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# whisper-base-cs

This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the CommonVoice11 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3785
- Wer: 35.4791

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
- mixed_precision_training: Native AMP

### Training results


BASE-CS baseline performance:
{'eval_loss': 2.514087438583374,
 'eval_wer': 82.45662504144104,
 'eval_runtime': 2620.0407,
 'eval_samples_per_second': 2.944,
 'eval_steps_per_second': 0.368}  

| Training Loss | Epoch | Step | Validation Loss | Wer     |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| ------        | 0.00  | 0000 | 2.5141          | 82.4566 |
| 0.2991        | 1.44  | 1000 | 0.4414          | 42.2993 |
| 0.1776        | 2.89  | 2000 | 0.3818          | 36.7573 |
| 0.0916        | 4.33  | 3000 | 0.3774          | 35.6080 |
| 0.076         | 5.78  | 4000 | 0.3785          | 35.4791 |


### Framework versions

- Transformers 4.36.0
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["wer"], "base_model": "openai/whisper-base", "model-index": [{"name": "whisper-base-cs", "results": []}]}
transformers tensorboard safetensors whisper automatic-speech-recognition generated_from_trainer base_model:openai/whisper-base license:apache-2.0 endpoints_compatible region:us