Automatic Speech Recognition
Transformers
Safetensors
wav2vec2
Generated from Trainer
Eval Results (legacy)
Instructions to use mouseyy/uk_wav2vec2_with_stress_mark with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mouseyy/uk_wav2vec2_with_stress_mark with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="mouseyy/uk_wav2vec2_with_stress_mark")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("mouseyy/uk_wav2vec2_with_stress_mark") model = AutoModelForCTC.from_pretrained("mouseyy/uk_wav2vec2_with_stress_mark") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6db6b5f333d4b7af602c774726895946ec1ce9a621c18fa87265c9631b4d673b
- Size of remote file:
- 5.37 kB
- SHA256:
- 04562d4aec1a0109d261ae381b7857cbdeddef7649082faeecfd4ebdf549e8e2
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.