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Teamim-AllNusah-whisper-medium_Warmup_steps-1000_LR-1e-05_Random-True – AI Model by cantillation | AlphaNeural AI
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cantillation
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Teamim-AllNusah-whisper-medium_Warmup_steps-1000_LR-1e-05_Random-True
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transformers
tensorboard
safetensors
whisper
automatic-speech-recognition
hf-asr-leaderboard
generated_from_trainer
he
openai/whisper-medium
finetune
apache-2.0
endpoints_compatible
us
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he-cantillation
This model is a fine-tuned version of
openai/whisper-medium
on an unknown dataset. It achieves the following results on the evaluation set:
eval_loss: 0.1666
eval_wer: 10.9128
eval_avg_precision_Exact: 0.9184
eval_avg_recall_Exact: 0.9197
eval_avg_f1_Exact: 0.9188
eval_avg_precision_Letter_Shift: 0.9365
eval_avg_recall_Letter_Shift: 0.9379
eval_avg_f1_Letter_Shift: 0.9369
eval_avg_precision_Word_Level: 0.9382
eval_avg_recall_Word_Level: 0.9395
eval_avg_f1_Word_Level: 0.9385
eval_avg_precision_Word_Shift: 0.9779
eval_avg_recall_Word_Shift: 0.9797
eval_avg_f1_Word_Shift: 0.9784
eval_precision_median_exact: 1.0
eval_recall_median_exact: 1.0
eval_f1_median_exact: 1.0
eval_precision_max_exact: 1.0
eval_recall_max_exact: 1.0
eval_f1_max_exact: 1.0
eval_precision_min_Exact: 0.0
eval_recall_min_Exact: 0.0
eval_f1_min_Exact: 0.0
eval_precision_min_Letter_Shift: 0.0
eval_recall_min_Letter_Shift: 0.0
eval_f1_min_Letter_Shift: 0.0
eval_precision_min_Word_Level: 0.0
eval_recall_min_Word_Level: 0.0
eval_f1_min_Word_Level: 0.0
eval_precision_min_Word_Shift: 0.1429
eval_recall_min_Word_Shift: 0.1111
eval_f1_min_Word_Shift: 0.125
eval_runtime: 1554.5785
eval_samples_per_second: 1.732
eval_steps_per_second: 0.055
epoch: 4.0
step: 50000
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: 32
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 1000
training_steps: 50000
mixed_precision_training: Native AMP
Framework versions
Transformers 4.39.0.dev0
Pytorch 2.2.1+cu121
Datasets 2.16.1
Tokenizers 0.15.0
