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test1000v2 – AI Model by minjibi | AlphaNeural AI
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minjibi
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test1000v2
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transformers
pytorch
tensorboard
wav2vec2
automatic-speech-recognition
generated_from_trainer
apache-2.0
endpoints_compatible
us
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Model card
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test1000v2
This model is a fine-tuned version of
facebook/wav2vec2-large-xlsr-53
on the None dataset. It achieves the following results on the evaluation set:
Loss: 0.7873
Wer: 0.6162
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: 16
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 32
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 500
num_epochs: 20
mixed_precision_training: Native AMP
Training results
Training Loss
Epoch
Step
Validation Loss
Wer
7.7913
3.22
100
3.3481
1.0
3.3831
6.44
200
3.3229
1.0
3.3778
9.67
300
3.3211
1.0
3.3671
12.89
400
3.2973
1.0
3.3528
16.13
500
3.1349
1.0
1.8611
19.35
600
0.7873
0.6162
Framework versions
Transformers 4.22.2
Pytorch 1.10.0+cu102
Datasets 1.4.1
Tokenizers 0.12.1
