Instructions to use abdulsammad1090/whisper-v3turbo-Romanurdu with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use abdulsammad1090/whisper-v3turbo-Romanurdu with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="abdulsammad1090/whisper-v3turbo-Romanurdu")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("abdulsammad1090/whisper-v3turbo-Romanurdu") model = AutoModelForSpeechSeq2Seq.from_pretrained("abdulsammad1090/whisper-v3turbo-Romanurdu") - Notebooks
- Google Colab
- Kaggle
whisperturboraomanurdu_abdulsammad
This model is a fine-tuned version of openai/whisper-small on the None dataset.
Model description
More information needed
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: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3.0
Framework versions
- Transformers 4.47.1
- Pytorch 2.5.1+cu118
- Datasets 3.2.0
- Tokenizers 0.21.0
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Model tree for abdulsammad1090/whisper-v3turbo-Romanurdu
Base model
openai/whisper-small