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whisper-medium-ft – AI Model by nocturneFlow | AlphaNeural AI
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whisper-medium-ft
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transformers
tensorboard
safetensors
whisper
automatic-speech-recognition
generated_from_trainer
kk
google/fleurs
mozilla-foundation/common_voice_17_0
openai/whisper-medium
finetune
apache-2.0
endpoints_compatible
us
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Whisper Medium KK - Kazakh - Fleurs - Common Voice
This model is a fine-tuned version of
openai/whisper-medium
on the None dataset. It achieves the following results on the evaluation set:
Loss: 0.3910
Wer: 21.2101
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 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
0.0045
7.5725
1000
0.3121
23.2826
0.0003
15.1507
2000
0.3523
21.3939
0.0001
22.7232
3000
0.3738
21.3661
0.0001
30.3013
4000
0.3863
21.3772
0.0001
37.8738
5000
0.3910
21.2101
Framework versions
Transformers 4.51.3
Pytorch 2.6.0+cu118
Datasets 3.6.0
Tokenizers 0.21.0
