---
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-us_en_bs128
  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: 0.3417945690672963
---

<!-- 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-us_en_bs128

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: 0.8372
- Wer Ortho: 0.3399
- Wer: 0.3418

## 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: 0.0001
- train_batch_size: 128
- eval_batch_size: 128
- 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_steps: 50
- training_steps: 500
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer Ortho | Wer    |
|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|
| 0.1633        | 6.25   | 25   | 0.5503          | 0.3177    | 0.3164 |
| 0.0027        | 12.5   | 50   | 0.6676          | 0.3288    | 0.3294 |
| 0.0011        | 18.75  | 75   | 0.7095          | 0.3134    | 0.3182 |
| 0.0012        | 25.0   | 100  | 0.7296          | 0.3196    | 0.3176 |
| 0.0014        | 31.25  | 125  | 0.7460          | 0.3541    | 0.3583 |
| 0.005         | 37.5   | 150  | 0.7059          | 0.4405    | 0.4610 |
| 0.0009        | 43.75  | 175  | 0.7803          | 0.3924    | 0.3961 |
| 0.0004        | 50.0   | 200  | 0.7996          | 0.3455    | 0.3512 |
| 0.0001        | 56.25  | 225  | 0.8074          | 0.3411    | 0.3442 |
| 0.0001        | 62.5   | 250  | 0.8146          | 0.3424    | 0.3459 |
| 0.0001        | 68.75  | 275  | 0.8197          | 0.3430    | 0.3459 |
| 0.0001        | 75.0   | 300  | 0.8239          | 0.3399    | 0.3424 |
| 0.0001        | 81.25  | 325  | 0.8274          | 0.3374    | 0.3400 |
| 0.0001        | 87.5   | 350  | 0.8303          | 0.3356    | 0.3383 |
| 0.0001        | 93.75  | 375  | 0.8324          | 0.3368    | 0.3400 |
| 0.0001        | 100.0  | 400  | 0.8341          | 0.3368    | 0.3388 |
| 0.0001        | 106.25 | 425  | 0.8354          | 0.3405    | 0.3424 |
| 0.0001        | 112.5  | 450  | 0.8364          | 0.3399    | 0.3418 |
| 0.0001        | 118.75 | 475  | 0.8371          | 0.3399    | 0.3418 |
| 0.0001        | 125.0  | 500  | 0.8372          | 0.3399    | 0.3418 |


### Framework versions

- Transformers 4.46.2
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3