metadata

license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - fleurs
metrics:
  - wer
model-index:
  - name: whisper-small-amet
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: fleurs
          type: fleurs
          config: am_et
          split: validation
          args: am_et
        metrics:
          - name: Wer
            type: wer
            value: 103.08219178082192

whisper-small-amet

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

Loss: 6.8839
Wer: 103.0822

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: 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: 2000
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
0.9013	100.0	100	2.7090	171.5753
0.0002	200.0	200	3.7303	298.6301
0.0001	300.0	300	3.8287	239.3836
0.0001	400.0	400	3.8877	234.9315
0.0001	500.0	500	4.0561	316.4384
0.0001	600.0	600	4.2706	189.0411
0.0	700.0	700	4.4524	229.4521
0.0	800.0	800	4.6250	308.5616
0.0	900.0	900	4.7844	429.4521
0.0405	1000.0	1000	4.6182	206.8493
0.0002	1100.0	1100	5.5423	159.9315
0.0002	1200.0	1200	6.0517	151.7123
0.0002	1300.0	1300	6.3493	154.7945
0.0002	1400.0	1400	6.5431	138.6986
0.0002	1500.0	1500	6.6699	158.5616
0.0001	1600.0	1600	6.7591	160.2740
0.0001	1700.0	1700	6.8209	103.0822
0.0001	1800.0	1800	6.8562	103.0822
0.0001	1900.0	1900	6.8758	103.0822
0.0001	2000.0	2000	6.8839	103.0822

Framework versions

Transformers 4.26.0.dev0
Pytorch 1.13.1+cu117
Datasets 2.8.1.dev0
Tokenizers 0.13.2