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whisper-medium-tw – AI Model by JacobLinCool | AlphaNeural AI
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whisper-medium-tw
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transformers
tensorboard
safetensors
whisper
automatic-speech-recognition
hf-asr-leaderboard
generated_from_trainer
zh
mozilla-foundation/common_voice_16_0
openai/whisper-medium
finetune
apache-2.0
endpoints_compatible
us
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Whisper Small zh-TW - Chinese
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: 0.1496
Cer: 99.9924
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: 4
seed: 42
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
Cer
0.1829
0.66
1000
0.1742
100.0076
0.0495
1.33
2000
0.1629
99.9824
0.044
1.99
3000
0.1497
99.9849
0.0193
2.65
4000
0.1496
99.9924
Framework versions
Transformers 4.36.2
Pytorch 2.1.0.post301
Datasets 2.16.1
Tokenizers 0.15.0
