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w2vbert-clean-silence-v2 – AI Model by GodwillN | AlphaNeural AI
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GodwillN
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w2vbert-clean-silence-v2
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transformers
safetensors
wav2vec2-bert
automatic-speech-recognition
generated_from_trainer
GodwillN/w2vbert-waxal-corrected
finetune
mit
endpoints_compatible
us
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w2vbert-clean-silence-v2
This model is a fine-tuned version of
GodwillN/w2vbert-waxal-corrected
on the None dataset. It achieves the following results on the evaluation set:
Loss: 0.4585
Wer: 0.2618
Cer: 0.0846
Combined Err: 0.1732
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: 5e-06
train_batch_size: 1
eval_batch_size: 1
seed: 42
gradient_accumulation_steps: 16
total_train_batch_size: 16
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: cosine
lr_scheduler_warmup_ratio: 0.02
num_epochs: 1
Training results
Training Loss
Epoch
Step
Validation Loss
Wer
Cer
Combined Err
0.1955
1.0
2242
0.4585
0.2618
0.0846
0.1732
Framework versions
Transformers 4.53.2
Pytorch 2.12.0+cu130
Datasets 3.6.0
Tokenizers 0.21.4
