Instructions to use basilkr/Malasar_48WER with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use basilkr/Malasar_48WER with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="basilkr/Malasar_48WER")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("basilkr/Malasar_48WER") model = AutoModelForSpeechSeq2Seq.from_pretrained("basilkr/Malasar_48WER", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6f496f00fd588105ebae3f7a8baa2da859533a157d0259681057213253ad335c
- Size of remote file:
- 6.17 GB
- SHA256:
- d97a4a915747350cc60b635f726fd5f571779160026faa19ae735eabea785a7e
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