transformers tensorboard safetensors whisper automatic-speech-recognition generated_from_trainer dataset:common_voice_11_0 base_model:openai/whisper-base license:apache-2.0 model-index endpoints_compatible region:us
---
license: apache-2.0
base_model: openai/whisper-base
tags:
- generated_from_trainer
datasets:
- common_voice_11_0
metrics:
- wer
model-index:
- name: whisper-base-sw-common-voice
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: common_voice_11_0
      type: common_voice_11_0
      config: sw
      split: test
      args: sw
    metrics:
    - name: Wer
      type: wer
      value: 47.437754327884676
---

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# whisper-base-sw-common-voice

This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the common_voice_11_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6719
- Wer: 47.4378

## 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: 4
- eval_batch_size: 4
- 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: 4000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer     |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.701         | 0.11  | 1000 | 0.8962          | 59.3501 |
| 0.7017        | 0.22  | 2000 | 0.7536          | 52.9887 |
| 0.541         | 0.33  | 3000 | 0.6957          | 47.0676 |
| 0.4735        | 0.43  | 4000 | 0.6719          | 47.4378 |


### Framework versions

- Transformers 4.37.0.dev0
- Pytorch 2.1.1+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
