README.md

metadata

language:
  - sr
license: apache-2.0
base_model: openai/whisper-medium
tags:
  - generated_from_trainer
datasets:
  - mozilla-foundation/common_voice_13_0
metrics:
  - wer
model-index:
  - name: Whisper Large v3 cmb
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice 13
          type: mozilla-foundation/common_voice_13_0
          config: sr
          split: test
          args: sr
        metrics:
          - name: Wer
            type: wer
            value: 0.04148566463944396

Whisper Large v3 cmb

This model is a fine-tuned version of openai/whisper-medium on the Common Voice 13 dataset. It achieves the following results on the evaluation set:

• Loss: 0.1111
• Wer Ortho: 0.1339
• Wer: 0.0415

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: 2
• eval_batch_size: 8
• seed: 42
• gradient_accumulation_steps: 8
• total_train_batch_size: 16
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 50
• training_steps: 1500
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer Ortho	Wer
0.2766	0.48	500	0.1350	0.1670	0.0595
0.2813	0.95	1000	0.1134	0.1426	0.0491
0.1858	1.43	1500	0.1111	0.1339	0.0415

Framework versions

• Transformers 4.35.2
• Pytorch 2.0.1+cu117
• Datasets 2.14.5
• Tokenizers 0.14.1