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wave2vec – AI Model by AAK1423 | AlphaNeural AI
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AAK1423
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wave2vec
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transformers
tensorboard
safetensors
wav2vec2
automatic-speech-recognition
generated_from_trainer
facebook/wav2vec2-xls-r-300m
finetune
apache-2.0
endpoints_compatible
us
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wave2vec
This model is a fine-tuned version of
facebook/wav2vec2-xls-r-300m
on the None dataset. It achieves the following results on the evaluation set:
Loss: 0.0799
Wer: 0.1099
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: 0.0002
train_batch_size: 8
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 4
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_ratio: 0.1
num_epochs: 10
mixed_precision_training: Native AMP
Training results
Training Loss
Epoch
Step
Validation Loss
Wer
8.6004
1.9561
200
3.2878
1.0
2.0684
3.9171
400
0.8551
0.8022
0.2506
5.8780
600
0.1461
0.1567
0.0596
7.8390
800
0.1018
0.1296
0.0289
9.8
1000
0.0799
0.1099
Framework versions
Transformers 4.49.0
Pytorch 2.5.1+cu124
Datasets 3.3.2
Tokenizers 0.21.0
