Instructions to use basilkr/Whisper_Malasar_50 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use basilkr/Whisper_Malasar_50 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="basilkr/Whisper_Malasar_50")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("basilkr/Whisper_Malasar_50") model = AutoModelForSpeechSeq2Seq.from_pretrained("basilkr/Whisper_Malasar_50", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7a2f3c40738c701277d6e164b97ed32748e659607ec9537c219130265ee2d629
- Size of remote file:
- 3.58 kB
- SHA256:
- 2e4b685e8410e4a5ea7a05c9de564d4112717a643f733c245cf6f019147b615b
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