Instructions to use sulaimank/whisper-small-lg-GRAIN-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sulaimank/whisper-small-lg-GRAIN-v3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="sulaimank/whisper-small-lg-GRAIN-v3")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("sulaimank/whisper-small-lg-GRAIN-v3") model = AutoModelForSpeechSeq2Seq.from_pretrained("sulaimank/whisper-small-lg-GRAIN-v3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e9ac955df17cc4eb640f3066bfd62783cd552c6a0cb3a31873a4739dc5c80dba
- Size of remote file:
- 5.5 kB
- SHA256:
- b01555d913f68cd84eaccb413c913859af6fd395faf7b1586ef0902273d6fdc7
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