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:
- ff75626af18c92e6f9d7be7c2eadeacd5dbaec743cc483a9e7e87e009834f599
- Size of remote file:
- 31.6 MB
- SHA256:
- 8ccae3e99cbf37991db216176b431ada1e9d962a207fb761d4ddbc5b5a97680c
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