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ft-whisperl-ja – AI Model by HuyHoang1977 | AlphaNeural AI
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ft-whisperl-ja
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transformers
tensorboard
safetensors
whisper
automatic-speech-recognition
generated_from_trainer
openai/whisper-large-v3
finetune
apache-2.0
endpoints_compatible
us
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ft-whisperl-ja
This model is a fine-tuned version of
openai/whisper-large-v3
on an unknown dataset. It achieves the following results on the evaluation set:
Loss: 0.4399
Wer: 71.8333
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: 2e-05
train_batch_size: 6
eval_batch_size: 1
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 24
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: cosine
lr_scheduler_warmup_steps: 200
training_steps: 2500
mixed_precision_training: Native AMP
Training results
Training Loss
Epoch
Step
Validation Loss
Wer
0.2888
0.7361
500
0.4336
75.5
0.1475
1.4711
1000
0.4332
75.6667
0.0567
2.2061
1500
0.4210
73.1667
0.0433
2.9422
2000
0.4117
71.6667
0.0137
3.6772
2500
0.4399
71.8333
Framework versions
Transformers 4.56.1
Pytorch 2.8.0+cu126
Datasets 4.0.0
Tokenizers 0.22.0
