Instructions to use golesheed/wav2vec2-large-xls-r-1b-frisian-cv-13-elderly-augmented with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use golesheed/wav2vec2-large-xls-r-1b-frisian-cv-13-elderly-augmented with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="golesheed/wav2vec2-large-xls-r-1b-frisian-cv-13-elderly-augmented")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("golesheed/wav2vec2-large-xls-r-1b-frisian-cv-13-elderly-augmented") model = AutoModelForCTC.from_pretrained("golesheed/wav2vec2-large-xls-r-1b-frisian-cv-13-elderly-augmented", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c9e8686aa64329cbf204c13a5c14c1c56e09f00b09b06f08fd04e2363890ca29
- Size of remote file:
- 3.85 GB
- SHA256:
- d5ddd6d7b9eaa880de26058e6651e2eb6fb70657e98b96e7a5bcd89b61bb0254
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