Instructions to use vtlustos/whisper-large-v2_lora_int8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vtlustos/whisper-large-v2_lora_int8 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="vtlustos/whisper-large-v2_lora_int8")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("vtlustos/whisper-large-v2_lora_int8") model = AutoModelForSpeechSeq2Seq.from_pretrained("vtlustos/whisper-large-v2_lora_int8", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 01e4ec2141438559144f4566348e2880df3e2ef975942c995b82665bcfbed252
- Size of remote file:
- 1.66 GB
- SHA256:
- e0e980c39adb29670eda5b23206bc2eed3750c36ea59157b4ac6dfc53ecc9e4a
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