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whisper-muong-clean-v2 – AI Model by longgb | AlphaNeural AI
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whisper-muong-clean-v2
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peft
tensorboard
safetensors
adapter
lora
transformers
openai/whisper-large-v3
apache-2.0
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whisper-muong-clean-v2
This model is a fine-tuned version of
openai/whisper-large-v3
on the None dataset. It achieves the following results on the evaluation set:
Loss: 0.8842
Wer: 69.7173
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: 0.001
train_batch_size: 4
eval_batch_size: 4
seed: 42
gradient_accumulation_steps: 8
total_train_batch_size: 32
optimizer: Use OptimizerNames.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: 50
training_steps: 800
mixed_precision_training: Native AMP
Training results
Training Loss
Epoch
Step
Validation Loss
Wer
0.7886
1.6393
200
0.8212
71.0578
0.3878
3.2787
400
0.7774
66.3295
0.2446
4.9180
600
0.8035
69.9244
0.0913
6.5574
800
0.8842
69.7173
Framework versions
PEFT 0.18.0
Transformers 4.57.3
Pytorch 2.6.0+cu124
Datasets 4.4.1
Tokenizers 0.22.1
