Feature Extraction
Transformers
PyTorch
Safetensors
Fairseq
French
pantagruel_uni
data2vec2
JEPA
speech
custom_code
Instructions to use PantagrueLLM/speech-base-14K with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use PantagrueLLM/speech-base-14K with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="PantagrueLLM/speech-base-14K", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("PantagrueLLM/speech-base-14K", trust_remote_code=True, dtype="auto") - Fairseq
How to use PantagrueLLM/speech-base-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-base-14K" ) - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d9471a9584d68d71bfb8bf3e51e2b9774201346a439feb5387e787364adfaa95
- Size of remote file:
- 373 MB
- SHA256:
- a2bc89546f9ebc7ea1a997cb3bc131e2c3c9d969c2bbd03404a639c35031b6f2
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