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asr_2500 – AI Model by William-Ndoni-254 | AlphaNeural AI
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asr_2500
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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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asr_2500
This model is a fine-tuned version of
facebook/wav2vec2-xls-r-300m
on an unknown dataset. It achieves the following results on the evaluation set:
Loss: 0.4736
Wer: 0.3085
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: 0.0003
train_batch_size: 8
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 4
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: 30
mixed_precision_training: Native AMP
Training results
Training Loss
Epoch
Step
Validation Loss
Wer
3.6647
7.3059
400
0.6404
0.5682
0.2773
14.6119
800
0.4814
0.4010
0.0989
21.9178
1200
0.4779
0.3385
0.0534
29.2237
1600
0.4736
0.3085
Framework versions
Transformers 4.40.1
Pytorch 2.2.1+cu121
Datasets 2.19.0
Tokenizers 0.19.1
