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
| from transformers import PretrainedConfig | |
| class ResEncoderConfig(PretrainedConfig): | |
| model_type = "modified_resnet" | |
| def __init__( | |
| self, | |
| relu_type="prelu", | |
| frontend_nout=64, | |
| backend_out=512, | |
| **kwargs, | |
| ): | |
| self.relu_type = relu_type | |
| self.frontend_nout = frontend_nout | |
| self.backend_out = backend_out | |
| super().__init__(**kwargs) | |