my_model / README.md

End of training

782c956 almost 3 years ago

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language:
  - ru
license: apache-2.0
base_model: openai/whisper-small
tags:
  - generated_from_trainer
datasets:
  - bond005/sberdevices_golos_10h_crowd
metrics:
  - wer
model-index:
  - name: my_model - Val123val
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Sberdevices_golos_10h_crowd
          type: bond005/sberdevices_golos_10h_crowd
          args: 'split: test'
        metrics:
          - name: Wer
            type: wer
            value: 42.241139818232334

my_model - Val123val

This model is a fine-tuned version of openai/whisper-small on the Sberdevices_golos_10h_crowd dataset. It achieves the following results on the evaluation set:

Loss: 0.1761
Wer: 42.2411

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: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 500
training_steps: 5000

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
0.1521	0.91	500	0.1824	29.3408
0.0824	1.82	1000	0.1702	27.5291
0.0304	2.73	1500	0.1726	45.1046
0.0114	3.64	2000	0.1704	40.1238
0.0039	4.55	2500	0.1692	32.1903
0.0013	5.45	3000	0.1704	34.0298
0.0029	6.36	3500	0.1712	39.8976
0.0007	7.27	4000	0.1738	39.4273
0.0006	8.18	4500	0.1755	41.0664
0.0005	9.09	5000	0.1761	42.2411

Framework versions

Transformers 4.36.2
Pytorch 2.1.2+cpu
Datasets 2.16.0
Tokenizers 0.15.0