metadata

license: apache-2.0
base_model: openai/whisper-large-v3
tags:
  - generated_from_trainer
datasets:
  - honzapucalek/p6_moderate
metrics:
  - wer
model-index:
  - name: p6_moderate
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: honzapucalek/p6_moderate cs
          type: honzapucalek/p6_moderate
          config: cs
          split: test
          args: cs
        metrics:
          - name: Wer
            type: wer
            value: 0.2500697155605131

p6_moderate

This model is a fine-tuned version of openai/whisper-large-v3 on the honzapucalek/p6_moderate cs dataset. It achieves the following results on the evaluation set:

Loss: 0.8973
Wer: 0.2501

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: 8
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 16
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 500
training_steps: 5000
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
0.0015	27.78	1000	0.7347	0.2520
0.0008	55.56	2000	0.7309	0.2523
0.0001	83.33	3000	0.8548	0.2531
0.0	111.11	4000	0.8869	0.2503
0.0	138.89	5000	0.8973	0.2501

Framework versions

Transformers 4.37.2
Pytorch 2.1.2+cu121
Datasets 2.16.1
Tokenizers 0.15.1