Instructions to use arthoho66/model_005_2000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use arthoho66/model_005_2000 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="arthoho66/model_005_2000")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("arthoho66/model_005_2000") model = AutoModelForSpeechSeq2Seq.from_pretrained("arthoho66/model_005_2000", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 55c884fb241b01ab29b56ebdb3e3561badac03e1df733eec363447581f403bf8
- Size of remote file:
- 6.11 GB
- SHA256:
- aa2a5436fb9ead7c9b22b964a12c4dd836c519e0500183dbf7ad87123e6f6535
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.