Instructions to use Hanhpt23/whisper-medium-Encode-engmed with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Hanhpt23/whisper-medium-Encode-engmed with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Hanhpt23/whisper-medium-Encode-engmed")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("Hanhpt23/whisper-medium-Encode-engmed") model = AutoModelForSpeechSeq2Seq.from_pretrained("Hanhpt23/whisper-medium-Encode-engmed", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Hanhpt23/whisper-medium-Encode-engmed: direct link, hf CLI and curl.
- Browser
- Download file 5.3 kB
-
https://huggingface.co/Hanhpt23/whisper-medium-Encode-engmed/resolve/main/training_args.bin
- Command line
-
hf download hf://Hanhpt23/whisper-medium-Encode-engmed/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Hanhpt23/whisper-medium-Encode-engmed/resolve/main/training_args.bin
5.3 kB
- Xet hash:
- 1a3477bf37b5cdd4e19231d08494e535b4ccb25cbcebc65d0ae44867a55b6732
- Size of remote file:
- 5.3 kB
- SHA256:
- acafe318aaf08bd7c69f179d2163c20772b4187b2738bee4ffc15438cc7842ba
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