Feature Extraction
Transformers
PyTorch
Safetensors
Fairseq
French
pantagruel_uni
data2vec2
JEPA
speech
custom_code
Instructions to use PantagrueLLM/speech-base-1K with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use PantagrueLLM/speech-base-1K with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="PantagrueLLM/speech-base-1K", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("PantagrueLLM/speech-base-1K", trust_remote_code=True, device_map="auto") - Fairseq
How to use PantagrueLLM/speech-base-1K 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-1K" ) - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 356f775e601a469c3fe894a554f56acbbf26e2ad9b784177cd00f14c7a7f2283
- Size of remote file:
- 373 MB
- SHA256:
- 5ef26e1ab373a39a8b2ff3f9a03e3ad0da2c6f5a221467bd5bb4e6c40c8226db
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