whisper-large-v3-turbo-verbatim-3-lora

This model is a fine-tuned version of openai/whisper-large-v3-turbo on the JacobLinCool/ami-disfluent dataset. It achieves the following results on the evaluation set:

• Loss: 0.1459
• Wer: 7.7269
• Cer: 3.2519
• Decode Runtime: 111.0004
• Wer Runtime: 0.0705
• Cer Runtime: 0.0932

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: 5e-05
• train_batch_size: 4
• eval_batch_size: 32
• seed: 42
• gradient_accumulation_steps: 16
• total_train_batch_size: 64
• optimizer: Use OptimizerNames.ADAMW_TORCH 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: 100
• training_steps: 1000

Training results

Framework versions

• PEFT 0.14.0
• Transformers 4.48.0
• Pytorch 2.4.1+cu124
• Datasets 3.2.0
• Tokenizers 0.21.0