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
  - Sagicc/audio-lmb-ds
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

Whisper Medium sr v2

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

Loss: 0.2216
Wer Ortho: 0.1663
Wer: 0.0738

Model description

This is a fine tunned on merged datasets Common Voice 16 + Fleurs + Juzne vesti (South news) + LBM

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
0.3634	0.40	500	0.1619	0.1953	0.0921
0.3185	0.81	1000	0.1423	0.175	0.0800
0.2216	1.21	1500	0.137	0.1663	0.0738

Framework versions

Transformers 4.35.2
Pytorch 2.0.1+cu117
Datasets 2.14.5
Tokenizers 0.14.1