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---
base_model: openai/whisper-small
datasets:
- fleurs
license: apache-2.0
metrics:
- wer
tags:
- generated_from_trainer
model-index:
- name: whisper-small-wolof
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: fleurs
type: fleurs
config: wo_sn
split: test
args: wo_sn
metrics:
- type: wer
value: 0.9217902350813744
name: Wer
---
<!-- 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-small-wolof
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co./openai/whisper-small) on the fleurs dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8726
- Wer: 0.9218
## 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
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 4.5232 | 0.9790 | 35 | 3.5807 | 1.3809 |
| 3.7127 | 1.9860 | 71 | 2.4567 | 1.1817 |
| 2.2111 | 2.9371 | 105 | 1.8726 | 0.9218 |
### Framework versions
- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|