metadata

license: apache-2.0
tags:
  - hf-asr-leaderboard
  - generated_from_trainer
  - whisper-event
datasets:
  - common_voice_11_0
metrics:
  - wer
model-index:
  - name: Whisper Medium Sv
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: common_voice_11_0
          type: common_voice_11_0
          config: sv-SE
          split: test
          args: sv-SE
        metrics:
          - name: Wer
            type: wer
            value: 10.712174146734748

openai/whisper-medium

This model is a fine-tuned version of openai/whisper-medium trained on NST Swedish ASR and evaluated on Common Voice 11 testset. It achieves the following results on the evaluation set:

Loss: 0.2636
Wer: 10.7122

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: 16
seed: 42
gradient_accumulation_steps: 2
total_train_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: 5000
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
0.0746	0.2	1000	0.2904	13.4695
0.0564	0.4	2000	0.3121	13.2384
0.0532	0.6	3000	0.2862	11.9726
0.0387	0.8	4000	0.2629	11.6931
0.0279	1.14	5000	0.2636	10.7122

Framework versions

Transformers 4.26.0.dev0
Pytorch 1.13.0+cu117
Datasets 2.7.1.dev0
Tokenizers 0.13.2