---
license: apache-2.0
base_model: openai/whisper-medium
tags:
- generated_from_trainer
datasets:
- common_voice_17_0
metrics:
- wer
model-index:
- name: whisper_medium_finetuning_maior4s_8kh
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: common_voice_17_0
      type: common_voice_17_0
      config: pt
      split: None
      args: pt
    metrics:
    - name: Wer
      type: wer
      value: 26.357459011283584
---

<!-- 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_medium_finetuning_maior4s_8kh

This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the common_voice_17_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2421
- Wer: 26.3575

## 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-06
- train_batch_size: 16
- eval_batch_size: 8
- 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: 8000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer     |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.1168        | 1.0707 | 1000 | 0.1729          | 24.3127 |
| 0.087         | 2.1413 | 2000 | 0.1663          | 18.2013 |
| 0.058         | 3.2120 | 3000 | 0.1709          | 19.9302 |
| 0.0499        | 4.2827 | 4000 | 0.1780          | 21.3661 |
| 0.0336        | 5.3533 | 5000 | 0.1948          | 25.4951 |
| 0.029         | 6.4240 | 6000 | 0.2105          | 27.6541 |
| 0.0245        | 7.4946 | 7000 | 0.2315          | 26.5528 |
| 0.0195        | 8.5653 | 8000 | 0.2421          | 26.3575 |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.2.1
- Datasets 2.19.2
- Tokenizers 0.19.1