---
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-en-US
  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: 0.34946871310507677
---

<!-- 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-US

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: 0.7334
- Wer Ortho: 0.3701
- Wer: 0.3495

## 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: 48
- eval_batch_size: 32
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: constant_with_warmup
- lr_scheduler_warmup_steps: 50
- num_epochs: 20
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|
| 2.5445        | 1.0   | 10   | 2.4472          | 0.5379    | 0.3985 |
| 2.0492        | 2.0   | 20   | 1.8586          | 0.5287    | 0.3973 |
| 1.3657        | 3.0   | 30   | 1.1065          | 0.4867    | 0.4038 |
| 0.6326        | 4.0   | 40   | 0.5885          | 0.4769    | 0.4115 |
| 0.3984        | 5.0   | 50   | 0.5155          | 0.4399    | 0.3861 |
| 0.2907        | 6.0   | 60   | 0.4921          | 0.3849    | 0.3347 |
| 0.236         | 7.0   | 70   | 0.4864          | 0.3886    | 0.3459 |
| 0.14          | 8.0   | 80   | 0.4936          | 0.3677    | 0.3264 |
| 0.106         | 9.0   | 90   | 0.5082          | 0.3917    | 0.3518 |
| 0.0837        | 10.0  | 100  | 0.5316          | 0.3819    | 0.3347 |
| 0.0458        | 11.0  | 110  | 0.5475          | 0.3899    | 0.3489 |
| 0.0201        | 12.0  | 120  | 0.5706          | 0.3893    | 0.3536 |
| 0.0099        | 13.0  | 130  | 0.5851          | 0.3831    | 0.3495 |
| 0.0067        | 14.0  | 140  | 0.6010          | 0.3769    | 0.3489 |
| 0.0036        | 15.0  | 150  | 0.6196          | 0.3819    | 0.3506 |
| 0.0021        | 16.0  | 160  | 0.6377          | 0.3782    | 0.3530 |
| 0.0013        | 17.0  | 170  | 0.6539          | 0.3708    | 0.3453 |
| 0.0006        | 18.0  | 180  | 0.6831          | 0.3720    | 0.3506 |
| 0.0004        | 19.0  | 190  | 0.7018          | 0.3732    | 0.3512 |
| 0.0003        | 20.0  | 200  | 0.7334          | 0.3701    | 0.3495 |


### Framework versions

- Transformers 4.46.1
- Pytorch 2.3.1
- Datasets 3.0.2
- Tokenizers 0.20.1