README.md

metadata

language:
  - sr
license: apache-2.0
base_model: openai/whisper-large-v3
tags:
  - generated_from_trainer
datasets:
  - mozilla-foundation/common_voice_16_1
  - google/fleurs
  - Sagicc/audio-lmb-ds
  - classla/ParlaSpeech-RS
metrics:
  - wer
model-index:
  - name: Whisper Large v2
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice 16.1
          type: mozilla-foundation/common_voice_16_1
          config: sr
          split: test
          args: sr
        metrics:
          - name: Wer
            type: wer
            value: 0.06891082129009517

Whisper Large v2

This model is a fine-tuned version of openai/whisper-large-v3 on the Common Voice 16.1 dataset. It achieves the following results on the evaluation set:

• Loss: 0.1401
• Wer Ortho: 0.1663
• Wer: 0.0689

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
0.1691	0.03	500	0.1776	0.2060	0.0941
0.1538	0.05	1000	0.1459	0.1743	0.0730
0.1522	0.08	1500	0.1401	0.1663	0.0689

Framework versions

• Transformers 4.37.2
• Pytorch 2.0.1+cu117
• Datasets 2.16.1
• Tokenizers 0.15.1