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--- |
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license: apache-2.0 |
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base_model: openai/whisper-large-v2 |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: whisper-large-final |
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results: [] |
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language: |
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- mn |
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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-final |
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This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- eval_loss: 0.0112 |
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- eval_wer: 1.1712 |
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- eval_runtime: 982.7637 |
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- eval_samples_per_second: 1.892 |
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- eval_steps_per_second: 0.237 |
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- epoch: 6.4205 |
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- step: 4000 |
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## Model description |
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Step Training Loss Validation Loss Wer |
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500 0.431500 0.412413 48.265244 |
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1000 0.244500 0.230148 29.284654 |
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1500 0.134300 0.122366 16.588772 |
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2000 0.055800 0.069241 10.551493 |
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2500 0.045700 0.035967 4.860615 |
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3000 0.027900 0.024117 3.425524 |
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3500 0.011000 0.016053 1.770495 |
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4000 0.004800 0.011227 1.171166 |
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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: 16 |
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- eval_batch_size: 8 |
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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: 1000 |
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- training_steps: 5000 |
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- mixed_precision_training: Native AMP |
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### Framework versions |
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- Transformers 4.40.2 |
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- Pytorch 2.2.0 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
