Instructions to use rasgaard/whisper-large-v3-turbo-encoder-pruned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rasgaard/whisper-large-v3-turbo-encoder-pruned with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="rasgaard/whisper-large-v3-turbo-encoder-pruned")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("rasgaard/whisper-large-v3-turbo-encoder-pruned") model = AutoModelForSpeechSeq2Seq.from_pretrained("rasgaard/whisper-large-v3-turbo-encoder-pruned", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Add library_name, pipeline_tag, license, and paper/code links
#1
by nielsr HF Staff - opened
This PR improves the model card by adding:
pipeline_tag: automatic-speech-recognitionso the model can be filtered in the Hub's task-based search.library_name: transformersto enable the automated "Use in Transformers" snippet (evidence:config.jsonusesWhisperForConditionalGenerationand the model card already includes a workingtransformersusage snippet).license: mit, matching the base model.- Links to the paper page on Hugging Face and the GitHub repository in the markdown content.
rasgaard changed pull request status to merged