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whisper-base – AI Model by halcyonzhou | AlphaNeural AI
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halcyonzhou
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whisper-base
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transformers
tensorboard
safetensors
whisper
automatic-speech-recognition
generated_from_trainer
openai/whisper-base
finetune
apache-2.0
endpoints_compatible
us
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whisper-base
This model is a fine-tuned version of
openai/whisper-base
on an unknown dataset. It achieves the following results on the evaluation set:
eval_loss: 1.0027
eval_wer: 0.3287
eval_runtime: 19.3097
eval_samples_per_second: 5.852
eval_steps_per_second: 0.207
epoch: 47.5333
step: 380
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: 32
eval_batch_size: 32
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 64
optimizer: Use adafactor and the args are: No additional optimizer arguments
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 100
training_steps: 1000
Framework versions
Transformers 4.51.0
Pytorch 2.8.0+cu129
Datasets 3.6.0
Tokenizers 0.21.4
