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:
- cdf12df78c78a64e81f8f6fdd81859d30d409588732bc3b3afae203e26bf510d
- Size of remote file:
- 967 MB
- SHA256:
- f9aaf9dce8bd6f39443b033fdf3dd60b3b4e4220f933f9a53eb556e9c7e9fd44
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