whisper-small-lingala-qlora – AI Model by Elod1e | AlphaNeural AI

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whisper-small-lingala-qlora

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automatic-speech-recognition

openai/whisper-small

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whisper-small-lingala-qlora

This model is a fine-tuned version of openai/whisper-small on the None dataset. It achieves the following results on the evaluation set:

Loss: 4.7410
Wer: 0.9973
Cer: 0.9793

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: 0.0001
train_batch_size: 1
eval_batch_size: 1
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 4
optimizer: Use OptimizerNames.PAGED_ADAMW_8BIT 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: 50
training_steps: 500

Training results

Training Loss	Epoch	Step	Validation Loss	Wer	Cer
22.1056	0.4306	100	5.4382	1.0155	0.9853
19.9251	0.8611	200	5.0677	0.9898	0.9502
19.3998	1.2885	300	4.8921	0.9763	0.9285
18.6250	1.7191	400	4.7937	0.9905	0.9595
18.0820	2.1464	500	4.7410	0.9973	0.9793

Framework versions

PEFT 0.19.1
Transformers 5.13.0
Pytorch 2.11.0+cu128
Datasets 5.0.0
Tokenizers 0.22.2