Automatic Speech Recognition
Transformers
PyTorch
Belarusian
wav2vec2
audio
speech
Eval Results (legacy)
Instructions to use ales/wav2vec2-cv-be with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ales/wav2vec2-cv-be with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="ales/wav2vec2-cv-be")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("ales/wav2vec2-cv-be") model = AutoModelForCTC.from_pretrained("ales/wav2vec2-cv-be", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c96ff7e9cf2856d1569e2fb97a51d09542c8b32c58b17a41717e37101115a3d5
- Size of remote file:
- 378 MB
- SHA256:
- b55c0aa0c1647ce563973ffa030dfacede44a21bae775b2bb4388e8e9a166f4f
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