metadata

language:
  - sw
license: apache-2.0
base_model: openai/whisper-large
tags:
  - hf-asr-leaderboard
  - generated_from_trainer
datasets:
  - mozilla-foundation/common_voice_14_0
metrics:
  - wer
model-index:
  - name: Whisper small  - Denis Musinguzi
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice 14.0
          type: mozilla-foundation/common_voice_14_0
          config: sw
          split: None
          args: 'config: sw, split: test'
        metrics:
          - name: Wer
            type: wer
            value: 0.25130933149495305

Whisper small - Denis Musinguzi

This model is a fine-tuned version of openai/whisper-large on the Common Voice 14.0 dataset. It achieves the following results on the evaluation set:

Loss: 0.4428
Wer: 0.2513
Cer: 0.0983

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: 32
eval_batch_size: 32
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 500
training_steps: 10000
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Cer	Validation Loss	Wer
0.9179	0.51	800	0.1412	0.5355	0.3693
0.3078	1.02	1600	0.1196	0.4343	0.3152
0.1959	1.53	2400	0.1172	0.4068	0.2822
0.1737	2.04	3200	0.1145	0.3922	0.2721
0.1046	2.55	4000	0.1084	0.3958	0.2634
0.1019	3.06	4800	0.1029	0.3957	0.2578
0.0588	3.57	5600	0.1132	0.4013	0.2666
0.0545	4.08	6400	0.1009	0.4112	0.2510
0.0305	4.59	7200	0.0941	0.4183	0.2442
0.0275	5.1	8000	0.1005	0.4303	0.2549
0.0153	5.61	8800	0.4374	0.2407	0.0908
0.014	6.12	9600	0.4428	0.2513	0.0983

Framework versions

Transformers 4.38.1
Pytorch 2.2.1
Datasets 2.17.0
Tokenizers 0.15.2