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@@ -70,38 +70,38 @@ The following hyperparameters were used during training:
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  ### Training results
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  / Step / Training Loss / Validation Loss / Wer /
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- 400 / 3.794000 / 3.532227 / 1.000000 /
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- 800 / 3.362400 / 3.359044 / 1.000000 /
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- 1200 / 2.293900 / 1.011279 / 0.829924 /
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- 1600 / 1.233000 / 0.502743 / 0.593662 /
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- 2000 / 0.962600 / 0.412519 / 0.496992 /
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- 2400 / 0.831800 / 0.402903 / 0.493783 /
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- 2800 / 0.737000 / 0.389773 / 0.469314 /
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- 3200 / 0.677100 / 0.373987 / 0.436021 /
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- 3600 / 0.634400 / 0.383823 / 0.432010 /
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- 4000 / 0.586000 / 0.375610 / 0.419575 /
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- 4400 / 0.561000 / 0.387891 / 0.418371 /
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- 4800 / 0.518500 / 0.386357 / 0.417569 /
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- 5200 / 0.515300 / 0.415069 / 0.430004 /
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- 5600 / 0.478100 / 0.399211 / 0.408744 /
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- 6000 / 0.468100 / 0.424542 / 0.402327 /
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- 6400 / 0.439400 / 0.430979 / 0.410750 /
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- 6800 / 0.429600 / 0.427700 / 0.409146 /
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- 7200 / 0.400300 / 0.451111 / 0.419976 /
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- 7600 / 0.395100 / 0.463446 / 0.405134 /
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- 8000 / 0.381800 / 0.454752 / 0.407942 /
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- 8400 / 0.371500 / 0.461547 / 0.404733 /
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- 8800 / 0.362500 / 0.461543 / 0.411151 /
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- 9200 / 0.338200 / 0.468299 / 0.417168 /
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- 9600 / 0.338800 / 0.480989 / 0.412355 /
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- 10000 / 0.317600 / 0.475700 / 0.410750 /
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- 10400 / 0.315100 / 0.478920 / 0.403530 /
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- 10800 / 0.296200 / 0.480600 / 0.398315 /
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- 11200 / 0.299000 / 0.477083 / 0.393502 /
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- 11600 / 0.290000 / 0.465646 / 0.393903 /
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- 12000 / 0.290900 / 0.490041 / 0.405937 /
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- 12400 / 0.275600 / 0.489354 / 0.399519 /
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- 12800 / 0.272600 / 0.494580 / 0.39 /909
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  ### Framework versions
 
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  ### Training results
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  / Step / Training Loss / Validation Loss / Wer /
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+ / 400 / 3.794000 / 3.532227 / 1.000000 /
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+ / 800 / 3.362400 / 3.359044 / 1.000000 /
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+ / 1200 / 2.293900 / 1.011279 / 0.829924 /
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+ / 1600 / 1.233000 / 0.502743 / 0.593662 /
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+ / 2000 / 0.962600 / 0.412519 / 0.496992 /
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+ / 2400 / 0.831800 / 0.402903 / 0.493783 /
79
+ / 2800 / 0.737000 / 0.389773 / 0.469314 /
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+ / 3200 / 0.677100 / 0.373987 / 0.436021 /
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+ / 3600 / 0.634400 / 0.383823 / 0.432010 /
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+ / 4000 / 0.586000 / 0.375610 / 0.419575 /
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+ / 4400 / 0.561000 / 0.387891 / 0.418371 /
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+ / 4800 / 0.518500 / 0.386357 / 0.417569 /
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+ / 5200 / 0.515300 / 0.415069 / 0.430004 /
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+ / 5600 / 0.478100 / 0.399211 / 0.408744 /
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+ / 6000 / 0.468100 / 0.424542 / 0.402327 /
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+ / 6400 / 0.439400 / 0.430979 / 0.410750 /
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+ / 6800 / 0.429600 / 0.427700 / 0.409146 /
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+ / 7200 / 0.400300 / 0.451111 / 0.419976 /
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+ / 7600 / 0.395100 / 0.463446 / 0.405134 /
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+ / 8000 / 0.381800 / 0.454752 / 0.407942 /
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+ / 8400 / 0.371500 / 0.461547 / 0.404733 /
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+ / 8800 / 0.362500 / 0.461543 / 0.411151 /
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+ / 9200 / 0.338200 / 0.468299 / 0.417168 /
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+ / 9600 / 0.338800 / 0.480989 / 0.412355 /
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+ / 10000 / 0.317600 / 0.475700 / 0.410750 /
98
+ / 10400 / 0.315100 / 0.478920 / 0.403530 /
99
+ / 10800 / 0.296200 / 0.480600 / 0.398315 /
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+ / 11200 / 0.299000 / 0.477083 / 0.393502 /
101
+ / 11600 / 0.290000 / 0.465646 / 0.393903 /
102
+ / 12000 / 0.290900 / 0.490041 / 0.405937 /
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+ / 12400 / 0.275600 / 0.489354 / 0.399519 /
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+ / 12800 / 0.272600 / 0.494580 / 0.395909 /
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  ### Framework versions