Automatic Speech Recognition
Transformers
PyTorch
JAX
Tamil
whisper
whisper-event
Eval Results (legacy)
Instructions to use vasista22/whisper-tamil-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vasista22/whisper-tamil-small with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="vasista22/whisper-tamil-small")# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("vasista22/whisper-tamil-small") model = AutoModelForMultimodalLM.from_pretrained("vasista22/whisper-tamil-small") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 54846aea8818a608f59bf5c5e36ca7e8686d53888a2f6b9a2123ff52a9a49b19
- Size of remote file:
- 3.63 kB
- SHA256:
- 75e7483235a07623468a8532633ae46653bc2c8d524f968df47b303a3726f6bd
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.