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mimi
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wynehills-mimi-ASR

Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
wav2vec2
Generated from Trainer
Inference Endpoints
Model card Files Metrics Community
mimi commited on
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update model card README.md

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  1. README.md +47 -47
README.md CHANGED
@@ -12,8 +12,8 @@ should probably proofread and complete it, then remove this comment. -->
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  This model was trained from scratch on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 1.4183
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- - Wer: 0.6593
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  ## Model description
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@@ -46,51 +46,51 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Wer |
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  |:-------------:|:-----:|:----:|:---------------:|:------:|
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- | No log | 1.54 | 20 | 10.9202 | 1.0 |
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- | No log | 3.08 | 40 | 8.2842 | 1.0 |
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- | No log | 4.62 | 60 | 5.8541 | 1.0 |
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- | No log | 6.15 | 80 | 3.9558 | 1.0315 |
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- | No log | 7.69 | 100 | 3.0441 | 1.0 |
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- | No log | 9.23 | 120 | 2.8603 | 1.0 |
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- | No log | 10.77 | 140 | 2.1605 | 0.9905 |
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- | No log | 12.31 | 160 | 1.4021 | 0.8076 |
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- | No log | 13.85 | 180 | 1.2697 | 0.7445 |
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- | No log | 15.38 | 200 | 1.2202 | 0.7066 |
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- | No log | 16.92 | 220 | 1.1530 | 0.6877 |
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- | No log | 18.46 | 240 | 1.1781 | 0.6719 |
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- | No log | 20.0 | 260 | 1.1623 | 0.6814 |
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- | No log | 21.54 | 280 | 1.1963 | 0.6530 |
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- | No log | 23.08 | 300 | 1.2303 | 0.6719 |
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- | No log | 24.62 | 320 | 1.2280 | 0.6625 |
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- | No log | 26.15 | 340 | 1.2206 | 0.6278 |
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- | No log | 27.69 | 360 | 1.2751 | 0.6309 |
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- | No log | 29.23 | 380 | 1.3254 | 0.6656 |
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- | No log | 30.77 | 400 | 1.3888 | 0.6625 |
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- | No log | 32.31 | 420 | 1.3669 | 0.6088 |
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- | No log | 33.85 | 440 | 1.2586 | 0.6183 |
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- | No log | 35.38 | 460 | 1.3708 | 0.6309 |
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- | No log | 36.92 | 480 | 1.4403 | 0.6656 |
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- | 1.9109 | 38.46 | 500 | 1.3880 | 0.6341 |
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- | 1.9109 | 40.0 | 520 | 1.3798 | 0.6498 |
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- | 1.9109 | 41.54 | 540 | 1.3963 | 0.6372 |
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- | 1.9109 | 43.08 | 560 | 1.4237 | 0.6467 |
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- | 1.9109 | 44.62 | 580 | 1.5438 | 0.6719 |
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- | 1.9109 | 46.15 | 600 | 1.5361 | 0.6530 |
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- | 1.9109 | 47.69 | 620 | 1.3929 | 0.6215 |
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- | 1.9109 | 49.23 | 640 | 1.4913 | 0.6404 |
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- | 1.9109 | 50.77 | 660 | 1.3698 | 0.6530 |
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- | 1.9109 | 52.31 | 680 | 1.5211 | 0.6530 |
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- | 1.9109 | 53.85 | 700 | 1.5811 | 0.6782 |
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- | 1.9109 | 55.38 | 720 | 1.4518 | 0.6372 |
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- | 1.9109 | 56.92 | 740 | 1.4725 | 0.6372 |
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- | 1.9109 | 58.46 | 760 | 1.3257 | 0.6278 |
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- | 1.9109 | 60.0 | 780 | 1.5451 | 0.6341 |
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- | 1.9109 | 61.54 | 800 | 1.6654 | 0.6467 |
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- | 1.9109 | 63.08 | 820 | 1.6500 | 0.6782 |
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- | 1.9109 | 64.62 | 840 | 1.5503 | 0.5962 |
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- | 1.9109 | 66.15 | 860 | 1.4508 | 0.6593 |
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- | 1.9109 | 67.69 | 880 | 1.5145 | 0.6814 |
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- | 1.9109 | 69.23 | 900 | 1.4183 | 0.6593 |
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  ### Framework versions
 
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  This model was trained from scratch on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 1.3822
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+ - Wer: 0.6309
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Wer |
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  |:-------------:|:-----:|:----:|:---------------:|:------:|
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+ | No log | 1.54 | 20 | 1.4018 | 0.6435 |
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+ | No log | 3.08 | 40 | 1.4704 | 0.6593 |
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+ | No log | 4.62 | 60 | 1.4898 | 0.6625 |
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+ | No log | 6.15 | 80 | 1.4560 | 0.6404 |
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+ | No log | 7.69 | 100 | 1.3822 | 0.6309 |
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+ | No log | 9.23 | 120 | 1.3822 | 0.6309 |
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+ | No log | 10.77 | 140 | 1.3822 | 0.6309 |
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+ | No log | 12.31 | 160 | 1.3822 | 0.6309 |
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+ | No log | 13.85 | 180 | 1.3822 | 0.6309 |
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+ | No log | 15.38 | 200 | 1.3822 | 0.6309 |
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+ | No log | 16.92 | 220 | 1.3822 | 0.6309 |
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+ | No log | 18.46 | 240 | 1.3822 | 0.6309 |
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+ | No log | 20.0 | 260 | 1.3822 | 0.6309 |
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+ | No log | 21.54 | 280 | 1.3822 | 0.6309 |
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+ | No log | 23.08 | 300 | 1.3822 | 0.6309 |
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+ | No log | 24.62 | 320 | 1.3822 | 0.6309 |
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+ | No log | 26.15 | 340 | 1.3822 | 0.6309 |
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+ | No log | 27.69 | 360 | 1.3822 | 0.6309 |
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+ | No log | 29.23 | 380 | 1.3822 | 0.6309 |
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+ | No log | 30.77 | 400 | 1.3822 | 0.6309 |
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+ | No log | 32.31 | 420 | 1.3822 | 0.6309 |
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+ | No log | 33.85 | 440 | 1.3822 | 0.6309 |
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+ | No log | 35.38 | 460 | 1.3822 | 0.6309 |
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+ | No log | 36.92 | 480 | 1.3822 | 0.6309 |
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+ | 0.0918 | 38.46 | 500 | 1.3822 | 0.6309 |
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+ | 0.0918 | 40.0 | 520 | 1.3822 | 0.6309 |
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+ | 0.0918 | 41.54 | 540 | 1.3822 | 0.6309 |
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+ | 0.0918 | 43.08 | 560 | 1.3822 | 0.6309 |
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+ | 0.0918 | 44.62 | 580 | 1.3822 | 0.6309 |
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+ | 0.0918 | 46.15 | 600 | 1.3822 | 0.6309 |
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+ | 0.0918 | 47.69 | 620 | 1.3822 | 0.6309 |
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+ | 0.0918 | 49.23 | 640 | 1.3822 | 0.6309 |
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+ | 0.0918 | 50.77 | 660 | 1.3822 | 0.6309 |
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+ | 0.0918 | 52.31 | 680 | 1.3822 | 0.6309 |
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+ | 0.0918 | 53.85 | 700 | 1.3822 | 0.6309 |
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+ | 0.0918 | 55.38 | 720 | 1.3822 | 0.6309 |
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+ | 0.0918 | 56.92 | 740 | 1.3822 | 0.6309 |
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+ | 0.0918 | 58.46 | 760 | 1.3822 | 0.6309 |
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+ | 0.0918 | 60.0 | 780 | 1.3822 | 0.6309 |
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+ | 0.0918 | 61.54 | 800 | 1.3822 | 0.6309 |
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+ | 0.0918 | 63.08 | 820 | 1.3822 | 0.6309 |
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+ | 0.0918 | 64.62 | 840 | 1.3822 | 0.6309 |
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+ | 0.0918 | 66.15 | 860 | 1.3822 | 0.6309 |
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+ | 0.0918 | 67.69 | 880 | 1.3822 | 0.6309 |
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+ | 0.0918 | 69.23 | 900 | 1.3822 | 0.6309 |
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  ### Framework versions