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--- |
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base_model: openai/whisper-large-v3 |
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datasets: |
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- google/fleurs |
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language: |
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- pl |
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license: apache-2.0 |
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metrics: |
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- wer |
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tags: |
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- hf-asr-leaderboard |
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- generated_from_trainer |
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model-index: |
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- name: Whisper Large V3 pl Fleurs Aug 2 - Chee Li |
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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: Google Fleurs |
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type: google/fleurs |
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config: pl_pl |
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split: None |
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args: 'config: pl split: test' |
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metrics: |
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- type: wer |
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value: 402.6139222812413 |
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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 V3 pl Fleurs Aug 2 - Chee Li |
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This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co./openai/whisper-large-v3) on the Google Fleurs dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1295 |
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- Wer: 402.6139 |
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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: 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: 750 |
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- training_steps: 6000 |
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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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| 0.0563 | 1.2579 | 1000 | 0.1102 | 448.8748 | |
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| 0.0144 | 2.5157 | 2000 | 0.1207 | 354.0117 | |
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| 0.0035 | 3.7736 | 3000 | 0.1205 | 514.6701 | |
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| 0.0009 | 5.0314 | 4000 | 0.1263 | 391.4104 | |
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| 0.0003 | 6.2893 | 5000 | 0.1280 | 385.1901 | |
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| 0.0001 | 7.5472 | 6000 | 0.1295 | 402.6139 | |
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### Framework versions |
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- Transformers 4.42.4 |
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- Pytorch 2.3.1+cu121 |
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- Datasets 2.20.0 |
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- Tokenizers 0.19.1 |
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