Feature Extraction
Transformers
PyTorch
Safetensors
Fairseq
French
pantagruel_uni
data2vec2
JEPA
speech
custom_code
Instructions to use PantagrueLLM/speech-large-14K with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use PantagrueLLM/speech-large-14K with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="PantagrueLLM/speech-large-14K", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("PantagrueLLM/speech-large-14K", trust_remote_code=True, device_map="auto") - Fairseq
How to use PantagrueLLM/speech-large-14K with Fairseq:
from fairseq.checkpoint_utils import load_model_ensemble_and_task_from_hf_hub models, cfg, task = load_model_ensemble_and_task_from_hf_hub( "PantagrueLLM/speech-large-14K" ) - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 61179f5a1694bf635b826645693771d63e2a308edc9d2455e820d830b065774b
- Size of remote file:
- 1.25 GB
- SHA256:
- 4814ed2002ee9f1e21a4af3385e3291cba310b1e1a02bed6968e3faf976922f3
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.