---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- PolyAI/minds14
metrics:
- wer
model-index:
- name: whisper-tiny-en-finetune-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: 0.3382526564344746
---

<!-- 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-finetune-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: 0.6541
- Wer Ortho: 0.3399
- Wer: 0.3383

## 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: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- training_steps: 1000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|
| 0.3136        | 3.57  | 100  | 0.4883          | 0.3640    | 0.3524 |
| 0.0417        | 7.14  | 200  | 0.5146          | 0.3560    | 0.3442 |
| 0.0066        | 10.71 | 300  | 0.5736          | 0.3411    | 0.3353 |
| 0.0017        | 14.29 | 400  | 0.6040          | 0.3455    | 0.3418 |
| 0.0013        | 17.86 | 500  | 0.6226          | 0.3393    | 0.3365 |
| 0.0009        | 21.43 | 600  | 0.6352          | 0.3393    | 0.3365 |
| 0.0007        | 25.0  | 700  | 0.6436          | 0.3399    | 0.3371 |
| 0.0006        | 28.57 | 800  | 0.6492          | 0.3399    | 0.3383 |
| 0.0006        | 32.14 | 900  | 0.6530          | 0.3399    | 0.3383 |
| 0.0006        | 35.71 | 1000 | 0.6541          | 0.3399    | 0.3383 |


### Framework versions

- Transformers 4.29.2
- Pytorch 1.13.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3