Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
Korean
whisper
hf-asr-leaderboard
Generated from Trainer
Instructions to use gingercake01/STT0529_1000audio_base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gingercake01/STT0529_1000audio_base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="gingercake01/STT0529_1000audio_base")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("gingercake01/STT0529_1000audio_base") model = AutoModelForSpeechSeq2Seq.from_pretrained("gingercake01/STT0529_1000audio_base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from gingercake01/STT0529_1000audio_base: direct link, hf CLI and curl.
- Browser
- Download file 5.24 kB
-
https://huggingface.co/gingercake01/STT0529_1000audio_base/resolve/main/training_args.bin
- Command line
-
hf download hf://gingercake01/STT0529_1000audio_base/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/gingercake01/STT0529_1000audio_base/resolve/main/training_args.bin
5.24 kB
- Xet hash:
- b41e0857b229c3cd22ec14b70509a874c45f76f0fec55b60150c19c05dcd1a83
- Size of remote file:
- 5.24 kB
- SHA256:
- 6dc2f2c40e08c6fad1db0b02f897c3826b326e7fd526f1892e6d22acc53f5ed2
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