transformers tensorboard safetensors whisper automatic-speech-recognition generated_from_trainer dataset:fleurs base_model:cifope/whisper-small-wolof license:apache-2.0 model-index endpoints_compatible region:us
---
license: apache-2.0
base_model: cifope/whisper-small-wolof
tags:
- generated_from_trainer
datasets:
- fleurs
metrics:
- wer
model-index:
- name: whisper-small-wolof
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: fleurs
      type: fleurs
      config: wo_sn
      split: test
      args: wo_sn
    metrics:
    - name: Wer
      type: wer
      value: 64.90514905149053
---

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# whisper-small-wolof

This model is a fine-tuned version of [cifope/whisper-small-wolof](https://huggingface.co/cifope/whisper-small-wolof) on the fleurs dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4778
- Wer: 64.9051

## 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
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 50
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer     |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 1.3863        | 1.4   | 50   | 1.4778          | 64.9051 |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
