Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
Shona
wav2vec2
Generated from Trainer
Eval Results (legacy)
Instructions to use CasperMuz/wav2vec2-base-sna-cleaned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CasperMuz/wav2vec2-base-sna-cleaned with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="CasperMuz/wav2vec2-base-sna-cleaned")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("CasperMuz/wav2vec2-base-sna-cleaned") model = AutoModelForCTC.from_pretrained("CasperMuz/wav2vec2-base-sna-cleaned", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d18cede05fff66791c101601844956f37feff992d375d02d1166918bead59247
- Size of remote file:
- 5.27 kB
- SHA256:
- 1db1049ab344140250f2b2e455f551213ac486976a5146b0e3a8384a5660df09
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