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chickenrice0721
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whisper-large-v3-translate-zh-v0.1-lt

Automatic Speech Recognition
Transformers
Safetensors
Chinese
whisper
audio
translate
Generated from Trainer
Inference Endpoints
Model card Files Community
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Update README.md

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@@ -43,48 +43,48 @@ The following hyperparameters were used during training:
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  ### Training results
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- | Training Loss | Epoch | Step | Cer | Validation Loss | Wer |
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- |:-------------:|:------:|:-----:|:------:|:---------------:|:------:|
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- | 2.1282 | 0.0739 | 1000 | 2.1852 | 1.9014 | 4.4904 |
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- | 1.8567 | 0.1478 | 2000 | 1.8366 | 1.7295 | 3.8716 |
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- | 1.6968 | 0.2217 | 3000 | 1.2615 | 1.6279 | 2.4825 |
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- | 1.6264 | 0.2956 | 4000 | 1.0536 | 1.5625 | 1.8101 |
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- | 1.5687 | 0.3695 | 5000 | 1.0932 | 1.5410 | 2.1218 |
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- | 1.531 | 0.4433 | 6000 | 1.5156 | 1.2533 | 2.3689 |
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- | 1.4875 | 0.5172 | 7000 | 1.4697 | 0.9560 | 1.5588 |
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- | 1.4518 | 0.5911 | 8000 | 1.4521 | 1.0170 | 1.6392 |
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- | 1.4472 | 0.6650 | 9000 | 1.4463 | 1.0084 | 1.6420 |
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- | 1.3991 | 0.7389 | 10000 | 1.4238 | 0.9266 | 1.6992 |
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- | 1.4266 | 0.8128 | 11000 | 1.4141 | 0.8365 | 1.3056 |
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- | 1.3755 | 0.8867 | 12000 | 1.4033 | 0.7904 | 1.3119 |
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- | 1.3833 | 0.9606 | 13000 | 1.4004 | 0.8600 | 1.3333 |
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- | 1.3224 | 1.0345 | 14000 | 1.3770 | 0.8243 | 1.4560 |
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- | 1.3295 | 1.1084 | 15000 | 1.3770 | 0.7852 | 1.4298 |
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- | 1.3136 | 1.1823 | 16000 | 1.3564 | 0.7176 | 1.1826 |
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- | 1.2832 | 1.2561 | 17000 | 1.3535 | 0.6767 | 1.1781 |
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- | 1.2917 | 1.3300 | 18000 | 1.3584 | 0.7255 | 1.1218 |
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- | 1.27 | 1.4039 | 19000 | 1.3330 | 0.6590 | 1.1242 |
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- | 1.2704 | 1.4778 | 20000 | 1.3379 | 0.6934 | 1.1944 |
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- | 1.2614 | 1.5517 | 21000 | 1.3330 | 0.6949 | 1.1820 |
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- | 1.2455 | 1.6256 | 22000 | 1.3350 | 0.6931 | 1.0892 |
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- | 1.2475 | 1.6995 | 23000 | 1.3154 | 0.6662 | 1.1576 |
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- | 1.2583 | 1.7734 | 24000 | 1.3164 | 0.6490 | 1.0705 |
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- | 1.2333 | 1.8473 | 25000 | 1.3184 | 0.6833 | 1.1480 |
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- | 1.2462 | 1.9212 | 26000 | 1.3125 | 0.6672 | 1.1612 |
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- | 1.2279 | 1.9950 | 27000 | 1.3047 | 0.6644 | 1.2179 |
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- | 1.1908 | 2.0689 | 28000 | 1.3047 | 0.6938 | 1.2221 |
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- | 1.1831 | 2.1428 | 29000 | 1.2998 | 0.6316 | 1.0717 |
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- | 1.1705 | 2.2167 | 30000 | 1.3018 | 0.6165 | 1.0958 |
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- | 1.171 | 2.2906 | 31000 | 1.3027 | 0.6109 | 1.0868 |
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- | 1.1567 | 2.3645 | 32000 | 1.3037 | 0.6485 | 1.1736 |
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- | 1.1705 | 2.4384 | 33000 | 1.2969 | 0.6078 | 1.0515 |
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- | 1.1819 | 2.5123 | 34000 | 1.2949 | 0.6158 | 1.0362 |
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- | 1.1447 | 2.5862 | 35000 | 1.2920 | 0.6365 | 1.0558 |
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- | 1.17 | 2.6601 | 36000 | 1.2881 | 0.6339 | 1.0868 |
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- | 1.1495 | 2.7340 | 37000 | 1.2949 | 0.6297 | 1.0437 |
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- | 1.1395 | 2.8078 | 38000 | 1.2900 | 0.6285 | 1.1221 |
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- | 1.15 | 2.8817 | 39000 | 1.2891 | 0.5997 | 1.0217 |
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- | 1.1623 | 2.9556 | 40000 | 1.2881 | 0.6085 | 1.0395 |
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  ### Framework versions
 
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  ### Training results
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+ | Training Loss | Epoch | Step | Validation Loss | Cer | Wer |
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+ |:-------------:|:------:|:-----:|:---------------:|:------:|:------:|
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+ | 2.1282 | 0.0739 | 1000 | 2.1852 | 1.9014 | 4.4904 |
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+ | 1.8567 | 0.1478 | 2000 | 1.8366 | 1.7295 | 3.8716 |
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+ | 1.6968 | 0.2217 | 3000 | 1.2615 | 1.6279 | 2.4825 |
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+ | 1.6264 | 0.2956 | 4000 | 1.0536 | 1.5625 | 1.8101 |
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+ | 1.5687 | 0.3695 | 5000 | 1.0932 | 1.5410 | 2.1218 |
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+ | 1.531 | 0.4433 | 6000 | 1.5156 | 1.2533 | 2.3689 |
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+ | 1.4875 | 0.5172 | 7000 | 1.4697 | 0.9560 | 1.5588 |
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+ | 1.4518 | 0.5911 | 8000 | 1.4521 | 1.0170 | 1.6392 |
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+ | 1.4472 | 0.6650 | 9000 | 1.4463 | 1.0084 | 1.6420 |
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+ | 1.3991 | 0.7389 | 10000 | 1.4238 | 0.9266 | 1.6992 |
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+ | 1.4266 | 0.8128 | 11000 | 1.4141 | 0.8365 | 1.3056 |
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+ | 1.3755 | 0.8867 | 12000 | 1.4033 | 0.7904 | 1.3119 |
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+ | 1.3833 | 0.9606 | 13000 | 1.4004 | 0.8600 | 1.3333 |
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+ | 1.3224 | 1.0345 | 14000 | 1.3770 | 0.8243 | 1.4560 |
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+ | 1.3295 | 1.1084 | 15000 | 1.3770 | 0.7852 | 1.4298 |
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+ | 1.3136 | 1.1823 | 16000 | 1.3564 | 0.7176 | 1.1826 |
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+ | 1.2832 | 1.2561 | 17000 | 1.3535 | 0.6767 | 1.1781 |
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+ | 1.2917 | 1.3300 | 18000 | 1.3584 | 0.7255 | 1.1218 |
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+ | 1.27 | 1.4039 | 19000 | 1.3330 | 0.6590 | 1.1242 |
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+ | 1.2704 | 1.4778 | 20000 | 1.3379 | 0.6934 | 1.1944 |
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+ | 1.2614 | 1.5517 | 21000 | 1.3330 | 0.6949 | 1.1820 |
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+ | 1.2455 | 1.6256 | 22000 | 1.3350 | 0.6931 | 1.0892 |
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+ | 1.2475 | 1.6995 | 23000 | 1.3154 | 0.6662 | 1.1576 |
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+ | 1.2583 | 1.7734 | 24000 | 1.3164 | 0.6490 | 1.0705 |
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+ | 1.2333 | 1.8473 | 25000 | 1.3184 | 0.6833 | 1.1480 |
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+ | 1.2462 | 1.9212 | 26000 | 1.3125 | 0.6672 | 1.1612 |
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+ | 1.2279 | 1.9950 | 27000 | 1.3047 | 0.6644 | 1.2179 |
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+ | 1.1908 | 2.0689 | 28000 | 1.3047 | 0.6938 | 1.2221 |
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+ | 1.1831 | 2.1428 | 29000 | 1.2998 | 0.6316 | 1.0717 |
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+ | 1.1705 | 2.2167 | 30000 | 1.3018 | 0.6165 | 1.0958 |
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+ | 1.171 | 2.2906 | 31000 | 1.3027 | 0.6109 | 1.0868 |
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+ | 1.1567 | 2.3645 | 32000 | 1.3037 | 0.6485 | 1.1736 |
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+ | 1.1705 | 2.4384 | 33000 | 1.2969 | 0.6078 | 1.0515 |
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+ | 1.1819 | 2.5123 | 34000 | 1.2949 | 0.6158 | 1.0362 |
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+ | 1.1447 | 2.5862 | 35000 | 1.2920 | 0.6365 | 1.0558 |
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+ | 1.17 | 2.6601 | 36000 | 1.2881 | 0.6339 | 1.0868 |
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+ | 1.1495 | 2.7340 | 37000 | 1.2949 | 0.6297 | 1.0437 |
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+ | 1.1395 | 2.8078 | 38000 | 1.2900 | 0.6285 | 1.1221 |
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+ | 1.15 | 2.8817 | 39000 | 1.2891 | 0.5997 | 1.0217 |
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+ | 1.1623 | 2.9556 | 40000 | 1.2881 | 0.6085 | 1.0395 |
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  ### Framework versions