Video-Text-to-Text
Transformers
Safetensors
English
gemma4
image-text-to-text
video-captioning
multimodal
gemma
parakeet
Instructions to use vdaular/sulphur-caption with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vdaular/sulphur-caption with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("vdaular/sulphur-caption") model = AutoModelForMultimodalLM.from_pretrained("vdaular/sulphur-caption", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download parakeet/processor_config.json from vdaular/sulphur-caption: direct link, hf CLI and curl.
- Browser
- Download file 392 Bytes
-
https://huggingface.co/vdaular/sulphur-caption/resolve/main/parakeet/processor_config.json
- Command line
-
hf download hf://vdaular/sulphur-caption/parakeet/processor_config.json
-
curl -L -o processor_config.json https://huggingface.co/vdaular/sulphur-caption/resolve/main/parakeet/processor_config.json
392 Bytes
| { | |
| "blank_token": "<blank>", | |
| "feature_extractor": { | |
| "feature_extractor_type": "ParakeetFeatureExtractor", | |
| "feature_size": 128, | |
| "hop_length": 160, | |
| "n_fft": 512, | |
| "padding_side": "right", | |
| "padding_value": 0.0, | |
| "preemphasis": 0.97, | |
| "return_attention_mask": true, | |
| "sampling_rate": 16000, | |
| "win_length": 400 | |
| }, | |
| "processor_class": "ParakeetProcessor" | |
| } | |