Feature Extraction
Transformers
Safetensors
English
speech_jepa
speech
audio
self-supervised-learning
jepa
speech-jepa
custom_code
Instructions to use labhamlet/speech-jepa-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use labhamlet/speech-jepa-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="labhamlet/speech-jepa-base", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("labhamlet/speech-jepa-base", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Links to fine-tuned CTC checkpoints lead to missing repos
#1
by shubham-fit-but-brno - opened
Hi, thanks for releasing Speech-JEPA! The model card links to fine-tuned CTC checkpoints like, speech-jepa-base-asr-100h, but it don’t appear to be public yet. Are there plans to release them? I’d like to benchmark them for edge ASR.
Hey Shubham,
We are working on releasing the CTC fine-tuned checkpoints and finalizing the paper details this week. They will be available the latest next Monday!
Meanwhile, you could try to fine-tune the base model using the ASR fine-tuning scripts we've provided in the repo.
Kind Regards,
Goksenin Yuksel