Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
Slovenian
whisper
whisper-event
Generated from Trainer
Eval Results (legacy)
Instructions to use mikr/whisper-medium-sl-cv11 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mikr/whisper-medium-sl-cv11 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="mikr/whisper-medium-sl-cv11")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("mikr/whisper-medium-sl-cv11") model = AutoModelForSpeechSeq2Seq.from_pretrained("mikr/whisper-medium-sl-cv11", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from mikr/whisper-medium-sl-cv11: direct link, hf CLI and curl.
- Browser
- Download file 4.73 kB
-
https://huggingface.co/mikr/whisper-medium-sl-cv11/resolve/main/training_args.bin
- Command line
-
hf download hf://mikr/whisper-medium-sl-cv11/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/mikr/whisper-medium-sl-cv11/resolve/main/training_args.bin
4.73 kB
- Xet hash:
- 5872fe9c26d1819dcc603b4a16a9b2792fb8ef90b1659d0dc163925580665a8a
- Size of remote file:
- 4.73 kB
- SHA256:
- 991fcb7c09f1ae880be5d3641b320e5728327b09ed188a260e1beaa7cc8ca4d1
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