Instructions to use stackintel/whisper-small-ro with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use stackintel/whisper-small-ro with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="stackintel/whisper-small-ro")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("stackintel/whisper-small-ro") model = AutoModelForSpeechSeq2Seq.from_pretrained("stackintel/whisper-small-ro", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from stackintel/whisper-small-ro: direct link, hf CLI and curl.
- Browser
- Download file 967 MB
-
https://huggingface.co/stackintel/whisper-small-ro/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://stackintel/whisper-small-ro/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/stackintel/whisper-small-ro/resolve/main/pytorch_model.bin
967 MB
- Xet hash:
- 2c0cb17969cf13eca47c1d92ada17028c592071b3078deedf8b4fc3ee6ce9c92
- Size of remote file:
- 967 MB
- SHA256:
- 0a51ccdb39a0ea995e2027360afc8bc39435f7fbda9ad520f46cac6253cdfe3d
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