Instructions to use vrclc/whisper-small-ta-en with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vrclc/whisper-small-ta-en with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="vrclc/whisper-small-ta-en")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("vrclc/whisper-small-ta-en") model = AutoModelForSpeechSeq2Seq.from_pretrained("vrclc/whisper-small-ta-en", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f1d2e0abcb8ab281feedc0e1760d2150c2e11b2d85d9d2b1c6d80610cd28052e
- Size of remote file:
- 5.56 kB
- SHA256:
- cad40920378aacd376e8182c495d28baf9ec0084ea108022e382e735e9b2eef5
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.