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--- |
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library_name: transformers |
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base_model: microsoft/wavlm-base |
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tags: |
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- automatic-speech-recognition |
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- Sunbird/salt |
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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: wavlm-salt-eng |
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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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# wavlm-salt-eng |
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This model is a fine-tuned version of [microsoft/wavlm-base](https://huggingface.co/microsoft/wavlm-base) on the SUNBIRD/SALT - MULTISPEAKER-ENG dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2244 |
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- Wer: 0.2118 |
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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: 0.0003 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 16 |
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- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 500.0 |
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- training_steps: 30000 |
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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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| 0.3803 | 3.3228 | 1000 | 0.3107 | 0.3624 | |
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| 0.1663 | 6.6456 | 2000 | 0.2740 | 0.3097 | |
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| 0.1206 | 9.9684 | 3000 | 0.2486 | 0.2903 | |
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| 0.0938 | 13.2895 | 4000 | 0.2588 | 0.2828 | |
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| 0.0816 | 16.6123 | 5000 | 0.2769 | 0.2785 | |
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| 0.0689 | 19.9351 | 6000 | 0.2457 | 0.2882 | |
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| 0.0642 | 23.2562 | 7000 | 0.2639 | 0.2914 | |
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| 0.0566 | 26.5790 | 8000 | 0.2954 | 0.2828 | |
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| 0.049 | 29.9018 | 9000 | 0.3172 | 0.2763 | |
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| 0.0454 | 33.2230 | 10000 | 0.3186 | 0.2828 | |
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| 0.0395 | 36.5458 | 11000 | 0.2782 | 0.2817 | |
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| 0.0389 | 39.8686 | 12000 | 0.2857 | 0.2828 | |
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| 0.0321 | 43.1897 | 13000 | 0.2692 | 0.2527 | |
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| 0.0282 | 46.5125 | 14000 | 0.2570 | 0.2559 | |
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| 0.0269 | 49.8353 | 15000 | 0.2446 | 0.2624 | |
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| 0.0233 | 53.1564 | 16000 | 0.2383 | 0.2473 | |
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| 0.0224 | 56.4792 | 17000 | 0.2805 | 0.2473 | |
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| 0.0198 | 59.8020 | 18000 | 0.2555 | 0.2516 | |
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| 0.0159 | 63.1231 | 19000 | 0.2097 | 0.2409 | |
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| 0.015 | 66.4459 | 20000 | 0.2367 | 0.2505 | |
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| 0.015 | 69.7687 | 21000 | 0.2486 | 0.2527 | |
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| 0.0122 | 73.0899 | 22000 | 0.2475 | 0.2527 | |
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| 0.0104 | 76.4126 | 23000 | 0.2377 | 0.2344 | |
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| 0.008 | 79.7354 | 24000 | 0.2363 | 0.2441 | |
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| 0.0081 | 83.0566 | 25000 | 0.2347 | 0.2333 | |
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| 0.0072 | 86.3794 | 26000 | 0.2232 | 0.2290 | |
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| 0.0064 | 89.7022 | 27000 | 0.2212 | 0.2280 | |
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| 0.0044 | 93.0233 | 28000 | 0.2287 | 0.2258 | |
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| 0.004 | 96.3461 | 29000 | 0.2295 | 0.2344 | |
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| 0.0037 | 99.6689 | 30000 | 0.2243 | 0.2204 | |
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### Framework versions |
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- Transformers 5.0.0.dev0 |
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- Pytorch 2.9.1+cu128 |
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- Datasets 3.6.0 |
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- Tokenizers 0.22.1 |
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