metadata

language:
  - sr
license: apache-2.0
base_model: openai/whisper-medium
tags:
  - generated_from_trainer
datasets:
  - mozilla-foundation/common_voice_13_0
  - google/fleurs
metrics:
  - wer
model-index:
  - name: Whisper Medium 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: 0.0658123370981755

Update

Use an updated fine tunned version Sagicc/whisper-medium-sr-v2 with new 10+ hours of dataset.

Whisper Medium 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: 0.1374
Wer Ortho: 0.1589
Wer: 0.0658

Model description

This is a fine tunned on merged datasets Common Voice 13 + Fleurs + Juzne vesti (South news)

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: 4
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 4
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
0.342	0.48	500	0.1604	0.1863	0.0862
0.3454	0.95	1000	0.1388	0.1589	0.0667
0.2247	1.43	1500	0.1374	0.1589	0.0658

Framework versions

Transformers 4.35.2
Pytorch 2.0.1+cu117
Datasets 2.14.5
Tokenizers 0.14.1