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Whisper Waxal Checkpoints

This model is a fine-tuned version of openai/whisper-small on the None dataset. It achieves the following results on the evaluation set: Loss: 0.5937 Wer: 26.8326 Cer: 9.9005

ArtText modelBasismodellopenai/whisper-smallVersionmodelCBewertung

Modellquelle

Art
Text model
Dateigröße
922 MB
Basismodell
openai/whisper-small
Version
vmodel
Aktualisiert
27. Sept. 2026
Downloads
3
Quelle
Hugging Face

Quellenbeschreibung

whisper-waxal-checkpoints

This model is a fine-tuned version of openai/whisper-small on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.5937
  • Wer: 26.8326
  • Cer: 9.9005

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: 1
  • eval_batch_size: 1
  • seed: 42
  • gradient_accumulation_steps: 8
  • total_train_batch_size: 8
  • optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 200
  • training_steps: 4000
  • mixed_precision_training: Native AMP

Training results

Training LossEpochStepCerValidation LossWer
3.25370.062525030.96240.934363.2244
2.48870.12550026.62790.761553.3804
3.98300.187575012.02670.739532.5904
3.95340.25100011.61500.723431.8722
3.28360.3125125011.43640.701530.9807
3.97590.375150011.17410.676729.9282
3.72710.4375175011.07020.665329.7424
4.42420.5200010.70040.649129.0738
3.65410.5625225010.79150.633528.6033
3.22830.625250010.38150.630128.2442
2.90010.6875275010.39610.625928.0461
3.04380.75300010.37610.617127.7737
3.55000.8125325010.07910.609327.1421
3.53080.875350010.00800.599426.8945
3.66160.937537500.593326.70889.9297
3.74141.040000.593726.83269.9005

Framework versions

  • Transformers 5.13.1
  • Pytorch 2.11.0+cu128
  • Datasets 5.0.0
  • Tokenizers 0.22.2

Quellen

1 Quelle
Hugging FacePrimärVersion 566ff8ceb79bf4988749726b236b9266febb6874
huggingface.co/medbechir/whisper-waxal-checkpoints/tree/566ff8ceb79bf4988749726b236b9266febb6874

Verifiziert 30. Aug.

Modellartefakte

1 Artefakt

model.safetensors

safetensors · 922 MB · SHA-256 aa6086593d34…a594 · Hugging Face

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Quellenauszüge

2 Auszüge

--- library_name: transformers license: apache-2.0 base_model: openai/whisper-small tags: - generated_from_trainer metrics: - wer model-index: - name: whisper-waxal-checkpoints results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # whisper-waxal-checkpoints This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5937 - Wer: 26.8326 - Cer: 9.9005 ## 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: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 200 - training_steps: 4000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Cer | Validation Loss | Wer | |:-------------:|:------:|:----:|:-------:|:---------------:|:-------:| | 3.2537 | 0.0625 | 250 | 30.9624 | 0.9343 | 63.2244 | | 2.4887 | 0.125 | 500 | 26.6279 | 0.7615 | 53.3804 | | 3.9830 | 0.1875 | 750 | 12.0267 | 0.7395 | 32.5904 | | 3.9534 | 0.25 | 1000 | 11.6150 | 0.7234 | 31.8722 | | 3.2836 | 0.3125 | 1250 | 11.4364 | 0.7015 | 30.9807 | | 3.9759 | 0.375 | 1500 | 11.1741 | 0.6767 | 29.9282 | | 3.7271 | 0.4375 | 1750 | 11.0702 | 0.6653 | 29.7424 | | 4.4242 | 0.5 | 2000 | 10.7004 | 0.6491 | 29.0738 | | 3.6541 | 0.5625 | 2250 | 10.7915 | 0.6335 | 28.6033 | | 3.2283 | 0.625 | 2500 | 10.3815 | 0.6301 | 28.2442 | | 2.9001 | 0.6875 | 2750 | 10.3961 | 0.6259 | 28.0461 | | 3.0438 | 0.75 | 3000 | 10.3761 | 0.6171 | 27.7737 | | 3.5500 | 0.8125 | 3250 | 10.0791 | 0.6093 | 27.1421 | | 3.5308 | 0.875 | 3500 | 10.0080 | 0.5994 | 26.8945 | | 3.6616 | 0.9375 | 3750 | 0.5933 | 26.7088 | 9.9297 | | 3.7414 | 1.0 | 4000 | 0.5937 | 26.8326 | 9.9005 | ### Framework versions - Transformers 5.13...

Source context: 3 downloads · 0 likes · Pipeline automatic-speech-recognition · Library transformers · Repo medbechir/whisper-waxal-checkpoints