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wav2vec2-base-Malawi-small – AI Model by Anujgr8 | AlphaNeural AI
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Anujgr8
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wav2vec2-base-Malawi-small
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transformers
tensorboard
safetensors
wav2vec2
automatic-speech-recognition
generated_from_trainer
facebook/wav2vec2-base-960h
finetune
apache-2.0
endpoints_compatible
us
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wav2vec2-base-Malawi-small
This model is a fine-tuned version of
facebook/wav2vec2-base-960h
on the None dataset. It achieves the following results on the evaluation set:
Loss: 0.8095
Wer: 0.4636
Cer: 0.1534
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: 6
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 24
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 500
training_steps: 900
mixed_precision_training: Native AMP
Training results
Training Loss
Epoch
Step
Validation Loss
Wer
Cer
7.3684
14.6341
300
3.2704
1.0
1.0
1.9223
29.2683
600
0.8459
0.5406
0.1849
0.2949
43.9024
900
0.8095
0.4636
0.1534
Framework versions
Transformers 4.44.2
Pytorch 2.5.0+cu121
Datasets 1.18.3
Tokenizers 0.19.1
