metadata

library_name: transformers
license: apache-2.0
base_model: openai/whisper-medium
tags:
  - generated_from_trainer
datasets:
  - bigcgen
metrics:
  - wer
model-index:
  - name: whisper-medium-bigcgen-combined-20hrs-model
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: bigcgen
          type: bigcgen
        metrics:
          - name: Wer
            type: wer
            value: 0.4210649229332088

whisper-medium-bigcgen-combined-20hrs-model

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

Loss: 0.5368
Wer: 0.4211

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: 1.75e-05
train_batch_size: 2
eval_batch_size: 2
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 8
optimizer: Use 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: 500
training_steps: 5000
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
3.7679	0.1521	200	0.8942	0.6455
3.4733	0.3042	400	0.7609	0.5650
2.6698	0.4564	600	0.6958	0.5375
2.5647	0.6085	800	0.6685	0.5496
2.1636	0.7606	1000	0.6228	0.5103
2.7265	0.9127	1200	0.5869	0.4706
1.5404	1.0654	1400	0.5990	0.4542
1.9844	1.2175	1600	0.5893	0.4643
1.6926	1.3697	1800	0.5730	0.4413
1.8654	1.5218	2000	0.5550	0.4599
1.8045	1.6739	2200	0.5445	0.4178
1.8258	1.8260	2400	0.5368	0.4211
1.543	1.9781	2600	0.5371	0.4245
0.9667	2.1308	2800	0.5587	0.4545
1.0216	2.2829	3000	0.5617	0.4096

Framework versions

Transformers 4.47.1
Pytorch 2.5.1+cu124
Datasets 3.2.0
Tokenizers 0.21.0