---
library_name: transformers
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_trainer
datasets:
- PolyAI/minds14
metrics:
- wer
model-index:
- name: whisper-tiny-finetuned-minds14
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: PolyAI/minds14
      type: PolyAI/minds14
      config: en-US
      split: train[450:]
      args: en-US
    metrics:
    - name: Wer
      type: wer
      value: 0.30460448642266824
---

<!-- 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-finetuned-minds14

This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the PolyAI/minds14 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5352
- Wer Ortho: 0.3029
- Wer: 0.3046

## Model description

I have made it for audio corse Unit 5 Hands-on.
Here is some additional info https://outleys.site/en/development/AI/hugface-unit-5-excercise-guide/

## 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: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- lr_scheduler_warmup_steps: 50
- num_epochs: 6

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer Ortho | Wer    |
|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|
| 1.1444        | 0.8850 | 100  | 0.4740          | 0.3411    | 0.3388 |
| 0.2788        | 1.7699 | 200  | 0.4633          | 0.2986    | 0.3017 |
| 0.1377        | 2.6549 | 300  | 0.4969          | 0.3048    | 0.3052 |
| 0.0561        | 3.5398 | 400  | 0.5145          | 0.3017    | 0.3034 |
| 0.0177        | 4.4248 | 500  | 0.5241          | 0.3091    | 0.3117 |
| 0.01          | 5.3097 | 600  | 0.5352          | 0.3029    | 0.3046 |


### Framework versions

- Transformers 4.46.2
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3