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whisper-medium-aug-05-june – AI Model by PhanithLIM | AlphaNeural AI
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PhanithLIM
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whisper-medium-aug-05-june
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transformers
safetensors
whisper
automatic-speech-recognition
generated_from_trainer
km
PhanithLIM/ams-speech-dataset
openslr/openslr
google/fleurs
PhanithLIM/kh-wmc
PhanithLIM/wmc-international-news
PhanithLIM/rfi-news-dataset
PhanithLIM/aakanee-kh
rinabuoy/khm-asr-open
seanghay/khmer_grkpp_speech
seanghay/khmer_mpwt_speech
seanghay/km-speech-corpus
openai/whisper-medium
finetune
apache-2.0
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whisper-medium-aug-05-june
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: 0.0721
Wer: 78.5554
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: 2
total_train_batch_size: 16
optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: constant
lr_scheduler_warmup_steps: 1000
num_epochs: 5
mixed_precision_training: Native AMP
Training results
Training Loss
Epoch
Step
Validation Loss
Wer
0.1867
1.0
2847
0.0867
78.8824
0.0689
2.0
5694
0.0720
75.8348
0.0485
3.0
8541
0.0706
77.7656
0.0362
4.0
11388
0.0690
77.5133
0.0274
5.0
14235
0.0721
78.5554
Framework versions
Transformers 4.51.3
Pytorch 2.7.0+cu128
Datasets 3.6.0
Tokenizers 0.21.1
