Instructions to use vrclc/whisper-medium-ta-en with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vrclc/whisper-medium-ta-en with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="vrclc/whisper-medium-ta-en")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("vrclc/whisper-medium-ta-en") model = AutoModelForSpeechSeq2Seq.from_pretrained("vrclc/whisper-medium-ta-en", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5d02a5bbd359cb3f2dc29f3512bd33af2731128e87b32ffcd86e16bd16a21c3e
- Size of remote file:
- 5.5 kB
- SHA256:
- dd9786ffcc39efdfb0358e0c95e5ee1c84a06e9099ac4ae2a0b1db17f8807a8f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.