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 training_args.bin from stackintel/whisper-small-en: direct link, hf CLI and curl.
- Browser
- Download file 3.63 kB
-
https://huggingface.co/stackintel/whisper-small-en/resolve/main/training_args.bin
- Command line
-
hf download hf://stackintel/whisper-small-en/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/stackintel/whisper-small-en/resolve/main/training_args.bin
3.63 kB
- Xet hash:
- bdf0c2f01ca3b584f3c65ffa58956236eb10245a7ecf220513cf214012db8125
- Size of remote file:
- 3.63 kB
- SHA256:
- 69c914b2b3dfaca70c081147725f725bc9b50abc59a5b1bde4b4b76e986b051d
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