Instructions to use stackintel/whisper-small-en with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use stackintel/whisper-small-en with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="stackintel/whisper-small-en")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("stackintel/whisper-small-en") model = AutoModelForSpeechSeq2Seq.from_pretrained("stackintel/whisper-small-en", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from stackintel/whisper-small-en: direct link, hf CLI and curl.
- Browser
- Download file 967 MB
-
https://huggingface.co/stackintel/whisper-small-en/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://stackintel/whisper-small-en/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/stackintel/whisper-small-en/resolve/main/pytorch_model.bin
967 MB
- Xet hash:
- bf045e779af6bf78cfcc97ffe22f79713edd5f85760af95d4bffa1de425807f3
- Size of remote file:
- 967 MB
- SHA256:
- 96e21546e0fbc7b2ed13ce8c035595561f14758b1f37ad094d41e21f6b8e07c2
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