arielcerdap/whisper-medium-fluencybank

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Whisper fine-tuned on FluencyBank — openai/whisper-medium

This model is a fine-tuned version of openai/whisper-medium on the FluencyBank Timestamped dataset. It achieves the following results on the evaluation set:

• —Loss: 1.8983
• —Wer: 15.9086
• —Cer: 10.9154

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: 8e-06
• —trainbatchsize: 32
• —evalbatchsize: 32
• —seed: 42
• —gradientaccumulationsteps: 4
• —totaltrainbatch_size: 128
• —optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• —lrschedulertype: cosine
• —lrschedulerwarmup_ratio: 0.1
• —training_steps: 2500
• —labelsmoothingfactor: 0.1

Training results

Framework versions

• —Transformers 4.45.2
• —Pytorch 2.10.0+cu128
• —Datasets 4.0.0
• —Tokenizers 0.20.3