Instructions to use Kathernie/vasista-whisper-small-ta_moe with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Kathernie/vasista-whisper-small-ta_moe with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Kathernie/vasista-whisper-small-ta_moe")# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("Kathernie/vasista-whisper-small-ta_moe") model = AutoModelForMultimodalLM.from_pretrained("Kathernie/vasista-whisper-small-ta_moe") - Notebooks
- Google Colab
- Kaggle
Whisper Small Tamil Filtered
This model is a fine-tuned version of vasista22/whisper-tamil-small on the Learn Tamil 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- 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: 200
- training_steps: 1000
- mixed_precision_training: Native AMP
Framework versions
- Transformers 4.37.2
- Pytorch 2.1.1
- Datasets 2.15.0
- Tokenizers 0.15.1
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Model tree for Kathernie/vasista-whisper-small-ta_moe
Base model
vasista22/whisper-tamil-small