Whisper Tiny - finetuned on weather and horoscope
This model is a fine-tuned version of openai/whisper-tiny on the Vreme ProTV and Horoscop Neti datasets. It achieves the following results on the evaluation set:
- Loss: 0.0053
- Wer: 17.14
Model description
This is a fine-tuned version of the Whisper Tiny model, specifically adapted for Romanian language Automatic Speech Recognition (ASR) in the domains of weather forecasts and horoscopes. The model has been trained on two custom datasets to improve its performance in transcribing Romanian speech in these specific contexts.
Training procedure
The model was fine-tuned using transfer learning techniques on the pre-trained Whisper Tiny model. Two custom datasets were used: audio recordings of weather forecasts and horoscopes in Romanian.
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 3000
- mixed_precision_training: Native AMP
Training results
Epoch | Step | Validation Loss | WER |
---|---|---|---|
3.75 | 1000 | 0.1388 | 18.7298 |
7.69 | 2000 | 0.036 | 17.5637 |
11.63 | 3000 | 0.0089 | 17.3574 |
15.38 | 4000 | 0.0053 | 17.1421 |
Framework versions
- Transformers 4.39.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
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Base model
openai/whisper-tinyDatasets used to train iulik-pisik/all_data_model_tiny
Evaluation results
- Wer on Vreme ProTV and Horoscop Netitest set self-reported17.140