Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use fontadie/whisper-tiny-openslrdev with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fontadie/whisper-tiny-openslrdev with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="fontadie/whisper-tiny-openslrdev")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("fontadie/whisper-tiny-openslrdev") model = AutoModelForSpeechSeq2Seq.from_pretrained("fontadie/whisper-tiny-openslrdev", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from fontadie/whisper-tiny-openslrdev: direct link, hf CLI and curl.
- Browser
- Download file 5.11 kB
-
https://huggingface.co/fontadie/whisper-tiny-openslrdev/resolve/main/training_args.bin
- Command line
-
hf download hf://fontadie/whisper-tiny-openslrdev/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/fontadie/whisper-tiny-openslrdev/resolve/main/training_args.bin
5.11 kB
- Xet hash:
- b51e3192388c347de52fd97082645f5271e99cbaf489342fab254849efa75f2e
- Size of remote file:
- 5.11 kB
- SHA256:
- f8e5944be09f4736b75df337c8284285d48573db73d073dc5082c7987bfdff61
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