transformers tensorboard safetensors whisper automatic-speech-recognition generated_from_trainer ta dataset:whisper-small-preon-test-1 base_model:openai/whisper-small license:apache-2.0 model-index endpoints_compatible region:us
---
language:
- ta
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- whisper-small-preon-test-1
metrics:
- wer
model-index:
- name: Whisper small
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: custom dataset
      type: whisper-small-preon-test-1
    metrics:
    - name: Wer
      type: wer
      value: 11.920529801324504
---

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# Whisper small

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the custom dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1046
- Wer Ortho: 11.8421
- Wer: 11.9205

## 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: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant_with_warmup
- lr_scheduler_warmup_steps: 50
- training_steps: 500
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer     |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:-------:|
| 0.4335        | 5.0   | 100  | 0.1326          | 11.8421   | 9.2715  |
| 0.0049        | 10.0  | 200  | 0.1332          | 15.7895   | 13.9073 |
| 0.0001        | 15.0  | 300  | 0.1019          | 11.8421   | 11.9205 |
| 0.0           | 20.0  | 400  | 0.1041          | 11.8421   | 11.9205 |
| 0.0           | 25.0  | 500  | 0.1046          | 11.8421   | 11.9205 |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
