metadata

language:
  - sr
license: apache-2.0
base_model: openai/whisper-large-v3
tags:
  - generated_from_trainer
datasets:
  - mozilla-foundation/common_voice_13_0
  - google/fleurs
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-large-v3 on the Common Voice 13, Google Fleurs and juzne vesti dataset. It achieves the following results on the evaluation set:

Loss: 0.1111
Wer Ortho: 0.1339
Wer: 0.0415

Model description

Dataset Juzne vesti is published by

Rupnik, Peter and Ljubešić, Nikola, 2022,
ASR training dataset for Serbian JuzneVesti-SR v1.0, Slovenian language resource repository CLARIN.SI, ISSN 2820-4042,
http://hdl.handle.net/11356/1679.

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