Instructions to use lgris/distil-whisper-large-v2-pt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lgris/distil-whisper-large-v2-pt with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="lgris/distil-whisper-large-v2-pt")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("lgris/distil-whisper-large-v2-pt") model = AutoModelForSpeechSeq2Seq.from_pretrained("lgris/distil-whisper-large-v2-pt", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c306c98c50f0b87010b42e23c88566ed7a62f920639166394cf0ed75dfc9331e
- Size of remote file:
- 956 MB
- SHA256:
- 850f45f3e333777383d12e87e877cbdd9bd40927a85d37961afc42e76a42a532
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