Instructions to use enactic/avista-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use enactic/avista-large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="enactic/avista-large", trust_remote_code=True)# Load model directly from transformers import AutoModelForSpeechSeq2Seq model = AutoModelForSpeechSeq2Seq.from_pretrained("enactic/avista-large", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 25b6869f19167b6a66de96d300e9d05f5e2955ebe32ffffe77b5a8c71d87c543
- Size of remote file:
- 1.92 GB
- SHA256:
- 07f75f8271f093890da16f01bb34b411eac5fd3abee1048a1fcef7c21e199214
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