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# whisperfinetune_modelcheckpoints

This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1735
- Wer: 9.5428

## 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: 16
- eval_batch_size: 8
- seed: 42
- 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

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.0005        | 10.42 | 1000 | 0.1472          | 7.7246 |
| 0.0002        | 20.83 | 2000 | 0.1626          | 9.3706 |
| 0.0001        | 31.25 | 3000 | 0.1705          | 9.4890 |
| 0.0001        | 41.67 | 4000 | 0.1735          | 9.5428 |


### Framework versions

- Transformers 4.36.0.dev0
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0

{"tags": ["generated_from_trainer"], "metrics": ["wer"], "model-index": [{"name": "whisperfinetune_modelcheckpoints", "results": []}]}
transformers tensorboard safetensors whisper automatic-speech-recognition generated_from_trainer endpoints_compatible has_space region:us