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:
- 8a1b5a8b919c0672b000a1f35d90fbbdc07b450940f382450628621e1990de3a
- Size of remote file:
- 3.64 kB
- SHA256:
- db2236957e6f7cb0f540b92b0c911ef1c2c114b0115c97360d9f16cca730cc45
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