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 training_args.bin from codeinout/whisper-small-mine: direct link, hf CLI and curl.
- Browser
- Download file 4.09 kB
-
https://huggingface.co/codeinout/whisper-small-mine/resolve/main/training_args.bin
- Command line
-
hf download hf://codeinout/whisper-small-mine/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/codeinout/whisper-small-mine/resolve/main/training_args.bin
4.09 kB
- Xet hash:
- 5e4b979ff92a170c1d20cddcc7a12e8b6b00304ef3aee1e30651f4dc3e843199
- Size of remote file:
- 4.09 kB
- SHA256:
- fee60be1a477e209ad346ba0fb4390d3db193ffe59b4cef90a09e153c7b6ead9
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