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whisper-fine-tuned – AI Model by shReYas0363 | AlphaNeural AI
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whisper-fine-tuned
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transformers
tensorboard
safetensors
whisper
automatic-speech-recognition
generated_from_trainer
en
openai/whisper-base
finetune
apache-2.0
endpoints_compatible
us
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whisperbase-shreyas
This model is a fine-tuned version of
openai/whisper-base
on the AI4Bharat-svarah dataset. It achieves the following results on the evaluation set:
Loss: 0.1469
Wer: 23.6355
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: 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
0.1893
2.6702
1000
0.3790
22.8606
0.0709
5.3458
2000
0.1469
23.6355
Framework versions
Transformers 4.45.2
Pytorch 2.4.1
Datasets 2.14.7
Tokenizers 0.20.1
