metadata

library_name: transformers
license: apache-2.0
base_model: openai/whisper-medium
tags:
  - generated_from_trainer
datasets:
  - wjbmattingly/zulu_merged_audio
metrics:
  - wer
model-index:
  - name: whisper-zulu-medium
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: wjbmattingly/zulu_merged_audio
          type: wjbmattingly/zulu_merged_audio
        metrics:
          - name: Wer
            type: wer
            value: 0.1993037098042152

whisper-zulu-medium

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

Loss: 0.2949
Wer: 0.1993

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: 16
eval_batch_size: 16
seed: 42
distributed_type: multi-GPU
num_devices: 4
total_train_batch_size: 64
total_eval_batch_size: 64
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 500
training_steps: 400
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
0.8378	1.25	100	0.7290	0.5605
0.3624	2.5	200	0.4048	0.2791
0.2279	3.75	300	0.3236	0.2187
0.1524	5.0	400	0.2949	0.1993

Framework versions

Transformers 4.45.0.dev0
Pytorch 2.0.1+cu117
Datasets 2.21.0
Tokenizers 0.19.1