Instructions to use aggtamv/Wav2vec2Askisi with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aggtamv/Wav2vec2Askisi with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="aggtamv/Wav2vec2Askisi")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("aggtamv/Wav2vec2Askisi") model = AutoModelForCTC.from_pretrained("aggtamv/Wav2vec2Askisi", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3919fbda4f4df057d9e910903925a650ad612b09cc24664aaf7b559862691896
- Size of remote file:
- 1.26 GB
- SHA256:
- 6f76197d3a0d2fd28ea0687a7933740c5487aa64a0e6ff1ae9f7c1c8022b4466
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.