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Whisper Medium Combined Lin Sna Lug Olusola

This model is a fine-tuned version of openai/whisper-medium on the None dataset. It achieves the following results on the evaluation set: Loss: 0.5651 Wer: 0.3436 Cer: 0.1221

Fuentehuggingface.co/samolusola-personal/whisper-medium-combined-lin-sna-lug-olusola/tree/975065e3cf15803d6b0636b0731fc6c4c14d02fa
TipoText modelModelo baseopenai/whisper-mediumVersión

Fuente del modelo

Tipo
Text model
Tamaño del archivo
2,85 GB
Modelo base
openai/whisper-medium
Versión
vmodel
Actualizado
14 sept 2026
Descargas
2
Fuente
Hugging Face

Descripción de la fuente

whisper-medium-combined-lin-sna-lug-olusola

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

  • Loss: 0.5651
  • Wer: 0.3436
  • Cer: 0.1221

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: 0.0001
  • train_batch_size: 8
  • eval_batch_size: 16
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 32
  • 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: 500
  • num_epochs: 30

Training results

Training LossEpochStepValidation LossWerCer
2.55400.50215000.63110.46090.1696
2.03451.004010000.55280.38690.1481
1.71641.506215000.53350.43070.1922
1.38032.008020000.51640.46130.1879
1.26232.510225000.50280.32830.1112
0.99713.012130000.52250.35350.1288
0.89553.514235000.53320.33750.1205
0.70474.016140000.56510.34360.1221

Framework versions

  • Transformers 5.12.1
  • Pytorch 2.11.0+cu128
  • Datasets 4.0.0
  • Tokenizers 0.22.2

Fuentes

1 fuente
Hugging FacePrincipalVersión 975065e3cf15803d6b0636b0731fc6c4c14d02fa
huggingface.co/samolusola-personal/whisper-medium-combined-lin-sna-lug-olusola/tree/975065e3cf15803d6b0636b0731fc6c4c14d02fa

Verificado 14 sept

Artefactos del modelo

1 artefacto

last-checkpoint/model.safetensors

safetensors · 2,85 GB · SHA-256 f59a3d76998b…0bf7 · Hugging Face

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Extractos de fuentes

2 extractos
Readme14 septhuggingface.co/samolusola-personal/whisper-medium-combined-lin-sna-lug-olusola

--- library_name: transformers license: apache-2.0 base_model: openai/whisper-medium tags: - generated_from_trainer metrics: - wer model-index: - name: whisper-medium-combined-lin-sna-lug-olusola 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-medium-combined-lin-sna-lug-olusola This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5651 - Wer: 0.3436 - Cer: 0.1221 ## 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: 0.0001 - train_batch_size: 8 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - 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: 500 - num_epochs: 30 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |:-------------:|:------:|:----:|:---------------:|:------:|:------:| | 2.5540 | 0.5021 | 500 | 0.6311 | 0.4609 | 0.1696 | | 2.0345 | 1.0040 | 1000 | 0.5528 | 0.3869 | 0.1481 | | 1.7164 | 1.5062 | 1500 | 0.5335 | 0.4307 | 0.1922 | | 1.3803 | 2.0080 | 2000 | 0.5164 | 0.4613 | 0.1879 | | 1.2623 | 2.5102 | 2500 | 0.5028 | 0.3283 | 0.1112 | | 0.9971 | 3.0121 | 3000 | 0.5225 | 0.3535 | 0.1288 | | 0.8955 | 3.5142 | 3500 | 0.5332 | 0.3375 | 0.1205 | | 0.7047 | 4.0161 | 4000 | 0.5651 | 0.3436 | 0.1221 | ### Framework versions - Transformers 5.12.1 - Pytorch 2.11.0+cu128 - Datasets 4.0.0 - Tokenizers 0.22.2

Note14 septhuggingface.co/samolusola-personal/whisper-medium-combined-lin-sna-lug-olusola

Source context: 2 downloads · 0 likes · Pipeline automatic-speech-recognition · Library transformers · Repo samolusola-personal/whisper-medium-combined-lin-sna-lug-olusola