Instructions to use biodatlab/whisper-th-medium with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use biodatlab/whisper-th-medium with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="biodatlab/whisper-th-medium")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("biodatlab/whisper-th-medium") model = AutoModelForSpeechSeq2Seq.from_pretrained("biodatlab/whisper-th-medium") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fa50cf2f52bc016807554cdb850f89de469b68f55413f11a8957eb123d447d10
- Size of remote file:
- 6.58 kB
- SHA256:
- d65c7141c951dc7605cc9c2ddd9eb6941801c5b7457ef2b0361da3a02351a620
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.