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--- |
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library_name: transformers |
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license: mit |
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base_model: openai/whisper-large-v3-turbo |
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tags: |
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- generated_from_trainer |
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datasets: |
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- fsicoli/common_voice_18_0 |
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metrics: |
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- wer |
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model-index: |
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- name: Whisper Turbo Train |
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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: Common Voice 18.0 |
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type: fsicoli/common_voice_18_0 |
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split: None |
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metrics: |
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- name: Wer |
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type: wer |
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value: 15.246076710047603 |
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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 Turbo Train |
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This model is a fine-tuned version of [openai/whisper-large-v3-turbo](https://huggingface.co/openai/whisper-large-v3-turbo) on the Common Voice 18.0 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1156 |
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- Wer: 15.2461 |
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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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- 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: 8000 |
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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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| 0.3715 | 0.4257 | 1000 | 0.3457 | 40.4692 | |
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| 0.251 | 0.8514 | 2000 | 0.2181 | 27.7065 | |
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| 0.1569 | 1.2771 | 3000 | 0.1814 | 24.1533 | |
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| 0.1436 | 1.7029 | 4000 | 0.1531 | 20.3812 | |
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| 0.0931 | 2.1286 | 5000 | 0.1374 | 18.4662 | |
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| 0.0891 | 2.5543 | 6000 | 0.1252 | 16.9349 | |
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| 0.0738 | 2.9800 | 7000 | 0.1199 | 15.5610 | |
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| 0.0544 | 3.4057 | 8000 | 0.1156 | 15.2461 | |
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### Framework versions |
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- Transformers 4.45.1 |
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- Pytorch 2.1.0 |
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- Datasets 3.0.1 |
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- Tokenizers 0.20.0 |
