---
language:
- sq
license: apache-2.0
library_name: transformers
tags:
- generated_from_trainer
base_model: openai/whisper-medium
datasets:
- Kushtrim/common_voice_19_sq
metrics:
- wer
model-index:
- name: Whisper Medium SQ
  results:
  - task:
      type: automatic-speech-recognition
      name: Automatic Speech Recognition
    dataset:
      name: Common Voice 19.0
      type: Kushtrim/common_voice_19_sq
      args: 'config: sq, split: test'
    metrics:
    - type: wer
      value: 7.322033898305085
      name: Wer
---

<!-- 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 SQ

This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the Common Voice 19.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0979
- Wer: 7.3220

## 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: 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
- num_epochs: 4
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer     |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.5161        | 0.4237 | 250  | 0.4949          | 38.8136 |
| 0.3207        | 0.8475 | 500  | 0.3084          | 29.4689 |
| 0.1595        | 1.2712 | 750  | 0.2255          | 21.6949 |
| 0.1239        | 1.6949 | 1000 | 0.1733          | 16.4859 |
| 0.0488        | 2.1186 | 1250 | 0.1338          | 13.0847 |
| 0.04          | 2.5424 | 1500 | 0.1188          | 10.8136 |
| 0.0241        | 2.9661 | 1750 | 0.1023          | 8.7684  |
| 0.0075        | 3.3898 | 2000 | 0.1022          | 8.1695  |
| 0.0065        | 3.8136 | 2250 | 0.0979          | 7.3220  |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 3.0.0
- Tokenizers 0.19.1