whisper-3000ms-v2 – AI Model by devkyle | AlphaNeural AI

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devkyle/
whisper-3000ms-v2

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automatic-speech-recognition

generated_from_trainer

openai/whisper-tiny

endpoints_compatible

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whisper-tiny-akan

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

Loss: 1.1467
Wer: 45.0456

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: 32
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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
0.5414	10.0	250	0.7596	55.6922
0.09	20.0	500	0.9048	52.3362
0.0275	30.0	750	1.0300	49.0091
0.0114	40.0	1000	1.0959	47.4902
0.004	50.0	1250	1.1247	45.3783
0.0017	60.0	1500	1.1412	45.3493
0.0009	70.0	1750	1.1437	44.6261
0.0007	80.0	2000	1.1467	45.0456

Framework versions

Transformers 4.44.2
Pytorch 2.4.0+cu121
Datasets 3.0.0
Tokenizers 0.19.1