---
license: apache-2.0
base_model: openai/whisper-base
tags:
- generated_from_trainer
datasets:
- common_voice_13_0
metrics:
- wer
model-index:
- name: openai/whisper-base
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: common_voice_13_0
      type: common_voice_13_0
      config: gl
      split: test
      args: gl
    metrics:
    - name: Wer
      type: wer
      value: 19.112168874172188
---

<!-- 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-base

This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the common_voice_13_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5307
- Wer: 19.1122

## 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: 2.5e-05
- train_batch_size: 128
- eval_batch_size: 64
- 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: 5000

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer     |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.0088        | 9.02  | 1000 | 0.4219          | 18.7776 |
| 0.0015        | 19.02 | 2000 | 0.4754          | 18.6879 |
| 0.0008        | 29.02 | 3000 | 0.5036          | 18.9000 |
| 0.0005        | 39.02 | 4000 | 0.5225          | 19.0553 |
| 0.0004        | 49.02 | 5000 | 0.5307          | 19.1122 |


### Framework versions

- Transformers 4.33.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.14.4
- Tokenizers 0.13.3