Instructions to use codeinout/whisper-small-mine with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use codeinout/whisper-small-mine with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="codeinout/whisper-small-mine")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("codeinout/whisper-small-mine") model = AutoModelForSpeechSeq2Seq.from_pretrained("codeinout/whisper-small-mine", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from codeinout/whisper-small-mine: direct link, hf CLI and curl.
- Browser
- Download file 967 MB
-
https://huggingface.co/codeinout/whisper-small-mine/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://codeinout/whisper-small-mine/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/codeinout/whisper-small-mine/resolve/main/pytorch_model.bin
967 MB
- Xet hash:
- e3b6e3b5088094e77ebfdd5cc677b0be86083844de495c6af25981815b43eba6
- Size of remote file:
- 967 MB
- SHA256:
- 59d0209583a257907e121b6f934ff874c4424de6570a0359c185a5018cee75ab
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