Whisper Base ig

This model is a fine-tuned version of openai/whisper-base on the google/fleurs dataset. It achieves the following results on the evaluation set:

Loss: 1.0933
Wer: 54.9487
Cer: 21.3532

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: 64
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_ratio: 0.04
training_steps: 5000

Training results

Training Loss	Epoch	Step	Validation Loss	Wer	Cer
0.2087	0.2	1000	0.8427	54.4143	20.1160
0.0734	1.0814	2000	0.9702	55.5707	21.6200
0.0609	1.2814	3000	1.0272	54.0256	20.4927
0.0336	2.1628	4000	1.0804	54.4337	20.4677
0.0341	3.0442	5000	1.0933	54.9487	21.3532

Framework versions

Transformers 4.42.0.dev0
Pytorch 2.3.0+cu121
Datasets 2.19.1
Tokenizers 0.19.1

Citation

Please cite the model using the following BibTeX entry:

@misc{deepdml/whisper-base-ig-mix-norm,
      title={Fine-tuned Whisper base ASR model for speech recognition in Lingala},
      author={Jimenez, David},
      howpublished={\url{https://huggingface.co/deepdml/whisper-base-ig-mix-norm}},
      year={2025}
    }