---
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_trainer
datasets:
- PolyAI/minds14
metrics:
- wer
model-index:
- name: whisper-tiny-en
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: minds14
      type: minds14
      config: en-US
      split: train
      args: en-US
    metrics:
    - name: Wer
      type: wer
      value: 0.2883917775090689
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# whisper-tiny-en

This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the minds14 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7626
- Wer Ortho: 0.2891
- Wer: 0.2884

## 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: 32
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- training_steps: 4000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer Ortho | Wer    |
|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|
| 0.0005        | 35.71  | 500  | 0.6319          | 0.2684    | 0.2684 |
| 0.0002        | 71.43  | 1000 | 0.6820          | 0.2709    | 0.2709 |
| 0.0001        | 107.14 | 1500 | 0.7092          | 0.2740    | 0.2739 |
| 0.0001        | 142.86 | 2000 | 0.7275          | 0.2854    | 0.2848 |
| 0.0001        | 178.57 | 2500 | 0.7423          | 0.2885    | 0.2878 |
| 0.0           | 214.29 | 3000 | 0.7531          | 0.2898    | 0.2890 |
| 0.0           | 250.0  | 3500 | 0.7604          | 0.2898    | 0.2890 |
| 0.0           | 285.71 | 4000 | 0.7626          | 0.2891    | 0.2884 |


### Framework versions

- Transformers 4.39.2
- Pytorch 1.13.0+cu117
- Datasets 2.16.1
- Tokenizers 0.15.1