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whisper-small-balti – AI Model by NasuAhmed | AlphaNeural AI
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whisper-small-balti
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transformers
tensorboard
safetensors
whisper
automatic-speech-recognition
generated_from_trainer
audiofolder
openai/whisper-small
finetune
apache-2.0
endpoints_compatible
us
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whisper-small-balti
This model is a fine-tuned version of
openai/whisper-small
on the audiofolder dataset. It achieves the following results on the evaluation set:
Loss: 0.2596
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: 8
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 16
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: 2000
mixed_precision_training: Native AMP
Training results
Training Loss
Epoch
Step
Validation Loss
0.3248
0.9372
500
0.3322
0.2153
1.8735
1000
0.2739
0.1187
2.8097
1500
0.2582
0.0747
3.7460
2000
0.2596
Framework versions
Transformers 4.57.3
Pytorch 2.9.0+cu126
Datasets 4.0.0
Tokenizers 0.22.1
