Hugging Face's logo

cantillation
/
Teamim-large-v2-pd1-e1_WeightDecay-0.05_Augmented_Combined-Data_date-14-07-2024_18-24

Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
Hebrew
whisper
hf-asr-leaderboard
Generated from Trainer
Inference Endpoints
Model card Files Metrics Community
Teamim-large-v2-pd1-e1_WeightDecay-0.05_Augmented_Combined-Data_date-14-07-2024_18-24 / README.md
cantillation's picture
cantillation
End of training
f9fd0ed verified
preview code
|
raw
history blame contribute delete
10.2 kB
metadata
language:
  - he
base_model: ivrit-ai/whisper-v2-pd1-e1
tags:
  - hf-asr-leaderboard
  - generated_from_trainer
metrics:
  - wer
model-index:
  - name: he-cantillation
    results: []

he-cantillation

This model is a fine-tuned version of ivrit-ai/whisper-v2-pd1-e1 on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.0952
  • Wer: 7.8511
  • Avg Precision Exact: 0.9335
  • Avg Recall Exact: 0.9352
  • Avg F1 Exact: 0.9340
  • Avg Precision Letter Shift: 0.9440
  • Avg Recall Letter Shift: 0.9458
  • Avg F1 Letter Shift: 0.9446
  • Avg Precision Word Level: 0.9462
  • Avg Recall Word Level: 0.9479
  • Avg F1 Word Level: 0.9467
  • Avg Precision Word Shift: 0.9714
  • Avg Recall Word Shift: 0.9736
  • Avg F1 Word Shift: 0.9721
  • Precision Median Exact: 1.0
  • Recall Median Exact: 1.0
  • F1 Median Exact: 1.0
  • Precision Max Exact: 1.0
  • Recall Max Exact: 1.0
  • F1 Max Exact: 1.0
  • Precision Min Exact: 0.0
  • Recall Min Exact: 0.0
  • F1 Min Exact: 0.0
  • Precision Min Letter Shift: 0.0
  • Recall Min Letter Shift: 0.0
  • F1 Min Letter Shift: 0.0
  • Precision Min Word Level: 0.0
  • Recall Min Word Level: 0.0
  • F1 Min Word Level: 0.0
  • Precision Min Word Shift: 0.1429
  • Recall Min Word Shift: 0.125
  • F1 Min Word Shift: 0.1333

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: 500
  • training_steps: 80000
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer Avg Precision Exact Avg Recall Exact Avg F1 Exact Avg Precision Letter Shift Avg Recall Letter Shift Avg F1 Letter Shift Avg Precision Word Level Avg Recall Word Level Avg F1 Word Level Avg Precision Word Shift Avg Recall Word Shift Avg F1 Word Shift Precision Median Exact Recall Median Exact F1 Median Exact Precision Max Exact Recall Max Exact F1 Max Exact Precision Min Exact Recall Min Exact F1 Min Exact Precision Min Letter Shift Recall Min Letter Shift F1 Min Letter Shift Precision Min Word Level Recall Min Word Level F1 Min Word Level Precision Min Word Shift Recall Min Word Shift F1 Min Word Shift
No log 0.0001 1 5.0835 121.5079 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0.0423 0.5167 10000 0.1010 13.7858 0.8705 0.8797 0.8745 0.8866 0.8961 0.8908 0.8899 0.8991 0.8939 0.9426 0.9519 0.9466 0.9286 0.9412 0.9474 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0139 1.0334 20000 0.0950 10.6832 0.9090 0.9076 0.9079 0.9219 0.9205 0.9208 0.9251 0.9237 0.9239 0.9610 0.9611 0.9605 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1429 0.1111 0.125
0.0089 1.5501 30000 0.0914 10.2458 0.9091 0.9077 0.9081 0.9208 0.9196 0.9198 0.9231 0.9220 0.9222 0.9596 0.9590 0.9589 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.125 0.1111
0.0032 2.0668 40000 0.0922 9.3269 0.9163 0.9159 0.9157 0.9282 0.9279 0.9277 0.9307 0.9303 0.9301 0.9666 0.9676 0.9667 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0909 0.1111 0.1176
0.0025 2.5834 50000 0.0924 9.0500 0.9171 0.9179 0.9172 0.9283 0.9292 0.9284 0.9307 0.9314 0.9307 0.9656 0.9669 0.9659 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1429 0.125 0.1333
0.0022 3.1001 60000 0.0933 8.3137 0.9272 0.9266 0.9266 0.9377 0.9371 0.9371 0.9399 0.9393 0.9393 0.9702 0.9702 0.9698 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1429 0.125 0.1333
0.0006 3.6168 70000 0.0947 8.0682 0.9287 0.9302 0.9291 0.9393 0.9409 0.9398 0.9417 0.9430 0.9420 0.9706 0.9723 0.9710 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1429 0.125 0.1333
0.0002 4.1335 80000 0.0952 7.8511 0.9335 0.9352 0.9340 0.9440 0.9458 0.9446 0.9462 0.9479 0.9467 0.9714 0.9736 0.9721 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1429 0.125 0.1333

Framework versions

  • Transformers 4.41.2
  • Pytorch 2.2.1
  • Datasets 2.20.0
  • Tokenizers 0.19.1