Automatic Speech Recognition
Transformers
PyTorch
JAX
French
wav2vec2
audio
hf-asr-leaderboard
mozilla-foundation/common_voice_6_0
robust-speech-event
speech
xlsr-fine-tuning-week
Eval Results (legacy)
Instructions to use bonvent/test2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bonvent/test2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="bonvent/test2")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("bonvent/test2") model = AutoModelForCTC.from_pretrained("bonvent/test2") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7dce8e3ee949acd50b1d20bf18ccc08d57162889b299058daccb18753c1d4741
- Size of remote file:
- 1.26 GB
- SHA256:
- 2fc4760bab7bf7d0d6c06e9b7f829a27a3e66eefee3733f53c88dbedf6607f2e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.