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--- |
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language: |
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- se |
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license: apache-2.0 |
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tags: |
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- whisper-event |
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- generated_from_trainer |
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datasets: |
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- audiofolder |
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metrics: |
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- wer |
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base_model: openai/whisper-large-v2 |
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model-index: |
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- name: Whisper Large Northern Sámi |
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results: |
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- task: |
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type: automatic-speech-recognition |
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name: Automatic Speech Recognition |
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dataset: |
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name: audiofolder |
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type: audiofolder |
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config: default |
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split: test |
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args: default |
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metrics: |
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- type: wer |
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value: 24.914285714285715 |
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name: Wer |
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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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# Whisper Large Northern Sámi |
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This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the audiofolder dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5559 |
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- Wer: 24.9143 |
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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: 12 |
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- eval_batch_size: 6 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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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: 60000 |
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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 | |
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|:-------------:|:------:|:-----:|:---------------:|:-------:| |
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| 0.4665 | 58.0 | 1000 | 0.8572 | 54.5143 | |
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| 0.3041 | 117.0 | 2000 | 0.6711 | 44.1143 | |
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| 0.2671 | 176.0 | 3000 | 0.5794 | 39.7714 | |
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| 0.1761 | 235.0 | 4000 | 0.5357 | 35.0857 | |
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| 0.2089 | 294.0 | 5000 | 0.5094 | 33.6 | |
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| 0.1456 | 352.0 | 6000 | 0.4959 | 33.0286 | |
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| 0.1514 | 411.0 | 7000 | 0.4864 | 32.5714 | |
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| 0.1203 | 470.0 | 8000 | 0.4625 | 31.4286 | |
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| 0.0879 | 529.0 | 9000 | 0.4916 | 45.4857 | |
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| 0.0825 | 588.0 | 10000 | 0.4962 | 30.6286 | |
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| 0.0753 | 647.0 | 11000 | 0.4723 | 31.2 | |
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| 0.0812 | 705.0 | 12000 | 0.4574 | 28.6857 | |
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| 0.062 | 764.0 | 13000 | 0.4628 | 28.8000 | |
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| 0.0604 | 823.0 | 14000 | 0.4668 | 28.0000 | |
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| 0.0666 | 882.0 | 15000 | 0.4697 | 28.6857 | |
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| 0.0405 | 941.0 | 16000 | 0.4908 | 54.6286 | |
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| 0.0349 | 999.0 | 17000 | 0.4728 | 28.4571 | |
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| 0.0409 | 1058.0 | 18000 | 0.4884 | 28.4571 | |
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| 0.0292 | 1117.0 | 19000 | 0.4576 | 27.3143 | |
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| 0.0247 | 1176.0 | 20000 | 0.4734 | 28.9143 | |
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| 0.0229 | 1235.0 | 21000 | 0.4899 | 29.9429 | |
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| 0.0271 | 1294.0 | 22000 | 0.4790 | 28.1143 | |
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| 0.0271 | 1352.0 | 23000 | 0.5012 | 30.1714 | |
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| 0.0184 | 1411.0 | 24000 | 0.5008 | 27.3143 | |
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| 0.0211 | 1470.0 | 25000 | 0.5118 | 27.6571 | |
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| 0.0183 | 1529.0 | 26000 | 0.5398 | 30.0571 | |
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| 0.0164 | 1588.0 | 27000 | 0.5006 | 27.3143 | |
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| 0.0169 | 1647.0 | 28000 | 0.5059 | 27.0857 | |
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| 0.0147 | 1705.0 | 29000 | 0.5325 | 27.7714 | |
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| 0.0104 | 1764.0 | 30000 | 0.4818 | 26.1714 | |
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| 0.0128 | 1823.0 | 31000 | 0.5259 | 28.3429 | |
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| 0.0145 | 1882.0 | 32000 | 0.5299 | 26.2857 | |
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| 0.0075 | 1941.0 | 33000 | 0.5082 | 27.4286 | |
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| 0.0087 | 1999.0 | 34000 | 0.5144 | 26.6286 | |
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| 0.005 | 2058.0 | 35000 | 0.5590 | 27.0857 | |
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| 0.0099 | 2117.0 | 36000 | 0.5546 | 28.9143 | |
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| 0.007 | 2176.0 | 37000 | 0.5364 | 26.8571 | |
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| 0.0045 | 2235.0 | 38000 | 0.5574 | 27.2000 | |
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| 0.0064 | 2294.0 | 39000 | 0.5051 | 25.7143 | |
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| 0.0079 | 2352.0 | 40000 | 0.5247 | 25.9429 | |
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| 0.0083 | 2411.0 | 41000 | 0.5514 | 25.6 | |
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| 0.0101 | 2470.0 | 42000 | 0.5710 | 25.6 | |
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| 0.0062 | 2529.0 | 43000 | 0.5830 | 28.0000 | |
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| 0.0046 | 2588.0 | 44000 | 0.5828 | 26.8571 | |
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| 0.0053 | 2647.0 | 45000 | 0.5621 | 27.4286 | |
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| 0.0047 | 2705.0 | 46000 | 0.5673 | 25.9429 | |
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| 0.0045 | 2764.0 | 47000 | 0.5220 | 25.6 | |
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| 0.0065 | 2823.0 | 48000 | 0.5704 | 27.7714 | |
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| 0.0039 | 2882.0 | 49000 | 0.5741 | 27.7714 | |
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| 0.0027 | 2941.0 | 50000 | 0.5762 | 26.0571 | |
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| 0.0019 | 2999.0 | 51000 | 0.5559 | 24.9143 | |
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| 0.0015 | 3058.0 | 52000 | 0.5777 | 28.5714 | |
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| 0.0026 | 3117.0 | 53000 | 0.5589 | 25.2571 | |
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| 0.0032 | 3176.0 | 54000 | 0.6061 | 26.9714 | |
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| 0.0025 | 3235.0 | 55000 | 0.5776 | 25.1429 | |
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| 0.0046 | 3294.0 | 56000 | 0.5753 | 27.3143 | |
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| 0.0015 | 3352.0 | 57000 | 0.5736 | 27.2000 | |
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| 0.003 | 3411.0 | 58000 | 0.5933 | 25.6 | |
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| 0.002 | 3470.0 | 59000 | 0.6036 | 25.6 | |
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| 0.0007 | 58.0 | 60000 | 0.5975 | 25.2571 | |
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### Framework versions |
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- Transformers 4.26.0.dev0 |
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- Pytorch 1.13.0+cu117 |
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- Datasets 2.7.1.dev0 |
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- Tokenizers 0.11.0 |
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