metadata

library_name: transformers
license: apache-2.0
base_model: openai/whisper-large
tags:
  - generated_from_trainer
datasets:
  - common_voice_17_0
metrics:
  - wer
model-index:
  - name: whisper-swahili-large-v0.1
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: common_voice_17_0
          type: common_voice_17_0
          config: sw
          split: test
          args: sw
        metrics:
          - name: Wer
            type: wer
            value: 26.32352799853929

whisper-swahili-large-v0.1

This model is a fine-tuned version of openai/whisper-large on the common_voice_17_0 dataset. It achieves the following results on the evaluation set:

Loss: 0.4162
Wer: 26.3235

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: 8
seed: 42
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: 500
training_steps: 1000
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
1.1388	0.0681	250	0.6673	65.1304
0.4408	0.1362	500	0.5600	33.9259
0.3612	0.2042	750	0.4609	30.1521
0.3057	0.2723	1000	0.4162	26.3235

Framework versions

Transformers 4.52.0.dev0
Pytorch 2.6.0+cu126
Datasets 3.5.0
Tokenizers 0.21.1