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whisper-base-ta – AI Model by navin-kumar-j | AlphaNeural AI
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transformers
tensorboard
safetensors
whisper
automatic-speech-recognition
generated_from_trainer
ta
mozilla-foundation/common_voice_17_0
openai/whisper-base
finetune
apache-2.0
model-index
endpoints_compatible
us
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Whisper Base Ta - Navin Kumar J
This model is a fine-tuned version of
openai/whisper-base
on the Common Voice 17.0 dataset. It achieves the following results on the evaluation set:
Loss: 0.2913
Wer: 1.5234
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: 16
eval_batch_size: 8
seed: 42
optimizer: Use adamw_torch 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: 500
training_steps: 4000
mixed_precision_training: Native AMP
Training results
Training Loss
Epoch
Step
Validation Loss
Wer
0.2192
0.2773
1000
0.3592
1.1204
0.2076
0.5546
2000
0.3164
1.1192
0.1881
0.8319
3000
0.2993
1.5272
0.1504
1.1093
4000
0.2913
1.5234
Framework versions
Transformers 4.51.3
Pytorch 2.7.0+cu126
Datasets 3.5.0
Tokenizers 0.21.1
