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--- |
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language: |
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- he |
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license: apache-2.0 |
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base_model: openai/whisper-small |
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tags: |
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- hf-asr-leaderboard |
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- generated_from_trainer |
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metrics: |
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- wer |
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model-index: |
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- name: he-cantillation |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# he-cantillation |
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This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2357 |
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- Wer: 12.4316 |
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- Avg Precision Exact: 0.8908 |
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- Avg Recall Exact: 0.8920 |
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- Avg F1 Exact: 0.8911 |
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- Avg Precision Letter Shift: 0.9098 |
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- Avg Recall Letter Shift: 0.9110 |
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- Avg F1 Letter Shift: 0.9100 |
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- Avg Precision Word Level: 0.9122 |
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- Avg Recall Word Level: 0.9134 |
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- Avg F1 Word Level: 0.9125 |
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- Avg Precision Word Shift: 0.9710 |
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- Avg Recall Word Shift: 0.9730 |
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- Avg F1 Word Shift: 0.9716 |
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- Precision Median Exact: 1.0 |
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- Recall Median Exact: 1.0 |
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- F1 Median Exact: 1.0 |
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- Precision Max Exact: 1.0 |
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- Recall Max Exact: 1.0 |
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- F1 Max Exact: 1.0 |
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- Precision Min Exact: 0.0 |
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- Recall Min Exact: 0.0 |
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- F1 Min Exact: 0.0 |
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- Precision Min Letter Shift: 0.0 |
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- Recall Min Letter Shift: 0.0 |
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- F1 Min Letter Shift: 0.0 |
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- Precision Min Word Level: 0.0 |
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- Recall Min Word Level: 0.0 |
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- F1 Min Word Level: 0.0 |
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- Precision Min Word Shift: 0.1429 |
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- Recall Min Word Shift: 0.1111 |
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- F1 Min Word Shift: 0.125 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 1e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 32 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 500 |
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- training_steps: 200000 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Wer | Avg Precision Exact | Avg Recall Exact | Avg F1 Exact | Avg Precision Letter Shift | Avg Recall Letter Shift | Avg F1 Letter Shift | Avg Precision Word Level | Avg Recall Word Level | Avg F1 Word Level | Avg Precision Word Shift | Avg Recall Word Shift | Avg F1 Word Shift | Precision Median Exact | Recall Median Exact | F1 Median Exact | Precision Max Exact | Recall Max Exact | F1 Max Exact | Precision Min Exact | Recall Min Exact | F1 Min Exact | Precision Min Letter Shift | Recall Min Letter Shift | F1 Min Letter Shift | Precision Min Word Level | Recall Min Word Level | F1 Min Word Level | Precision Min Word Shift | Recall Min Word Shift | F1 Min Word Shift | |
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|:-------------:|:-------:|:------:|:---------------:|:--------:|:-------------------:|:----------------:|:------------:|:--------------------------:|:-----------------------:|:-------------------:|:------------------------:|:---------------------:|:-----------------:|:------------------------:|:---------------------:|:-----------------:|:----------------------:|:-------------------:|:---------------:|:-------------------:|:----------------:|:------------:|:-------------------:|:----------------:|:------------:|:--------------------------:|:-----------------------:|:-------------------:|:------------------------:|:---------------------:|:-----------------:|:------------------------:|:---------------------:|:-----------------:| |
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| No log | 0.0001 | 1 | 8.6313 | 200.3067 | 0.0004 | 0.0014 | 0.0004 | 0.0046 | 0.0050 | 0.0041 | 0.0031 | 0.0092 | 0.0033 | 0.0326 | 0.0360 | 0.0303 | 0.0 | 0.0 | 0.0 | 0.1111 | 1.0 | 0.2000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | |
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| 0.0158 | 0.9410 | 10000 | 0.1501 | 17.4538 | 0.8417 | 0.8453 | 0.8428 | 0.8645 | 0.8682 | 0.8656 | 0.8682 | 0.8723 | 0.8695 | 0.9449 | 0.9497 | 0.9463 | 0.9231 | 0.9231 | 0.9231 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0909 | 0.0909 | 0.1111 | |
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| 0.0149 | 1.8820 | 20000 | 0.1635 | 15.7724 | 0.8627 | 0.8659 | 0.8639 | 0.8851 | 0.8885 | 0.8864 | 0.8881 | 0.8915 | 0.8894 | 0.9560 | 0.9594 | 0.9571 | 0.9286 | 0.9333 | 0.9412 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | |
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| 0.0053 | 2.8230 | 30000 | 0.1755 | 14.9261 | 0.8767 | 0.8763 | 0.8761 | 0.8990 | 0.8986 | 0.8983 | 0.9016 | 0.9013 | 0.9010 | 0.9609 | 0.9621 | 0.9610 | 0.9333 | 0.9375 | 0.9474 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1429 | 0.125 | 0.1333 | |
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| 0.0034 | 3.7640 | 40000 | 0.1817 | 15.5691 | 0.8584 | 0.8592 | 0.8584 | 0.8794 | 0.8804 | 0.8794 | 0.8823 | 0.8833 | 0.8824 | 0.9538 | 0.9566 | 0.9547 | 0.9333 | 0.9333 | 0.9444 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | |
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| 0.0005 | 4.7050 | 50000 | 0.1857 | 14.4050 | 0.8793 | 0.8793 | 0.8789 | 0.8998 | 0.8999 | 0.8994 | 0.9024 | 0.9028 | 0.9022 | 0.9629 | 0.9637 | 0.9628 | 0.9474 | 1.0 | 0.9565 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1429 | 0.1 | 0.1176 | |
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| 0.0021 | 5.6460 | 60000 | 0.1945 | 14.5196 | 0.8716 | 0.8762 | 0.8734 | 0.8930 | 0.8978 | 0.8949 | 0.8960 | 0.9006 | 0.8979 | 0.9607 | 0.9659 | 0.9628 | 0.9333 | 1.0 | 0.9565 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0769 | 0.1 | 0.0952 | |
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| 0.0016 | 6.5870 | 70000 | 0.1987 | 14.0872 | 0.8773 | 0.8781 | 0.8772 | 0.8985 | 0.8993 | 0.8984 | 0.9015 | 0.9025 | 0.9015 | 0.9641 | 0.9661 | 0.9646 | 0.9412 | 1.0 | 0.9600 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1429 | 0.1111 | 0.125 | |
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| 0.0005 | 7.5280 | 80000 | 0.2020 | 13.7288 | 0.8801 | 0.8825 | 0.8809 | 0.9015 | 0.9041 | 0.9024 | 0.9044 | 0.9069 | 0.9052 | 0.9673 | 0.9709 | 0.9687 | 1.0 | 1.0 | 0.9630 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1429 | 0.1 | 0.1176 | |
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| 0.0021 | 8.4690 | 90000 | 0.2088 | 14.3829 | 0.8767 | 0.8782 | 0.8770 | 0.8980 | 0.8997 | 0.8984 | 0.9009 | 0.9026 | 0.9013 | 0.9629 | 0.9664 | 0.9640 | 0.9412 | 1.0 | 0.9600 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | |
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| 0.0013 | 9.4100 | 100000 | 0.2041 | 13.4146 | 0.8826 | 0.8837 | 0.8828 | 0.9025 | 0.9037 | 0.9027 | 0.9052 | 0.9063 | 0.9054 | 0.9681 | 0.9697 | 0.9684 | 1.0 | 1.0 | 0.9630 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1429 | 0.1111 | 0.125 | |
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| 0.0002 | 10.3510 | 110000 | 0.2110 | 13.5329 | 0.8876 | 0.8891 | 0.8880 | 0.9086 | 0.9101 | 0.9089 | 0.9112 | 0.9127 | 0.9115 | 0.9681 | 0.9710 | 0.9690 | 1.0 | 1.0 | 0.9630 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1429 | 0.1 | 0.1176 | |
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| 0.0003 | 11.2920 | 120000 | 0.2123 | 13.5033 | 0.8822 | 0.8828 | 0.8821 | 0.9023 | 0.9029 | 0.9022 | 0.9048 | 0.9056 | 0.9048 | 0.9679 | 0.9698 | 0.9684 | 1.0 | 1.0 | 0.9677 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1429 | 0.1 | 0.1176 | |
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| 0.0011 | 12.2330 | 130000 | 0.2082 | 13.5070 | 0.8884 | 0.8881 | 0.8878 | 0.9085 | 0.9083 | 0.9080 | 0.9109 | 0.9107 | 0.9104 | 0.9677 | 0.9683 | 0.9675 | 1.0 | 1.0 | 0.9677 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0769 | 0.0769 | 0.0769 | |
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| 0.0 | 13.1740 | 140000 | 0.2181 | 13.4220 | 0.8835 | 0.8860 | 0.8844 | 0.9028 | 0.9054 | 0.9038 | 0.9056 | 0.9082 | 0.9065 | 0.9671 | 0.9706 | 0.9684 | 1.0 | 1.0 | 0.9677 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1429 | 0.1111 | 0.125 | |
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| 0.0 | 14.1150 | 150000 | 0.2240 | 13.0266 | 0.8847 | 0.8860 | 0.8850 | 0.9043 | 0.9057 | 0.9046 | 0.9073 | 0.9086 | 0.9075 | 0.9692 | 0.9719 | 0.9701 | 1.0 | 1.0 | 0.9677 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1429 | 0.1111 | 0.125 | |
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| 0.0002 | 15.0560 | 160000 | 0.2245 | 13.0155 | 0.8828 | 0.8831 | 0.8826 | 0.9019 | 0.9022 | 0.9017 | 0.9049 | 0.9053 | 0.9048 | 0.9701 | 0.9717 | 0.9704 | 1.0 | 1.0 | 0.9687 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1429 | 0.1111 | 0.125 | |
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| 0.0001 | 15.9970 | 170000 | 0.2272 | 13.0155 | 0.8855 | 0.8861 | 0.8854 | 0.9043 | 0.9049 | 0.9042 | 0.9070 | 0.9078 | 0.9070 | 0.9693 | 0.9710 | 0.9697 | 1.0 | 1.0 | 0.9677 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1429 | 0.1111 | 0.125 | |
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| 0.0 | 16.9380 | 180000 | 0.2312 | 12.6053 | 0.8875 | 0.8892 | 0.8880 | 0.9062 | 0.9079 | 0.9067 | 0.9089 | 0.9106 | 0.9093 | 0.9703 | 0.9728 | 0.9711 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1429 | 0.1111 | 0.125 | |
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| 0.0 | 17.8790 | 190000 | 0.2324 | 12.5684 | 0.8898 | 0.8910 | 0.8900 | 0.9089 | 0.9102 | 0.9092 | 0.9113 | 0.9127 | 0.9116 | 0.9703 | 0.9726 | 0.9710 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1429 | 0.1111 | 0.125 | |
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| 0.0 | 18.8200 | 200000 | 0.2357 | 12.4316 | 0.8908 | 0.8920 | 0.8911 | 0.9098 | 0.9110 | 0.9100 | 0.9122 | 0.9134 | 0.9125 | 0.9710 | 0.9730 | 0.9716 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1429 | 0.1111 | 0.125 | |
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### Framework versions |
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- Transformers 4.41.2 |
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- Pytorch 2.2.1 |
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- Datasets 2.20.0 |
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- Tokenizers 0.19.1 |
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