---
library_name: transformers
language:
- uz
license: apache-2.0
base_model: openai/whisper-medium
tags:
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_17_0
metrics:
- wer
model-index:
- name: Whisper medium uz - Yorkerdev
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Common Voice 17.0
      type: mozilla-foundation/common_voice_17_0
      config: uz
      split: test
      args: 'config: uz, split: test'
    metrics:
    - name: Wer
      type: wer
      value: 30.004243528618858
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# Whisper medium uz - Yorkerdev

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: 0.3029
- Wer: 30.0042

## 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: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 16
- 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: 500
- training_steps: 4000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer     |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.4938        | 0.2640 | 1000 | 0.4440          | 39.7007 |
| 0.3856        | 0.5280 | 2000 | 0.3592          | 33.4303 |
| 0.3381        | 0.7919 | 3000 | 0.3203          | 31.1263 |
| 0.2389        | 1.0557 | 4000 | 0.3029          | 30.0042 |


### Framework versions

- Transformers 4.49.0
- Pytorch 2.6.0+cu118
- Datasets 3.3.2
- Tokenizers 0.21.0