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whisper_medium – AI Model by anhphuong | AlphaNeural AI
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whisper_medium
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transformers
tensorboard
safetensors
whisper
automatic-speech-recognition
generated_from_trainer
vi
mozilla-foundation/common_voice_11_0
openai/whisper-medium
finetune
apache-2.0
model-index
endpoints_compatible
us
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Whisper Medium Vi - Anh Phuong
This model is a fine-tuned version of
openai/whisper-medium
on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set:
Loss: 0.6608
Wer: 21.8884
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: 4
eval_batch_size: 4
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 16
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 500
training_steps: 4000
mixed_precision_training: Native AMP
Training results
Training Loss
Epoch
Step
Validation Loss
Wer
0.0213
5.7637
1000
0.5477
23.9281
0.0012
11.5274
2000
0.6165
22.5354
0.0001
17.2911
3000
0.6494
21.8664
0.0001
23.0548
4000
0.6608
21.8884
Framework versions
Transformers 4.41.2
Pytorch 2.3.0+cu121
Datasets 2.20.0
Tokenizers 0.19.1
