Instructions to use Yapeng/whisper-small-pt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Yapeng/whisper-small-pt with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Yapeng/whisper-small-pt")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("Yapeng/whisper-small-pt") model = AutoModelForSpeechSeq2Seq.from_pretrained("Yapeng/whisper-small-pt", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Yapeng/whisper-small-pt: direct link, hf CLI and curl.
- Browser
- Download file 967 MB
-
https://huggingface.co/Yapeng/whisper-small-pt/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Yapeng/whisper-small-pt/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Yapeng/whisper-small-pt/resolve/main/pytorch_model.bin
967 MB
- Xet hash:
- 287be5eb5884b185da5f2c0802c42937fec647339effb973a9188efdefb3a1f0
- Size of remote file:
- 967 MB
- SHA256:
- c8e6f210df456ac2581e6730c1d42ad5fdf5d3b368d8c891a2d9d85c5364af79
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