Instructions to use emilios/whisper-lg-v2-farsipal-el-3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use emilios/whisper-lg-v2-farsipal-el-3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="emilios/whisper-lg-v2-farsipal-el-3")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("emilios/whisper-lg-v2-farsipal-el-3") model = AutoModelForSpeechSeq2Seq.from_pretrained("emilios/whisper-lg-v2-farsipal-el-3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 61445cdb5038364013156190f92578ec3584516ecf2216df752df835f2df709c
- Size of remote file:
- 3.09 GB
- SHA256:
- 477ea268157101cf655cafde46578ccc38ae399b557b918fe2fba37fdbc78f1d
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