Instructions to use enactic/avista-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use enactic/avista-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="enactic/avista-base", trust_remote_code=True)# Load model directly from transformers import AutoModelForSpeechSeq2Seq model = AutoModelForSpeechSeq2Seq.from_pretrained("enactic/avista-base", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,050 Bytes
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"auto_map": {
"AutoFeatureExtractor": "feature_extraction_avhubert.AVHubertFeatureExtractor",
"AutoProcessor": "processing_avhubert.AVHubertProcessor"
},
"feature_extractor_type": "AVHubertFeatureExtractor",
"image_crop_size": 88,
"landmark_indices": [
5,
411,
199,
187
],
"max_sample_size": null,
"min_detection_confidence": 0.5,
"min_tracking_confidence": 0.5,
"normalize": true,
"processor_class": "AVHubertProcessor",
"refine_landmarks": false,
"sr": 16000,
"stack_order_audio": 4,
"static_image_mode": false,
"transforms": [
{
"training": "True",
"transforms_type": "ToImage"
},
{
"size": "(88, 88)",
"training": "True",
"transforms_type": "CenterCrop"
},
{
"dtype": "torch.float32",
"scale": "True",
"training": "True",
"transforms_type": "ToDtype"
},
{
"inplace": "False",
"mean": "[0.421]",
"std": "[0.165]",
"training": "True",
"transforms_type": "Normalize"
}
]
}
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