Instructions to use classla/wav2vec2-xls-r-parlaspeech-hr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use classla/wav2vec2-xls-r-parlaspeech-hr with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="classla/wav2vec2-xls-r-parlaspeech-hr")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("classla/wav2vec2-xls-r-parlaspeech-hr") model = AutoModelForCTC.from_pretrained("classla/wav2vec2-xls-r-parlaspeech-hr", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download optimizer.pt from classla/wav2vec2-xls-r-parlaspeech-hr: direct link, hf CLI and curl.
- Browser
- Download file 2.49 GB
-
https://huggingface.co/classla/wav2vec2-xls-r-parlaspeech-hr/resolve/main/optimizer.pt
- Command line
-
hf download hf://classla/wav2vec2-xls-r-parlaspeech-hr/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/classla/wav2vec2-xls-r-parlaspeech-hr/resolve/main/optimizer.pt
2.49 GB
- Xet hash:
- 134651f6ede4c93aa872e65d09caeaad0399b9d70586456a5f7c434b3c295cbf
- Size of remote file:
- 2.49 GB
- SHA256:
- dbbd74245a32dfee65153384591d3e157fbaadbfa9ed6ba3e84bde67ecc0d26b
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