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:
- b948fc0604b7fa724217adf26917c35defa1c943880fde45c42e2cd7d4a21314
- Size of remote file:
- 1.93 GB
- SHA256:
- 54a52a1a14f68dc47375eac8d1417729b3a349bccbe1379edeccec2a98ee91f0
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.