---
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_trainer
datasets:
- common_voice_16_1
metrics:
- wer
model-index:
- name: openai/whisper-tiny
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: common_voice_16_1
      type: common_voice_16_1
      config: eu
      split: test
      args: eu
    metrics:
    - name: Wer
      type: wer
      value: 19.196498358605595
---

<!-- 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. -->

# openai/whisper-tiny

This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the common_voice_16_1 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6095
- Wer: 19.1965

## 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: 3.75e-05
- train_batch_size: 256
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 40000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer     |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|
| 0.0426        | 10.0  | 1000  | 0.3451          | 23.2003 |
| 0.0077        | 20.0  | 2000  | 0.4123          | 22.6053 |
| 0.0013        | 30.0  | 3000  | 0.4288          | 21.1965 |
| 0.0004        | 40.0  | 4000  | 0.4538          | 21.1926 |
| 0.0003        | 50.0  | 5000  | 0.4757          | 21.1808 |
| 0.0206        | 60.0  | 6000  | 0.4172          | 22.2751 |
| 0.0003        | 70.0  | 7000  | 0.4374          | 19.5131 |
| 0.0002        | 80.0  | 8000  | 0.4547          | 19.5091 |
| 0.0001        | 90.0  | 9000  | 0.4697          | 19.5062 |
| 0.0001        | 100.0 | 10000 | 0.4853          | 19.5199 |
| 0.0001        | 110.0 | 11000 | 0.5009          | 19.5687 |
| 0.0           | 120.0 | 12000 | 0.5175          | 19.6586 |
| 0.0           | 130.0 | 13000 | 0.5348          | 19.7729 |
| 0.0           | 140.0 | 14000 | 0.5531          | 19.7847 |
| 0.0002        | 150.0 | 15000 | 0.4626          | 19.4730 |
| 0.0001        | 160.0 | 16000 | 0.4813          | 19.2199 |
| 0.0           | 170.0 | 17000 | 0.4932          | 19.1691 |
| 0.0           | 180.0 | 18000 | 0.5041          | 19.1291 |
| 0.0           | 190.0 | 19000 | 0.5146          | 19.0949 |
| 0.0           | 200.0 | 20000 | 0.5254          | 19.1232 |
| 0.0           | 210.0 | 21000 | 0.5369          | 19.1369 |
| 0.0           | 220.0 | 22000 | 0.5484          | 19.1125 |
| 0.0           | 230.0 | 23000 | 0.5606          | 19.1330 |
| 0.0           | 240.0 | 24000 | 0.5732          | 19.1965 |
| 0.0           | 250.0 | 25000 | 0.5864          | 19.2219 |
| 0.0           | 260.0 | 26000 | 0.6003          | 19.3108 |
| 0.0           | 270.0 | 27000 | 0.6140          | 19.3714 |
| 0.0034        | 280.0 | 28000 | 0.5536          | 20.6630 |
| 0.0           | 290.0 | 29000 | 0.5486          | 19.3391 |
| 0.0           | 300.0 | 30000 | 0.5591          | 19.3059 |
| 0.0           | 310.0 | 31000 | 0.5669          | 19.3137 |
| 0.0           | 320.0 | 32000 | 0.5737          | 19.3225 |
| 0.0           | 330.0 | 33000 | 0.5798          | 19.2883 |
| 0.0           | 340.0 | 34000 | 0.5856          | 19.2668 |
| 0.0           | 350.0 | 35000 | 0.5911          | 19.2346 |
| 0.0           | 360.0 | 36000 | 0.5962          | 19.2287 |
| 0.0           | 370.0 | 37000 | 0.6010          | 19.2326 |
| 0.0           | 380.0 | 38000 | 0.6050          | 19.2287 |
| 0.0           | 390.0 | 39000 | 0.6081          | 19.2375 |
| 0.0           | 400.0 | 40000 | 0.6095          | 19.1965 |


### Framework versions

- Transformers 4.37.2
- Pytorch 2.2.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1