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)# pip install -U transformers accelerate # 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
File size: 211 Bytes
4be1a68 | 1 2 3 4 5 6 7 8 9 | {
"do_normalize": true,
"feature_extractor_type": "Wav2Vec2FeatureExtractor",
"feature_size": 1,
"padding_side": "right",
"padding_value": 0,
"return_attention_mask": true,
"sampling_rate": 16000
} |