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whisper-base-sw – AI Model by emason | AlphaNeural AI
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whisper-base-sw
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transformers
tensorboard
safetensors
whisper
automatic-speech-recognition
generated_from_trainer
sw
mozilla-foundation/common_voice_17_0
openai/whisper-base
finetune
apache-2.0
model-index
endpoints_compatible
us
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Whisper Base Swahili - Munene Mutuma
This model is a fine-tuned version of
openai/whisper-base
on the Common Voice 17.0 dataset. It achieves the following results on the evaluation set:
Loss: 0.6033
Wer: 39.3269
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: 64
eval_batch_size: 32
seed: 42
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_steps: 500
training_steps: 5000
mixed_precision_training: Native AMP
Training results
Training Loss
Epoch
Step
Validation Loss
Wer
0.5121
1.0893
1000
0.7500
50.1768
0.3802
2.1786
2000
0.6447
43.3016
0.31
3.2680
3000
0.6139
39.4864
0.2768
4.3573
4000
0.6029
39.2423
0.266
5.4466
5000
0.6033
39.3269
Framework versions
Transformers 4.48.0
Pytorch 2.7.0a0+79aa17489c.nv25.04
Datasets 3.6.0
Tokenizers 0.21.4
