whisper-small-dioula – AI Model by Isaacbmb992 | AlphaNeural AI

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Isaacbmb992/
whisper-small-dioula

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automatic-speech-recognition

generated_from_trainer

openai/whisper-small

endpoints_compatible

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whisper-small-dioula

This model is a fine-tuned version of openai/whisper-small on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 1.2308
Wer: 79.9333

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: 5e-06
train_batch_size: 16
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 32
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: 100
training_steps: 1500
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
5.6770	0.9901	250	1.3940	85.7063
4.6025	1.9782	500	1.2605	84.8053
3.7246	2.9663	750	1.2230	84.4716
3.3528	3.9545	1000	1.2163	79.9555
3.0999	4.9426	1250	1.2202	80.5117
2.8991	5.9307	1500	1.2308	79.9333

Framework versions

Transformers 5.0.0
Pytorch 2.10.0+cu128
Datasets 4.8.3
Tokenizers 0.22.2