Instructions to use EducativeCS2023/whisper-en-tiny-trained with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use EducativeCS2023/whisper-en-tiny-trained with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="EducativeCS2023/whisper-en-tiny-trained")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("EducativeCS2023/whisper-en-tiny-trained") model = AutoModelForSpeechSeq2Seq.from_pretrained("EducativeCS2023/whisper-en-tiny-trained", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from EducativeCS2023/whisper-en-tiny-trained: direct link, hf CLI and curl.
- Browser
- Download file 4.09 kB
-
https://huggingface.co/EducativeCS2023/whisper-en-tiny-trained/resolve/main/training_args.bin
- Command line
-
hf download hf://EducativeCS2023/whisper-en-tiny-trained/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/EducativeCS2023/whisper-en-tiny-trained/resolve/main/training_args.bin
4.09 kB
- Xet hash:
- 23b973618dd672ede51c894896f3fb1897e111684552bc7e9d35d56b754dfbe6
- Size of remote file:
- 4.09 kB
- SHA256:
- 206a3d5ae93cc83f9612d668007c0c0a07fcaf7bf0773c1cd3780c36adfa79e2
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