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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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- fleurs |
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metrics: |
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- wer |
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model-index: |
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- name: whisper-training-blog |
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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: fleurs |
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type: fleurs |
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config: sv_se |
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split: validation |
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args: sv_se |
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metrics: |
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- name: Wer |
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type: wer |
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value: 191.23423279578478 |
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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-training-blog |
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This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co./openai/whisper-tiny) on the fleurs dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.0050 |
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- Wer: 191.2342 |
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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: 7.5e-06 |
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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_ratio: 0.3 |
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- training_steps: 448 |
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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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| 1.4111 | 0.1 | 44 | 1.4919 | 245.3457 | |
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| 1.0501 | 0.2 | 88 | 1.2255 | 225.8822 | |
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| 0.9032 | 0.29 | 132 | 1.1203 | 211.6558 | |
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| 0.8141 | 1.06 | 176 | 1.0675 | 184.6240 | |
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| 0.8029 | 1.16 | 220 | 1.0394 | 178.4129 | |
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| 0.6325 | 1.25 | 264 | 1.0301 | 216.6374 | |
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| 0.6971 | 2.02 | 308 | 1.0135 | 184.4004 | |
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| 0.6051 | 2.12 | 352 | 1.0065 | 194.7150 | |
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| 0.6047 | 2.21 | 396 | 1.0029 | 166.9328 | |
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| 0.585 | 2.31 | 440 | 1.0050 | 191.2342 | |
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### Framework versions |
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- Transformers 4.29.2 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.12.0 |
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- Tokenizers 0.13.3 |
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