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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- dataset/riksdagen |
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metrics: |
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- wer |
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model-index: |
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- name: whisper-tiny-sv |
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results: |
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- task: |
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name: Automatic Speech Recognition |
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type: automatic-speech-recognition |
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dataset: |
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name: dataset/riksdagen audiofolder |
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type: dataset/riksdagen |
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config: audiofolder |
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split: train |
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args: audiofolder |
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metrics: |
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- name: Wer |
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type: wer |
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value: 0.3700987201570632 |
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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-tiny-sv |
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This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co./openai/whisper-tiny) on the dataset/riksdagen audiofolder dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.6435 |
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- Wer: 0.3701 |
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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: 32 |
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- eval_batch_size: 32 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- num_devices: 4 |
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- total_train_batch_size: 128 |
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- total_eval_batch_size: 128 |
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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: 100 |
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- training_steps: 5000 |
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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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| 1.0032 | 0.08 | 250 | 1.0075 | 0.5063 | |
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| 0.8983 | 0.17 | 500 | 0.8945 | 0.4649 | |
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| 0.8227 | 0.25 | 750 | 0.8336 | 0.4491 | |
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| 0.777 | 0.33 | 1000 | 0.7931 | 0.4314 | |
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| 0.7728 | 0.42 | 1250 | 0.7640 | 0.4217 | |
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| 0.7141 | 0.5 | 1500 | 0.7407 | 0.4134 | |
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| 0.7208 | 0.58 | 1750 | 0.7225 | 0.4023 | |
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| 0.6911 | 0.66 | 2000 | 0.7083 | 0.3942 | |
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| 0.6924 | 0.75 | 2250 | 0.6948 | 0.3911 | |
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| 0.6702 | 0.83 | 2500 | 0.6849 | 0.3884 | |
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| 0.663 | 0.91 | 2750 | 0.6766 | 0.3769 | |
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| 0.6548 | 1.0 | 3000 | 0.6686 | 0.3759 | |
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| 0.638 | 1.08 | 3250 | 0.6627 | 0.3728 | |
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| 0.6222 | 1.16 | 3500 | 0.6574 | 0.3733 | |
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| 0.6323 | 1.25 | 3750 | 0.6528 | 0.3691 | |
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| 0.6192 | 1.33 | 4000 | 0.6498 | 0.3688 | |
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| 0.633 | 1.41 | 4250 | 0.6469 | 0.3677 | |
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| 0.6229 | 1.5 | 4500 | 0.6451 | 0.3681 | |
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| 0.6246 | 1.58 | 4750 | 0.6439 | 0.3706 | |
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| 0.6214 | 1.66 | 5000 | 0.6435 | 0.3701 | |
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### Framework versions |
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- Transformers 4.26.0.dev0 |
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- Pytorch 1.12.0a0+8a1a93a |
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- Datasets 2.7.1 |
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- Tokenizers 0.13.2 |
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