metadata

language:
  - fi
license: apache-2.0
tags:
  - whisper-event
  - generated_from_trainer
datasets:
  - mozilla-foundation/common_voice_11_0
metrics:
  - wer
model-index:
  - name: Whisper Small Finnish all
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: mozilla-foundation/common_voice_11_0 fi
          type: mozilla-foundation/common_voice_11_0
          config: fi
          split: test
          args: fi
        metrics:
          - name: Wer
            type: wer
            value: 25.43330821401658

Whisper Small Finnish all

This model is a fine-tuned version of openai/whisper-small on the mozilla-foundation/common_voice_11_0 fi dataset. It achieves the following results on the evaluation set:

Loss: 0.5334
Wer: 25.4333

Model description

The Model is fine-tuned for 5000 steps/updates on CV11 Finnish train+valiation data.

Zero-shot - 30.5 (CV9 test data, even on CV11 the WER is closer a bit higher than this)
Fine-tuned - 25.43 (CV11 test data)

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: 64
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: 5000
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
0.0025	19.0	1000	0.4265	24.8493
0.0005	38.0	2000	0.4785	25.3203
0.0003	57.01	3000	0.5073	25.3956
0.0002	76.01	4000	0.5253	25.4333
0.0002	96.0	5000	0.5334	25.4333

Framework versions

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