noirlab/whisper-large-v3-lingala-asr

Whisper Large v3 Lingala fine-tune

This model is a fine-tuned version of openai/whisper-large-v3 on the Google FLEURS dataset. It achieves the following results on the evaluation set:

• Loss: 0.4297
• Wer: 14.8266

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
• optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 500
• training_steps: 2000
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
0.0012	8.9686	2000	0.4297	14.8266

Framework versions

• Transformers 4.56.2
• Pytorch 2.8.0+cu126
• Datasets 3.6.0
• Tokenizers 0.22.1