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smangrul
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xls-r-mr-model

Automatic Speech Recognition
Transformers
PyTorch
Marathi
mozilla-foundation/common_voice_8_0
openslr
Generated from Trainer
robust-speech-event
hf-asr-leaderboard
Eval Results
Inference Endpoints
Model card Files Community
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Update README.md

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@@ -69,40 +69,39 @@ The following hyperparameters were used during training:
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  ### Training results
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- [12991/14200 9:46:02 < 54:32, 0.37 it/s, Epoch 182.96/200]
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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 0.395909
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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 /
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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