Instructions to use hr16/PhoWhisper-large-flax with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hr16/PhoWhisper-large-flax with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="hr16/PhoWhisper-large-flax")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("hr16/PhoWhisper-large-flax") model = AutoModelForSpeechSeq2Seq.from_pretrained("hr16/PhoWhisper-large-flax") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 374cc86c8be6efd705c1c73b7dcecdfc69dec5876faa454125efa9dcd83c7f97
- Size of remote file:
- 6.17 GB
- SHA256:
- 7ceb92460f9e7bb2a1167ed769a5fda204e1aa597569931279266490de355081
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