Instructions to use lilgoose777/tibetan_small_whisper_final with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lilgoose777/tibetan_small_whisper_final with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="lilgoose777/tibetan_small_whisper_final")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("lilgoose777/tibetan_small_whisper_final") model = AutoModelForSpeechSeq2Seq.from_pretrained("lilgoose777/tibetan_small_whisper_final", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8b28bbed3132df29e41574d4af42291caf49c151a852f51bf6a47e954762fc83
- Size of remote file:
- 967 MB
- SHA256:
- 14d0795e9fb6ff3403af34aa355cdb419fee2cc65997f5d22bad8a614468dbf5
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