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whisper-amharic-finetuned – AI Model by Dawit-H | AlphaNeural AI
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Dawit-H
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whisper-amharic-finetuned
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transformers
tensorboard
safetensors
whisper
automatic-speech-recognition
generated_from_trainer
endpoints_compatible
us
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whisper-amharic-finetuned
This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set:
Loss: 0.2306
Wer: 33.9300
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: 1e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 32
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED 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: 80
training_steps: 800
mixed_precision_training: Native AMP
Training results
Training Loss
Epoch
Step
Validation Loss
Wer
0.0399
2.2472
200
0.2047
36.2313
0.0184
4.4944
400
0.2085
34.5201
0.0034
6.7416
600
0.2241
34.3824
0.0014
8.9888
800
0.2306
33.9300
Framework versions
Transformers 5.10.0.dev0
Pytorch 2.9.0+cu126
Datasets 3.0.0
Tokenizers 0.22.2
