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
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