Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
Mongolian
wav2vec2-bert
Generated from Trainer
Instructions to use Cafet/w2v-bert-version-final with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Cafet/w2v-bert-version-final with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Cafet/w2v-bert-version-final")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("Cafet/w2v-bert-version-final") model = AutoModelForCTC.from_pretrained("Cafet/w2v-bert-version-final", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- aacc14880626f481993bc3dc9035d09bf2acb714a36e6e01f136daa9723ffd7f
- Size of remote file:
- 2.42 GB
- SHA256:
- 27baa35c5d19566c00ee9dfb94ef4f62d9118e834c5fd6ef8e711b6a18530e41
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.