transformers pytorch tensorboard safetensors whisper automatic-speech-recognition generated_from_trainer dataset:arrow license:apache-2.0 model-index endpoints_compatible region:us
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- arrow
metrics:
- wer
model-index:
- name: whisper-small-hy
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: arrow
      type: arrow
      config: default
      split: test
      args: default
    metrics:
    - name: Wer
      type: wer
      value: 40.83972054534355
---

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# whisper-small-hy

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the arrow dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2581
- Wer: 40.8397

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.2078        | 1.77  | 1000 | 0.2089          | 45.6217 |
| 0.0948        | 3.54  | 2000 | 0.2087          | 41.2060 |
| 0.0453        | 5.31  | 3000 | 0.2385          | 40.9550 |
| 0.0315        | 7.08  | 4000 | 0.2581          | 40.8397 |


### Framework versions

- Transformers 4.26.1
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.13.3
