Instructions to use lgris/whisper-tiny-cv11-pt-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lgris/whisper-tiny-cv11-pt-2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="lgris/whisper-tiny-cv11-pt-2")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("lgris/whisper-tiny-cv11-pt-2") model = AutoModelForSpeechSeq2Seq.from_pretrained("lgris/whisper-tiny-cv11-pt-2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a20ada9ebe3183fd24d6ccd86f3038ece87c0f83315f14323809cafee0bc1cfc
- Size of remote file:
- 3.52 kB
- SHA256:
- 8a8924437d24b190a373ce6f0ee360993ee1699b66c9361b85a2147f38b0717f
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