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wav2vec2-base-demo-colab – AI Model by Munia-ak | AlphaNeural AI | AlphaNeural AI
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Munia-ak
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wav2vec2-base-demo-colab
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transformers
tensorboard
safetensors
wav2vec2
automatic-speech-recognition
generated_from_trainer
facebook/wav2vec2-base
finetune
apache-2.0
endpoints_compatible
us
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wav2vec2-base-demo-colab
This model is a fine-tuned version of
facebook/wav2vec2-base
on the None dataset. It achieves the following results on the evaluation set:
Loss: 0.3900
Wer: 0.8889
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: 0.0001
train_batch_size: 32
eval_batch_size: 8
seed: 42
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: 1000
num_epochs: 5
mixed_precision_training: Native AMP
Training results
Training Loss
Epoch
Step
Validation Loss
Wer
0.7643
2.5381
500
0.3900
0.8889
Framework versions
Transformers 4.52.4
Pytorch 2.6.0+cu124
Datasets 3.6.0
Tokenizers 0.21.1
