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wav2vec2-finetuned – AI Model by dennohpeter | AlphaNeural AI
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wav2vec2-finetuned
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transformers
tensorboard
safetensors
wav2vec2
automatic-speech-recognition
generated_from_trainer
fleurs
facebook/wav2vec2-base
finetune
apache-2.0
model-index
endpoints_compatible
us
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wav2vec2-finetuned
This model is a fine-tuned version of
facebook/wav2vec2-base
on the fleurs dataset. It achieves the following results on the evaluation set:
Loss: 0.4598
Wer: 0.9991
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: 8
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 2
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: linear
lr_scheduler_warmup_steps: 500
training_steps: 2000
mixed_precision_training: Native AMP
Training results
Training Loss
Epoch
Step
Validation Loss
Wer
0.4879
31.2540
1000
0.4964
0.9991
0.439
62.5079
2000
0.4598
0.9991
Framework versions
Transformers 4.53.0
Pytorch 2.7.1+cu126
Datasets 3.6.0
Tokenizers 0.21.2
