---
library_name: transformers
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_trainer
datasets:
- common_voice_11_0
metrics:
- wer
model-index:
- name: whisper-tiny-bg
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: common_voice_11_0
      type: common_voice_11_0
      config: bg
      split: None
      args: bg
    metrics:
    - name: Wer
      type: wer
      value: 58.93870930367281
---

<!-- 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-bg

This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the common_voice_11_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8746
- Wer: 58.9387

## 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: 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     |
|:-------------:|:-------:|:----:|:---------------:|:-------:|
| 0.3458        | 3.6630  | 1000 | 0.7458          | 60.0684 |
| 0.1146        | 7.3260  | 2000 | 0.7719          | 58.7417 |
| 0.0475        | 10.9890 | 3000 | 0.8278          | 57.8149 |
| 0.0245        | 14.6520 | 4000 | 0.8746          | 58.9387 |


### Framework versions

- Transformers 4.46.1
- Pytorch 2.4.1+cu121
- Datasets 3.0.2
- Tokenizers 0.20.1