Instructions to use Aravindhan0107/whisper-small-ex with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Aravindhan0107/whisper-small-ex with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Aravindhan0107/whisper-small-ex")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("Aravindhan0107/whisper-small-ex") model = AutoModelForSpeechSeq2Seq.from_pretrained("Aravindhan0107/whisper-small-ex") - Notebooks
- Google Colab
- Kaggle
whisper-small-ex
This model is a fine-tuned version of openai/whisper-tiny on an unknown 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: 1e-05
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 5000
Training results
Framework versions
- Transformers 4.38.2
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
- Downloads last month
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Model tree for Aravindhan0107/whisper-small-ex
Base model
openai/whisper-tiny