": 50914,
+  "<|11.02|>": 50915,
+  "<|11.04|>": 50916,
+  "<|11.06|>": 50917,
+  "<|11.08|>": 50918,
+  "<|11.10|>": 50919,
+  "<|11.12|>": 50920,
+  "<|11.14|>": 50921,
+  "<|11.16|>": 50922,
+  "<|11.18|>": 50923,
+  "<|11.20|>": 50924,
+  "<|11.22|>": 50925,
+  "<|11.24|>": 50926,
+  "<|11.26|>": 50927,
+  "<|11.28|>": 50928,
+  "<|11.30|>": 50929,
+  "<|11.32|>": 50930,
+  "<|11.34|>": 50931,
+  "<|11.36|>": 50932,
+  "<|11.38|>": 50933,
+  "<|11.40|>": 50934,
+  "<|11.42|>": 50935,
+  "<|11.44|>": 50936,
+  "<|11.46|>": 50937,
+  "<|11.48|>": 50938,
+  "<|11.50|>": 50939,
+  "<|11.52|>": 50940,
+  "<|11.54|>": 50941,
+  "<|11.56|>": 50942,
+  "<|11.58|>": 50943,
+  "<|11.60|>": 50944,
+  "<|11.62|>": 50945,
+  "<|11.64|>": 50946,
+  "<|11.66|>": 50947,
+  "<|11.68|>": 50948,
+  "<|11.70|>": 50949,
+  "<|11.72|>": 50950,
+  "<|11.74|>": 50951,
+  "<|11.76|>": 50952,
+  "<|11.78|>": 50953,
+  "<|11.80|>": 50954,
+  "<|11.82|>": 50955,
+  "<|11.84|>": 50956,
+  "<|11.86|>": 50957,
+  "<|11.88|>": 50958,
+  "<|11.90|>": 50959,
+  "<|11.92|>": 50960,
+  "<|11.94|>": 50961,
+  "<|11.96|>": 50962,
+  "<|11.98|>": 50963,
+  "<|12.00|>": 50964,
+  "<|12.02|>": 50965,
+  "<|12.04|>": 50966,
+  "<|12.06|>": 50967,
+  "<|12.08|>": 50968,
+  "<|12.10|>": 50969,
+  "<|12.12|>": 50970,
+  "<|12.14|>": 50971,
+  "<|12.16|>": 50972,
+  "<|12.18|>": 50973,
+  "<|12.20|>": 50974,
+  "<|12.22|>": 50975,
+  "<|12.24|>": 50976,
+  "<|12.26|>": 50977,
+  "<|12.28|>": 50978,
+  "<|12.30|>": 50979,
+  "<|12.32|>": 50980,
+  "<|12.34|>": 50981,
+  "<|12.36|>": 50982,
+  "<|12.38|>": 50983,
+  "<|12.40|>": 50984,
+  "<|12.42|>": 50985,
+  "<|12.44|>": 50986,
+  "<|12.46|>": 50987,
+  "<|12.48|>": 50988,
+  "<|12.50|>": 50989,
+  "<|12.52|>": 50990,
+  "<|12.54|>": 50991,
+  "<|12.56|>": 50992,
+  "<|12.58|>": 50993,
+  "<|12.60|>": 50994,
+  "<|12.62|>": 50995,
+  "<|12.64|>": 50996,
+  "<|12.66|>": 50997,
+  "<|12.68|>": 50998,
+  "<|12.70|>": 50999,
+  "<|12.72|>": 51000,
+  "<|12.74|>": 51001,
+  "<|12.76|>": 51002,
+  "<|12.78|>": 51003,
+  "<|12.80|>": 51004,
+  "<|12.82|>": 51005,
+  "<|12.84|>": 51006,
+  "<|12.86|>": 51007,
+  "<|12.88|>": 51008,
+  "<|12.90|>": 51009,
+  "<|12.92|>": 51010,
+  "<|12.94|>": 51011,
+  "<|12.96|>": 51012,
+  "<|12.98|>": 51013,
+  "<|13.00|>": 51014,
+  "<|13.02|>": 51015,
+  "<|13.04|>": 51016,
+  "<|13.06|>": 51017,
+  "<|13.08|>": 51018,
+  "<|13.10|>": 51019,
+  "<|13.12|>": 51020,
+  "<|13.14|>": 51021,
+  "<|13.16|>": 51022,
+  "<|13.18|>": 51023,
+  "<|13.20|>": 51024,
+  "<|13.22|>": 51025,
+  "<|13.24|>": 51026,
+  "<|13.26|>": 51027,
+  "<|13.28|>": 51028,
+  "<|13.30|>": 51029,
+  "<|13.32|>": 51030,
+  "<|13.34|>": 51031,
+  "<|13.36|>": 51032,
+  "<|13.38|>": 51033,
+  "<|13.40|>": 51034,
+  "<|13.42|>": 51035,
+  "<|13.44|>": 51036,
+  "<|13.46|>": 51037,
+  "<|13.48|>": 51038,
+  "<|13.50|>": 51039,
+  "<|13.52|>": 51040,
+  "<|13.54|>": 51041,
+  "<|13.56|>": 51042,
+  "<|13.58|>": 51043,
+  "<|13.60|>": 51044,
+  "<|13.62|>": 51045,
+  "<|13.64|>": 51046,
+  "<|13.66|>": 51047,
+  "<|13.68|>": 51048,
+  "<|13.70|>": 51049,
+  "<|13.72|>": 51050,
+  "<|13.74|>": 51051,
+  "<|13.76|>": 51052,
+  "<|13.78|>": 51053,
+  "<|13.80|>": 51054,
+  "<|13.82|>": 51055,
+  "<|13.84|>": 51056,
+  "<|13.86|>": 51057,
+  "<|13.88|>": 51058,
+  "<|13.90|>": 51059,
+  "<|13.92|>": 51060,
+  "<|13.94|>": 51061,
+  "<|13.96|>": 51062,
+  "<|13.98|>": 51063,
+  "<|14.00|>": 51064,
+  "<|14.02|>": 51065,
+  "<|14.04|>": 51066,
+  "<|14.06|>": 51067,
+  "<|14.08|>": 51068,
+  "<|14.10|>": 51069,
+  "<|14.12|>": 51070,
+  "<|14.14|>": 51071,
+  "<|14.16|>": 51072,
+  "<|14.18|>": 51073,
+  "<|14.20|>": 51074,
+  "<|14.22|>": 51075,
+  "<|14.24|>": 51076,
+  "<|14.26|>": 51077,
+  "<|14.28|>": 51078,
+  "<|14.30|>": 51079,
+  "<|14.32|>": 51080,
+  "<|14.34|>": 51081,
+  "<|14.36|>": 51082,
+  "<|14.38|>": 51083,
+  "<|14.40|>": 51084,
+  "<|14.42|>": 51085,
+  "<|14.44|>": 51086,
+  "<|14.46|>": 51087,
+  "<|14.48|>": 51088,
+  "<|14.50|>": 51089,
+  "<|14.52|>": 51090,
+  "<|14.54|>": 51091,
+  "<|14.56|>": 51092,
+  "<|14.58|>": 51093,
+  "<|14.60|>": 51094,
+  "<|14.62|>": 51095,
+  "<|14.64|>": 51096,
+  "<|14.66|>": 51097,
+  "<|14.68|>": 51098,
+  "<|14.70|>": 51099,
+  "<|14.72|>": 51100,
+  "<|14.74|>": 51101,
+  "<|14.76|>": 51102,
+  "<|14.78|>": 51103,
+  "<|14.80|>": 51104,
+  "<|14.82|>": 51105,
+  "<|14.84|>": 51106,
+  "<|14.86|>": 51107,
+  "<|14.88|>": 51108,
+  "<|14.90|>": 51109,
+  "<|14.92|>": 51110,
+  "<|14.94|>": 51111,
+  "<|14.96|>": 51112,
+  "<|14.98|>": 51113,
+  "<|15.00|>": 51114,
+  "<|15.02|>": 51115,
+  "<|15.04|>": 51116,
+  "<|15.06|>": 51117,
+  "<|15.08|>": 51118,
+  "<|15.10|>": 51119,
+  "<|15.12|>": 51120,
+  "<|15.14|>": 51121,
+  "<|15.16|>": 51122,
+  "<|15.18|>": 51123,
+  "<|15.20|>": 51124,
+  "<|15.22|>": 51125,
+  "<|15.24|>": 51126,
+  "<|15.26|>": 51127,
+  "<|15.28|>": 51128,
+  "<|15.30|>": 51129,
+  "<|15.32|>": 51130,
+  "<|15.34|>": 51131,
+  "<|15.36|>": 51132,
+  "<|15.38|>": 51133,
+  "<|15.40|>": 51134,
+  "<|15.42|>": 51135,
+  "<|15.44|>": 51136,
+  "<|15.46|>": 51137,
+  "<|15.48|>": 51138,
+  "<|15.50|>": 51139,
+  "<|15.52|>": 51140,
+  "<|15.54|>": 51141,
+  "<|15.56|>": 51142,
+  "<|15.58|>": 51143,
+  "<|15.60|>": 51144,
+  "<|15.62|>": 51145,
+  "<|15.64|>": 51146,
+  "<|15.66|>": 51147,
+  "<|15.68|>": 51148,
+  "<|15.70|>": 51149,
+  "<|15.72|>": 51150,
+  "<|15.74|>": 51151,
+  "<|15.76|>": 51152,
+  "<|15.78|>": 51153,
+  "<|15.80|>": 51154,
+  "<|15.82|>": 51155,
+  "<|15.84|>": 51156,
+  "<|15.86|>": 51157,
+  "<|15.88|>": 51158,
+  "<|15.90|>": 51159,
+  "<|15.92|>": 51160,
+  "<|15.94|>": 51161,
+  "<|15.96|>": 51162,
+  "<|15.98|>": 51163,
+  "<|16.00|>": 51164,
+  "<|16.02|>": 51165,
+  "<|16.04|>": 51166,
+  "<|16.06|>": 51167,
+  "<|16.08|>": 51168,
+  "<|16.10|>": 51169,
+  "<|16.12|>": 51170,
+  "<|16.14|>": 51171,
+  "<|16.16|>": 51172,
+  "<|16.18|>": 51173,
+  "<|16.20|>": 51174,
+  "<|16.22|>": 51175,
+  "<|16.24|>": 51176,
+  "<|16.26|>": 51177,
+  "<|16.28|>": 51178,
+  "<|16.30|>": 51179,
+  "<|16.32|>": 51180,
+  "<|16.34|>": 51181,
+  "<|16.36|>": 51182,
+  "<|16.38|>": 51183,
+  "<|16.40|>": 51184,
+  "<|16.42|>": 51185,
+  "<|16.44|>": 51186,
+  "<|16.46|>": 51187,
+  "<|16.48|>": 51188,
+  "<|16.50|>": 51189,
+  "<|16.52|>": 51190,
+  "<|16.54|>": 51191,
+  "<|16.56|>": 51192,
+  "<|16.58|>": 51193,
+  "<|16.60|>": 51194,
+  "<|16.62|>": 51195,
+  "<|16.64|>": 51196,
+  "<|16.66|>": 51197,
+  "<|16.68|>": 51198,
+  "<|16.70|>": 51199,
+  "<|16.72|>": 51200,
+  "<|16.74|>": 51201,
+  "<|16.76|>": 51202,
+  "<|16.78|>": 51203,
+  "<|16.80|>": 51204,
+  "<|16.82|>": 51205,
+  "<|16.84|>": 51206,
+  "<|16.86|>": 51207,
+  "<|16.88|>": 51208,
+  "<|16.90|>": 51209,
+  "<|16.92|>": 51210,
+  "<|16.94|>": 51211,
+  "<|16.96|>": 51212,
+  "<|16.98|>": 51213,
+  "<|17.00|>": 51214,
+  "<|17.02|>": 51215,
+  "<|17.04|>": 51216,
+  "<|17.06|>": 51217,
+  "<|17.08|>": 51218,
+  "<|17.10|>": 51219,
+  "<|17.12|>": 51220,
+  "<|17.14|>": 51221,
+  "<|17.16|>": 51222,
+  "<|17.18|>": 51223,
+  "<|17.20|>": 51224,
+  "<|17.22|>": 51225,
+  "<|17.24|>": 51226,
+  "<|17.26|>": 51227,
+  "<|17.28|>": 51228,
+  "<|17.30|>": 51229,
+  "<|17.32|>": 51230,
+  "<|17.34|>": 51231,
+  "<|17.36|>": 51232,
+  "<|17.38|>": 51233,
+  "<|17.40|>": 51234,
+  "<|17.42|>": 51235,
+  "<|17.44|>": 51236,
+  "<|17.46|>": 51237,
+  "<|17.48|>": 51238,
+  "<|17.50|>": 51239,
+  "<|17.52|>": 51240,
+  "<|17.54|>": 51241,
+  "<|17.56|>": 51242,
+  "<|17.58|>": 51243,
+  "<|17.60|>": 51244,
+  "<|17.62|>": 51245,
+  "<|17.64|>": 51246,
+  "<|17.66|>": 51247,
+  "<|17.68|>": 51248,
+  "<|17.70|>": 51249,
+  "<|17.72|>": 51250,
+  "<|17.74|>": 51251,
+  "<|17.76|>": 51252,
+  "<|17.78|>": 51253,
+  "<|17.80|>": 51254,
+  "<|17.82|>": 51255,
+  "<|17.84|>": 51256,
+  "<|17.86|>": 51257,
+  "<|17.88|>": 51258,
+  "<|17.90|>": 51259,
+  "<|17.92|>": 51260,
+  "<|17.94|>": 51261,
+  "<|17.96|>": 51262,
+  "<|17.98|>": 51263,
+  "<|18.00|>": 51264,
+  "<|18.02|>": 51265,
+  "<|18.04|>": 51266,
+  "<|18.06|>": 51267,
+  "<|18.08|>": 51268,
+  "<|18.10|>": 51269,
+  "<|18.12|>": 51270,
+  "<|18.14|>": 51271,
+  "<|18.16|>": 51272,
+  "<|18.18|>": 51273,
+  "<|18.20|>": 51274,
+  "<|18.22|>": 51275,
+  "<|18.24|>": 51276,
+  "<|18.26|>": 51277,
+  "<|18.28|>": 51278,
+  "<|18.30|>": 51279,
+  "<|18.32|>": 51280,
+  "<|18.34|>": 51281,
+  "<|18.36|>": 51282,
+  "<|18.38|>": 51283,
+  "<|18.40|>": 51284,
+  "<|18.42|>": 51285,
+  "<|18.44|>": 51286,
+  "<|18.46|>": 51287,
+  "<|18.48|>": 51288,
+  "<|18.50|>": 51289,
+  "<|18.52|>": 51290,
+  "<|18.54|>": 51291,
+  "<|18.56|>": 51292,
+  "<|18.58|>": 51293,
+  "<|18.60|>": 51294,
+  "<|18.62|>": 51295,
+  "<|18.64|>": 51296,
+  "<|18.66|>": 51297,
+  "<|18.68|>": 51298,
+  "<|18.70|>": 51299,
+  "<|18.72|>": 51300,
+  "<|18.74|>": 51301,
+  "<|18.76|>": 51302,
+  "<|18.78|>": 51303,
+  "<|18.80|>": 51304,
+  "<|18.82|>": 51305,
+  "<|18.84|>": 51306,
+  "<|18.86|>": 51307,
+  "<|18.88|>": 51308,
+  "<|18.90|>": 51309,
+  "<|18.92|>": 51310,
+  "<|18.94|>": 51311,
+  "<|18.96|>": 51312,
+  "<|18.98|>": 51313,
+  "<|19.00|>": 51314,
+  "<|19.02|>": 51315,
+  "<|19.04|>": 51316,
+  "<|19.06|>": 51317,
+  "<|19.08|>": 51318,
+  "<|19.10|>": 51319,
+  "<|19.12|>": 51320,
+  "<|19.14|>": 51321,
+  "<|19.16|>": 51322,
+  "<|19.18|>": 51323,
+  "<|19.20|>": 51324,
+  "<|19.22|>": 51325,
+  "<|19.24|>": 51326,
+  "<|19.26|>": 51327,
+  "<|19.28|>": 51328,
+  "<|19.30|>": 51329,
+  "<|19.32|>": 51330,
+  "<|19.34|>": 51331,
+  "<|19.36|>": 51332,
+  "<|19.38|>": 51333,
+  "<|19.40|>": 51334,
+  "<|19.42|>": 51335,
+  "<|19.44|>": 51336,
+  "<|19.46|>": 51337,
+  "<|19.48|>": 51338,
+  "<|19.50|>": 51339,
+  "<|19.52|>": 51340,
+  "<|19.54|>": 51341,
+  "<|19.56|>": 51342,
+  "<|19.58|>": 51343,
+  "<|19.60|>": 51344,
+  "<|19.62|>": 51345,
+  "<|19.64|>": 51346,
+  "<|19.66|>": 51347,
+  "<|19.68|>": 51348,
+  "<|19.70|>": 51349,
+  "<|19.72|>": 51350,
+  "<|19.74|>": 51351,
+  "<|19.76|>": 51352,
+  "<|19.78|>": 51353,
+  "<|19.80|>": 51354,
+  "<|19.82|>": 51355,
+  "<|19.84|>": 51356,
+  "<|19.86|>": 51357,
+  "<|19.88|>": 51358,
+  "<|19.90|>": 51359,
+  "<|19.92|>": 51360,
+  "<|19.94|>": 51361,
+  "<|19.96|>": 51362,
+  "<|19.98|>": 51363,
+  "<|2.00|>": 50464,
+  "<|2.02|>": 50465,
+  "<|2.04|>": 50466,
+  "<|2.06|>": 50467,
+  "<|2.08|>": 50468,
+  "<|2.10|>": 50469,
+  "<|2.12|>": 50470,
+  "<|2.14|>": 50471,
+  "<|2.16|>": 50472,
+  "<|2.18|>": 50473,
+  "<|2.20|>": 50474,
+  "<|2.22|>": 50475,
+  "<|2.24|>": 50476,
+  "<|2.26|>": 50477,
+  "<|2.28|>": 50478,
+  "<|2.30|>": 50479,
+  "<|2.32|>": 50480,
+  "<|2.34|>": 50481,
+  "<|2.36|>": 50482,
+  "<|2.38|>": 50483,
+  "<|2.40|>": 50484,
+  "<|2.42|>": 50485,
+  "<|2.44|>": 50486,
+  "<|2.46|>": 50487,
+  "<|2.48|>": 50488,
+  "<|2.50|>": 50489,
+  "<|2.52|>": 50490,
+  "<|2.54|>": 50491,
+  "<|2.56|>": 50492,
+  "<|2.58|>": 50493,
+  "<|2.60|>": 50494,
+  "<|2.62|>": 50495,
+  "<|2.64|>": 50496,
+  "<|2.66|>": 50497,
+  "<|2.68|>": 50498,
+  "<|2.70|>": 50499,
+  "<|2.72|>": 50500,
+  "<|2.74|>": 50501,
+  "<|2.76|>": 50502,
+  "<|2.78|>": 50503,
+  "<|2.80|>": 50504,
+  "<|2.82|>": 50505,
+  "<|2.84|>": 50506,
+  "<|2.86|>": 50507,
+  "<|2.88|>": 50508,
+  "<|2.90|>": 50509,
+  "<|2.92|>": 50510,
+  "<|2.94|>": 50511,
+  "<|2.96|>": 50512,
+  "<|2.98|>": 50513,
+  "<|20.00|>": 51364,
+  "<|20.02|>": 51365,
+  "<|20.04|>": 51366,
+  "<|20.06|>": 51367,
+  "<|20.08|>": 51368,
+  "<|20.10|>": 51369,
+  "<|20.12|>": 51370,
+  "<|20.14|>": 51371,
+  "<|20.16|>": 51372,
+  "<|20.18|>": 51373,
+  "<|20.20|>": 51374,
+  "<|20.22|>": 51375,
+  "<|20.24|>": 51376,
+  "<|20.26|>": 51377,
+  "<|20.28|>": 51378,
+  "<|20.30|>": 51379,
+  "<|20.32|>": 51380,
+  "<|20.34|>": 51381,
+  "<|20.36|>": 51382,
+  "<|20.38|>": 51383,
+  "<|20.40|>": 51384,
+  "<|20.42|>": 51385,
+  "<|20.44|>": 51386,
+  "<|20.46|>": 51387,
+  "<|20.48|>": 51388,
+  "<|20.50|>": 51389,
+  "<|20.52|>": 51390,
+  "<|20.54|>": 51391,
+  "<|20.56|>": 51392,
+  "<|20.58|>": 51393,
+  "<|20.60|>": 51394,
+  "<|20.62|>": 51395,
+  "<|20.64|>": 51396,
+  "<|20.66|>": 51397,
+  "<|20.68|>": 51398,
+  "<|20.70|>": 51399,
+  "<|20.72|>": 51400,
+  "<|20.74|>": 51401,
+  "<|20.76|>": 51402,
+  "<|20.78|>": 51403,
+  "<|20.80|>": 51404,
+  "<|20.82|>": 51405,
+  "<|20.84|>": 51406,
+  "<|20.86|>": 51407,
+  "<|20.88|>": 51408,
+  "<|20.90|>": 51409,
+  "<|20.92|>": 51410,
+  "<|20.94|>": 51411,
+  "<|20.96|>": 51412,
+  "<|20.98|>": 51413,
+  "<|21.00|>": 51414,
+  "<|21.02|>": 51415,
+  "<|21.04|>": 51416,
+  "<|21.06|>": 51417,
+  "<|21.08|>": 51418,
+  "<|21.10|>": 51419,
+  "<|21.12|>": 51420,
+  "<|21.14|>": 51421,
+  "<|21.16|>": 51422,
+  "<|21.18|>": 51423,
+  "<|21.20|>": 51424,
+  "<|21.22|>": 51425,
+  "<|21.24|>": 51426,
+  "<|21.26|>": 51427,
+  "<|21.28|>": 51428,
+  "<|21.30|>": 51429,
+  "<|21.32|>": 51430,
+  "<|21.34|>": 51431,
+  "<|21.36|>": 51432,
+  "<|21.38|>": 51433,
+  "<|21.40|>": 51434,
+  "<|21.42|>": 51435,
+  "<|21.44|>": 51436,
+  "<|21.46|>": 51437,
+  "<|21.48|>": 51438,
+  "<|21.50|>": 51439,
+  "<|21.52|>": 51440,
+  "<|21.54|>": 51441,
+  "<|21.56|>": 51442,
+  "<|21.58|>": 51443,
+  "<|21.60|>": 51444,
+  "<|21.62|>": 51445,
+  "<|21.64|>": 51446,
+  "<|21.66|>": 51447,
+  "<|21.68|>": 51448,
+  "<|21.70|>": 51449,
+  "<|21.72|>": 51450,
+  "<|21.74|>": 51451,
+  "<|21.76|>": 51452,
+  "<|21.78|>": 51453,
+  "<|21.80|>": 51454,
+  "<|21.82|>": 51455,
+  "<|21.84|>": 51456,
+  "<|21.86|>": 51457,
+  "<|21.88|>": 51458,
+  "<|21.90|>": 51459,
+  "<|21.92|>": 51460,
+  "<|21.94|>": 51461,
+  "<|21.96|>": 51462,
+  "<|21.98|>": 51463,
+  "<|22.00|>": 51464,
+  "<|22.02|>": 51465,
+  "<|22.04|>": 51466,
+  "<|22.06|>": 51467,
+  "<|22.08|>": 51468,
+  "<|22.10|>": 51469,
+  "<|22.12|>": 51470,
+  "<|22.14|>": 51471,
+  "<|22.16|>": 51472,
+  "<|22.18|>": 51473,
+  "<|22.20|>": 51474,
+  "<|22.22|>": 51475,
+  "<|22.24|>": 51476,
+  "<|22.26|>": 51477,
+  "<|22.28|>": 51478,
+  "<|22.30|>": 51479,
+  "<|22.32|>": 51480,
+  "<|22.34|>": 51481,
+  "<|22.36|>": 51482,
+  "<|22.38|>": 51483,
+  "<|22.40|>": 51484,
+  "<|22.42|>": 51485,
+  "<|22.44|>": 51486,
+  "<|22.46|>": 51487,
+  "<|22.48|>": 51488,
+  "<|22.50|>": 51489,
+  "<|22.52|>": 51490,
+  "<|22.54|>": 51491,
+  "<|22.56|>": 51492,
+  "<|22.58|>": 51493,
+  "<|22.60|>": 51494,
+  "<|22.62|>": 51495,
+  "<|22.64|>": 51496,
+  "<|22.66|>": 51497,
+  "<|22.68|>": 51498,
+  "<|22.70|>": 51499,
+  "<|22.72|>": 51500,
+  "<|22.74|>": 51501,
+  "<|22.76|>": 51502,
+  "<|22.78|>": 51503,
+  "<|22.80|>": 51504,
+  "<|22.82|>": 51505,
+  "<|22.84|>": 51506,
+  "<|22.86|>": 51507,
+  "<|22.88|>": 51508,
+  "<|22.90|>": 51509,
+  "<|22.92|>": 51510,
+  "<|22.94|>": 51511,
+  "<|22.96|>": 51512,
+  "<|22.98|>": 51513,
+  "<|23.00|>": 51514,
+  "<|23.02|>": 51515,
+  "<|23.04|>": 51516,
+  "<|23.06|>": 51517,
+  "<|23.08|>": 51518,
+  "<|23.10|>": 51519,
+  "<|23.12|>": 51520,
+  "<|23.14|>": 51521,
+  "<|23.16|>": 51522,
+  "<|23.18|>": 51523,
+  "<|23.20|>": 51524,
+  "<|23.22|>": 51525,
+  "<|23.24|>": 51526,
+  "<|23.26|>": 51527,
+  "<|23.28|>": 51528,
+  "<|23.30|>": 51529,
+  "<|23.32|>": 51530,
+  "<|23.34|>": 51531,
+  "<|23.36|>": 51532,
+  "<|23.38|>": 51533,
+  "<|23.40|>": 51534,
+  "<|23.42|>": 51535,
+  "<|23.44|>": 51536,
+  "<|23.46|>": 51537,
+  "<|23.48|>": 51538,
+  "<|23.50|>": 51539,
+  "<|23.52|>": 51540,
+  "<|23.54|>": 51541,
+  "<|23.56|>": 51542,
+  "<|23.58|>": 51543,
+  "<|23.60|>": 51544,
+  "<|23.62|>": 51545,
+  "<|23.64|>": 51546,
+  "<|23.66|>": 51547,
+  "<|23.68|>": 51548,
+  "<|23.70|>": 51549,
+  "<|23.72|>": 51550,
+  "<|23.74|>": 51551,
+  "<|23.76|>": 51552,
+  "<|23.78|>": 51553,
+  "<|23.80|>": 51554,
+  "<|23.82|>": 51555,
+  "<|23.84|>": 51556,
+  "<|23.86|>": 51557,
+  "<|23.88|>": 51558,
+  "<|23.90|>": 51559,
+  "<|23.92|>": 51560,
+  "<|23.94|>": 51561,
+  "<|23.96|>": 51562,
+  "<|23.98|>": 51563,
+  "<|24.00|>": 51564,
+  "<|24.02|>": 51565,
+  "<|24.04|>": 51566,
+  "<|24.06|>": 51567,
+  "<|24.08|>": 51568,
+  "<|24.10|>": 51569,
+  "<|24.12|>": 51570,
+  "<|24.14|>": 51571,
+  "<|24.16|>": 51572,
+  "<|24.18|>": 51573,
+  "<|24.20|>": 51574,
+  "<|24.22|>": 51575,
+  "<|24.24|>": 51576,
+  "<|24.26|>": 51577,
+  "<|24.28|>": 51578,
+  "<|24.30|>": 51579,
+  "<|24.32|>": 51580,
+  "<|24.34|>": 51581,
+  "<|24.36|>": 51582,
+  "<|24.38|>": 51583,
+  "<|24.40|>": 51584,
+  "<|24.42|>": 51585,
+  "<|24.44|>": 51586,
+  "<|24.46|>": 51587,
+  "<|24.48|>": 51588,
+  "<|24.50|>": 51589,
+  "<|24.52|>": 51590,
+  "<|24.54|>": 51591,
+  "<|24.56|>": 51592,
+  "<|24.58|>": 51593,
+  "<|24.60|>": 51594,
+  "<|24.62|>": 51595,
+  "<|24.64|>": 51596,
+  "<|24.66|>": 51597,
+  "<|24.68|>": 51598,
+  "<|24.70|>": 51599,
+  "<|24.72|>": 51600,
+  "<|24.74|>": 51601,
+  "<|24.76|>": 51602,
+  "<|24.78|>": 51603,
+  "<|24.80|>": 51604,
+  "<|24.82|>": 51605,
+  "<|24.84|>": 51606,
+  "<|24.86|>": 51607,
+  "<|24.88|>": 51608,
+  "<|24.90|>": 51609,
+  "<|24.92|>": 51610,
+  "<|24.94|>": 51611,
+  "<|24.96|>": 51612,
+  "<|24.98|>": 51613,
+  "<|25.00|>": 51614,
+  "<|25.02|>": 51615,
+  "<|25.04|>": 51616,
+  "<|25.06|>": 51617,
+  "<|25.08|>": 51618,
+  "<|25.10|>": 51619,
+  "<|25.12|>": 51620,
+  "<|25.14|>": 51621,
+  "<|25.16|>": 51622,
+  "<|25.18|>": 51623,
+  "<|25.20|>": 51624,
+  "<|25.22|>": 51625,
+  "<|25.24|>": 51626,
+  "<|25.26|>": 51627,
+  "<|25.28|>": 51628,
+  "<|25.30|>": 51629,
+  "<|25.32|>": 51630,
+  "<|25.34|>": 51631,
+  "<|25.36|>": 51632,
+  "<|25.38|>": 51633,
+  "<|25.40|>": 51634,
+  "<|25.42|>": 51635,
+  "<|25.44|>": 51636,
+  "<|25.46|>": 51637,
+  "<|25.48|>": 51638,
+  "<|25.50|>": 51639,
+  "<|25.52|>": 51640,
+  "<|25.54|>": 51641,
+  "<|25.56|>": 51642,
+  "<|25.58|>": 51643,
+  "<|25.60|>": 51644,
+  "<|25.62|>": 51645,
+  "<|25.64|>": 51646,
+  "<|25.66|>": 51647,
+  "<|25.68|>": 51648,
+  "<|25.70|>": 51649,
+  "<|25.72|>": 51650,
+  "<|25.74|>": 51651,
+  "<|25.76|>": 51652,
+  "<|25.78|>": 51653,
+  "<|25.80|>": 51654,
+  "<|25.82|>": 51655,
+  "<|25.84|>": 51656,
+  "<|25.86|>": 51657,
+  "<|25.88|>": 51658,
+  "<|25.90|>": 51659,
+  "<|25.92|>": 51660,
+  "<|25.94|>": 51661,
+  "<|25.96|>": 51662,
+  "<|25.98|>": 51663,
+  "<|26.00|>": 51664,
+  "<|26.02|>": 51665,
+  "<|26.04|>": 51666,
+  "<|26.06|>": 51667,
+  "<|26.08|>": 51668,
+  "<|26.10|>": 51669,
+  "<|26.12|>": 51670,
+  "<|26.14|>": 51671,
+  "<|26.16|>": 51672,
+  "<|26.18|>": 51673,
+  "<|26.20|>": 51674,
+  "<|26.22|>": 51675,
+  "<|26.24|>": 51676,
+  "<|26.26|>": 51677,
+  "<|26.28|>": 51678,
+  "<|26.30|>": 51679,
+  "<|26.32|>": 51680,
+  "<|26.34|>": 51681,
+  "<|26.36|>": 51682,
+  "<|26.38|>": 51683,
+  "<|26.40|>": 51684,
+  "<|26.42|>": 51685,
+  "<|26.44|>": 51686,
+  "<|26.46|>": 51687,
+  "<|26.48|>": 51688,
+  "<|26.50|>": 51689,
+  "<|26.52|>": 51690,
+  "<|26.54|>": 51691,
+  "<|26.56|>": 51692,
+  "<|26.58|>": 51693,
+  "<|26.60|>": 51694,
+  "<|26.62|>": 51695,
+  "<|26.64|>": 51696,
+  "<|26.66|>": 51697,
+  "<|26.68|>": 51698,
+  "<|26.70|>": 51699,
+  "<|26.72|>": 51700,
+  "<|26.74|>": 51701,
+  "<|26.76|>": 51702,
+  "<|26.78|>": 51703,
+  "<|26.80|>": 51704,
+  "<|26.82|>": 51705,
+  "<|26.84|>": 51706,
+  "<|26.86|>": 51707,
+  "<|26.88|>": 51708,
+  "<|26.90|>": 51709,
+  "<|26.92|>": 51710,
+  "<|26.94|>": 51711,
+  "<|26.96|>": 51712,
+  "<|26.98|>": 51713,
+  "<|27.00|>": 51714,
+  "<|27.02|>": 51715,
+  "<|27.04|>": 51716,
+  "<|27.06|>": 51717,
+  "<|27.08|>": 51718,
+  "<|27.10|>": 51719,
+  "<|27.12|>": 51720,
+  "<|27.14|>": 51721,
+  "<|27.16|>": 51722,
+  "<|27.18|>": 51723,
+  "<|27.20|>": 51724,
+  "<|27.22|>": 51725,
+  "<|27.24|>": 51726,
+  "<|27.26|>": 51727,
+  "<|27.28|>": 51728,
+  "<|27.30|>": 51729,
+  "<|27.32|>": 51730,
+  "<|27.34|>": 51731,
+  "<|27.36|>": 51732,
+  "<|27.38|>": 51733,
+  "<|27.40|>": 51734,
+  "<|27.42|>": 51735,
+  "<|27.44|>": 51736,
+  "<|27.46|>": 51737,
+  "<|27.48|>": 51738,
+  "<|27.50|>": 51739,
+  "<|27.52|>": 51740,
+  "<|27.54|>": 51741,
+  "<|27.56|>": 51742,
+  "<|27.58|>": 51743,
+  "<|27.60|>": 51744,
+  "<|27.62|>": 51745,
+  "<|27.64|>": 51746,
+  "<|27.66|>": 51747,
+  "<|27.68|>": 51748,
+  "<|27.70|>": 51749,
+  "<|27.72|>": 51750,
+  "<|27.74|>": 51751,
+  "<|27.76|>": 51752,
+  "<|27.78|>": 51753,
+  "<|27.80|>": 51754,
+  "<|27.82|>": 51755,
+  "<|27.84|>": 51756,
+  "<|27.86|>": 51757,
+  "<|27.88|>": 51758,
+  "<|27.90|>": 51759,
+  "<|27.92|>": 51760,
+  "<|27.94|>": 51761,
+  "<|27.96|>": 51762,
+  "<|27.98|>": 51763,
+  "<|28.00|>": 51764,
+  "<|28.02|>": 51765,
+  "<|28.04|>": 51766,
+  "<|28.06|>": 51767,
+  "<|28.08|>": 51768,
+  "<|28.10|>": 51769,
+  "<|28.12|>": 51770,
+  "<|28.14|>": 51771,
+  "<|28.16|>": 51772,
+  "<|28.18|>": 51773,
+  "<|28.20|>": 51774,
+  "<|28.22|>": 51775,
+  "<|28.24|>": 51776,
+  "<|28.26|>": 51777,
+  "<|28.28|>": 51778,
+  "<|28.30|>": 51779,
+  "<|28.32|>": 51780,
+  "<|28.34|>": 51781,
+  "<|28.36|>": 51782,
+  "<|28.38|>": 51783,
+  "<|28.40|>": 51784,
+  "<|28.42|>": 51785,
+  "<|28.44|>": 51786,
+  "<|28.46|>": 51787,
+  "<|28.48|>": 51788,
+  "<|28.50|>": 51789,
+  "<|28.52|>": 51790,
+  "<|28.54|>": 51791,
+  "<|28.56|>": 51792,
+  "<|28.58|>": 51793,
+  "<|28.60|>": 51794,
+  "<|28.62|>": 51795,
+  "<|28.64|>": 51796,
+  "<|28.66|>": 51797,
+  "<|28.68|>": 51798,
+  "<|28.70|>": 51799,
+  "<|28.72|>": 51800,
+  "<|28.74|>": 51801,
+  "<|28.76|>": 51802,
+  "<|28.78|>": 51803,
+  "<|28.80|>": 51804,
+  "<|28.82|>": 51805,
+  "<|28.84|>": 51806,
+  "<|28.86|>": 51807,
+  "<|28.88|>": 51808,
+  "<|28.90|>": 51809,
+  "<|28.92|>": 51810,
+  "<|28.94|>": 51811,
+  "<|28.96|>": 51812,
+  "<|28.98|>": 51813,
+  "<|29.00|>": 51814,
+  "<|29.02|>": 51815,
+  "<|29.04|>": 51816,
+  "<|29.06|>": 51817,
+  "<|29.08|>": 51818,
+  "<|29.10|>": 51819,
+  "<|29.12|>": 51820,
+  "<|29.14|>": 51821,
+  "<|29.16|>": 51822,
+  "<|29.18|>": 51823,
+  "<|29.20|>": 51824,
+  "<|29.22|>": 51825,
+  "<|29.24|>": 51826,
+  "<|29.26|>": 51827,
+  "<|29.28|>": 51828,
+  "<|29.30|>": 51829,
+  "<|29.32|>": 51830,
+  "<|29.34|>": 51831,
+  "<|29.36|>": 51832,
+  "<|29.38|>": 51833,
+  "<|29.40|>": 51834,
+  "<|29.42|>": 51835,
+  "<|29.44|>": 51836,
+  "<|29.46|>": 51837,
+  "<|29.48|>": 51838,
+  "<|29.50|>": 51839,
+  "<|29.52|>": 51840,
+  "<|29.54|>": 51841,
+  "<|29.56|>": 51842,
+  "<|29.58|>": 51843,
+  "<|29.60|>": 51844,
+  "<|29.62|>": 51845,
+  "<|29.64|>": 51846,
+  "<|29.66|>": 51847,
+  "<|29.68|>": 51848,
+  "<|29.70|>": 51849,
+  "<|29.72|>": 51850,
+  "<|29.74|>": 51851,
+  "<|29.76|>": 51852,
+  "<|29.78|>": 51853,
+  "<|29.80|>": 51854,
+  "<|29.82|>": 51855,
+  "<|29.84|>": 51856,
+  "<|29.86|>": 51857,
+  "<|29.88|>": 51858,
+  "<|29.90|>": 51859,
+  "<|29.92|>": 51860,
+  "<|29.94|>": 51861,
+  "<|29.96|>": 51862,
+  "<|29.98|>": 51863,
+  "<|3.00|>": 50514,
+  "<|3.02|>": 50515,
+  "<|3.04|>": 50516,
+  "<|3.06|>": 50517,
+  "<|3.08|>": 50518,
+  "<|3.10|>": 50519,
+  "<|3.12|>": 50520,
+  "<|3.14|>": 50521,
+  "<|3.16|>": 50522,
+  "<|3.18|>": 50523,
+  "<|3.20|>": 50524,
+  "<|3.22|>": 50525,
+  "<|3.24|>": 50526,
+  "<|3.26|>": 50527,
+  "<|3.28|>": 50528,
+  "<|3.30|>": 50529,
+  "<|3.32|>": 50530,
+  "<|3.34|>": 50531,
+  "<|3.36|>": 50532,
+  "<|3.38|>": 50533,
+  "<|3.40|>": 50534,
+  "<|3.42|>": 50535,
+  "<|3.44|>": 50536,
+  "<|3.46|>": 50537,
+  "<|3.48|>": 50538,
+  "<|3.50|>": 50539,
+  "<|3.52|>": 50540,
+  "<|3.54|>": 50541,
+  "<|3.56|>": 50542,
+  "<|3.58|>": 50543,
+  "<|3.60|>": 50544,
+  "<|3.62|>": 50545,
+  "<|3.64|>": 50546,
+  "<|3.66|>": 50547,
+  "<|3.68|>": 50548,
+  "<|3.70|>": 50549,
+  "<|3.72|>": 50550,
+  "<|3.74|>": 50551,
+  "<|3.76|>": 50552,
+  "<|3.78|>": 50553,
+  "<|3.80|>": 50554,
+  "<|3.82|>": 50555,
+  "<|3.84|>": 50556,
+  "<|3.86|>": 50557,
+  "<|3.88|>": 50558,
+  "<|3.90|>": 50559,
+  "<|3.92|>": 50560,
+  "<|3.94|>": 50561,
+  "<|3.96|>": 50562,
+  "<|3.98|>": 50563,
+  "<|30.00|>": 51864,
+  "<|4.00|>": 50564,
+  "<|4.02|>": 50565,
+  "<|4.04|>": 50566,
+  "<|4.06|>": 50567,
+  "<|4.08|>": 50568,
+  "<|4.10|>": 50569,
+  "<|4.12|>": 50570,
+  "<|4.14|>": 50571,
+  "<|4.16|>": 50572,
+  "<|4.18|>": 50573,
+  "<|4.20|>": 50574,
+  "<|4.22|>": 50575,
+  "<|4.24|>": 50576,
+  "<|4.26|>": 50577,
+  "<|4.28|>": 50578,
+  "<|4.30|>": 50579,
+  "<|4.32|>": 50580,
+  "<|4.34|>": 50581,
+  "<|4.36|>": 50582,
+  "<|4.38|>": 50583,
+  "<|4.40|>": 50584,
+  "<|4.42|>": 50585,
+  "<|4.44|>": 50586,
+  "<|4.46|>": 50587,
+  "<|4.48|>": 50588,
+  "<|4.50|>": 50589,
+  "<|4.52|>": 50590,
+  "<|4.54|>": 50591,
+  "<|4.56|>": 50592,
+  "<|4.58|>": 50593,
+  "<|4.60|>": 50594,
+  "<|4.62|>": 50595,
+  "<|4.64|>": 50596,
+  "<|4.66|>": 50597,
+  "<|4.68|>": 50598,
+  "<|4.70|>": 50599,
+  "<|4.72|>": 50600,
+  "<|4.74|>": 50601,
+  "<|4.76|>": 50602,
+  "<|4.78|>": 50603,
+  "<|4.80|>": 50604,
+  "<|4.82|>": 50605,
+  "<|4.84|>": 50606,
+  "<|4.86|>": 50607,
+  "<|4.88|>": 50608,
+  "<|4.90|>": 50609,
+  "<|4.92|>": 50610,
+  "<|4.94|>": 50611,
+  "<|4.96|>": 50612,
+  "<|4.98|>": 50613,
+  "<|5.00|>": 50614,
+  "<|5.02|>": 50615,
+  "<|5.04|>": 50616,
+  "<|5.06|>": 50617,
+  "<|5.08|>": 50618,
+  "<|5.10|>": 50619,
+  "<|5.12|>": 50620,
+  "<|5.14|>": 50621,
+  "<|5.16|>": 50622,
+  "<|5.18|>": 50623,
+  "<|5.20|>": 50624,
+  "<|5.22|>": 50625,
+  "<|5.24|>": 50626,
+  "<|5.26|>": 50627,
+  "<|5.28|>": 50628,
+  "<|5.30|>": 50629,
+  "<|5.32|>": 50630,
+  "<|5.34|>": 50631,
+  "<|5.36|>": 50632,
+  "<|5.38|>": 50633,
+  "<|5.40|>": 50634,
+  "<|5.42|>": 50635,
+  "<|5.44|>": 50636,
+  "<|5.46|>": 50637,
+  "<|5.48|>": 50638,
+  "<|5.50|>": 50639,
+  "<|5.52|>": 50640,
+  "<|5.54|>": 50641,
+  "<|5.56|>": 50642,
+  "<|5.58|>": 50643,
+  "<|5.60|>": 50644,
+  "<|5.62|>": 50645,
+  "<|5.64|>": 50646,
+  "<|5.66|>": 50647,
+  "<|5.68|>": 50648,
+  "<|5.70|>": 50649,
+  "<|5.72|>": 50650,
+  "<|5.74|>": 50651,
+  "<|5.76|>": 50652,
+  "<|5.78|>": 50653,
+  "<|5.80|>": 50654,
+  "<|5.82|>": 50655,
+  "<|5.84|>": 50656,
+  "<|5.86|>": 50657,
+  "<|5.88|>": 50658,
+  "<|5.90|>": 50659,
+  "<|5.92|>": 50660,
+  "<|5.94|>": 50661,
+  "<|5.96|>": 50662,
+  "<|5.98|>": 50663,
+  "<|6.00|>": 50664,
+  "<|6.02|>": 50665,
+  "<|6.04|>": 50666,
+  "<|6.06|>": 50667,
+  "<|6.08|>": 50668,
+  "<|6.10|>": 50669,
+  "<|6.12|>": 50670,
+  "<|6.14|>": 50671,
+  "<|6.16|>": 50672,
+  "<|6.18|>": 50673,
+  "<|6.20|>": 50674,
+  "<|6.22|>": 50675,
+  "<|6.24|>": 50676,
+  "<|6.26|>": 50677,
+  "<|6.28|>": 50678,
+  "<|6.30|>": 50679,
+  "<|6.32|>": 50680,
+  "<|6.34|>": 50681,
+  "<|6.36|>": 50682,
+  "<|6.38|>": 50683,
+  "<|6.40|>": 50684,
+  "<|6.42|>": 50685,
+  "<|6.44|>": 50686,
+  "<|6.46|>": 50687,
+  "<|6.48|>": 50688,
+  "<|6.50|>": 50689,
+  "<|6.52|>": 50690,
+  "<|6.54|>": 50691,
+  "<|6.56|>": 50692,
+  "<|6.58|>": 50693,
+  "<|6.60|>": 50694,
+  "<|6.62|>": 50695,
+  "<|6.64|>": 50696,
+  "<|6.66|>": 50697,
+  "<|6.68|>": 50698,
+  "<|6.70|>": 50699,
+  "<|6.72|>": 50700,
+  "<|6.74|>": 50701,
+  "<|6.76|>": 50702,
+  "<|6.78|>": 50703,
+  "<|6.80|>": 50704,
+  "<|6.82|>": 50705,
+  "<|6.84|>": 50706,
+  "<|6.86|>": 50707,
+  "<|6.88|>": 50708,
+  "<|6.90|>": 50709,
+  "<|6.92|>": 50710,
+  "<|6.94|>": 50711,
+  "<|6.96|>": 50712,
+  "<|6.98|>": 50713,
+  "<|7.00|>": 50714,
+  "<|7.02|>": 50715,
+  "<|7.04|>": 50716,
+  "<|7.06|>": 50717,
+  "<|7.08|>": 50718,
+  "<|7.10|>": 50719,
+  "<|7.12|>": 50720,
+  "<|7.14|>": 50721,
+  "<|7.16|>": 50722,
+  "<|7.18|>": 50723,
+  "<|7.20|>": 50724,
+  "<|7.22|>": 50725,
+  "<|7.24|>": 50726,
+  "<|7.26|>": 50727,
+  "<|7.28|>": 50728,
+  "<|7.30|>": 50729,
+  "<|7.32|>": 50730,
+  "<|7.34|>": 50731,
+  "<|7.36|>": 50732,
+  "<|7.38|>": 50733,
+  "<|7.40|>": 50734,
+  "<|7.42|>": 50735,
+  "<|7.44|>": 50736,
+  "<|7.46|>": 50737,
+  "<|7.48|>": 50738,
+  "<|7.50|>": 50739,
+  "<|7.52|>": 50740,
+  "<|7.54|>": 50741,
+  "<|7.56|>": 50742,
+  "<|7.58|>": 50743,
+  "<|7.60|>": 50744,
+  "<|7.62|>": 50745,
+  "<|7.64|>": 50746,
+  "<|7.66|>": 50747,
+  "<|7.68|>": 50748,
+  "<|7.70|>": 50749,
+  "<|7.72|>": 50750,
+  "<|7.74|>": 50751,
+  "<|7.76|>": 50752,
+  "<|7.78|>": 50753,
+  "<|7.80|>": 50754,
+  "<|7.82|>": 50755,
+  "<|7.84|>": 50756,
+  "<|7.86|>": 50757,
+  "<|7.88|>": 50758,
+  "<|7.90|>": 50759,
+  "<|7.92|>": 50760,
+  "<|7.94|>": 50761,
+  "<|7.96|>": 50762,
+  "<|7.98|>": 50763,
+  "<|8.00|>": 50764,
+  "<|8.02|>": 50765,
+  "<|8.04|>": 50766,
+  "<|8.06|>": 50767,
+  "<|8.08|>": 50768,
+  "<|8.10|>": 50769,
+  "<|8.12|>": 50770,
+  "<|8.14|>": 50771,
+  "<|8.16|>": 50772,
+  "<|8.18|>": 50773,
+  "<|8.20|>": 50774,
+  "<|8.22|>": 50775,
+  "<|8.24|>": 50776,
+  "<|8.26|>": 50777,
+  "<|8.28|>": 50778,
+  "<|8.30|>": 50779,
+  "<|8.32|>": 50780,
+  "<|8.34|>": 50781,
+  "<|8.36|>": 50782,
+  "<|8.38|>": 50783,
+  "<|8.40|>": 50784,
+  "<|8.42|>": 50785,
+  "<|8.44|>": 50786,
+  "<|8.46|>": 50787,
+  "<|8.48|>": 50788,
+  "<|8.50|>": 50789,
+  "<|8.52|>": 50790,
+  "<|8.54|>": 50791,
+  "<|8.56|>": 50792,
+  "<|8.58|>": 50793,
+  "<|8.60|>": 50794,
+  "<|8.62|>": 50795,
+  "<|8.64|>": 50796,
+  "<|8.66|>": 50797,
+  "<|8.68|>": 50798,
+  "<|8.70|>": 50799,
+  "<|8.72|>": 50800,
+  "<|8.74|>": 50801,
+  "<|8.76|>": 50802,
+  "<|8.78|>": 50803,
+  "<|8.80|>": 50804,
+  "<|8.82|>": 50805,
+  "<|8.84|>": 50806,
+  "<|8.86|>": 50807,
+  "<|8.88|>": 50808,
+  "<|8.90|>": 50809,
+  "<|8.92|>": 50810,
+  "<|8.94|>": 50811,
+  "<|8.96|>": 50812,
+  "<|8.98|>": 50813,
+  "<|9.00|>": 50814,
+  "<|9.02|>": 50815,
+  "<|9.04|>": 50816,
+  "<|9.06|>": 50817,
+  "<|9.08|>": 50818,
+  "<|9.10|>": 50819,
+  "<|9.12|>": 50820,
+  "<|9.14|>": 50821,
+  "<|9.16|>": 50822,
+  "<|9.18|>": 50823,
+  "<|9.20|>": 50824,
+  "<|9.22|>": 50825,
+  "<|9.24|>": 50826,
+  "<|9.26|>": 50827,
+  "<|9.28|>": 50828,
+  "<|9.30|>": 50829,
+  "<|9.32|>": 50830,
+  "<|9.34|>": 50831,
+  "<|9.36|>": 50832,
+  "<|9.38|>": 50833,
+  "<|9.40|>": 50834,
+  "<|9.42|>": 50835,
+  "<|9.44|>": 50836,
+  "<|9.46|>": 50837,
+  "<|9.48|>": 50838,
+  "<|9.50|>": 50839,
+  "<|9.52|>": 50840,
+  "<|9.54|>": 50841,
+  "<|9.56|>": 50842,
+  "<|9.58|>": 50843,
+  "<|9.60|>": 50844,
+  "<|9.62|>": 50845,
+  "<|9.64|>": 50846,
+  "<|9.66|>": 50847,
+  "<|9.68|>": 50848,
+  "<|9.70|>": 50849,
+  "<|9.72|>": 50850,
+  "<|9.74|>": 50851,
+  "<|9.76|>": 50852,
+  "<|9.78|>": 50853,
+  "<|9.80|>": 50854,
+  "<|9.82|>": 50855,
+  "<|9.84|>": 50856,
+  "<|9.86|>": 50857,
+  "<|9.88|>": 50858,
+  "<|9.90|>": 50859,
+  "<|9.92|>": 50860,
+  "<|9.94|>": 50861,
+  "<|9.96|>": 50862,
+  "<|9.98|>": 50863,
+  "<|af|>": 50327,
+  "<|am|>": 50334,
+  "<|ar|>": 50272,
+  "<|as|>": 50350,
+  "<|az|>": 50304,
+  "<|ba|>": 50355,
+  "<|be|>": 50330,
+  "<|bg|>": 50292,
+  "<|bn|>": 50302,
+  "<|bo|>": 50347,
+  "<|br|>": 50309,
+  "<|bs|>": 50315,
+  "<|ca|>": 50270,
+  "<|cs|>": 50283,
+  "<|cy|>": 50297,
+  "<|da|>": 50285,
+  "<|de|>": 50261,
+  "<|el|>": 50281,
+  "<|en|>": 50259,
+  "<|es|>": 50262,
+  "<|et|>": 50307,
+  "<|eu|>": 50310,
+  "<|fa|>": 50300,
+  "<|fi|>": 50277,
+  "<|fo|>": 50338,
+  "<|fr|>": 50265,
+  "<|gl|>": 50319,
+  "<|gu|>": 50333,
+  "<|haw|>": 50352,
+  "<|ha|>": 50354,
+  "<|he|>": 50279,
+  "<|hi|>": 50276,
+  "<|hr|>": 50291,
+  "<|ht|>": 50339,
+  "<|hu|>": 50286,
+  "<|hy|>": 50312,
+  "<|id|>": 50275,
+  "<|is|>": 50311,
+  "<|it|>": 50274,
+  "<|ja|>": 50266,
+  "<|jw|>": 50356,
+  "<|ka|>": 50329,
+  "<|kk|>": 50316,
+  "<|km|>": 50323,
+  "<|kn|>": 50306,
+  "<|ko|>": 50264,
+  "<|la|>": 50294,
+  "<|lb|>": 50345,
+  "<|ln|>": 50353,
+  "<|lo|>": 50336,
+  "<|lt|>": 50293,
+  "<|lv|>": 50301,
+  "<|mg|>": 50349,
+  "<|mi|>": 50295,
+  "<|mk|>": 50308,
+  "<|ml|>": 50296,
+  "<|mn|>": 50314,
+  "<|mr|>": 50320,
+  "<|ms|>": 50282,
+  "<|mt|>": 50343,
+  "<|my|>": 50346,
+  "<|ne|>": 50313,
+  "<|nl|>": 50271,
+  "<|nn|>": 50342,
+  "<|nocaptions|>": 50362,
+  "<|notimestamps|>": 50363,
+  "<|no|>": 50288,
+  "<|oc|>": 50328,
+  "<|pa|>": 50321,
+  "<|pl|>": 50269,
+  "<|ps|>": 50340,
+  "<|pt|>": 50267,
+  "<|ro|>": 50284,
+  "<|ru|>": 50263,
+  "<|sa|>": 50344,
+  "<|sd|>": 50332,
+  "<|si|>": 50322,
+  "<|sk|>": 50298,
+  "<|sl|>": 50305,
+  "<|sn|>": 50324,
+  "<|so|>": 50326,
+  "<|sq|>": 50317,
+  "<|sr|>": 50303,
+  "<|startoflm|>": 50360,
+  "<|startofprev|>": 50361,
+  "<|startoftranscript|>": 50258,
+  "<|su|>": 50357,
+  "<|sv|>": 50273,
+  "<|sw|>": 50318,
+  "<|ta|>": 50287,
+  "<|te|>": 50299,
+  "<|tg|>": 50331,
+  "<|th|>": 50289,
+  "<|tk|>": 50341,
+  "<|tl|>": 50348,
+  "<|transcribe|>": 50359,
+  "<|translate|>": 50358,
+  "<|tr|>": 50268,
+  "<|tt|>": 50351,
+  "<|uk|>": 50280,
+  "<|ur|>": 50290,
+  "<|uz|>": 50337,
+  "<|vi|>": 50278,
+  "<|yi|>": 50335,
+  "<|yo|>": 50325,
+  "<|zh|>": 50260
+}

checkpoint-1000-epoch-0/model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5846539ce04532d21ae1018baad4bd0a3389ec6969bfa7327a995a96fe03ff44
+size 3024943976

checkpoint-1000-epoch-0/model_1.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:496c403608ba899060ddd5259b16f3de8536ae526a06c259fd871a2a26a318b8
+size 3086761032

checkpoint-1000-epoch-0/optimizer.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c0d86c554f72013a349c5dd47f1d18b5ec93818136e8f7dbd644709a847184cd
+size 955529338

checkpoint-1000-epoch-0/random_states_0.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7e1c07ea5661032b9d319bf0337abf2b19f6a28556ad1936bac5b28f44a4fef9
+size 14344

checkpoint-1000-epoch-0/scheduler.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c8089e940f36553a3c1c923efdeec31d242baa919d942c67c2c633343ea9b7c8
+size 1064

config.json

@@ -0,0 +1,154 @@
+{
+  "_name_or_path": "openai/whisper-large-v2",
+  "activation_dropout": 0.0,
+  "activation_function": "gelu",
+  "apply_spec_augment": false,
+  "architectures": [
+    "WhisperForConditionalGeneration"
+  ],
+  "attention_dropout": 0.0,
+  "begin_suppress_tokens": [
+    220,
+    50257
+  ],
+  "bos_token_id": 50257,
+  "classifier_proj_size": 256,
+  "d_model": 1280,
+  "decoder_attention_heads": 20,
+  "decoder_ffn_dim": 5120,
+  "decoder_layerdrop": 0.0,
+  "decoder_layers": 2,
+  "decoder_start_token_id": 50258,
+  "dropout": 0.0,
+  "encoder_attention_heads": 20,
+  "encoder_ffn_dim": 5120,
+  "encoder_layerdrop": 0.0,
+  "encoder_layers": 32,
+  "eos_token_id": 50257,
+  "forced_decoder_ids": [
+    [
+      1,
+      50259
+    ],
+    [
+      2,
+      50359
+    ],
+    [
+      3,
+      50363
+    ]
+  ],
+  "init_std": 0.02,
+  "is_encoder_decoder": true,
+  "mask_feature_length": 10,
+  "mask_feature_min_masks": 0,
+  "mask_feature_prob": 0.0,
+  "mask_time_length": 10,
+  "mask_time_min_masks": 2,
+  "mask_time_prob": 0.05,
+  "max_length": 448,
+  "max_source_positions": 1500,
+  "max_target_positions": 448,
+  "median_filter_width": 7,
+  "model_type": "whisper",
+  "num_hidden_layers": 32,
+  "num_mel_bins": 80,
+  "pad_token_id": 50257,
+  "scale_embedding": false,
+  "suppress_tokens": [
+    1,
+    2,
+    7,
+    8,
+    9,
+    10,
+    14,
+    25,
+    26,
+    27,
+    28,
+    29,
+    31,
+    58,
+    59,
+    60,
+    61,
+    62,
+    63,
+    90,
+    91,
+    92,
+    93,
+    359,
+    503,
+    522,
+    542,
+    873,
+    893,
+    902,
+    918,
+    922,
+    931,
+    1350,
+    1853,
+    1982,
+    2460,
+    2627,
+    3246,
+    3253,
+    3268,
+    3536,
+    3846,
+    3961,
+    4183,
+    4667,
+    6585,
+    6647,
+    7273,
+    9061,
+    9383,
+    10428,
+    10929,
+    11938,
+    12033,
+    12331,
+    12562,
+    13793,
+    14157,
+    14635,
+    15265,
+    15618,
+    16553,
+    16604,
+    18362,
+    18956,
+    20075,
+    21675,
+    22520,
+    26130,
+    26161,
+    26435,
+    28279,
+    29464,
+    31650,
+    32302,
+    32470,
+    36865,
+    42863,
+    47425,
+    49870,
+    50254,
+    50258,
+    50358,
+    50359,
+    50360,
+    50361,
+    50362
+  ],
+  "torch_dtype": "float32",
+  "transformers_version": "4.36.2",
+  "use_cache": true,
+  "use_weighted_layer_sum": false,
+  "vocab_size": 51865
+}

create_student_model.py

@@ -0,0 +1,214 @@
+#!/usr/bin/env python
+# coding=utf-8
+# Copyright 2023 The HuggingFace Inc. 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.
+"""
+Initialise a student Whisper model from a pre-trained teacher model for
+teacher-student distillation.
+"""
+
+import argparse
+import copy
+import logging
+
+import numpy as np
+import torch
+from transformers import GenerationConfig, WhisperForConditionalGeneration, WhisperProcessor
+
+
+logger = logging.getLogger(__name__)
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description="Initialise a student Whisper model from a teacher model, copying the relevant layer weights and adjusting the processor as necessary."
+    )
+    parser.add_argument(
+        "--teacher_checkpoint",
+        type=str,
+        required=True,
+        help="The HF Hub ID of the teacher checkpoint.",
+    )
+    parser.add_argument(
+        "--subfolder",
+        type=str,
+        default="",
+        help="In case the relevant teacher weights are located inside a subfolder of the model repo on huggingface.co, you "
+        "can specify the folder name here.",
+    )
+    parser.add_argument(
+        "--encoder_layers",
+        type=int,
+        default=None,
+        help="Number of encoder layers to use in the student model. Defaults to all layers from the teacher.",
+    )
+    parser.add_argument(
+        "--decoder_layers",
+        type=int,
+        default=2,
+        help="Number of decoder layers to use in the student model. Defaults to 2 layers.",
+    )
+    parser.add_argument(
+        "--save_dir",
+        type=str,
+        required=True,
+        help="Where to save the student weights and processor.",
+    )
+    parser.add_argument(
+        "--push_to_hub",
+        type=bool,
+        required=False,
+        default=False,
+        help="Whether to push the student weights and processor to the Hub.",
+    )
+    parser.add_argument(
+        "--cache_dir",
+        type=str,
+        default=None,
+        help="Where to store the pretrained models downloaded from huggingface.co",
+    )
+
+    args = parser.parse_args()
+    return args
+
+
+def init_student_model_from_teacher(
+    teacher_checkpoint,
+    encoder_layers=None,
+    decoder_layers=2,
+    save_dir=None,
+    push_to_hub=None,
+    cache_dir=None,
+    subfolder="",
+):
+    teacher_model = WhisperForConditionalGeneration.from_pretrained(
+        teacher_checkpoint,
+        cache_dir=cache_dir,
+        subfolder=subfolder,
+        low_cpu_mem_usage=True,
+    )
+    processor = WhisperProcessor.from_pretrained(teacher_checkpoint)
+    generation_config = GenerationConfig.from_pretrained(teacher_checkpoint)
+
+    teacher_config = teacher_model.config
+    teacher_encoder_layers = teacher_config.encoder_layers
+    teacher_decoder_layers = teacher_config.decoder_layers
+
+    student_config = copy.deepcopy(teacher_config)
+    student_config.update(
+        {
+            "encoder_layers": encoder_layers if encoder_layers is not None else teacher_encoder_layers,
+            "decoder_layers": decoder_layers,
+        }
+    )
+
+    encoder_mapping = np.linspace(0, teacher_encoder_layers - 1, student_config.encoder_layers, dtype=int)
+    encoder_mapping[-1] = teacher_encoder_layers - 1
+
+    encoder_map = {}
+    for student_layer, teacher_layer in enumerate(encoder_mapping):
+        encoder_map[teacher_layer] = student_layer
+
+    decoder_mapping = np.linspace(0, teacher_decoder_layers - 1, student_config.decoder_layers, dtype=int)
+    decoder_mapping[-1] = teacher_decoder_layers - 1
+
+    decoder_map = {}
+    for student_layer, teacher_layer in enumerate(decoder_mapping):
+        decoder_map[teacher_layer] = student_layer
+
+    # init the student params from the teacher model
+    student_model = WhisperForConditionalGeneration(student_config)
+    missing_keys, unexpected_keys = student_model.load_state_dict(teacher_model.state_dict(), strict=False)
+    if len(missing_keys) > 0:
+        raise RuntimeError(
+            "Error(s) in loading state_dict for WhisperForConditionalGeneration. \n"
+            f"Missing key(s) in state_dict: {missing_keys}"
+        )
+    if decoder_layers == teacher_decoder_layers:
+        decoder_keys = [key for key in unexpected_keys if "model.decoder.layers" in key]
+        if len(decoder_keys) > 0:
+            raise RuntimeError(
+                "Error(s) in loading state_dict for WhisperForConditionalGeneration. \n"
+                f"Unexpected key(s) in state_dict: {decoder_keys}"
+            )
+    if encoder_layers == teacher_encoder_layers:
+        encoder_keys = [key for key in unexpected_keys if "model.encoder.layers" in key]
+        if len(encoder_keys) > 0:
+            raise RuntimeError(
+                "Error(s) in loading state_dict for WhisperForConditionalGeneration. \n"
+                f"Unexpected key(s) in state_dict: {encoder_keys}"
+            )
+
+    for layer in range(teacher_decoder_layers):
+        if layer in decoder_map:
+            # re-introduce pre-defined layers from the teacher
+            student_model.model.decoder.layers[decoder_map[layer]].load_state_dict(
+                teacher_model.model.decoder.layers[layer].state_dict()
+            )
+
+    if encoder_layers is not None:
+        for layer in range(teacher_encoder_layers):
+            if layer in encoder_map:
+                # re-introduce pre-defined layers from the teacher
+                student_model.model.encoder.layers[encoder_map[layer]].load_state_dict(
+                    teacher_model.model.encoder.layers[layer].state_dict()
+                )
+
+    # remove the teacher params and model
+    del teacher_model
+
+    # save the converted weights and model
+    if save_dir is not None:
+        student_model.save_pretrained(save_dir)
+        # we also need to correctly save the processor and generation config
+        processor.save_pretrained(save_dir)
+        generation_config.save_pretrained(save_dir)
+
+    # check we can do a forward pass with the saved model - first load the weights and processor
+    logger.info("Checking we can load the saved model...")
+    student_model = WhisperForConditionalGeneration.from_pretrained(
+        save_dir,
+        low_cpu_mem_usage=True,
+    )
+    processor = WhisperProcessor.from_pretrained(save_dir)
+
+    # define some random inputs
+    input_features = processor(np.ones(16000), sampling_rate=16000, return_tensors="pt").input_features
+    decoder_start_token_id = student_model.config.decoder_start_token_id
+    decoder_input_ids = torch.ones((input_features.shape[0], 1), dtype=torch.long) * decoder_start_token_id
+
+    # do a forward pass - outputs will be gibberish for the initialised model so we can't check them
+    # but we make can sure the model runs as expected
+    logger.info("Checking we can run the converted model forward...")
+    _ = student_model(input_features, decoder_input_ids=decoder_input_ids).logits
+    logger.info("Conversion successful!")
+
+    if push_to_hub:
+        student_model.push_to_hub(save_dir)
+        processor.push_to_hub(save_dir)
+        generation_config.push_to_hub(save_dir)
+
+
+if __name__ == "__main__":
+    args = parse_args()
+
+    init_student_model_from_teacher(
+        teacher_checkpoint=args.teacher_checkpoint,
+        encoder_layers=args.encoder_layers,
+        decoder_layers=args.decoder_layers,
+        save_dir=args.save_dir,
+        push_to_hub=args.push_to_hub,
+        cache_dir=args.cache_dir,
+        subfolder=args.subfolder,
+    )

create_student_model.sh

@@ -0,0 +1,5 @@
+python create_student_model.py \
+  --teacher_checkpoint "openai/whisper-large-v2" \
+  --encoder_layers 32 \
+  --decoder_layers 2 \
+  --save_dir "./distil-large-v2-init"

distil-large-v2-init/added_tokens.json

@@ -0,0 +1,1609 @@
+{
+  "<|0.00|>": 50364,
+  "<|0.02|>": 50365,
+  "<|0.04|>": 50366,
+  "<|0.06|>": 50367,
+  "<|0.08|>": 50368,
+  "<|0.10|>": 50369,
+  "<|0.12|>": 50370,
+  "<|0.14|>": 50371,
+  "<|0.16|>": 50372,
+  "<|0.18|>": 50373,
+  "<|0.20|>": 50374,
+  "<|0.22|>": 50375,
+  "<|0.24|>": 50376,
+  "<|0.26|>": 50377,
+  "<|0.28|>": 50378,
+  "<|0.30|>": 50379,
+  "<|0.32|>": 50380,
+  "<|0.34|>": 50381,
+  "<|0.36|>": 50382,
+  "<|0.38|>": 50383,
+  "<|0.40|>": 50384,
+  "<|0.42|>": 50385,
+  "<|0.44|>": 50386,
+  "<|0.46|>": 50387,
+  "<|0.48|>": 50388,
+  "<|0.50|>": 50389,
+  "<|0.52|>": 50390,
+  "<|0.54|>": 50391,
+  "<|0.56|>": 50392,
+  "<|0.58|>": 50393,
+  "<|0.60|>": 50394,
+  "<|0.62|>": 50395,
+  "<|0.64|>": 50396,
+  "<|0.66|>": 50397,
+  "<|0.68|>": 50398,
+  "<|0.70|>": 50399,
+  "<|0.72|>": 50400,
+  "<|0.74|>": 50401,
+  "<|0.76|>": 50402,
+  "<|0.78|>": 50403,
+  "<|0.80|>": 50404,
+  "<|0.82|>": 50405,
+  "<|0.84|>": 50406,
+  "<|0.86|>": 50407,
+  "<|0.88|>": 50408,
+  "<|0.90|>": 50409,
+  "<|0.92|>": 50410,
+  "<|0.94|>": 50411,
+  "<|0.96|>": 50412,
+  "<|0.98|>": 50413,
+  "<|1.00|>": 50414,
+  "<|1.02|>": 50415,
+  "<|1.04|>": 50416,
+  "<|1.06|>": 50417,
+  "<|1.08|>": 50418,
+  "<|1.10|>": 50419,
+  "<|1.12|>": 50420,
+  "<|1.14|>": 50421,
+  "<|1.16|>": 50422,
+  "<|1.18|>": 50423,
+  "<|1.20|>": 50424,
+  "<|1.22|>": 50425,
+  "<|1.24|>": 50426,
+  "<|1.26|>": 50427,
+  "<|1.28|>": 50428,
+  "<|1.30|>": 50429,
+  "<|1.32|>": 50430,
+  "<|1.34|>": 50431,
+  "<|1.36|>": 50432,
+  "<|1.38|>": 50433,
+  "<|1.40|>": 50434,
+  "<|1.42|>": 50435,
+  "<|1.44|>": 50436,
+  "<|1.46|>": 50437,
+  "<|1.48|>": 50438,
+  "<|1.50|>": 50439,
+  "<|1.52|>": 50440,
+  "<|1.54|>": 50441,
+  "<|1.56|>": 50442,
+  "<|1.58|>": 50443,
+  "<|1.60|>": 50444,
+  "<|1.62|>": 50445,
+  "<|1.64|>": 50446,
+  "<|1.66|>": 50447,
+  "<|1.68|>": 50448,
+  "<|1.70|>": 50449,
+  "<|1.72|>": 50450,
+  "<|1.74|>": 50451,
+  "<|1.76|>": 50452,
+  "<|1.78|>": 50453,
+  "<|1.80|>": 50454,
+  "<|1.82|>": 50455,
+  "<|1.84|>": 50456,
+  "<|1.86|>": 50457,
+  "<|1.88|>": 50458,
+  "<|1.90|>": 50459,
+  "<|1.92|>": 50460,
+  "<|1.94|>": 50461,
+  "<|1.96|>": 50462,
+  "<|1.98|>": 50463,
+  "<|10.00|>": 50864,
+  "<|10.02|>": 50865,
+  "<|10.04|>": 50866,
+  "<|10.06|>": 50867,
+  "<|10.08|>": 50868,
+  "<|10.10|>": 50869,
+  "<|10.12|>": 50870,
+  "<|10.14|>": 50871,
+  "<|10.16|>": 50872,
+  "<|10.18|>": 50873,
+  "<|10.20|>": 50874,
+  "<|10.22|>": 50875,
+  "<|10.24|>": 50876,
+  "<|10.26|>": 50877,
+  "<|10.28|>": 50878,
+  "<|10.30|>": 50879,
+  "<|10.32|>": 50880,
+  "<|10.34|>": 50881,
+  "<|10.36|>": 50882,
+  "<|10.38|>": 50883,
+  "<|10.40|>": 50884,
+  "<|10.42|>": 50885,
+  "<|10.44|>": 50886,
+  "<|10.46|>": 50887,
+  "<|10.48|>": 50888,
+  "<|10.50|>": 50889,
+  "<|10.52|>": 50890,
+  "<|10.54|>": 50891,
+  "<|10.56|>": 50892,
+  "<|10.58|>": 50893,
+  "<|10.60|>": 50894,
+  "<|10.62|>": 50895,
+  "<|10.64|>": 50896,
+  "<|10.66|>": 50897,
+  "<|10.68|>": 50898,
+  "<|10.70|>": 50899,
+  "<|10.72|>": 50900,
+  "<|10.74|>": 50901,
+  "<|10.76|>": 50902,
+  "<|10.78|>": 50903,
+  "<|10.80|>": 50904,
+  "<|10.82|>": 50905,
+  "<|10.84|>": 50906,
+  "<|10.86|>": 50907,
+  "<|10.88|>": 50908,
+  "<|10.90|>": 50909,
+  "<|10.92|>": 50910,
+  "<|10.94|>": 50911,
+  "<|10.96|>": 50912,
+  "<|10.98|>": 50913,
+  "<|11.00|>": 50914,
+  "<|11.02|>": 50915,
+  "<|11.04|>": 50916,
+  "<|11.06|>": 50917,
+  "<|11.08|>": 50918,
+  "<|11.10|>": 50919,
+  "<|11.12|>": 50920,
+  "<|11.14|>": 50921,
+  "<|11.16|>": 50922,
+  "<|11.18|>": 50923,
+  "<|11.20|>": 50924,
+  "<|11.22|>": 50925,
+  "<|11.24|>": 50926,
+  "<|11.26|>": 50927,
+  "<|11.28|>": 50928,
+  "<|11.30|>": 50929,
+  "<|11.32|>": 50930,
+  "<|11.34|>": 50931,
+  "<|11.36|>": 50932,
+  "<|11.38|>": 50933,
+  "<|11.40|>": 50934,
+  "<|11.42|>": 50935,
+  "<|11.44|>": 50936,
+  "<|11.46|>": 50937,
+  "<|11.48|>": 50938,
+  "<|11.50|>": 50939,
+  "<|11.52|>": 50940,
+  "<|11.54|>": 50941,
+  "<|11.56|>": 50942,
+  "<|11.58|>": 50943,
+  "<|11.60|>": 50944,
+  "<|11.62|>": 50945,
+  "<|11.64|>": 50946,
+  "<|11.66|>": 50947,
+  "<|11.68|>": 50948,
+  "<|11.70|>": 50949,
+  "<|11.72|>": 50950,
+  "<|11.74|>": 50951,
+  "<|11.76|>": 50952,
+  "<|11.78|>": 50953,
+  "<|11.80|>": 50954,
+  "<|11.82|>": 50955,
+  "<|11.84|>": 50956,
+  "<|11.86|>": 50957,
+  "<|11.88|>": 50958,
+  "<|11.90|>": 50959,
+  "<|11.92|>": 50960,
+  "<|11.94|>": 50961,
+  "<|11.96|>": 50962,
+  "<|11.98|>": 50963,
+  "<|12.00|>": 50964,
+  "<|12.02|>": 50965,
+  "<|12.04|>": 50966,
+  "<|12.06|>": 50967,
+  "<|12.08|>": 50968,
+  "<|12.10|>": 50969,
+  "<|12.12|>": 50970,
+  "<|12.14|>": 50971,
+  "<|12.16|>": 50972,
+  "<|12.18|>": 50973,
+  "<|12.20|>": 50974,
+  "<|12.22|>": 50975,
+  "<|12.24|>": 50976,
+  "<|12.26|>": 50977,
+  "<|12.28|>": 50978,
+  "<|12.30|>": 50979,
+  "<|12.32|>": 50980,
+  "<|12.34|>": 50981,
+  "<|12.36|>": 50982,
+  "<|12.38|>": 50983,
+  "<|12.40|>": 50984,
+  "<|12.42|>": 50985,
+  "<|12.44|>": 50986,
+  "<|12.46|>": 50987,
+  "<|12.48|>": 50988,
+  "<|12.50|>": 50989,
+  "<|12.52|>": 50990,
+  "<|12.54|>": 50991,
+  "<|12.56|>": 50992,
+  "<|12.58|>": 50993,
+  "<|12.60|>": 50994,
+  "<|12.62|>": 50995,
+  "<|12.64|>": 50996,
+  "<|12.66|>": 50997,
+  "<|12.68|>": 50998,
+  "<|12.70|>": 50999,
+  "<|12.72|>": 51000,
+  "<|12.74|>": 51001,
+  "<|12.76|>": 51002,
+  "<|12.78|>": 51003,
+  "<|12.80|>": 51004,
+  "<|12.82|>": 51005,
+  "<|12.84|>": 51006,
+  "<|12.86|>": 51007,
+  "<|12.88|>": 51008,
+  "<|12.90|>": 51009,
+  "<|12.92|>": 51010,
+  "<|12.94|>": 51011,
+  "<|12.96|>": 51012,
+  "<|12.98|>": 51013,
+  "<|13.00|>": 51014,
+  "<|13.02|>": 51015,
+  "<|13.04|>": 51016,
+  "<|13.06|>": 51017,
+  "<|13.08|>": 51018,
+  "<|13.10|>": 51019,
+  "<|13.12|>": 51020,
+  "<|13.14|>": 51021,
+  "<|13.16|>": 51022,
+  "<|13.18|>": 51023,
+  "<|13.20|>": 51024,
+  "<|13.22|>": 51025,
+  "<|13.24|>": 51026,
+  "<|13.26|>": 51027,
+  "<|13.28|>": 51028,
+  "<|13.30|>": 51029,
+  "<|13.32|>": 51030,
+  "<|13.34|>": 51031,
+  "<|13.36|>": 51032,
+  "<|13.38|>": 51033,
+  "<|13.40|>": 51034,
+  "<|13.42|>": 51035,
+  "<|13.44|>": 51036,
+  "<|13.46|>": 51037,
+  "<|13.48|>": 51038,
+  "<|13.50|>": 51039,
+  "<|13.52|>": 51040,
+  "<|13.54|>": 51041,
+  "<|13.56|>": 51042,
+  "<|13.58|>": 51043,
+  "<|13.60|>": 51044,
+  "<|13.62|>": 51045,
+  "<|13.64|>": 51046,
+  "<|13.66|>": 51047,
+  "<|13.68|>": 51048,
+  "<|13.70|>": 51049,
+  "<|13.72|>": 51050,
+  "<|13.74|>": 51051,
+  "<|13.76|>": 51052,
+  "<|13.78|>": 51053,
+  "<|13.80|>": 51054,
+  "<|13.82|>": 51055,
+  "<|13.84|>": 51056,
+  "<|13.86|>": 51057,
+  "<|13.88|>": 51058,
+  "<|13.90|>": 51059,
+  "<|13.92|>": 51060,
+  "<|13.94|>": 51061,
+  "<|13.96|>": 51062,
+  "<|13.98|>": 51063,
+  "<|14.00|>": 51064,
+  "<|14.02|>": 51065,
+  "<|14.04|>": 51066,
+  "<|14.06|>": 51067,
+  "<|14.08|>": 51068,
+  "<|14.10|>": 51069,
+  "<|14.12|>": 51070,
+  "<|14.14|>": 51071,
+  "<|14.16|>": 51072,
+  "<|14.18|>": 51073,
+  "<|14.20|>": 51074,
+  "<|14.22|>": 51075,
+  "<|14.24|>": 51076,
+  "<|14.26|>": 51077,
+  "<|14.28|>": 51078,
+  "<|14.30|>": 51079,
+  "<|14.32|>": 51080,
+  "<|14.34|>": 51081,
+  "<|14.36|>": 51082,
+  "<|14.38|>": 51083,
+  "<|14.40|>": 51084,
+  "<|14.42|>": 51085,
+  "<|14.44|>": 51086,
+  "<|14.46|>": 51087,
+  "<|14.48|>": 51088,
+  "<|14.50|>": 51089,
+  "<|14.52|>": 51090,
+  "<|14.54|>": 51091,
+  "<|14.56|>": 51092,
+  "<|14.58|>": 51093,
+  "<|14.60|>": 51094,
+  "<|14.62|>": 51095,
+  "<|14.64|>": 51096,
+  "<|14.66|>": 51097,
+  "<|14.68|>": 51098,
+  "<|14.70|>": 51099,
+  "<|14.72|>": 51100,
+  "<|14.74|>": 51101,
+  "<|14.76|>": 51102,
+  "<|14.78|>": 51103,
+  "<|14.80|>": 51104,
+  "<|14.82|>": 51105,
+  "<|14.84|>": 51106,
+  "<|14.86|>": 51107,
+  "<|14.88|>": 51108,
+  "<|14.90|>": 51109,
+  "<|14.92|>": 51110,
+  "<|14.94|>": 51111,
+  "<|14.96|>": 51112,
+  "<|14.98|>": 51113,
+  "<|15.00|>": 51114,
+  "<|15.02|>": 51115,
+  "<|15.04|>": 51116,
+  "<|15.06|>": 51117,
+  "<|15.08|>": 51118,
+  "<|15.10|>": 51119,
+  "<|15.12|>": 51120,
+  "<|15.14|>": 51121,
+  "<|15.16|>": 51122,
+  "<|15.18|>": 51123,
+  "<|15.20|>": 51124,
+  "<|15.22|>": 51125,
+  "<|15.24|>": 51126,
+  "<|15.26|>": 51127,
+  "<|15.28|>": 51128,
+  "<|15.30|>": 51129,
+  "<|15.32|>": 51130,
+  "<|15.34|>": 51131,
+  "<|15.36|>": 51132,
+  "<|15.38|>": 51133,
+  "<|15.40|>": 51134,
+  "<|15.42|>": 51135,
+  "<|15.44|>": 51136,
+  "<|15.46|>": 51137,
+  "<|15.48|>": 51138,
+  "<|15.50|>": 51139,
+  "<|15.52|>": 51140,
+  "<|15.54|>": 51141,
+  "<|15.56|>": 51142,
+  "<|15.58|>": 51143,
+  "<|15.60|>": 51144,
+  "<|15.62|>": 51145,
+  "<|15.64|>": 51146,
+  "<|15.66|>": 51147,
+  "<|15.68|>": 51148,
+  "<|15.70|>": 51149,
+  "<|15.72|>": 51150,
+  "<|15.74|>": 51151,
+  "<|15.76|>": 51152,
+  "<|15.78|>": 51153,
+  "<|15.80|>": 51154,
+  "<|15.82|>": 51155,
+  "<|15.84|>": 51156,
+  "<|15.86|>": 51157,
+  "<|15.88|>": 51158,
+  "<|15.90|>": 51159,
+  "<|15.92|>": 51160,
+  "<|15.94|>": 51161,
+  "<|15.96|>": 51162,
+  "<|15.98|>": 51163,
+  "<|16.00|>": 51164,
+  "<|16.02|>": 51165,
+  "<|16.04|>": 51166,
+  "<|16.06|>": 51167,
+  "<|16.08|>": 51168,
+  "<|16.10|>": 51169,
+  "<|16.12|>": 51170,
+  "<|16.14|>": 51171,
+  "<|16.16|>": 51172,
+  "<|16.18|>": 51173,
+  "<|16.20|>": 51174,
+  "<|16.22|>": 51175,
+  "<|16.24|>": 51176,
+  "<|16.26|>": 51177,
+  "<|16.28|>": 51178,
+  "<|16.30|>": 51179,
+  "<|16.32|>": 51180,
+  "<|16.34|>": 51181,
+  "<|16.36|>": 51182,
+  "<|16.38|>": 51183,
+  "<|16.40|>": 51184,
+  "<|16.42|>": 51185,
+  "<|16.44|>": 51186,
+  "<|16.46|>": 51187,
+  "<|16.48|>": 51188,
+  "<|16.50|>": 51189,
+  "<|16.52|>": 51190,
+  "<|16.54|>": 51191,
+  "<|16.56|>": 51192,
+  "<|16.58|>": 51193,
+  "<|16.60|>": 51194,
+  "<|16.62|>": 51195,
+  "<|16.64|>": 51196,
+  "<|16.66|>": 51197,
+  "<|16.68|>": 51198,
+  "<|16.70|>": 51199,
+  "<|16.72|>": 51200,
+  "<|16.74|>": 51201,
+  "<|16.76|>": 51202,
+  "<|16.78|>": 51203,
+  "<|16.80|>": 51204,
+  "<|16.82|>": 51205,
+  "<|16.84|>": 51206,
+  "<|16.86|>": 51207,
+  "<|16.88|>": 51208,
+  "<|16.90|>": 51209,
+  "<|16.92|>": 51210,
+  "<|16.94|>": 51211,
+  "<|16.96|>": 51212,
+  "<|16.98|>": 51213,
+  "<|17.00|>": 51214,
+  "<|17.02|>": 51215,
+  "<|17.04|>": 51216,
+  "<|17.06|>": 51217,
+  "<|17.08|>": 51218,
+  "<|17.10|>": 51219,
+  "<|17.12|>": 51220,
+  "<|17.14|>": 51221,
+  "<|17.16|>": 51222,
+  "<|17.18|>": 51223,
+  "<|17.20|>": 51224,
+  "<|17.22|>": 51225,
+  "<|17.24|>": 51226,
+  "<|17.26|>": 51227,
+  "<|17.28|>": 51228,
+  "<|17.30|>": 51229,
+  "<|17.32|>": 51230,
+  "<|17.34|>": 51231,
+  "<|17.36|>": 51232,
+  "<|17.38|>": 51233,
+  "<|17.40|>": 51234,
+  "<|17.42|>": 51235,
+  "<|17.44|>": 51236,
+  "<|17.46|>": 51237,
+  "<|17.48|>": 51238,
+  "<|17.50|>": 51239,
+  "<|17.52|>": 51240,
+  "<|17.54|>": 51241,
+  "<|17.56|>": 51242,
+  "<|17.58|>": 51243,
+  "<|17.60|>": 51244,
+  "<|17.62|>": 51245,
+  "<|17.64|>": 51246,
+  "<|17.66|>": 51247,
+  "<|17.68|>": 51248,
+  "<|17.70|>": 51249,
+  "<|17.72|>": 51250,
+  "<|17.74|>": 51251,
+  "<|17.76|>": 51252,
+  "<|17.78|>": 51253,
+  "<|17.80|>": 51254,
+  "<|17.82|>": 51255,
+  "<|17.84|>": 51256,
+  "<|17.86|>": 51257,
+  "<|17.88|>": 51258,
+  "<|17.90|>": 51259,
+  "<|17.92|>": 51260,
+  "<|17.94|>": 51261,
+  "<|17.96|>": 51262,
+  "<|17.98|>": 51263,
+  "<|18.00|>": 51264,
+  "<|18.02|>": 51265,
+  "<|18.04|>": 51266,
+  "<|18.06|>": 51267,
+  "<|18.08|>": 51268,
+  "<|18.10|>": 51269,
+  "<|18.12|>": 51270,
+  "<|18.14|>": 51271,
+  "<|18.16|>": 51272,
+  "<|18.18|>": 51273,
+  "<|18.20|>": 51274,
+  "<|18.22|>": 51275,
+  "<|18.24|>": 51276,
+  "<|18.26|>": 51277,
+  "<|18.28|>": 51278,
+  "<|18.30|>": 51279,
+  "<|18.32|>": 51280,
+  "<|18.34|>": 51281,
+  "<|18.36|>": 51282,
+  "<|18.38|>": 51283,
+  "<|18.40|>": 51284,
+  "<|18.42|>": 51285,
+  "<|18.44|>": 51286,
+  "<|18.46|>": 51287,
+  "<|18.48|>": 51288,
+  "<|18.50|>": 51289,
+  "<|18.52|>": 51290,
+  "<|18.54|>": 51291,
+  "<|18.56|>": 51292,
+  "<|18.58|>": 51293,
+  "<|18.60|>": 51294,
+  "<|18.62|>": 51295,
+  "<|18.64|>": 51296,
+  "<|18.66|>": 51297,
+  "<|18.68|>": 51298,
+  "<|18.70|>": 51299,
+  "<|18.72|>": 51300,
+  "<|18.74|>": 51301,
+  "<|18.76|>": 51302,
+  "<|18.78|>": 51303,
+  "<|18.80|>": 51304,
+  "<|18.82|>": 51305,
+  "<|18.84|>": 51306,
+  "<|18.86|>": 51307,
+  "<|18.88|>": 51308,
+  "<|18.90|>": 51309,
+  "<|18.92|>": 51310,
+  "<|18.94|>": 51311,
+  "<|18.96|>": 51312,
+  "<|18.98|>": 51313,
+  "<|19.00|>": 51314,
+  "<|19.02|>": 51315,
+  "<|19.04|>": 51316,
+  "<|19.06|>": 51317,
+  "<|19.08|>": 51318,
+  "<|19.10|>": 51319,
+  "<|19.12|>": 51320,
+  "<|19.14|>": 51321,
+  "<|19.16|>": 51322,
+  "<|19.18|>": 51323,
+  "<|19.20|>": 51324,
+  "<|19.22|>": 51325,
+  "<|19.24|>": 51326,
+  "<|19.26|>": 51327,
+  "<|19.28|>": 51328,
+  "<|19.30|>": 51329,
+  "<|19.32|>": 51330,
+  "<|19.34|>": 51331,
+  "<|19.36|>": 51332,
+  "<|19.38|>": 51333,
+  "<|19.40|>": 51334,
+  "<|19.42|>": 51335,
+  "<|19.44|>": 51336,
+  "<|19.46|>": 51337,
+  "<|19.48|>": 51338,
+  "<|19.50|>": 51339,
+  "<|19.52|>": 51340,
+  "<|19.54|>": 51341,
+  "<|19.56|>": 51342,
+  "<|19.58|>": 51343,
+  "<|19.60|>": 51344,
+  "<|19.62|>": 51345,
+  "<|19.64|>": 51346,
+  "<|19.66|>": 51347,
+  "<|19.68|>": 51348,
+  "<|19.70|>": 51349,
+  "<|19.72|>": 51350,
+  "<|19.74|>": 51351,
+  "<|19.76|>": 51352,
+  "<|19.78|>": 51353,
+  "<|19.80|>": 51354,
+  "<|19.82|>": 51355,
+  "<|19.84|>": 51356,
+  "<|19.86|>": 51357,
+  "<|19.88|>": 51358,
+  "<|19.90|>": 51359,
+  "<|19.92|>": 51360,
+  "<|19.94|>": 51361,
+  "<|19.96|>": 51362,
+  "<|19.98|>": 51363,
+  "<|2.00|>": 50464,
+  "<|2.02|>": 50465,
+  "<|2.04|>": 50466,
+  "<|2.06|>": 50467,
+  "<|2.08|>": 50468,
+  "<|2.10|>": 50469,
+  "<|2.12|>": 50470,
+  "<|2.14|>": 50471,
+  "<|2.16|>": 50472,
+  "<|2.18|>": 50473,
+  "<|2.20|>": 50474,
+  "<|2.22|>": 50475,
+  "<|2.24|>": 50476,
+  "<|2.26|>": 50477,
+  "<|2.28|>": 50478,
+  "<|2.30|>": 50479,
+  "<|2.32|>": 50480,
+  "<|2.34|>": 50481,
+  "<|2.36|>": 50482,
+  "<|2.38|>": 50483,
+  "<|2.40|>": 50484,
+  "<|2.42|>": 50485,
+  "<|2.44|>": 50486,
+  "<|2.46|>": 50487,
+  "<|2.48|>": 50488,
+  "<|2.50|>": 50489,
+  "<|2.52|>": 50490,
+  "<|2.54|>": 50491,
+  "<|2.56|>": 50492,
+  "<|2.58|>": 50493,
+  "<|2.60|>": 50494,
+  "<|2.62|>": 50495,
+  "<|2.64|>": 50496,
+  "<|2.66|>": 50497,
+  "<|2.68|>": 50498,
+  "<|2.70|>": 50499,
+  "<|2.72|>": 50500,
+  "<|2.74|>": 50501,
+  "<|2.76|>": 50502,
+  "<|2.78|>": 50503,
+  "<|2.80|>": 50504,
+  "<|2.82|>": 50505,
+  "<|2.84|>": 50506,
+  "<|2.86|>": 50507,
+  "<|2.88|>": 50508,
+  "<|2.90|>": 50509,
+  "<|2.92|>": 50510,
+  "<|2.94|>": 50511,
+  "<|2.96|>": 50512,
+  "<|2.98|>": 50513,
+  "<|20.00|>": 51364,
+  "<|20.02|>": 51365,
+  "<|20.04|>": 51366,
+  "<|20.06|>": 51367,
+  "<|20.08|>": 51368,
+  "<|20.10|>": 51369,
+  "<|20.12|>": 51370,
+  "<|20.14|>": 51371,
+  "<|20.16|>": 51372,
+  "<|20.18|>": 51373,
+  "<|20.20|>": 51374,
+  "<|20.22|>": 51375,
+  "<|20.24|>": 51376,
+  "<|20.26|>": 51377,
+  "<|20.28|>": 51378,
+  "<|20.30|>": 51379,
+  "<|20.32|>": 51380,
+  "<|20.34|>": 51381,
+  "<|20.36|>": 51382,
+  "<|20.38|>": 51383,
+  "<|20.40|>": 51384,
+  "<|20.42|>": 51385,
+  "<|20.44|>": 51386,
+  "<|20.46|>": 51387,
+  "<|20.48|>": 51388,
+  "<|20.50|>": 51389,
+  "<|20.52|>": 51390,
+  "<|20.54|>": 51391,
+  "<|20.56|>": 51392,
+  "<|20.58|>": 51393,
+  "<|20.60|>": 51394,
+  "<|20.62|>": 51395,
+  "<|20.64|>": 51396,
+  "<|20.66|>": 51397,
+  "<|20.68|>": 51398,
+  "<|20.70|>": 51399,
+  "<|20.72|>": 51400,
+  "<|20.74|>": 51401,
+  "<|20.76|>": 51402,
+  "<|20.78|>": 51403,
+  "<|20.80|>": 51404,
+  "<|20.82|>": 51405,
+  "<|20.84|>": 51406,
+  "<|20.86|>": 51407,
+  "<|20.88|>": 51408,
+  "<|20.90|>": 51409,
+  "<|20.92|>": 51410,
+  "<|20.94|>": 51411,
+  "<|20.96|>": 51412,
+  "<|20.98|>": 51413,
+  "<|21.00|>": 51414,
+  "<|21.02|>": 51415,
+  "<|21.04|>": 51416,
+  "<|21.06|>": 51417,
+  "<|21.08|>": 51418,
+  "<|21.10|>": 51419,
+  "<|21.12|>": 51420,
+  "<|21.14|>": 51421,
+  "<|21.16|>": 51422,
+  "<|21.18|>": 51423,
+  "<|21.20|>": 51424,
+  "<|21.22|>": 51425,
+  "<|21.24|>": 51426,
+  "<|21.26|>": 51427,
+  "<|21.28|>": 51428,
+  "<|21.30|>": 51429,
+  "<|21.32|>": 51430,
+  "<|21.34|>": 51431,
+  "<|21.36|>": 51432,
+  "<|21.38|>": 51433,
+  "<|21.40|>": 51434,
+  "<|21.42|>": 51435,
+  "<|21.44|>": 51436,
+  "<|21.46|>": 51437,
+  "<|21.48|>": 51438,
+  "<|21.50|>": 51439,
+  "<|21.52|>": 51440,
+  "<|21.54|>": 51441,
+  "<|21.56|>": 51442,
+  "<|21.58|>": 51443,
+  "<|21.60|>": 51444,
+  "<|21.62|>": 51445,
+  "<|21.64|>": 51446,
+  "<|21.66|>": 51447,
+  "<|21.68|>": 51448,
+  "<|21.70|>": 51449,
+  "<|21.72|>": 51450,
+  "<|21.74|>": 51451,
+  "<|21.76|>": 51452,
+  "<|21.78|>": 51453,
+  "<|21.80|>": 51454,
+  "<|21.82|>": 51455,
+  "<|21.84|>": 51456,
+  "<|21.86|>": 51457,
+  "<|21.88|>": 51458,
+  "<|21.90|>": 51459,
+  "<|21.92|>": 51460,
+  "<|21.94|>": 51461,
+  "<|21.96|>": 51462,
+  "<|21.98|>": 51463,
+  "<|22.00|>": 51464,
+  "<|22.02|>": 51465,
+  "<|22.04|>": 51466,
+  "<|22.06|>": 51467,
+  "<|22.08|>": 51468,
+  "<|22.10|>": 51469,
+  "<|22.12|>": 51470,
+  "<|22.14|>": 51471,
+  "<|22.16|>": 51472,
+  "<|22.18|>": 51473,
+  "<|22.20|>": 51474,
+  "<|22.22|>": 51475,
+  "<|22.24|>": 51476,
+  "<|22.26|>": 51477,
+  "<|22.28|>": 51478,
+  "<|22.30|>": 51479,
+  "<|22.32|>": 51480,
+  "<|22.34|>": 51481,
+  "<|22.36|>": 51482,
+  "<|22.38|>": 51483,
+  "<|22.40|>": 51484,
+  "<|22.42|>": 51485,
+  "<|22.44|>": 51486,
+  "<|22.46|>": 51487,
+  "<|22.48|>": 51488,
+  "<|22.50|>": 51489,
+  "<|22.52|>": 51490,
+  "<|22.54|>": 51491,
+  "<|22.56|>": 51492,
+  "<|22.58|>": 51493,
+  "<|22.60|>": 51494,
+  "<|22.62|>": 51495,
+  "<|22.64|>": 51496,
+  "<|22.66|>": 51497,
+  "<|22.68|>": 51498,
+  "<|22.70|>": 51499,
+  "<|22.72|>": 51500,
+  "<|22.74|>": 51501,
+  "<|22.76|>": 51502,
+  "<|22.78|>": 51503,
+  "<|22.80|>": 51504,
+  "<|22.82|>": 51505,
+  "<|22.84|>": 51506,
+  "<|22.86|>": 51507,
+  "<|22.88|>": 51508,
+  "<|22.90|>": 51509,
+  "<|22.92|>": 51510,
+  "<|22.94|>": 51511,
+  "<|22.96|>": 51512,
+  "<|22.98|>": 51513,
+  "<|23.00|>": 51514,
+  "<|23.02|>": 51515,
+  "<|23.04|>": 51516,
+  "<|23.06|>": 51517,
+  "<|23.08|>": 51518,
+  "<|23.10|>": 51519,
+  "<|23.12|>": 51520,
+  "<|23.14|>": 51521,
+  "<|23.16|>": 51522,
+  "<|23.18|>": 51523,
+  "<|23.20|>": 51524,
+  "<|23.22|>": 51525,
+  "<|23.24|>": 51526,
+  "<|23.26|>": 51527,
+  "<|23.28|>": 51528,
+  "<|23.30|>": 51529,
+  "<|23.32|>": 51530,
+  "<|23.34|>": 51531,
+  "<|23.36|>": 51532,
+  "<|23.38|>": 51533,
+  "<|23.40|>": 51534,
+  "<|23.42|>": 51535,
+  "<|23.44|>": 51536,
+  "<|23.46|>": 51537,
+  "<|23.48|>": 51538,
+  "<|23.50|>": 51539,
+  "<|23.52|>": 51540,
+  "<|23.54|>": 51541,
+  "<|23.56|>": 51542,
+  "<|23.58|>": 51543,
+  "<|23.60|>": 51544,
+  "<|23.62|>": 51545,
+  "<|23.64|>": 51546,
+  "<|23.66|>": 51547,
+  "<|23.68|>": 51548,
+  "<|23.70|>": 51549,
+  "<|23.72|>": 51550,
+  "<|23.74|>": 51551,
+  "<|23.76|>": 51552,
+  "<|23.78|>": 51553,
+  "<|23.80|>": 51554,
+  "<|23.82|>": 51555,
+  "<|23.84|>": 51556,
+  "<|23.86|>": 51557,
+  "<|23.88|>": 51558,
+  "<|23.90|>": 51559,
+  "<|23.92|>": 51560,
+  "<|23.94|>": 51561,
+  "<|23.96|>": 51562,
+  "<|23.98|>": 51563,
+  "<|24.00|>": 51564,
+  "<|24.02|>": 51565,
+  "<|24.04|>": 51566,
+  "<|24.06|>": 51567,
+  "<|24.08|>": 51568,
+  "<|24.10|>": 51569,
+  "<|24.12|>": 51570,
+  "<|24.14|>": 51571,
+  "<|24.16|>": 51572,
+  "<|24.18|>": 51573,
+  "<|24.20|>": 51574,
+  "<|24.22|>": 51575,
+  "<|24.24|>": 51576,
+  "<|24.26|>": 51577,
+  "<|24.28|>": 51578,
+  "<|24.30|>": 51579,
+  "<|24.32|>": 51580,
+  "<|24.34|>": 51581,
+  "<|24.36|>": 51582,
+  "<|24.38|>": 51583,
+  "<|24.40|>": 51584,
+  "<|24.42|>": 51585,
+  "<|24.44|>": 51586,
+  "<|24.46|>": 51587,
+  "<|24.48|>": 51588,
+  "<|24.50|>": 51589,
+  "<|24.52|>": 51590,
+  "<|24.54|>": 51591,
+  "<|24.56|>": 51592,
+  "<|24.58|>": 51593,
+  "<|24.60|>": 51594,
+  "<|24.62|>": 51595,
+  "<|24.64|>": 51596,
+  "<|24.66|>": 51597,
+  "<|24.68|>": 51598,
+  "<|24.70|>": 51599,
+  "<|24.72|>": 51600,
+  "<|24.74|>": 51601,
+  "<|24.76|>": 51602,
+  "<|24.78|>": 51603,
+  "<|24.80|>": 51604,
+  "<|24.82|>": 51605,
+  "<|24.84|>": 51606,
+  "<|24.86|>": 51607,
+  "<|24.88|>": 51608,
+  "<|24.90|>": 51609,
+  "<|24.92|>": 51610,
+  "<|24.94|>": 51611,
+  "<|24.96|>": 51612,
+  "<|24.98|>": 51613,
+  "<|25.00|>": 51614,
+  "<|25.02|>": 51615,
+  "<|25.04|>": 51616,
+  "<|25.06|>": 51617,
+  "<|25.08|>": 51618,
+  "<|25.10|>": 51619,
+  "<|25.12|>": 51620,
+  "<|25.14|>": 51621,
+  "<|25.16|>": 51622,
+  "<|25.18|>": 51623,
+  "<|25.20|>": 51624,
+  "<|25.22|>": 51625,
+  "<|25.24|>": 51626,
+  "<|25.26|>": 51627,
+  "<|25.28|>": 51628,
+  "<|25.30|>": 51629,
+  "<|25.32|>": 51630,
+  "<|25.34|>": 51631,
+  "<|25.36|>": 51632,
+  "<|25.38|>": 51633,
+  "<|25.40|>": 51634,
+  "<|25.42|>": 51635,
+  "<|25.44|>": 51636,
+  "<|25.46|>": 51637,
+  "<|25.48|>": 51638,
+  "<|25.50|>": 51639,
+  "<|25.52|>": 51640,
+  "<|25.54|>": 51641,
+  "<|25.56|>": 51642,
+  "<|25.58|>": 51643,
+  "<|25.60|>": 51644,
+  "<|25.62|>": 51645,
+  "<|25.64|>": 51646,
+  "<|25.66|>": 51647,
+  "<|25.68|>": 51648,
+  "<|25.70|>": 51649,
+  "<|25.72|>": 51650,
+  "<|25.74|>": 51651,
+  "<|25.76|>": 51652,
+  "<|25.78|>": 51653,
+  "<|25.80|>": 51654,
+  "<|25.82|>": 51655,
+  "<|25.84|>": 51656,
+  "<|25.86|>": 51657,
+  "<|25.88|>": 51658,
+  "<|25.90|>": 51659,
+  "<|25.92|>": 51660,
+  "<|25.94|>": 51661,
+  "<|25.96|>": 51662,
+  "<|25.98|>": 51663,
+  "<|26.00|>": 51664,
+  "<|26.02|>": 51665,
+  "<|26.04|>": 51666,
+  "<|26.06|>": 51667,
+  "<|26.08|>": 51668,
+  "<|26.10|>": 51669,
+  "<|26.12|>": 51670,
+  "<|26.14|>": 51671,
+  "<|26.16|>": 51672,
+  "<|26.18|>": 51673,
+  "<|26.20|>": 51674,
+  "<|26.22|>": 51675,
+  "<|26.24|>": 51676,
+  "<|26.26|>": 51677,
+  "<|26.28|>": 51678,
+  "<|26.30|>": 51679,
+  "<|26.32|>": 51680,
+  "<|26.34|>": 51681,
+  "<|26.36|>": 51682,
+  "<|26.38|>": 51683,
+  "<|26.40|>": 51684,
+  "<|26.42|>": 51685,
+  "<|26.44|>": 51686,
+  "<|26.46|>": 51687,
+  "<|26.48|>": 51688,
+  "<|26.50|>": 51689,
+  "<|26.52|>": 51690,
+  "<|26.54|>": 51691,
+  "<|26.56|>": 51692,
+  "<|26.58|>": 51693,
+  "<|26.60|>": 51694,
+  "<|26.62|>": 51695,
+  "<|26.64|>": 51696,
+  "<|26.66|>": 51697,
+  "<|26.68|>": 51698,
+  "<|26.70|>": 51699,
+  "<|26.72|>": 51700,
+  "<|26.74|>": 51701,
+  "<|26.76|>": 51702,
+  "<|26.78|>": 51703,
+  "<|26.80|>": 51704,
+  "<|26.82|>": 51705,
+  "<|26.84|>": 51706,
+  "<|26.86|>": 51707,
+  "<|26.88|>": 51708,
+  "<|26.90|>": 51709,
+  "<|26.92|>": 51710,
+  "<|26.94|>": 51711,
+  "<|26.96|>": 51712,
+  "<|26.98|>": 51713,
+  "<|27.00|>": 51714,
+  "<|27.02|>": 51715,
+  "<|27.04|>": 51716,
+  "<|27.06|>": 51717,
+  "<|27.08|>": 51718,
+  "<|27.10|>": 51719,
+  "<|27.12|>": 51720,
+  "<|27.14|>": 51721,
+  "<|27.16|>": 51722,
+  "<|27.18|>": 51723,
+  "<|27.20|>": 51724,
+  "<|27.22|>": 51725,
+  "<|27.24|>": 51726,
+  "<|27.26|>": 51727,
+  "<|27.28|>": 51728,
+  "<|27.30|>": 51729,
+  "<|27.32|>": 51730,
+  "<|27.34|>": 51731,
+  "<|27.36|>": 51732,
+  "<|27.38|>": 51733,
+  "<|27.40|>": 51734,
+  "<|27.42|>": 51735,
+  "<|27.44|>": 51736,
+  "<|27.46|>": 51737,
+  "<|27.48|>": 51738,
+  "<|27.50|>": 51739,
+  "<|27.52|>": 51740,
+  "<|27.54|>": 51741,
+  "<|27.56|>": 51742,
+  "<|27.58|>": 51743,
+  "<|27.60|>": 51744,
+  "<|27.62|>": 51745,
+  "<|27.64|>": 51746,
+  "<|27.66|>": 51747,
+  "<|27.68|>": 51748,
+  "<|27.70|>": 51749,
+  "<|27.72|>": 51750,
+  "<|27.74|>": 51751,
+  "<|27.76|>": 51752,
+  "<|27.78|>": 51753,
+  "<|27.80|>": 51754,
+  "<|27.82|>": 51755,
+  "<|27.84|>": 51756,
+  "<|27.86|>": 51757,
+  "<|27.88|>": 51758,
+  "<|27.90|>": 51759,
+  "<|27.92|>": 51760,
+  "<|27.94|>": 51761,
+  "<|27.96|>": 51762,
+  "<|27.98|>": 51763,
+  "<|28.00|>": 51764,
+  "<|28.02|>": 51765,
+  "<|28.04|>": 51766,
+  "<|28.06|>": 51767,
+  "<|28.08|>": 51768,
+  "<|28.10|>": 51769,
+  "<|28.12|>": 51770,
+  "<|28.14|>": 51771,
+  "<|28.16|>": 51772,
+  "<|28.18|>": 51773,
+  "<|28.20|>": 51774,
+  "<|28.22|>": 51775,
+  "<|28.24|>": 51776,
+  "<|28.26|>": 51777,
+  "<|28.28|>": 51778,
+  "<|28.30|>": 51779,
+  "<|28.32|>": 51780,
+  "<|28.34|>": 51781,
+  "<|28.36|>": 51782,
+  "<|28.38|>": 51783,
+  "<|28.40|>": 51784,
+  "<|28.42|>": 51785,
+  "<|28.44|>": 51786,
+  "<|28.46|>": 51787,
+  "<|28.48|>": 51788,
+  "<|28.50|>": 51789,
+  "<|28.52|>": 51790,
+  "<|28.54|>": 51791,
+  "<|28.56|>": 51792,
+  "<|28.58|>": 51793,
+  "<|28.60|>": 51794,
+  "<|28.62|>": 51795,
+  "<|28.64|>": 51796,
+  "<|28.66|>": 51797,
+  "<|28.68|>": 51798,
+  "<|28.70|>": 51799,
+  "<|28.72|>": 51800,
+  "<|28.74|>": 51801,
+  "<|28.76|>": 51802,
+  "<|28.78|>": 51803,
+  "<|28.80|>": 51804,
+  "<|28.82|>": 51805,
+  "<|28.84|>": 51806,
+  "<|28.86|>": 51807,
+  "<|28.88|>": 51808,
+  "<|28.90|>": 51809,
+  "<|28.92|>": 51810,
+  "<|28.94|>": 51811,
+  "<|28.96|>": 51812,
+  "<|28.98|>": 51813,
+  "<|29.00|>": 51814,
+  "<|29.02|>": 51815,
+  "<|29.04|>": 51816,
+  "<|29.06|>": 51817,
+  "<|29.08|>": 51818,
+  "<|29.10|>": 51819,
+  "<|29.12|>": 51820,
+  "<|29.14|>": 51821,
+  "<|29.16|>": 51822,
+  "<|29.18|>": 51823,
+  "<|29.20|>": 51824,
+  "<|29.22|>": 51825,
+  "<|29.24|>": 51826,
+  "<|29.26|>": 51827,
+  "<|29.28|>": 51828,
+  "<|29.30|>": 51829,
+  "<|29.32|>": 51830,
+  "<|29.34|>": 51831,
+  "<|29.36|>": 51832,
+  "<|29.38|>": 51833,
+  "<|29.40|>": 51834,
+  "<|29.42|>": 51835,
+  "<|29.44|>": 51836,
+  "<|29.46|>": 51837,
+  "<|29.48|>": 51838,
+  "<|29.50|>": 51839,
+  "<|29.52|>": 51840,
+  "<|29.54|>": 51841,
+  "<|29.56|>": 51842,
+  "<|29.58|>": 51843,
+  "<|29.60|>": 51844,
+  "<|29.62|>": 51845,
+  "<|29.64|>": 51846,
+  "<|29.66|>": 51847,
+  "<|29.68|>": 51848,
+  "<|29.70|>": 51849,
+  "<|29.72|>": 51850,
+  "<|29.74|>": 51851,
+  "<|29.76|>": 51852,
+  "<|29.78|>": 51853,
+  "<|29.80|>": 51854,
+  "<|29.82|>": 51855,
+  "<|29.84|>": 51856,
+  "<|29.86|>": 51857,
+  "<|29.88|>": 51858,
+  "<|29.90|>": 51859,
+  "<|29.92|>": 51860,
+  "<|29.94|>": 51861,
+  "<|29.96|>": 51862,
+  "<|29.98|>": 51863,
+  "<|3.00|>": 50514,
+  "<|3.02|>": 50515,
+  "<|3.04|>": 50516,
+  "<|3.06|>": 50517,
+  "<|3.08|>": 50518,
+  "<|3.10|>": 50519,
+  "<|3.12|>": 50520,
+  "<|3.14|>": 50521,
+  "<|3.16|>": 50522,
+  "<|3.18|>": 50523,
+  "<|3.20|>": 50524,
+  "<|3.22|>": 50525,
+  "<|3.24|>": 50526,
+  "<|3.26|>": 50527,
+  "<|3.28|>": 50528,
+  "<|3.30|>": 50529,
+  "<|3.32|>": 50530,
+  "<|3.34|>": 50531,
+  "<|3.36|>": 50532,
+  "<|3.38|>": 50533,
+  "<|3.40|>": 50534,
+  "<|3.42|>": 50535,
+  "<|3.44|>": 50536,
+  "<|3.46|>": 50537,
+  "<|3.48|>": 50538,
+  "<|3.50|>": 50539,
+  "<|3.52|>": 50540,
+  "<|3.54|>": 50541,
+  "<|3.56|>": 50542,
+  "<|3.58|>": 50543,
+  "<|3.60|>": 50544,
+  "<|3.62|>": 50545,
+  "<|3.64|>": 50546,
+  "<|3.66|>": 50547,
+  "<|3.68|>": 50548,
+  "<|3.70|>": 50549,
+  "<|3.72|>": 50550,
+  "<|3.74|>": 50551,
+  "<|3.76|>": 50552,
+  "<|3.78|>": 50553,
+  "<|3.80|>": 50554,
+  "<|3.82|>": 50555,
+  "<|3.84|>": 50556,
+  "<|3.86|>": 50557,
+  "<|3.88|>": 50558,
+  "<|3.90|>": 50559,
+  "<|3.92|>": 50560,
+  "<|3.94|>": 50561,
+  "<|3.96|>": 50562,
+  "<|3.98|>": 50563,
+  "<|30.00|>": 51864,
+  "<|4.00|>": 50564,
+  "<|4.02|>": 50565,
+  "<|4.04|>": 50566,
+  "<|4.06|>": 50567,
+  "<|4.08|>": 50568,
+  "<|4.10|>": 50569,
+  "<|4.12|>": 50570,
+  "<|4.14|>": 50571,
+  "<|4.16|>": 50572,
+  "<|4.18|>": 50573,
+  "<|4.20|>": 50574,
+  "<|4.22|>": 50575,
+  "<|4.24|>": 50576,
+  "<|4.26|>": 50577,
+  "<|4.28|>": 50578,
+  "<|4.30|>": 50579,
+  "<|4.32|>": 50580,
+  "<|4.34|>": 50581,
+  "<|4.36|>": 50582,
+  "<|4.38|>": 50583,
+  "<|4.40|>": 50584,
+  "<|4.42|>": 50585,
+  "<|4.44|>": 50586,
+  "<|4.46|>": 50587,
+  "<|4.48|>": 50588,
+  "<|4.50|>": 50589,
+  "<|4.52|>": 50590,
+  "<|4.54|>": 50591,
+  "<|4.56|>": 50592,
+  "<|4.58|>": 50593,
+  "<|4.60|>": 50594,
+  "<|4.62|>": 50595,
+  "<|4.64|>": 50596,
+  "<|4.66|>": 50597,
+  "<|4.68|>": 50598,
+  "<|4.70|>": 50599,
+  "<|4.72|>": 50600,
+  "<|4.74|>": 50601,
+  "<|4.76|>": 50602,
+  "<|4.78|>": 50603,
+  "<|4.80|>": 50604,
+  "<|4.82|>": 50605,
+  "<|4.84|>": 50606,
+  "<|4.86|>": 50607,
+  "<|4.88|>": 50608,
+  "<|4.90|>": 50609,
+  "<|4.92|>": 50610,
+  "<|4.94|>": 50611,
+  "<|4.96|>": 50612,
+  "<|4.98|>": 50613,
+  "<|5.00|>": 50614,
+  "<|5.02|>": 50615,
+  "<|5.04|>": 50616,
+  "<|5.06|>": 50617,
+  "<|5.08|>": 50618,
+  "<|5.10|>": 50619,
+  "<|5.12|>": 50620,
+  "<|5.14|>": 50621,
+  "<|5.16|>": 50622,
+  "<|5.18|>": 50623,
+  "<|5.20|>": 50624,
+  "<|5.22|>": 50625,
+  "<|5.24|>": 50626,
+  "<|5.26|>": 50627,
+  "<|5.28|>": 50628,
+  "<|5.30|>": 50629,
+  "<|5.32|>": 50630,
+  "<|5.34|>": 50631,
+  "<|5.36|>": 50632,
+  "<|5.38|>": 50633,
+  "<|5.40|>": 50634,
+  "<|5.42|>": 50635,
+  "<|5.44|>": 50636,
+  "<|5.46|>": 50637,
+  "<|5.48|>": 50638,
+  "<|5.50|>": 50639,
+  "<|5.52|>": 50640,
+  "<|5.54|>": 50641,
+  "<|5.56|>": 50642,
+  "<|5.58|>": 50643,
+  "<|5.60|>": 50644,
+  "<|5.62|>": 50645,
+  "<|5.64|>": 50646,
+  "<|5.66|>": 50647,
+  "<|5.68|>": 50648,
+  "<|5.70|>": 50649,
+  "<|5.72|>": 50650,
+  "<|5.74|>": 50651,
+  "<|5.76|>": 50652,
+  "<|5.78|>": 50653,
+  "<|5.80|>": 50654,
+  "<|5.82|>": 50655,
+  "<|5.84|>": 50656,
+  "<|5.86|>": 50657,
+  "<|5.88|>": 50658,
+  "<|5.90|>": 50659,
+  "<|5.92|>": 50660,
+  "<|5.94|>": 50661,
+  "<|5.96|>": 50662,
+  "<|5.98|>": 50663,
+  "<|6.00|>": 50664,
+  "<|6.02|>": 50665,
+  "<|6.04|>": 50666,
+  "<|6.06|>": 50667,
+  "<|6.08|>": 50668,
+  "<|6.10|>": 50669,
+  "<|6.12|>": 50670,
+  "<|6.14|>": 50671,
+  "<|6.16|>": 50672,
+  "<|6.18|>": 50673,
+  "<|6.20|>": 50674,
+  "<|6.22|>": 50675,
+  "<|6.24|>": 50676,
+  "<|6.26|>": 50677,
+  "<|6.28|>": 50678,
+  "<|6.30|>": 50679,
+  "<|6.32|>": 50680,
+  "<|6.34|>": 50681,
+  "<|6.36|>": 50682,
+  "<|6.38|>": 50683,
+  "<|6.40|>": 50684,
+  "<|6.42|>": 50685,
+  "<|6.44|>": 50686,
+  "<|6.46|>": 50687,
+  "<|6.48|>": 50688,
+  "<|6.50|>": 50689,
+  "<|6.52|>": 50690,
+  "<|6.54|>": 50691,
+  "<|6.56|>": 50692,
+  "<|6.58|>": 50693,
+  "<|6.60|>": 50694,
+  "<|6.62|>": 50695,
+  "<|6.64|>": 50696,
+  "<|6.66|>": 50697,
+  "<|6.68|>": 50698,
+  "<|6.70|>": 50699,
+  "<|6.72|>": 50700,
+  "<|6.74|>": 50701,
+  "<|6.76|>": 50702,
+  "<|6.78|>": 50703,
+  "<|6.80|>": 50704,
+  "<|6.82|>": 50705,
+  "<|6.84|>": 50706,
+  "<|6.86|>": 50707,
+  "<|6.88|>": 50708,
+  "<|6.90|>": 50709,
+  "<|6.92|>": 50710,
+  "<|6.94|>": 50711,
+  "<|6.96|>": 50712,
+  "<|6.98|>": 50713,
+  "<|7.00|>": 50714,
+  "<|7.02|>": 50715,
+  "<|7.04|>": 50716,
+  "<|7.06|>": 50717,
+  "<|7.08|>": 50718,
+  "<|7.10|>": 50719,
+  "<|7.12|>": 50720,
+  "<|7.14|>": 50721,
+  "<|7.16|>": 50722,
+  "<|7.18|>": 50723,
+  "<|7.20|>": 50724,
+  "<|7.22|>": 50725,
+  "<|7.24|>": 50726,
+  "<|7.26|>": 50727,
+  "<|7.28|>": 50728,
+  "<|7.30|>": 50729,
+  "<|7.32|>": 50730,
+  "<|7.34|>": 50731,
+  "<|7.36|>": 50732,
+  "<|7.38|>": 50733,
+  "<|7.40|>": 50734,
+  "<|7.42|>": 50735,
+  "<|7.44|>": 50736,
+  "<|7.46|>": 50737,
+  "<|7.48|>": 50738,
+  "<|7.50|>": 50739,
+  "<|7.52|>": 50740,
+  "<|7.54|>": 50741,
+  "<|7.56|>": 50742,
+  "<|7.58|>": 50743,
+  "<|7.60|>": 50744,
+  "<|7.62|>": 50745,
+  "<|7.64|>": 50746,
+  "<|7.66|>": 50747,
+  "<|7.68|>": 50748,
+  "<|7.70|>": 50749,
+  "<|7.72|>": 50750,
+  "<|7.74|>": 50751,
+  "<|7.76|>": 50752,
+  "<|7.78|>": 50753,
+  "<|7.80|>": 50754,
+  "<|7.82|>": 50755,
+  "<|7.84|>": 50756,
+  "<|7.86|>": 50757,
+  "<|7.88|>": 50758,
+  "<|7.90|>": 50759,
+  "<|7.92|>": 50760,
+  "<|7.94|>": 50761,
+  "<|7.96|>": 50762,
+  "<|7.98|>": 50763,
+  "<|8.00|>": 50764,
+  "<|8.02|>": 50765,
+  "<|8.04|>": 50766,
+  "<|8.06|>": 50767,
+  "<|8.08|>": 50768,
+  "<|8.10|>": 50769,
+  "<|8.12|>": 50770,
+  "<|8.14|>": 50771,
+  "<|8.16|>": 50772,
+  "<|8.18|>": 50773,
+  "<|8.20|>": 50774,
+  "<|8.22|>": 50775,
+  "<|8.24|>": 50776,
+  "<|8.26|>": 50777,
+  "<|8.28|>": 50778,
+  "<|8.30|>": 50779,
+  "<|8.32|>": 50780,
+  "<|8.34|>": 50781,
+  "<|8.36|>": 50782,
+  "<|8.38|>": 50783,
+  "<|8.40|>": 50784,
+  "<|8.42|>": 50785,
+  "<|8.44|>": 50786,
+  "<|8.46|>": 50787,
+  "<|8.48|>": 50788,
+  "<|8.50|>": 50789,
+  "<|8.52|>": 50790,
+  "<|8.54|>": 50791,
+  "<|8.56|>": 50792,
+  "<|8.58|>": 50793,
+  "<|8.60|>": 50794,
+  "<|8.62|>": 50795,
+  "<|8.64|>": 50796,
+  "<|8.66|>": 50797,
+  "<|8.68|>": 50798,
+  "<|8.70|>": 50799,
+  "<|8.72|>": 50800,
+  "<|8.74|>": 50801,
+  "<|8.76|>": 50802,
+  "<|8.78|>": 50803,
+  "<|8.80|>": 50804,
+  "<|8.82|>": 50805,
+  "<|8.84|>": 50806,
+  "<|8.86|>": 50807,
+  "<|8.88|>": 50808,
+  "<|8.90|>": 50809,
+  "<|8.92|>": 50810,
+  "<|8.94|>": 50811,
+  "<|8.96|>": 50812,
+  "<|8.98|>": 50813,
+  "<|9.00|>": 50814,
+  "<|9.02|>": 50815,
+  "<|9.04|>": 50816,
+  "<|9.06|>": 50817,
+  "<|9.08|>": 50818,
+  "<|9.10|>": 50819,
+  "<|9.12|>": 50820,
+  "<|9.14|>": 50821,
+  "<|9.16|>": 50822,
+  "<|9.18|>": 50823,
+  "<|9.20|>": 50824,
+  "<|9.22|>": 50825,
+  "<|9.24|>": 50826,
+  "<|9.26|>": 50827,
+  "<|9.28|>": 50828,
+  "<|9.30|>": 50829,
+  "<|9.32|>": 50830,
+  "<|9.34|>": 50831,
+  "<|9.36|>": 50832,
+  "<|9.38|>": 50833,
+  "<|9.40|>": 50834,
+  "<|9.42|>": 50835,
+  "<|9.44|>": 50836,
+  "<|9.46|>": 50837,
+  "<|9.48|>": 50838,
+  "<|9.50|>": 50839,
+  "<|9.52|>": 50840,
+  "<|9.54|>": 50841,
+  "<|9.56|>": 50842,
+  "<|9.58|>": 50843,
+  "<|9.60|>": 50844,
+  "<|9.62|>": 50845,
+  "<|9.64|>": 50846,
+  "<|9.66|>": 50847,
+  "<|9.68|>": 50848,
+  "<|9.70|>": 50849,
+  "<|9.72|>": 50850,
+  "<|9.74|>": 50851,
+  "<|9.76|>": 50852,
+  "<|9.78|>": 50853,
+  "<|9.80|>": 50854,
+  "<|9.82|>": 50855,
+  "<|9.84|>": 50856,
+  "<|9.86|>": 50857,
+  "<|9.88|>": 50858,
+  "<|9.90|>": 50859,
+  "<|9.92|>": 50860,
+  "<|9.94|>": 50861,
+  "<|9.96|>": 50862,
+  "<|9.98|>": 50863,
+  "<|af|>": 50327,
+  "<|am|>": 50334,
+  "<|ar|>": 50272,
+  "<|as|>": 50350,
+  "<|az|>": 50304,
+  "<|ba|>": 50355,
+  "<|be|>": 50330,
+  "<|bg|>": 50292,
+  "<|bn|>": 50302,
+  "<|bo|>": 50347,
+  "<|br|>": 50309,
+  "<|bs|>": 50315,
+  "<|ca|>": 50270,
+  "<|cs|>": 50283,
+  "<|cy|>": 50297,
+  "<|da|>": 50285,
+  "<|de|>": 50261,
+  "<|el|>": 50281,
+  "<|en|>": 50259,
+  "<|es|>": 50262,
+  "<|et|>": 50307,
+  "<|eu|>": 50310,
+  "<|fa|>": 50300,
+  "<|fi|>": 50277,
+  "<|fo|>": 50338,
+  "<|fr|>": 50265,
+  "<|gl|>": 50319,
+  "<|gu|>": 50333,
+  "<|haw|>": 50352,
+  "<|ha|>": 50354,
+  "<|he|>": 50279,
+  "<|hi|>": 50276,
+  "<|hr|>": 50291,
+  "<|ht|>": 50339,
+  "<|hu|>": 50286,
+  "<|hy|>": 50312,
+  "<|id|>": 50275,
+  "<|is|>": 50311,
+  "<|it|>": 50274,
+  "<|ja|>": 50266,
+  "<|jw|>": 50356,
+  "<|ka|>": 50329,
+  "<|kk|>": 50316,
+  "<|km|>": 50323,
+  "<|kn|>": 50306,
+  "<|ko|>": 50264,
+  "<|la|>": 50294,
+  "<|lb|>": 50345,
+  "<|ln|>": 50353,
+  "<|lo|>": 50336,
+  "<|lt|>": 50293,
+  "<|lv|>": 50301,
+  "<|mg|>": 50349,
+  "<|mi|>": 50295,
+  "<|mk|>": 50308,
+  "<|ml|>": 50296,
+  "<|mn|>": 50314,
+  "<|mr|>": 50320,
+  "<|ms|>": 50282,
+  "<|mt|>": 50343,
+  "<|my|>": 50346,
+  "<|ne|>": 50313,
+  "<|nl|>": 50271,
+  "<|nn|>": 50342,
+  "<|nocaptions|>": 50362,
+  "<|notimestamps|>": 50363,
+  "<|no|>": 50288,
+  "<|oc|>": 50328,
+  "<|pa|>": 50321,
+  "<|pl|>": 50269,
+  "<|ps|>": 50340,
+  "<|pt|>": 50267,
+  "<|ro|>": 50284,
+  "<|ru|>": 50263,
+  "<|sa|>": 50344,
+  "<|sd|>": 50332,
+  "<|si|>": 50322,
+  "<|sk|>": 50298,
+  "<|sl|>": 50305,
+  "<|sn|>": 50324,
+  "<|so|>": 50326,
+  "<|sq|>": 50317,
+  "<|sr|>": 50303,
+  "<|startoflm|>": 50360,
+  "<|startofprev|>": 50361,
+  "<|startoftranscript|>": 50258,
+  "<|su|>": 50357,
+  "<|sv|>": 50273,
+  "<|sw|>": 50318,
+  "<|ta|>": 50287,
+  "<|te|>": 50299,
+  "<|tg|>": 50331,
+  "<|th|>": 50289,
+  "<|tk|>": 50341,
+  "<|tl|>": 50348,
+  "<|transcribe|>": 50359,
+  "<|translate|>": 50358,
+  "<|tr|>": 50268,
+  "<|tt|>": 50351,
+  "<|uk|>": 50280,
+  "<|ur|>": 50290,
+  "<|uz|>": 50337,
+  "<|vi|>": 50278,
+  "<|yi|>": 50335,
+  "<|yo|>": 50325,
+  "<|zh|>": 50260
+}

distil-large-v2-init/config.json

@@ -0,0 +1,154 @@
+{
+  "_name_or_path": "openai/whisper-large-v2",
+  "activation_dropout": 0.0,
+  "activation_function": "gelu",
+  "apply_spec_augment": false,
+  "architectures": [
+    "WhisperForConditionalGeneration"
+  ],
+  "attention_dropout": 0.0,
+  "begin_suppress_tokens": [
+    220,
+    50257
+  ],
+  "bos_token_id": 50257,
+  "classifier_proj_size": 256,
+  "d_model": 1280,
+  "decoder_attention_heads": 20,
+  "decoder_ffn_dim": 5120,
+  "decoder_layerdrop": 0.0,
+  "decoder_layers": 2,
+  "decoder_start_token_id": 50258,
+  "dropout": 0.0,
+  "encoder_attention_heads": 20,
+  "encoder_ffn_dim": 5120,
+  "encoder_layerdrop": 0.0,
+  "encoder_layers": 32,
+  "eos_token_id": 50257,
+  "forced_decoder_ids": [
+    [
+      1,
+      50259
+    ],
+    [
+      2,
+      50359
+    ],
+    [
+      3,
+      50363
+    ]
+  ],
+  "init_std": 0.02,
+  "is_encoder_decoder": true,
+  "mask_feature_length": 10,
+  "mask_feature_min_masks": 0,
+  "mask_feature_prob": 0.0,
+  "mask_time_length": 10,
+  "mask_time_min_masks": 2,
+  "mask_time_prob": 0.05,
+  "max_length": 448,
+  "max_source_positions": 1500,
+  "max_target_positions": 448,
+  "median_filter_width": 7,
+  "model_type": "whisper",
+  "num_hidden_layers": 32,
+  "num_mel_bins": 80,
+  "pad_token_id": 50257,
+  "scale_embedding": false,
+  "suppress_tokens": [
+    1,
+    2,
+    7,
+    8,
+    9,
+    10,
+    14,
+    25,
+    26,
+    27,
+    28,
+    29,
+    31,
+    58,
+    59,
+    60,
+    61,
+    62,
+    63,
+    90,
+    91,
+    92,
+    93,
+    359,
+    503,
+    522,
+    542,
+    873,
+    893,
+    902,
+    918,
+    922,
+    931,
+    1350,
+    1853,
+    1982,
+    2460,
+    2627,
+    3246,
+    3253,
+    3268,
+    3536,
+    3846,
+    3961,
+    4183,
+    4667,
+    6585,
+    6647,
+    7273,
+    9061,
+    9383,
+    10428,
+    10929,
+    11938,
+    12033,
+    12331,
+    12562,
+    13793,
+    14157,
+    14635,
+    15265,
+    15618,
+    16553,
+    16604,
+    18362,
+    18956,
+    20075,
+    21675,
+    22520,
+    26130,
+    26161,
+    26435,
+    28279,
+    29464,
+    31650,
+    32302,
+    32470,
+    36865,
+    42863,
+    47425,
+    49870,
+    50254,
+    50258,
+    50358,
+    50359,
+    50360,
+    50361,
+    50362
+  ],
+  "torch_dtype": "float32",
+  "transformers_version": "4.36.2",
+  "use_cache": true,
+  "use_weighted_layer_sum": false,
+  "vocab_size": 51865
+}

distil-large-v2-init/generation_config.json

@@ -0,0 +1,315 @@
+{
+  "alignment_heads": [
+    [
+      10,
+      12
+    ],
+    [
+      13,
+      17
+    ],
+    [
+      16,
+      11
+    ],
+    [
+      16,
+      12
+    ],
+    [
+      16,
+      13
+    ],
+    [
+      17,
+      15
+    ],
+    [
+      17,
+      16
+    ],
+    [
+      18,
+      4
+    ],
+    [
+      18,
+      11
+    ],
+    [
+      18,
+      19
+    ],
+    [
+      19,
+      11
+    ],
+    [
+      21,
+      2
+    ],
+    [
+      21,
+      3
+    ],
+    [
+      22,
+      3
+    ],
+    [
+      22,
+      9
+    ],
+    [
+      22,
+      12
+    ],
+    [
+      23,
+      5
+    ],
+    [
+      23,
+      7
+    ],
+    [
+      23,
+      13
+    ],
+    [
+      25,
+      5
+    ],
+    [
+      26,
+      1
+    ],
+    [
+      26,
+      12
+    ],
+    [
+      27,
+      15
+    ]
+  ],
+  "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
+    ]
+  ],
+  "is_multilingual": true,
+  "lang_to_id": {
+    "<|af|>": 50327,
+    "<|am|>": 50334,
+    "<|ar|>": 50272,
+    "<|as|>": 50350,
+    "<|az|>": 50304,
+    "<|ba|>": 50355,
+    "<|be|>": 50330,
+    "<|bg|>": 50292,
+    "<|bn|>": 50302,
+    "<|bo|>": 50347,
+    "<|br|>": 50309,
+    "<|bs|>": 50315,
+    "<|ca|>": 50270,
+    "<|cs|>": 50283,
+    "<|cy|>": 50297,
+    "<|da|>": 50285,
+    "<|de|>": 50261,
+    "<|el|>": 50281,
+    "<|en|>": 50259,
+    "<|es|>": 50262,
+    "<|et|>": 50307,
+    "<|eu|>": 50310,
+    "<|fa|>": 50300,
+    "<|fi|>": 50277,
+    "<|fo|>": 50338,
+    "<|fr|>": 50265,
+    "<|gl|>": 50319,
+    "<|gu|>": 50333,
+    "<|haw|>": 50352,
+    "<|ha|>": 50354,
+    "<|he|>": 50279,
+    "<|hi|>": 50276,
+    "<|hr|>": 50291,
+    "<|ht|>": 50339,
+    "<|hu|>": 50286,
+    "<|hy|>": 50312,
+    "<|id|>": 50275,
+    "<|is|>": 50311,
+    "<|it|>": 50274,
+    "<|ja|>": 50266,
+    "<|jw|>": 50356,
+    "<|ka|>": 50329,
+    "<|kk|>": 50316,
+    "<|km|>": 50323,
+    "<|kn|>": 50306,
+    "<|ko|>": 50264,
+    "<|la|>": 50294,
+    "<|lb|>": 50345,
+    "<|ln|>": 50353,
+    "<|lo|>": 50336,
+    "<|lt|>": 50293,
+    "<|lv|>": 50301,
+    "<|mg|>": 50349,
+    "<|mi|>": 50295,
+    "<|mk|>": 50308,
+    "<|ml|>": 50296,
+    "<|mn|>": 50314,
+    "<|mr|>": 50320,
+    "<|ms|>": 50282,
+    "<|mt|>": 50343,
+    "<|my|>": 50346,
+    "<|ne|>": 50313,
+    "<|nl|>": 50271,
+    "<|nn|>": 50342,
+    "<|no|>": 50288,
+    "<|oc|>": 50328,
+    "<|pa|>": 50321,
+    "<|pl|>": 50269,
+    "<|ps|>": 50340,
+    "<|pt|>": 50267,
+    "<|ro|>": 50284,
+    "<|ru|>": 50263,
+    "<|sa|>": 50344,
+    "<|sd|>": 50332,
+    "<|si|>": 50322,
+    "<|sk|>": 50298,
+    "<|sl|>": 50305,
+    "<|sn|>": 50324,
+    "<|so|>": 50326,
+    "<|sq|>": 50317,
+    "<|sr|>": 50303,
+    "<|su|>": 50357,
+    "<|sv|>": 50273,
+    "<|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,
+  "return_timestamps": false,
+  "suppress_tokens": [
+    1,
+    2,
+    7,
+    8,
+    9,
+    10,
+    14,
+    25,
+    26,
+    27,
+    28,
+    29,
+    31,
+    58,
+    59,
+    60,
+    61,
+    62,
+    63,
+    90,
+    91,
+    92,
+    93,
+    359,
+    503,
+    522,
+    542,
+    873,
+    893,
+    902,
+    918,
+    922,
+    931,
+    1350,
+    1853,
+    1982,
+    2460,
+    2627,
+    3246,
+    3253,
+    3268,
+    3536,
+    3846,
+    3961,
+    4183,
+    4667,
+    6585,
+    6647,
+    7273,
+    9061,
+    9383,
+    10428,
+    10929,
+    11938,
+    12033,
+    12331,
+    12562,
+    13793,
+    14157,
+    14635,
+    15265,
+    15618,
+    16553,
+    16604,
+    18362,
+    18956,
+    20075,
+    21675,
+    22520,
+    26130,
+    26161,
+    26435,
+    28279,
+    29464,
+    31650,
+    32302,
+    32470,
+    36865,
+    42863,
+    47425,
+    49870,
+    50254,
+    50258,
+    50358,
+    50359,
+    50360,
+    50361,
+    50362
+  ],
+  "task_to_id": {
+    "transcribe": 50359,
+    "translate": 50358
+  },
+  "transformers_version": "4.36.2"
+}

distil-large-v2-init/model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e8d8cfe7cdfe70dfd94f7625d7e6d6f8206de90541eb67d3687477be80512db5
+size 3024943976

distil-large-v2-init/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"
+}

distil-large-v2-init/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
+}

distil-large-v2-init/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
+  }
+}

distil-whisper/events.out.tfevents.1705577943.c066756f484e.16415.0

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:040cb9a816e9c46e96bc0d99f82c22904736ae79dafc9b4c76a4c855b9ba3eb5
+size 88

distil-whisper/events.out.tfevents.1705578047.c066756f484e.16497.0

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:212fcd3f0dcb0a927affdb9297d319c02fd21dbb54ebe378d472a669c48d2221
+size 88

distil-whisper/events.out.tfevents.1705578567.c066756f484e.17473.0

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:246c9f94dc40198fa3a927029edf3c6a7bbbc1a93cca3ae11036c818a7eeda1d
+size 88

distil-whisper/events.out.tfevents.1705578603.c066756f484e.17562.0

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2c6a96e58e2cf53dd01a5e99da9219b96fd4cb3e8f8520aced95e23ff2a6118f
+size 88

distil-whisper/events.out.tfevents.1705578645.c066756f484e.17648.0

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:647904c76334c6865112bbe61af3962e6585871165a9a967c893aea26becf5f6
+size 88

distil-whisper/events.out.tfevents.1705586109.c066756f484e.19847.0

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b26f34b02cfdb723ee4c13d2f5edf473c7fc36265e0c81a6669020427af1789d
+size 88

distil-whisper/events.out.tfevents.1705586128.c066756f484e.19901.0

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1e49b0ea2bc10c4de791e401639b3af14f17c58d7b8c428cff32908496c77acb
+size 88

distil-whisper/events.out.tfevents.1705587760.c066756f484e.20098.0

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3ff070f5139f4c6986e8abd63c6955ff984ff0240c331cf46e128d39e6c5e6fd
+size 12458

generation_config.json

@@ -0,0 +1,315 @@
+{
+  "alignment_heads": [
+    [
+      10,
+      12
+    ],
+    [
+      13,
+      17
+    ],
+    [
+      16,
+      11
+    ],
+    [
+      16,
+      12
+    ],
+    [
+      16,
+      13
+    ],
+    [
+      17,
+      15
+    ],
+    [
+      17,
+      16
+    ],
+    [
+      18,
+      4
+    ],
+    [
+      18,
+      11
+    ],
+    [
+      18,
+      19
+    ],
+    [
+      19,
+      11
+    ],
+    [
+      21,
+      2
+    ],
+    [
+      21,
+      3
+    ],
+    [
+      22,
+      3
+    ],
+    [
+      22,
+      9
+    ],
+    [
+      22,
+      12
+    ],
+    [
+      23,
+      5
+    ],
+    [
+      23,
+      7
+    ],
+    [
+      23,
+      13
+    ],
+    [
+      25,
+      5
+    ],
+    [
+      26,
+      1
+    ],
+    [
+      26,
+      12
+    ],
+    [
+      27,
+      15
+    ]
+  ],
+  "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
+    ]
+  ],
+  "is_multilingual": true,
+  "lang_to_id": {
+    "<|af|>": 50327,
+    "<|am|>": 50334,
+    "<|ar|>": 50272,
+    "<|as|>": 50350,
+    "<|az|>": 50304,
+    "<|ba|>": 50355,
+    "<|be|>": 50330,
+    "<|bg|>": 50292,
+    "<|bn|>": 50302,
+    "<|bo|>": 50347,
+    "<|br|>": 50309,
+    "<|bs|>": 50315,
+    "<|ca|>": 50270,
+    "<|cs|>": 50283,
+    "<|cy|>": 50297,
+    "<|da|>": 50285,
+    "<|de|>": 50261,
+    "<|el|>": 50281,
+    "<|en|>": 50259,
+    "<|es|>": 50262,
+    "<|et|>": 50307,
+    "<|eu|>": 50310,
+    "<|fa|>": 50300,
+    "<|fi|>": 50277,
+    "<|fo|>": 50338,
+    "<|fr|>": 50265,
+    "<|gl|>": 50319,
+    "<|gu|>": 50333,
+    "<|haw|>": 50352,
+    "<|ha|>": 50354,
+    "<|he|>": 50279,
+    "<|hi|>": 50276,
+    "<|hr|>": 50291,
+    "<|ht|>": 50339,
+    "<|hu|>": 50286,
+    "<|hy|>": 50312,
+    "<|id|>": 50275,
+    "<|is|>": 50311,
+    "<|it|>": 50274,
+    "<|ja|>": 50266,
+    "<|jw|>": 50356,
+    "<|ka|>": 50329,
+    "<|kk|>": 50316,
+    "<|km|>": 50323,
+    "<|kn|>": 50306,
+    "<|ko|>": 50264,
+    "<|la|>": 50294,
+    "<|lb|>": 50345,
+    "<|ln|>": 50353,
+    "<|lo|>": 50336,
+    "<|lt|>": 50293,
+    "<|lv|>": 50301,
+    "<|mg|>": 50349,
+    "<|mi|>": 50295,
+    "<|mk|>": 50308,
+    "<|ml|>": 50296,
+    "<|mn|>": 50314,
+    "<|mr|>": 50320,
+    "<|ms|>": 50282,
+    "<|mt|>": 50343,
+    "<|my|>": 50346,
+    "<|ne|>": 50313,
+    "<|nl|>": 50271,
+    "<|nn|>": 50342,
+    "<|no|>": 50288,
+    "<|oc|>": 50328,
+    "<|pa|>": 50321,
+    "<|pl|>": 50269,
+    "<|ps|>": 50340,
+    "<|pt|>": 50267,
+    "<|ro|>": 50284,
+    "<|ru|>": 50263,
+    "<|sa|>": 50344,
+    "<|sd|>": 50332,
+    "<|si|>": 50322,
+    "<|sk|>": 50298,
+    "<|sl|>": 50305,
+    "<|sn|>": 50324,
+    "<|so|>": 50326,
+    "<|sq|>": 50317,
+    "<|sr|>": 50303,
+    "<|su|>": 50357,
+    "<|sv|>": 50273,
+    "<|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,
+  "return_timestamps": false,
+  "suppress_tokens": [
+    1,
+    2,
+    7,
+    8,
+    9,
+    10,
+    14,
+    25,
+    26,
+    27,
+    28,
+    29,
+    31,
+    58,
+    59,
+    60,
+    61,
+    62,
+    63,
+    90,
+    91,
+    92,
+    93,
+    359,
+    503,
+    522,
+    542,
+    873,
+    893,
+    902,
+    918,
+    922,
+    931,
+    1350,
+    1853,
+    1982,
+    2460,
+    2627,
+    3246,
+    3253,
+    3268,
+    3536,
+    3846,
+    3961,
+    4183,
+    4667,
+    6585,
+    6647,
+    7273,
+    9061,
+    9383,
+    10428,
+    10929,
+    11938,
+    12033,
+    12331,
+    12562,
+    13793,
+    14157,
+    14635,
+    15265,
+    15618,
+    16553,
+    16604,
+    18362,
+    18956,
+    20075,
+    21675,
+    22520,
+    26130,
+    26161,
+    26435,
+    28279,
+    29464,
+    31650,
+    32302,
+    32470,
+    36865,
+    42863,
+    47425,
+    49870,
+    50254,
+    50258,
+    50358,
+    50359,
+    50360,
+    50361,
+    50362
+  ],
+  "task_to_id": {
+    "transcribe": 50359,
+    "translate": 50358
+  },
+  "transformers_version": "4.36.2"
+}

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"
+}

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
+}

run_distil.sh

@@ -0,0 +1,39 @@
+#!/usr/bin/env bash
+
+accelerate launch run_distillation.py \
+  --model_name_or_path "./distil-large-v2-init" \
+  --teacher_model_name_or_path "openai/whisper-large-v2" \
+  --train_dataset_name "../common_voice_13_0_pt_pseudo_labelled+../common_voice_13_0_pt_pseudo_labelled" \
+  --train_split_name "train+validation" \
+  --text_column_name "sentence+sentence" \
+  --train_dataset_samples "10+5" \
+  --eval_dataset_name "../common_voice_13_0_pt_pseudo_labelled" \
+  --eval_split_name "test" \
+  --eval_text_column_name "sentence" \
+  --eval_steps 1000 \
+  --save_steps 1000 \
+  --warmup_steps 50 \
+  --learning_rate 0.0001 \
+  --lr_scheduler_type "constant_with_warmup" \
+  --logging_steps 25 \
+  --save_total_limit 1 \
+  --max_steps 5000 \
+  --wer_threshold 10 \
+  --per_device_train_batch_size 16 \
+  --per_device_eval_batch_size 16 \
+  --dataloader_num_workers 4 \
+  --preprocessing_num_workers 4 \
+  --ddp_timeout 7200 \
+  --dtype "bfloat16" \
+  --output_dir "." \
+  --do_train \
+  --do_eval \
+  --gradient_checkpointing \
+  --overwrite_output_dir \
+  --predict_with_generate \
+  --freeze_encoder \
+  --streaming False \
+  --train_dataset_config_name "pt+pt" \
+  --eval_dataset_config_name "pt" \
+  --report_to "tensorboard" \
+  --push_to_hub

run_distillation.py

@@ -0,0 +1,1665 @@
+#!/usr/bin/env python
+# coding=utf-8
+# Copyright 2023 The HuggingFace Inc. 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.
+"""
+Training the Whisper model for sequence to sequence speech recognition via teacher-student distillation.
+"""
+# You can also adapt this script for your own distillation tasks. Pointers for this are left as comments.
+
+import logging
+import os
+import re
+import shutil
+import sys
+import time
+from dataclasses import dataclass, field
+from functools import partial
+from pathlib import Path
+from typing import Any, Dict, List, Optional, Union
+
+import datasets
+import evaluate
+import numpy as np
+import torch
+import torch.nn as nn
+import transformers
+from accelerate import Accelerator
+from accelerate.logging import get_logger
+from datasets import (
+    DatasetDict,
+    IterableDataset,
+    IterableDatasetDict,
+    concatenate_datasets,
+    interleave_datasets,
+    load_dataset,
+)
+from huggingface_hub import Repository, create_repo
+from torch.utils.data import DataLoader
+from tqdm import tqdm
+from transformers import (
+    AddedToken,
+    HfArgumentParser,
+    Seq2SeqTrainingArguments,
+    WhisperConfig,
+    WhisperFeatureExtractor,
+    WhisperForConditionalGeneration,
+    WhisperProcessor,
+    WhisperTokenizerFast,
+    get_scheduler,
+    set_seed,
+)
+from transformers.modeling_outputs import BaseModelOutput
+from transformers.models.whisper.english_normalizer import BasicTextNormalizer, EnglishTextNormalizer
+from transformers.utils import check_min_version
+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("4.34.0.dev0")
+
+require_version("datasets>=2.14.6", "To fix: `pip install --upgrade datasets`")
+
+logger = get_logger(__name__)
+
+
+@dataclass
+class ModelArguments:
+    """
+    Arguments pertaining to which model/config/tokenizer we are going to distill from.
+    """
+
+    model_name_or_path: str = field(
+        metadata={"help": "Path to pretrained Whisper model or model identifier from huggingface.co/models"}
+    )
+    teacher_model_name_or_path: str = field(
+        metadata={"help": "Path to pretrained teacher 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)."},
+    )
+    subfolder: str = field(
+        default="",
+        metadata={
+            "help": "In case the relevant files are located inside a subfolder of the model repo on huggingface.co, you can"
+            "specify the folder name here."
+        },
+    )
+    token: str = field(
+        default=None,
+        metadata={
+            "help": (
+                "The token to use as HTTP bearer authorization for remote files. If not specified, will use the token "
+                "generated when running `huggingface-cli login` (stored in `~/.huggingface`)."
+            )
+        },
+    )
+
+
+@dataclass
+class DataTrainingArguments:
+    """
+    Arguments pertaining to what data we are going to input our model for training and eval.
+    """
+
+    train_dataset_name: str = field(
+        default=None,
+        metadata={
+            "help": "The name of the training dataset to use (via the datasets library). Load and combine "
+            "multiple datasets by separating dataset ids by a '+' symbol. For example, to load LibriSpeech "
+            "and Common Voice, set `train_dataset_name='librispeech_asr+common_voice'`."
+        },
+    )
+    train_dataset_config_name: Optional[str] = field(
+        default=None,
+        metadata={
+            "help": "The configuration name of the training dataset to use (via the datasets library). Load and combine "
+            "multiple datasets by separating dataset configs by a '+' symbol. Note that the order of the configs should "
+            "match the order of the datasets."
+        },
+    )
+    train_dataset_samples: str = field(
+        default=None,
+        metadata={
+            "help": "Number of samples in each dataset when loading multiple datasets with streaming mode. "
+            "Not required when using one dataset or non-streaming mode. The sample values provide the sampling "
+            "probability for each dataset. Setting them equal to the number of sample values ensures that every "
+            "sample from every dataset is used once per epoch."
+        },
+    )
+    eval_dataset_name: str = field(
+        default=None,
+        metadata={
+            "help": "The name of the evaluation dataset to use (via the datasets library). Defaults to the training "
+            "dataset name if unspecified. Load multiple evaluation datasets by separating dataset "
+            "ids by a '+' symbol."
+        },
+    )
+    eval_dataset_config_name: Optional[str] = field(
+        default=None,
+        metadata={
+            "help": "The configuration name of the evaluation dataset to use (via the datasets library). Defaults to the "
+            "training dataset config name if unspecified."
+        },
+    )
+    dataset_cache_dir: Optional[str] = field(
+        default=None,
+        metadata={"help": "Path to cache directory for saving and loading datasets"},
+    )
+    overwrite_cache: bool = field(
+        default=False,
+        metadata={"help": "Overwrite the cached training and evaluation sets"},
+    )
+    preprocessing_num_workers: Optional[int] = field(
+        default=None,
+        metadata={"help": "The number of processes to use for the preprocessing if using non-streaming mode."},
+    )
+    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=None,
+        metadata={"help": "The name of the dataset column containing the text data in the training set."},
+    )
+    eval_text_column_name: str = field(
+        default="text",
+        metadata={"help": ("The name of the dataset column containing the text data in the evaluation set.")},
+    )
+    max_duration_in_seconds: float = field(
+        default=30.0,
+        metadata={"help": "Filter audio files that are longer than `max_duration_in_seconds` seconds"},
+    )
+    min_duration_in_seconds: float = field(
+        default=0.0,
+        metadata={"help": "Filter audio files that are shorter than `min_duration_in_seconds` seconds"},
+    )
+    max_label_length: int = field(
+        default=128,
+        metadata={"help": "Truncate transcriptions that are longer `max_label_length` tokens."},
+    )
+    pad_target_to_multiple_of: Optional[int] = field(
+        default=None,
+        metadata={
+            "help": (
+                "If set will pad the target sequence to a multiple of the provided"
+                " value. This is important to avoid triggering recompilations on TPU."
+                " If unspecified, will default to padding the targets to max length."
+            )
+        },
+    )
+    preprocessing_only: bool = field(
+        default=False,
+        metadata={
+            "help": (
+                "Whether to only do data preprocessing and skip training. This is"
+                " especially useful when data preprocessing errors out in distributed"
+                " training due to timeout. In this case, one should run the"
+                " preprocessing in a non-distributed setup with"
+                " `preprocessing_only=True` so that the cached datasets can"
+                " consequently be loaded in distributed training"
+            )
+        },
+    )
+    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="validation",
+        metadata={
+            "help": (
+                "The name of the evaluation data set split to use (via the datasets library). Defaults to 'validation'"
+            )
+        },
+    )
+    streaming: bool = field(
+        default=True,
+        metadata={"help": "Whether to use Datasets' streaming mode to load and pre-process the data."},
+    )
+    wer_threshold: float = field(
+        default=None,
+        metadata={
+            "help": "Filter training data with Whisper transcriptions that have greater than `wer_threshold` "
+            "WER with the normalised transcriptions. This only takes effect if training on pseudo-labels targets."
+            "If `--use_pseudo_labels=False`, then no WER filtering is performed, since we train directly on the text"
+            "transcriptions."
+        },
+    )
+    use_pseudo_labels: bool = field(
+        default=True,
+        metadata={
+            "help": "Whether or not to use pseudo-label transcriptions as the targets. If True, the pseudo-labels "
+            "must be in the dataset column `whisper_transcript` from the previous pseudo-labelling step. This is "
+            "not currently yet configurable."
+        },
+    )
+    timestamp_probability: float = field(
+        default=0.2, metadata={"help": "Probability for training on timestamped tokens if the data contains it."}
+    )
+    condition_on_prev_probability: float = field(
+        default=0.2, metadata={"help": "Probability for conditioning on the previous text example."}
+    )
+    return_timestamps: bool = field(
+        default=False, metadata={"help": "Whether or not to predict timestamps in the generation step."}
+    )
+    language: str = field(
+        default=None,
+        metadata={
+            "help": (
+                "Language for multilingual distillation. This argument should be set for multilingual distillation "
+                "only. For English speech recognition, it should be left as `None`."
+            )
+        },
+    )
+    task: str = field(
+        default="transcribe",
+        metadata={
+            "help": "Task, either `transcribe` for speech recognition or `translate` for speech translation."
+            "This argument should be set for multilingual distillation only. For English speech recognition, it should be left as `None`."
+        },
+    )
+    wandb_project: str = field(
+        default="distil-whisper",
+        metadata={"help": "The name of the wandb project."},
+    )
+
+
+@dataclass
+class DistillationTrainingArguments(Seq2SeqTrainingArguments):
+    freeze_encoder: Optional[bool] = field(
+        default=False,
+        metadata={
+            "help": (
+                "Whether to freeze the entire encoder model. Only recommended when the entire encoder has been "
+                "copied from the teacher model."
+            )
+        },
+    )
+    temperature: Optional[float] = field(
+        default=2.0, metadata={"help": "Temperature to anneal the logits when computing the softmax."}
+    )
+    kl_weight: Optional[float] = field(
+        default=1.0,
+        metadata={
+            "help": (
+                "Weighting assigned to the MSE loss in the KD formulation. MSE loss is "
+                "computed between the teacher-student hidden states and attentions."
+            )
+        },
+    )
+    dtype: Optional[str] = field(
+        default="float32",
+        metadata={
+            "help": (
+                "The data type (dtype) in which to run training. One of `float32` (full-precision), "
+                "`float16` or `bfloat16` (both half-precision)."
+            )
+        },
+    )
+
+
+@dataclass
+class DataCollatorSpeechSeq2SeqWithPadding:
+    """
+    Data collator that will dynamically pad the inputs received.
+    Args:
+        processor ([`Wav2Vec2Processor`])
+            The processor used for proccessing the data.
+        decoder_start_token_id (:obj: `int`)
+            The start-of-sequence token id of the decoder.
+        decoder_prev_token_id (:obj: `int`)
+            The start-of-prompt token id of the decoder
+        input_padding (:obj:`bool`, :obj:`str` or :class:`~transformers.tokenization_utils_base.PaddingStrategy`, `optional`, defaults to :obj:`True`):
+            Select a strategy to pad the returned input sequences (according to the model's padding side and padding index)
+            among:
+            * :obj:`True` or :obj:`'longest'`: Pad to the longest sequence in the batch (or no padding if only a single
+              sequence if provided).
+            * :obj:`'max_length'`: Pad to a maximum length specified with the argument :obj:`max_length` or to the
+              maximum acceptable input length for the model if that argument is not provided.
+            * :obj:`False` or :obj:`'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of
+              different lengths).
+        target_padding (:obj:`bool`, :obj:`str` or :class:`~transformers.tokenization_utils_base.PaddingStrategy`, `optional`, defaults to :obj:`True`):
+            Select a strategy to pad the returned target sequences (according to the model's padding side and padding index).
+            See above for details.
+        max_target_length (:obj:`int`, `optional`):
+            Maximum length of the ``labels`` of the returned list and optionally padding length (see above).
+    """
+
+    processor: Any
+    decoder_start_token_id: int
+    decoder_prev_token_id: int
+    input_padding: Union[bool, str] = "max_length"
+    target_padding: Union[bool, str] = "max_length"
+    max_target_length: Optional[int] = None
+
+    def __call__(self, features: List[Dict[str, Union[List[int], np.ndarray]]]) -> Dict[str, np.ndarray]:
+        # 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]
+
+        # dataloader returns a list of features which we convert to a dict
+        input_features = {model_input_name: [feature[model_input_name] for feature in features]}
+        label_features = {"input_ids": [feature["labels"] for feature in features]}
+
+        # reformat list to dict and set to pytorch format
+        batch = self.processor.feature_extractor.pad(
+            input_features,
+            padding=self.input_padding,
+            return_tensors="pt",
+        )
+
+        labels_batch = self.processor.tokenizer.pad(
+            label_features,
+            max_length=self.max_target_length,
+            padding=self.target_padding,
+            return_tensors="pt",
+        )
+
+        # shift labels to the right to get decoder input ids
+        labels = labels_batch["input_ids"]
+        decoder_input_ids = labels[:, :-1]
+        labels = labels[:, 1:]
+        labels_mask = labels_batch.attention_mask[:, 1:]
+
+        # replace padding with -100 to ignore correctly when computing the loss
+        labels = labels.masked_fill(labels_mask.ne(1), -100)
+
+        # replace initial prompt tokens with -100 to ignore correctly when computing the loss
+        bos_index = torch.argmax((labels == self.decoder_start_token_id).long(), dim=1)
+        prompt_mask = torch.arange(labels.shape[1]) < bos_index[:, None]
+        labels = torch.where(prompt_mask, -100, labels)
+
+        batch["labels"] = labels
+        batch["decoder_input_ids"] = decoder_input_ids
+
+        return batch
+
+
+def log_metric(
+    accelerator,
+    metrics: Dict,
+    train_time: float,
+    step: int,
+    epoch: int,
+    learning_rate: float = None,
+    prefix: str = "train",
+):
+    """Helper function to log all training/evaluation metrics with the correct prefixes and styling."""
+    log_metrics = {}
+    for k, v in metrics.items():
+        log_metrics[f"{prefix}/{k}"] = v
+    log_metrics[f"{prefix}/time"] = train_time
+    log_metrics[f"{prefix}/epoch"] = epoch
+    if learning_rate is not None:
+        log_metrics[f"{prefix}/learning_rate"] = learning_rate
+    accelerator.log(log_metrics, step=step)
+
+
+def log_pred(
+    accelerator,
+    pred_str: List[str],
+    label_str: List[str],
+    norm_pred_str: List[str],
+    norm_label_str: List[str],
+    step: int,
+    prefix: str = "eval",
+    num_lines: int = 200000,
+):
+    """Helper function to log target/predicted transcriptions to weights and biases (wandb)."""
+    if accelerator.is_main_process:
+        wandb_tracker = accelerator.get_tracker("wandb")
+        # pretty name for current step: step 50000 -> step 50k
+        cur_step_pretty = f"{int(step // 1000)}k" if step > 1000 else step
+        prefix_pretty = prefix.replace("/", "-")
+
+        # convert str data to a wandb compatible format
+        str_data = [[label_str[i], pred_str[i], norm_label_str[i], norm_pred_str[i]] for i in range(len(pred_str))]
+        # log as a table with the appropriate headers
+        wandb_tracker.log_table(
+            table_name=f"predictions/{prefix_pretty}-step-{cur_step_pretty}",
+            columns=["Target", "Pred", "Norm Target", "Norm Pred"],
+            data=str_data[:num_lines],
+            step=step,
+        )
+
+        # log incorrect normalised predictions
+        str_data = np.asarray(str_data)
+        str_data_incorrect = str_data[str_data[:, -2] != str_data[:, -1]]
+        # log as a table with the appropriate headers
+        wandb_tracker.log_table(
+            table_name=f"incorrect_predictions/{prefix_pretty}-step-{cur_step_pretty}",
+            columns=["Target", "Pred", "Norm Target", "Norm Pred"],
+            data=str_data_incorrect[:num_lines],
+            step=step,
+        )
+
+
+def convert_dataset_str_to_list(
+    dataset_names,
+    dataset_config_names,
+    splits=None,
+    text_column_names=None,
+    dataset_samples=None,
+    default_split="train",
+) -> List[Dict]:
+    """
+    Given three lists of dataset names, configs and splits, this function groups the corresponding
+    names/configs/splits. Each dataset is assigned a unique dictionary with these metadata values, and the
+    function returns a list of dictionaries, one for each dataset.
+    """
+    if isinstance(dataset_names, str):
+        dataset_names = dataset_names.split("+")
+        dataset_config_names = dataset_config_names.split("+")
+        splits = splits.split("+") if splits is not None else None
+        text_column_names = text_column_names.split("+") if text_column_names is not None else None
+        dataset_samples = dataset_samples.split("+") if dataset_samples is not None else None
+
+    # basic checks to ensure we've got the right number of datasets/configs/splits/columns/probs
+    if len(dataset_names) != len(dataset_config_names):
+        raise ValueError(
+            f"Ensure one config is passed for each dataset, got {len(dataset_names)} datasets and"
+            f" {len(dataset_config_names)} configs."
+        )
+
+    if splits is not None and len(splits) != len(dataset_names):
+        raise ValueError(
+            f"Ensure one split is passed for each dataset, got {len(dataset_names)} datasets and {len(splits)} splits."
+        )
+
+    if text_column_names is not None and len(text_column_names) != len(dataset_names):
+        raise ValueError(
+            f"Ensure one text column name is passed for each dataset, got {len(dataset_names)} datasets and"
+            f" {len(text_column_names)} text column names."
+        )
+
+    if dataset_samples is not None:
+        if len(dataset_samples) != len(dataset_names):
+            raise ValueError(
+                f"Ensure one sample is passed for each dataset, got {len(dataset_names)} datasets and "
+                f"{len(dataset_samples)} samples."
+            )
+        dataset_samples = [float(ds_sample) for ds_sample in dataset_samples]
+    else:
+        dataset_samples = [None] * len(dataset_names)
+
+    text_column_names = (
+        text_column_names if text_column_names is not None else ["text" for _ in range(len(dataset_names))]
+    )
+    splits = splits if splits is not None else [default_split for _ in range(len(dataset_names))]
+
+    dataset_names_dict = []
+    for i, ds_name in enumerate(dataset_names):
+        dataset_names_dict.append(
+            {
+                "name": ds_name,
+                "config": dataset_config_names[i],
+                "split": splits[i],
+                "text_column_name": text_column_names[i],
+                "samples": dataset_samples[i],
+            }
+        )
+    return dataset_names_dict
+
+
+def load_multiple_datasets(
+    dataset_names: Union[List, str],
+    dataset_config_names: Union[List, str],
+    splits: Optional[Union[List, str]] = None,
+    text_column_names: Optional[List] = None,
+    sampling_rate: Optional[int] = 16000,
+    stopping_strategy: Optional[str] = "first_exhausted",
+    dataset_samples: Optional[Union[List, np.array]] = None,
+    streaming: Optional[bool] = True,
+    seed: Optional[int] = None,
+    accelerator: Optional[Accelerator] = None,
+    use_pseudo_labels: float = None,
+    **kwargs,
+) -> IterableDataset:
+    dataset_names_dict = convert_dataset_str_to_list(
+        dataset_names, dataset_config_names, splits, text_column_names, dataset_samples
+    )
+
+    if dataset_samples is not None:
+        dataset_samples = [ds_dict["samples"] for ds_dict in dataset_names_dict]
+        probabilities = np.array(dataset_samples) / np.sum(dataset_samples)
+    else:
+        probabilities = None
+
+    all_datasets = []
+    # iterate over the datasets we want to interleave
+    for dataset_dict in tqdm(
+        dataset_names_dict,
+        desc="Combining datasets...",
+        disable=not accelerator.is_local_main_process if accelerator is not None else False,
+    ):
+        dataset = load_dataset(
+            dataset_dict["name"],
+            dataset_dict["config"],
+            split=dataset_dict["split"],
+            streaming=streaming,
+            **kwargs,
+        )
+        # resample to specified sampling rate
+        dataset = dataset.cast_column("audio", datasets.features.Audio(sampling_rate))
+        dataset_features = dataset.features.keys()
+        columns_to_keep = {"audio", "text"}
+
+        if dataset_dict["text_column_name"] not in dataset_features:
+            raise ValueError(
+                f"Text column name {dataset_dict['text_column_name']} not found in dataset"
+                f" '{dataset_dict['name']}'. Make sure to set `--text_column_name` to the"
+                f" correct text column - one of {', '.join(dataset_features)}."
+            )
+
+        # blanket renaming of all transcription columns to text
+        if dataset_dict["text_column_name"] != "text":
+            dataset = dataset.rename_column(dataset_dict["text_column_name"], "text")
+
+        if use_pseudo_labels:
+            if "whisper_transcript" not in dataset_features:
+                raise ValueError(
+                    f"Pseudo-label column `whisper_transcript` not found in dataset {dataset_dict['name']}. Ensure"
+                    "pseudo-labels are present in the dataset under this column name, or train directly on the text "
+                    "labels by setting `--use_pseudo_labels=False` and defining the appropriate `--text_column_name`."
+                )
+            columns_to_keep.add("whisper_transcript")
+        dataset_features = dataset.features.keys()
+        dataset = dataset.remove_columns(set(dataset_features - columns_to_keep))
+        all_datasets.append(dataset)
+
+    if len(all_datasets) == 1:
+        # we have a single dataset so just return it as is
+        return all_datasets[0]
+
+    if streaming:
+        interleaved_dataset = interleave_datasets(
+            all_datasets,
+            stopping_strategy=stopping_strategy,
+            probabilities=probabilities,
+            seed=seed,
+        )
+    else:
+        interleaved_dataset = concatenate_datasets(all_datasets)
+
+    return interleaved_dataset
+
+
+def get_layers_to_supervise(student_layers: int, teacher_layers: int) -> Dict:
+    """Helper function to map the student layer i to the teacher layer j whose output we'd like them to emulate. Used
+    for MSE loss terms in distillation (hidden-states and activations). Student layers are paired with teacher layers
+    in equal increments, e.g. for a 12-layer model distilled to a 3-layer model, student layer 0 emulates teacher layer
+    3 (such that it behaves like the first 4 teacher layers), student layer 1 emulates teacher layer 7, and student layer
+    2 emulates teacher layer 11. This mapping is summarised by the dictionary: {0: 3, 1: 7, 2: 11}, which is precisely
+    the output of this function for the arguments (student_layers=3, teacher_layers=12)."""
+    layer_intervals = np.linspace(teacher_layers // student_layers - 1, teacher_layers - 1, student_layers, dtype=int)
+    layer_intervals[-1] = teacher_layers - 1
+    layer_map = {}
+
+    for student_layer, teacher_layer in enumerate(layer_intervals):
+        layer_map[student_layer] = teacher_layer
+
+    return layer_map
+
+
+def sorted_checkpoints(output_dir=None, checkpoint_prefix="checkpoint") -> List[str]:
+    """Helper function to sort saved checkpoints from oldest to newest."""
+    ordering_and_checkpoint_path = []
+
+    glob_checkpoints = [str(x) for x in Path(output_dir).glob(f"{checkpoint_prefix}-*") if os.path.isdir(x)]
+
+    for path in glob_checkpoints:
+        regex_match = re.match(f".*{checkpoint_prefix}-([0-9]+)", path)
+        if regex_match is not None and regex_match.groups() is not None:
+            ordering_and_checkpoint_path.append((int(regex_match.groups()[0]), path))
+
+    checkpoints_sorted = sorted(ordering_and_checkpoint_path)
+    checkpoints_sorted = [checkpoint[1] for checkpoint in checkpoints_sorted]
+    return checkpoints_sorted
+
+
+def rotate_checkpoints(save_total_limit=None, output_dir=None, checkpoint_prefix="checkpoint") -> None:
+    """Helper function to delete old checkpoints."""
+    if save_total_limit is None or save_total_limit <= 0:
+        return
+    # Check if we should delete older checkpoint(s)
+    checkpoints_sorted = sorted_checkpoints(output_dir=output_dir, checkpoint_prefix=checkpoint_prefix)
+    if len(checkpoints_sorted) <= save_total_limit:
+        return
+
+    number_of_checkpoints_to_delete = max(0, len(checkpoints_sorted) - save_total_limit)
+    checkpoints_to_be_deleted = checkpoints_sorted[:number_of_checkpoints_to_delete]
+    for checkpoint in checkpoints_to_be_deleted:
+        logger.info(f"Deleting older checkpoint [{checkpoint}] due to args.save_total_limit")
+        shutil.rmtree(checkpoint, ignore_errors=True)
+
+
+_RE_CHECKPOINT = re.compile(r"^checkpoint-(\d+)-epoch-(\d+)$")
+
+
+def get_last_checkpoint(folder):
+    content = os.listdir(folder)
+    checkpoints = [
+        path
+        for path in content
+        if _RE_CHECKPOINT.search(path) is not None and os.path.isdir(os.path.join(folder, path))
+    ]
+    if len(checkpoints) == 0:
+        return
+    return os.path.join(folder, max(checkpoints, key=lambda x: int(_RE_CHECKPOINT.search(x).groups()[0])))
+
+
+def get_parameter_names(model, forbidden_layer_types, forbidden_module=None):
+    """
+    Returns the names of the model parameters that are not inside a forbidden layer or forbidden module.
+    Can be used to get a subset of parameter names for decay masks, or to exclude parameters from an optimiser
+    (e.g. if the module is frozen).
+    """
+    result = []
+    for name, child in model.named_children():
+        result += [
+            f"{name}.{n}"
+            for n in get_parameter_names(child, forbidden_layer_types, forbidden_module)
+            if not (
+                isinstance(child, tuple(forbidden_layer_types))
+                or (child in tuple(forbidden_module) if forbidden_module is not None else False)
+            )
+        ]
+    # Add model specific parameters (defined with nn.Parameter) since they are not in any child.
+    result += list(model._parameters.keys())
+    return result
+
+
+def main():
+    # 1. Parse input arguments
+    # We keep distinct sets of args, for cleaner separation of model/data/training related args
+    parser = HfArgumentParser((ModelArguments, DataTrainingArguments, DistillationTrainingArguments))
+
+    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()
+
+    # 2. Initialize the accelerator
+    # We will let the accelerator handle device placement for us in this example
+    # We simply have to specify the training precision and any trackers being used
+    # We'll use the same dtype arguments as our JAX/Flax training script and convert
+    # it to accelerate format
+    # The teacher model can safely be cast to the dtype of training since we don't
+    # update the params
+    if training_args.dtype == "float16":
+        mixed_precision = "fp16"
+        teacher_dtype = torch.float16
+    elif training_args.dtype == "bfloat16":
+        mixed_precision = "bf16"
+        teacher_dtype = torch.bfloat16
+    else:
+        mixed_precision = "no"
+        teacher_dtype = torch.float32
+
+    accelerator = Accelerator(
+        gradient_accumulation_steps=training_args.gradient_accumulation_steps,
+        mixed_precision=mixed_precision,
+        log_with=training_args.report_to,
+        project_dir=training_args.output_dir,
+    )
+
+    accelerator.init_trackers(project_name=data_args.wandb_project)
+
+    # 3. Set-up basic logging
+    # Create one log on every process with the configuration for debugging
+    logging.basicConfig(
+        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
+        datefmt="%m/%d/%Y %H:%M:%S",
+        level=logging.INFO,
+    )
+    # Log a small summary on each proces
+    logger.warning(
+        f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, "
+        f"distributed training: {training_args.parallel_mode.value == 'distributed'}, 16-bits training: {training_args.fp16}"
+    )
+
+    # Set the verbosity to info of the Transformers logger (on main process only)
+    if accelerator.is_local_main_process:
+        datasets.utils.logging.set_verbosity_warning()
+        transformers.utils.logging.set_verbosity_info()
+    else:
+        datasets.utils.logging.set_verbosity_error()
+        transformers.utils.logging.set_verbosity_error()
+    logger.info("Training/evaluation parameters %s", training_args)
+
+    # 4. 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."
+            )
+
+    # 5. Handle the repository creation
+    if accelerator.is_main_process:
+        if training_args.push_to_hub:
+            # Retrieve of infer repo_name
+            repo_name = training_args.hub_model_id
+            if repo_name is None:
+                repo_name = Path(training_args.output_dir).absolute().name
+            # Create repo and retrieve repo_id
+            repo_id = create_repo(repo_name, exist_ok=True, token=training_args.hub_token).repo_id
+            # Clone repo locally
+            repo = Repository(training_args.output_dir, clone_from=repo_id, token=training_args.hub_token)
+
+            with open(os.path.join(training_args.output_dir, ".gitignore"), "w+") as gitignore:
+                if "wandb" not in gitignore:
+                    gitignore.write("wandb\n")
+        elif training_args.output_dir is not None:
+            os.makedirs(training_args.output_dir, exist_ok=True)
+    accelerator.wait_for_everyone()
+
+    # 6. Load dataset - either streaming or non-streaming (offline)
+    raw_datasets = IterableDatasetDict() if data_args.streaming else DatasetDict()
+
+    # set seed for determinism
+    set_seed(training_args.seed)
+
+    if training_args.do_train:
+        raw_datasets["train"] = load_multiple_datasets(
+            data_args.train_dataset_name,
+            data_args.train_dataset_config_name,
+            splits=data_args.train_split_name,
+            text_column_names=data_args.text_column_name,
+            use_pseudo_labels=data_args.use_pseudo_labels,
+            streaming=data_args.streaming,
+            dataset_samples=data_args.train_dataset_samples,
+            seed=training_args.seed,
+            accelerator=accelerator,
+            cache_dir=data_args.dataset_cache_dir,
+            token=model_args.token,
+        )
+        raw_datasets_train_features = list(raw_datasets["train"].features.keys())
+
+    if training_args.do_eval:
+        dataset_names_dict = convert_dataset_str_to_list(
+            data_args.eval_dataset_name if data_args.eval_dataset_name else data_args.train_dataset_name,
+            data_args.eval_dataset_config_name
+            if data_args.eval_dataset_config_name
+            else data_args.train_dataset_config_name,
+            splits=data_args.eval_split_name,
+            text_column_names=data_args.eval_text_column_name,
+        )
+        all_eval_splits = []
+        if len(dataset_names_dict) == 1:
+            # load a single eval set
+            dataset_dict = dataset_names_dict[0]
+            all_eval_splits.append("eval")
+            raw_datasets["eval"] = load_dataset(
+                dataset_dict["name"],
+                dataset_dict["config"],
+                split=dataset_dict["split"],
+                cache_dir=data_args.dataset_cache_dir,
+                token=model_args.token,
+                streaming=data_args.streaming,
+            )
+            if data_args.eval_text_column_name != "text":
+                raw_datasets["eval"] = raw_datasets["eval"].rename_column(data_args.eval_text_column_name, "text")
+        else:
+            # load multiple eval sets
+            for dataset_dict in dataset_names_dict:
+                if dataset_dict["name"] == "esb/diagnostic-dataset":
+                    # for the ESB diagnostic dataset, the dataset name is effectively the config
+                    pretty_name = f"{dataset_dict['config']}-diagnostic/{dataset_dict['split']}"
+                else:
+                    pretty_name = f"{dataset_dict['name'].split('/')[-1]}/{dataset_dict['split'].replace('.', '-')}"
+                all_eval_splits.append(pretty_name)
+                raw_datasets[pretty_name] = load_dataset(
+                    dataset_dict["name"],
+                    dataset_dict["config"],
+                    split=dataset_dict["split"],
+                    cache_dir=data_args.dataset_cache_dir,
+                    token=model_args.token,
+                    streaming=data_args.streaming,
+                )
+                # make column names consistent (text, audio)
+                if dataset_dict["text_column_name"] != "text":
+                    raw_datasets[pretty_name] = raw_datasets[pretty_name].rename_column(
+                        dataset_dict["text_column_name"], "text"
+                    )
+                raw_datasets[pretty_name] = raw_datasets[pretty_name].remove_columns(
+                    set(raw_datasets[pretty_name].features.keys()) - {"audio", "text"}
+                )
+
+    if not training_args.do_train and not training_args.do_eval:
+        raise ValueError(
+            "Cannot not train and not do evaluation. At least one of training or evaluation has to be performed."
+        )
+
+    # 7. Load pretrained model, tokenizer, and feature extractor
+    config = WhisperConfig.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,
+        token=model_args.token,
+    )
+    feature_extractor = WhisperFeatureExtractor.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,
+        token=model_args.token,
+    )
+    tokenizer = WhisperTokenizerFast.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,
+        token=model_args.token,
+    )
+
+    # override timestamp tokens until tokenizer issues are fixed in transformers
+    timestamps = [AddedToken("<|%.2f|>" % (i * 0.02), lstrip=False, rstrip=False) for i in range(1500 + 1)]
+    tokenizer.add_tokens(timestamps)
+
+    teacher_model = WhisperForConditionalGeneration.from_pretrained(
+        model_args.teacher_model_name_or_path,
+        cache_dir=model_args.cache_dir,
+        token=model_args.token,
+        low_cpu_mem_usage=True,
+        torch_dtype=teacher_dtype,
+    )
+
+    student_model = WhisperForConditionalGeneration.from_pretrained(
+        model_args.model_name_or_path,
+        config=config,
+        cache_dir=model_args.cache_dir,
+        revision=model_args.model_revision,
+        subfolder=model_args.subfolder,
+        token=model_args.token,
+        low_cpu_mem_usage=True,
+    )
+
+    if student_model.config.decoder_start_token_id is None or teacher_model.config.decoder_start_token_id is None:
+        raise ValueError(
+            f"Make sure that `config.decoder_start_token_id` is correctly defined for both the "
+            f"student and teacher model. Got {student_model.config.decoder_start_token_id} for the "
+            f"student and {teacher_model.config.decoder_start_token_id} for the teacher."
+        )
+
+    share_hidden_states = training_args.freeze_encoder and student_model.config.d_model == teacher_model.config.d_model
+
+    # enable gradient checkpointing if necessary
+    if training_args.gradient_checkpointing:
+        student_model.gradient_checkpointing_enable()
+
+    # freeze student encoder if necessary
+    if training_args.freeze_encoder:
+        student_model.freeze_encoder()
+        student_model.model.encoder.gradient_checkpointing = False
+
+    # if share_hidden_states:
+    # tie the weights for the student encoder if we're freezing it and it's the same as the teacher
+    #    student_model.model.encoder = teacher_model.model.encoder
+
+    if hasattr(teacher_model.generation_config, "is_multilingual") and teacher_model.generation_config.is_multilingual:
+        # We need to set the language and task ids for previously multilingual checkpoints
+        is_multilingual = True
+        tokenizer.set_prefix_tokens(language=data_args.language, task=data_args.task, predict_timestamps=False)
+        student_model.generation_config.update(
+            **{
+                "language": data_args.language,
+                "task": data_args.task,
+            }
+        )
+    elif data_args.language is not None:
+        raise ValueError(
+            "Setting language token for an English-only checkpoint is not permitted. The language argument should "
+            "only be set for multilingual checkpoints."
+        )
+    else:
+        is_multilingual = False
+
+    # 8. Create a single speech processor - make sure all processes wait until data is saved
+    if accelerator.is_main_process:
+        feature_extractor.save_pretrained(training_args.output_dir)
+        tokenizer.save_pretrained(training_args.output_dir)
+        # save the config and generation config as well
+        config.save_pretrained(training_args.output_dir)
+        student_model.generation_config.save_pretrained(training_args.output_dir)
+
+    accelerator.wait_for_everyone()
+    processor = WhisperProcessor.from_pretrained(training_args.output_dir)
+
+    # 9. Resample speech dataset: `datasets` takes care of automatically loading and resampling the audio,
+    # so we just need to set the correct target sampling rate.
+    sampling_rate = feature_extractor.sampling_rate
+    raw_datasets = raw_datasets.cast_column(
+        data_args.audio_column_name,
+        datasets.features.Audio(sampling_rate=sampling_rate),
+    )
+
+    # 10. Preprocessing the datasets: we need to read the audio files as arrays and tokenize the targets.
+    # 10.1: Define the pre-processing constants
+    max_input_length = int(data_args.max_duration_in_seconds * sampling_rate)
+    min_input_length = int(data_args.min_duration_in_seconds * sampling_rate)
+    max_label_length = (
+        data_args.max_label_length if data_args.max_label_length is not None else student_model.config.max_length
+    )
+
+    timestamp_probability = data_args.timestamp_probability
+    condition_on_prev_probability = data_args.condition_on_prev_probability
+    return_timestamps = data_args.return_timestamps if timestamp_probability > 0 else False
+
+    timestamp_ids = tokenizer.timestamp_ids()
+    timestamp_begin = tokenizer.all_special_ids[-1]
+    timestamp_position = 3 if is_multilingual else 1
+
+    decoder_start_token_id = student_model.config.decoder_start_token_id  # <|startoftranscript|>
+    decoder_prev_token_id = tokenizer.all_special_ids[-3]  # <|startofprev|>
+    decoder_eot_token_id = tokenizer.eos_token_id
+
+    language = data_args.language
+    task = data_args.task
+
+    num_workers = data_args.preprocessing_num_workers
+    dataloader_num_workers = training_args.dataloader_num_workers
+
+    metric = evaluate.load("wer")
+    normalizer = (
+        BasicTextNormalizer() if language is not None else EnglishTextNormalizer(tokenizer.english_spelling_normalizer)
+    )
+    wer_threshold = data_args.wer_threshold
+    use_pseudo_labels = data_args.use_pseudo_labels
+    train_text_column_name = "whisper_transcript" if use_pseudo_labels else "text"
+
+    # 10.2: filter based on maximum number of training/evaluation samples
+    if training_args.do_train and data_args.max_train_samples is not None:
+        raw_datasets["train"] = (
+            raw_datasets["train"].take(data_args.max_train_samples)
+            if data_args.streaming
+            else raw_datasets["train"].select(range(data_args.max_train_samples))
+        )
+
+    if training_args.do_eval and data_args.max_eval_samples is not None:
+        for eval_split in all_eval_splits:
+            raw_datasets[eval_split] = (
+                raw_datasets[eval_split].take(data_args.max_eval_samples)
+                if data_args.streaming
+                else raw_datasets[eval_split].select(range(data_args.max_eval_samples))
+            )
+
+    # 10.3: filter training data based on WER threshold -> this is KEY to good distillation performance
+    def is_wer_in_range(ground_truth, whisper_transcript):
+        norm_ground_truth = normalizer(ground_truth)
+        if (
+            isinstance(whisper_transcript, str)
+            and whisper_transcript.startswith("[")
+            and whisper_transcript.endswith("]")
+        ):
+            whisper_transcript = re.findall(r"\d+", whisper_transcript)
+            whisper_transcript = [int(token) for token in whisper_transcript]
+        if isinstance(whisper_transcript, list):
+            whisper_transcript = tokenizer.decode(whisper_transcript, skip_special_tokens=True)
+        if len(norm_ground_truth) > 0 and whisper_transcript is not None:
+            norm_whisper_transcript = normalizer(whisper_transcript)
+            wer = 100 * metric.compute(predictions=[norm_whisper_transcript], references=[norm_ground_truth])
+            return wer < wer_threshold
+        else:
+            # filter automatically since we can't know the WER
+            return False
+
+    filter_by_wer_threshold = partial(
+        raw_datasets["train"].filter,
+        function=is_wer_in_range,
+        input_columns=["text", "whisper_transcript"],
+    )
+
+    if wer_threshold is not None and use_pseudo_labels:
+        raw_datasets["train"] = (
+            filter_by_wer_threshold(num_proc=num_workers, desc="filtering train dataset by wer")
+            if not data_args.streaming
+            else filter_by_wer_threshold()
+        )
+
+    # 10.4: pre-process training/evaluation datasets
+    def has_timestamp_tokens(input_str):
+        """
+        Identify whether the input string contains timestamp tokens, of the form <|0.00|>, by searching for
+        pairs of left and right-angle brackets.
+        """
+        return bool(re.search("\<[^\>]*\>", input_str))
+
+    def prepare_train_dataset(batch):
+        """
+        Pre-process the raw dataset in a three stage process:
+            1. Convert the audio arrays to log-mel spectrogram inputs
+            2. Possibly filter the timestamp tokens from the token ids (depending on the timestamp probability)
+            3. Possibly add prompt tokens if conditioning on previous text (depending on the conditioning probability)
+        TODO(SG): see whether we can 'pack' the audio inputs closer to 30 second chunks
+        """
+        # process audio input
+        audio = [sample["array"] for sample in batch["audio"]]
+        inputs = feature_extractor(audio, sampling_rate=sampling_rate)
+        batch["input_features"] = inputs.input_features
+        batch["input_length"] = [len(sample) for sample in audio]
+
+        # process text targets - for training these are the Whisper-generated pseudo-labels
+        input_str_batched = batch[train_text_column_name]
+
+        all_token_ids = []
+        all_token_ids_unprompted = []
+        for input_str in input_str_batched:
+            if isinstance(input_str, list):
+                # pseudo-labelled transcriptions have been retained as token ids (`decode_token_ids=False`)
+                token_ids = input_str
+            elif input_str[0].startswith("[") and input_str[0].endswith("]"):
+                token_ids = re.findall(r"\d+", input_str)
+                token_ids = [int(token) for token in token_ids]
+            else:
+                token_ids = None
+
+            if token_ids is not None:
+                # remove the EOT tokens to get the 'true' token length
+                token_ids = [token for token in token_ids if token != decoder_eot_token_id]
+                token_ids = token_ids + [decoder_eot_token_id]
+                # check whether we have timestamps in the PLs and filter if required
+                has_timestamps = len(set(token_ids) & set(timestamp_ids)) > 0
+                if has_timestamps:
+                    # sample from binomial distribution to get probability of training on timestamps
+                    predict_timestamps = bool(np.random.binomial(1, timestamp_probability))
+                    if not predict_timestamps:
+                        # filter timestamps and insert the <|notimestamps|> task token
+                        token_ids = [token for token in token_ids if token < timestamp_begin]
+                        token_ids.insert(timestamp_position, timestamp_begin)
+            else:
+                # pseudo-labelled transcriptions have been decoded to text (`decode_token_ids=True`)
+                has_timestamps = has_timestamp_tokens(input_str)
+
+                if has_timestamps:
+                    predict_timestamps = bool(np.random.binomial(1, timestamp_probability))
+                    if not predict_timestamps:
+                        # filter timestamp token ids if not part of the prediction task
+                        input_str = tokenizer._filter_timestamp_ids(input_str)
+                else:
+                    predict_timestamps = False
+
+                tokenizer.set_prefix_tokens(language=language, task=task, predict_timestamps=predict_timestamps)
+                token_ids = tokenizer(input_str).input_ids
+
+            all_token_ids_unprompted.append(token_ids)
+            # check whether to condition on previous text - we do this with probability condition_on_prev_probability
+            condition_on_prev = bool(np.random.binomial(1, condition_on_prev_probability))
+            if condition_on_prev and len(all_token_ids_unprompted) > 1:
+                # prompt ids are the penultimate token ids in the batch
+                prompt_ids = all_token_ids_unprompted[-2]
+                # strip timestamp tokens from prompt
+                prompt_ids = [token for token in prompt_ids if token < timestamp_begin]
+                if len(prompt_ids) > 0:
+                    # remove the standard task tokens and add the special <|startofprev|> token
+                    prompt_ids = [decoder_prev_token_id] + prompt_ids[timestamp_position:-1]
+                if len(prompt_ids + token_ids) < max_label_length:
+                    token_ids = prompt_ids + token_ids
+            all_token_ids.append(token_ids)
+
+        batch["labels"] = all_token_ids
+        return batch
+
+    def prepare_eval_dataset(batch):
+        # process audio input
+        sample = batch["audio"]
+        inputs = feature_extractor(sample["array"], sampling_rate=sample["sampling_rate"])
+        batch["input_features"] = inputs.input_features[0]
+        batch["input_length"] = len(sample["array"])
+
+        # process targets - for evaluation these are the ground-truth transcriptions
+        input_str = batch["text"]
+        batch["labels"] = tokenizer(input_str).input_ids
+        return batch
+
+    vectorized_datasets = IterableDatasetDict() if data_args.streaming else DatasetDict()
+    if training_args.do_train:
+        # with streaming mode we can only have 1 worker, whereas with non-streaming
+        # we can use `num_workers` (which is much faster)
+        # We gate the pre-processing function accordingly
+        map_fn_train = partial(
+            raw_datasets["train"].map,
+            function=prepare_train_dataset,
+            remove_columns=raw_datasets_train_features,
+            batched=True,
+            batch_size=max(
+                training_args.per_device_train_batch_size // 4, 4
+            ),  # TODO(SG) make data prep bs configurable
+        )
+        vectorized_datasets["train"] = (
+            map_fn_train(num_proc=num_workers, desc="preprocess train dataset")
+            if not data_args.streaming
+            else map_fn_train()
+        )
+    if training_args.do_eval:
+        for eval_split in all_eval_splits:
+            raw_datasets_eval_features = list(raw_datasets[eval_split].features.keys())
+            map_fn_eval = partial(
+                raw_datasets[eval_split].map, function=prepare_eval_dataset, remove_columns=raw_datasets_eval_features
+            )
+            if accelerator.is_main_process:
+                vectorized_datasets[eval_split] = (
+                    map_fn_eval(num_proc=num_workers, desc="preprocess eval dataset")
+                    if not data_args.streaming
+                    else map_fn_eval()
+                )
+
+    # 10.5: Filter training data with inputs longer than `max_input_length`
+    def is_audio_in_length_range(length):
+        return min_input_length < length < max_input_length
+
+    filter_by_audio_fn = partial(
+        vectorized_datasets.filter, function=is_audio_in_length_range, input_columns=["input_length"]
+    )
+    vectorized_datasets = (
+        filter_by_audio_fn(num_proc=num_workers, desc="filtering train dataset by audio length")
+        if not data_args.streaming
+        else filter_by_audio_fn()
+    )
+
+    # 10.6: Filter training data with labels longer than `max_label_length`
+    def is_labels_in_length_range(labels):
+        return 0 < len(labels) <= max_label_length
+
+    filter_by_labels_fn = partial(
+        vectorized_datasets.filter, function=is_labels_in_length_range, input_columns=["labels"]
+    )
+    vectorized_datasets = (
+        filter_by_labels_fn(num_proc=num_workers, desc="filtering train dataset")
+        if not data_args.streaming
+        else filter_by_labels_fn()
+    )
+
+    # Pre-processing complete!
+    # For large datasets it is advised to run the preprocessing on a
+    # single machine first with `--preprocessing_only` since there will mostly likely
+    # be a timeout when running the script in distributed mode.
+    # In a second step, `--preprocessing_only` can then be set to `False` to load the
+    # cached dataset
+    if data_args.preprocessing_only:
+        if data_args.streaming:
+            raise ValueError(
+                "When using streaming mode, dataset pre-processing is performed on the fly, hence there is no notion"
+                "of a cached pre-processed dataset. Remove the argument `--preprocessing_only` to run pre-processing "
+                "on the fly with streaming mode."
+            )
+        cache = {k: v.cache_files for k, v in vectorized_datasets.items()}
+        logger.info(f"Data preprocessing finished. Files cached at {cache}.")
+        return
+
+    # 11. Define Evaluation Metrics
+    def compute_metrics(preds, labels):
+        # replace padded labels by the padding token
+        for idx in range(len(labels)):
+            labels[idx][labels[idx] == -100] = tokenizer.pad_token_id
+
+        pred_str = tokenizer.batch_decode(preds, skip_special_tokens=True, decode_with_timestamps=return_timestamps)
+        # we do not want to group tokens when computing the metrics
+        label_str = tokenizer.batch_decode(labels, skip_special_tokens=True)
+        wer_ortho = 100 * metric.compute(predictions=pred_str, references=label_str)
+
+        # normalize everything and re-compute the WER
+        norm_pred_str = [normalizer(pred) for pred in pred_str]
+        norm_label_str = [normalizer(label) for label in label_str]
+        # for logging, we need the pred/labels to match the norm_pred/norm_labels, so discard any filtered samples here
+        pred_str = [pred_str[i] for i in range(len(norm_pred_str)) if len(norm_label_str[i]) > 0]
+        label_str = [label_str[i] for i in range(len(norm_label_str)) if len(norm_label_str[i]) > 0]
+        # filtering step to only evaluate the samples that correspond to non-zero normalized references:
+        norm_pred_str = [norm_pred_str[i] for i in range(len(norm_pred_str)) if len(norm_label_str[i]) > 0]
+        norm_label_str = [norm_label_str[i] for i in range(len(norm_label_str)) if len(norm_label_str[i]) > 0]
+
+        wer = 100 * metric.compute(predictions=norm_pred_str, references=norm_label_str)
+        return {"wer": wer, "wer_ortho": wer_ortho}, pred_str, label_str, norm_pred_str, norm_label_str
+
+    # 12. Define Training Schedule
+    # Store some constants
+    per_device_train_batch_size = int(training_args.per_device_train_batch_size)
+    train_batch_size = per_device_train_batch_size * accelerator.num_processes
+    gradient_accumulation_steps = int(training_args.gradient_accumulation_steps)
+    per_device_eval_batch_size = int(training_args.per_device_eval_batch_size)
+
+    if not data_args.streaming and training_args.max_steps < 0:
+        num_epochs = int(training_args.num_train_epochs)
+        steps_per_epoch = len(vectorized_datasets["train"]) // (train_batch_size * gradient_accumulation_steps)
+        total_train_steps = steps_per_epoch * num_epochs
+    elif training_args.max_steps > 0:
+        logger.info("max_steps is given, it will override any value given in num_train_epochs")
+        total_train_steps = int(training_args.max_steps)
+        # Setting a very large number of epochs so we go as many times as necessary over the iterator.
+        num_epochs = sys.maxsize
+        steps_per_epoch = total_train_steps
+    else:
+        raise ValueError("max_steps must be specified when training with a streaming (iterable) dataset")
+
+    if training_args.eval_steps is None:
+        logger.info(
+            f"eval_steps is not set, evaluating at the end of {'each epoch' if not data_args.streaming else 'training'}"
+        )
+        eval_steps = steps_per_epoch
+    else:
+        eval_steps = training_args.eval_steps
+
+    # 13. Define optimizer, LR scheduler, collator
+    decay_parameters = get_parameter_names(
+        student_model,
+        [nn.LayerNorm],
+        forbidden_module=[student_model.model.encoder] if training_args.freeze_encoder else None,
+    )
+    decay_parameters = [name for name in decay_parameters if "bias" not in name]
+    optimizer_grouped_parameters = [
+        {
+            "params": [param for name, param in student_model.named_parameters() if name in decay_parameters],
+            "weight_decay": training_args.weight_decay,
+        },
+        {
+            "params": [param for name, param in student_model.named_parameters() if name not in decay_parameters],
+            "weight_decay": 0.0,
+        },
+    ]
+    optimizer = torch.optim.AdamW(
+        params=optimizer_grouped_parameters,
+        lr=training_args.learning_rate,
+        betas=(training_args.adam_beta1, training_args.adam_beta2),
+        eps=training_args.adam_epsilon,
+    )
+
+    # LR scheduler gets stepped by `num_processes` each time -> account for this in warmup / total steps
+    lr_scheduler = get_scheduler(
+        name=training_args.lr_scheduler_type,
+        optimizer=optimizer,
+        num_warmup_steps=training_args.warmup_steps * accelerator.num_processes,
+        num_training_steps=total_train_steps * accelerator.num_processes,
+    )
+
+    data_collator = DataCollatorSpeechSeq2SeqWithPadding(
+        processor=processor,
+        decoder_start_token_id=decoder_start_token_id,
+        decoder_prev_token_id=decoder_prev_token_id,
+        input_padding="longest",
+        target_padding="max_length",
+        max_target_length=max_label_length,
+    )
+
+    # 14. Define generation arguments - we need to do this before we wrap the models in DDP
+    # so that we can still access the configs
+    num_beams = (
+        training_args.generation_num_beams
+        if training_args.generation_num_beams is not None
+        else getattr(student_model.generation_config, "num_beams", 1)
+    )
+
+    gen_kwargs = {
+        "max_length": max_label_length,
+        "num_beams": num_beams,
+        "return_timestamps": return_timestamps,
+    }
+    if hasattr(teacher_model.generation_config, "is_multilingual") and teacher_model.generation_config.is_multilingual:
+        # forcing the language and task tokens helps multilingual models in their generations
+        gen_kwargs.update(
+            {
+                "language": data_args.language,
+                "task": data_args.task,
+            }
+        )
+
+    # 15. Prepare everything with accelerate
+    student_model, teacher_model, optimizer, lr_scheduler = accelerator.prepare(
+        student_model, teacher_model, optimizer, lr_scheduler
+    )
+
+    def kl_divergence(target_distribution, log_predicted_distribution, labels):
+        kl_loss = nn.KLDivLoss(reduction="none")
+        divergence = kl_loss(log_predicted_distribution, target_distribution)
+        # ignore padded tokens from divergence, i.e. where labels are not set to -100
+        padding_mask = labels >= 0
+        padding_mask = padding_mask.unsqueeze(-1)
+        divergence = divergence * padding_mask
+        # take the average over the mini-batch
+        divergence = divergence.sum() / padding_mask.sum()
+        return divergence
+
+    # Define gradient update step fn
+    def train_step(
+        batch,
+        temperature=2.0,
+    ):
+        student_model.train()
+        teacher_model.eval()
+
+        student_outputs = student_model(**batch)
+        with torch.no_grad():
+            if share_hidden_states:
+                # if the student and teacher share the same frozen encoder then we don't have to recompute the
+                # encoder hidden-states for the teacher model, we can just re-use from the student
+                encoder_outputs = BaseModelOutput(student_outputs.encoder_last_hidden_state)
+                teacher_outputs = teacher_model(encoder_outputs=encoder_outputs, labels=batch["labels"])
+            else:
+                # do the full forward pass for the teacher model (encoder + decoder)
+                teacher_outputs = teacher_model(**batch)
+
+        # CE (data) loss
+        ce_loss = student_outputs.loss
+        # rescale distribution by temperature to ensure gradients scale correctly
+        teacher_distribution = nn.functional.softmax(teacher_outputs.logits / temperature, dim=-1)
+        # log softmax of student predictions for numerical stability
+        student_distribution = nn.functional.log_softmax(student_outputs.logits / temperature, dim=-1)
+        # KL-divergence loss (scaled by temperature)
+        kl_loss = kl_divergence(teacher_distribution, student_distribution, batch["labels"]) * temperature**2
+
+        # use Distil-Whisper formulation (fix weight of CE loss and tune KL weight)
+        loss = 0.8 * ce_loss + training_args.kl_weight * kl_loss
+        metrics = {"loss": loss, "ce_loss": ce_loss, "kl_loss": kl_loss}
+        return loss, metrics
+
+    # Define eval fn
+    def eval_step(batch):
+        student_model.eval()
+        teacher_model.eval()
+
+        with torch.no_grad():
+            student_outputs = student_model(**batch)
+            if share_hidden_states:
+                encoder_outputs = BaseModelOutput(student_outputs.encoder_last_hidden_state)
+                teacher_outputs = teacher_model(encoder_outputs=encoder_outputs, labels=batch["labels"])
+            else:
+                teacher_outputs = teacher_model(**batch)
+
+        # CE (data) loss
+        ce_loss = student_outputs.loss
+
+        # log softmax / softmax for numerical stability
+        student_distribution = nn.functional.log_softmax(student_outputs.logits, dim=-1)
+        teacher_distribution = nn.functional.softmax(teacher_outputs.logits, dim=-1)
+        # temperature is always 1 for eval
+        kl_loss = kl_divergence(teacher_distribution, student_distribution, batch["labels"])
+
+        # use Distil-Whisper formulation (fix weight of CE loss and tune KL weight)
+        loss = 0.8 * ce_loss + training_args.kl_weight * kl_loss
+        metrics = {"loss": loss, "ce_loss": ce_loss, "kl_loss": kl_loss}
+        return metrics
+
+    def generate_step(batch):
+        student_model.eval()
+        output_ids = accelerator.unwrap_model(student_model).generate(batch["input_features"], **gen_kwargs)
+        output_ids = accelerator.pad_across_processes(output_ids, dim=1, pad_index=tokenizer.pad_token_id)
+        return output_ids
+
+    logger.info("***** Running training *****")
+    logger.info(f"  Num examples = {total_train_steps * train_batch_size * gradient_accumulation_steps}")
+    logger.info("  Instantaneous batch size per device =" f" {training_args.per_device_train_batch_size}")
+    logger.info("  Gradient accumulation steps =" f" {gradient_accumulation_steps}")
+    logger.info(
+        f"  Total train batch size (w. parallel & distributed) = {train_batch_size * gradient_accumulation_steps}"
+    )
+    logger.info(f"  Total optimization steps = {total_train_steps}")
+
+    # ======================== Training ================================
+    train_time = 0
+    train_start = time.time()
+    steps_trained_progress_bar = tqdm(
+        range(total_train_steps), desc="Train steps ... ", position=0, disable=not accelerator.is_local_main_process
+    )
+    continue_training = True
+    epochs_trained = 0
+    cur_step = 0
+
+    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
+
+    if checkpoint is not None:
+        accelerator.load_state(checkpoint)
+        # Find num steps and epoch from saved state string pattern
+        pattern = r"checkpoint-(\d+)-epoch-(\d+)"
+        match = re.search(pattern, checkpoint)
+        cur_step = int(match.group(1))
+        epochs_trained = int(match.group(2))
+
+        logger.info("  Continuing training from checkpoint, will skip to saved global_step")
+        logger.info(f"  Continuing training from epoch {epochs_trained}")
+        logger.info(f"  Continuing training from global step {cur_step}")
+
+        steps_trained_progress_bar.update(cur_step)
+
+        for epoch in range(0, epochs_trained):
+            vectorized_datasets["train"] = vectorized_datasets["train"].shuffle(training_args.seed)
+
+        if not data_args.streaming and training_args.max_steps < 0:
+            # we know exactly the number of steps per epoch, so can skip through the required number of batches
+            resume_step = (cur_step - epochs_trained * steps_per_epoch) * gradient_accumulation_steps
+        else:
+            # Currently we don't know how many steps we've taken in the current epoch
+            # So we just shuffle the dataset one extra time and start from a fresh epoch
+            # This is "good enough" for our purposes but not fully correct
+            resume_step = None
+            vectorized_datasets["train"] = vectorized_datasets["train"].shuffle(training_args.seed)
+    else:
+        resume_step = None
+
+    for epoch in range(epochs_trained, num_epochs):
+        vectorized_datasets["train"] = vectorized_datasets["train"].shuffle(training_args.seed)
+        train_dataloader = DataLoader(
+            vectorized_datasets["train"],
+            collate_fn=data_collator,
+            batch_size=per_device_train_batch_size,
+            num_workers=dataloader_num_workers,
+            pin_memory=training_args.dataloader_pin_memory,
+        )
+        train_dataloader = accelerator.prepare(train_dataloader)
+        if hasattr(train_dataloader, "dataset") and isinstance(train_dataloader.dataset, IterableDataset):
+            train_dataloader.dataset.set_epoch(epoch)
+
+        if resume_step is not None:
+            # Skip the first N batches in the dataloader when resuming from a checkpoint
+            train_dataloader = accelerator.skip_first_batches(train_dataloader, resume_step)
+            resume_step = None
+
+        for batch in train_dataloader:
+            with accelerator.accumulate(student_model):
+                loss, train_metric = train_step(batch, temperature=training_args.temperature)
+                accelerator.backward(loss)
+                if accelerator.sync_gradients:
+                    accelerator.clip_grad_norm_(student_model.parameters(), training_args.max_grad_norm)
+                optimizer.step()
+                lr_scheduler.step()
+                optimizer.zero_grad()
+
+            # Check if the accelerator has performed an optimization step behind the scenes
+            if accelerator.sync_gradients:
+                steps_trained_progress_bar.update(1)
+                cur_step += 1
+
+                if cur_step % training_args.logging_steps == 0:
+                    steps_trained_progress_bar.write(
+                        f"Step... ({cur_step} / {total_train_steps} | Loss:"
+                        f" {train_metric['loss']}, Learning Rate:"
+                        f" {lr_scheduler.get_last_lr()[0]})"
+                    )
+                    log_metric(
+                        accelerator,
+                        metrics=train_metric,
+                        learning_rate=lr_scheduler.get_last_lr()[0],
+                        train_time=train_time + time.time() - train_start,
+                        step=cur_step,
+                        epoch=epoch,
+                        prefix="train",
+                    )
+
+                # save checkpoint and weights after each save_steps and at the end of training
+                if (cur_step % training_args.save_steps == 0) or cur_step == total_train_steps:
+                    intermediate_dir = os.path.join(training_args.output_dir, f"checkpoint-{cur_step}-epoch-{epoch}")
+                    accelerator.save_state(output_dir=intermediate_dir)
+                    accelerator.wait_for_everyone()
+                    if accelerator.is_main_process:
+                        rotate_checkpoints(training_args.save_total_limit, output_dir=training_args.output_dir)
+
+                        if cur_step == total_train_steps:
+                            # un-wrap student model for save
+                            student_model = accelerator.unwrap_model(student_model)
+                            student_model.save_pretrained(training_args.output_dir)
+                            # re-wrap student model for final eval
+                            student_model = accelerator.prepare(student_model)
+
+                        if training_args.push_to_hub:
+                            repo.push_to_hub(
+                                commit_message=f"Saving train state of step {cur_step}",
+                                blocking=False,
+                            )
+
+                if training_args.do_eval and (cur_step % eval_steps == 0 or cur_step == total_train_steps):
+                    train_time += time.time() - train_start
+                    student_model.eval()
+                    # ======================== Evaluating ==============================
+                    for eval_split in all_eval_splits:
+                        eval_metrics = []
+                        eval_preds = []
+                        eval_labels = []
+                        eval_start = time.time()
+
+                        validation_dataloader = DataLoader(
+                            vectorized_datasets[eval_split],
+                            collate_fn=data_collator,
+                            batch_size=per_device_eval_batch_size,
+                            drop_last=False,
+                            num_workers=dataloader_num_workers,
+                            pin_memory=training_args.dataloader_pin_memory,
+                        )
+                        validation_dataloader = accelerator.prepare(validation_dataloader)
+
+                        for batch in tqdm(
+                            validation_dataloader,
+                            desc=f"Evaluating {eval_split}...",
+                            position=2,
+                            disable=not accelerator.is_local_main_process,
+                        ):
+                            # Model forward
+                            eval_metric = eval_step(batch)
+                            eval_metric = accelerator.gather_for_metrics(eval_metric)
+                            eval_metrics.append(eval_metric)
+
+                            # generation
+                            if training_args.predict_with_generate:
+                                generated_ids = generate_step(batch)
+                                # Gather all predictions and targets
+                                generated_ids, labels = accelerator.gather_for_metrics(
+                                    (generated_ids, batch["labels"])
+                                )
+                                eval_preds.extend(generated_ids)
+                                eval_labels.extend(labels)
+
+                        eval_time = time.time() - eval_start
+                        # normalize eval metrics
+                        eval_metrics = {
+                            key: torch.mean(torch.stack([d[key] for d in eval_metrics])) for key in eval_metrics[0]
+                        }
+
+                        # compute WER metric
+                        wer_desc = ""
+                        if training_args.predict_with_generate:
+                            wer_metric, pred_str, label_str, norm_pred_str, norm_label_str = compute_metrics(
+                                eval_preds, eval_labels
+                            )
+                            eval_metrics.update(wer_metric)
+                            wer_desc = " ".join([f"Eval {key}: {value} |" for key, value in wer_metric.items()])
+                            log_pred(
+                                accelerator,
+                                pred_str,
+                                label_str,
+                                norm_pred_str,
+                                norm_label_str,
+                                step=cur_step,
+                                prefix=eval_split,
+                            )
+
+                        # Print metrics and update progress bar
+                        steps_trained_progress_bar.write(
+                            f"Eval results for step ({cur_step} / {total_train_steps} | Eval Loss: {eval_metrics['loss']} |"
+                            f" {wer_desc})"
+                        )
+
+                        log_metric(
+                            accelerator,
+                            metrics=eval_metrics,
+                            train_time=eval_time,
+                            step=cur_step,
+                            epoch=epoch,
+                            prefix=eval_split,
+                        )
+
+                    # flush the train metrics
+                    train_start = time.time()
+
+                # break condition
+                if cur_step == total_train_steps:
+                    continue_training = False
+                    break
+
+        if not continue_training:
+            break
+
+    accelerator.end_training()
+
+
+if __name__ == "__main__":
+    main()

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
+  }
+}