Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
Korean
whisper
hf-asr-leaderboard
Generated from Trainer
Instructions to use gingercake01/STT_15000_4method_audio_basev2_0607 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gingercake01/STT_15000_4method_audio_basev2_0607 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="gingercake01/STT_15000_4method_audio_basev2_0607")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("gingercake01/STT_15000_4method_audio_basev2_0607") model = AutoModelForSpeechSeq2Seq.from_pretrained("gingercake01/STT_15000_4method_audio_basev2_0607") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0289a557893443da6777fa47d309822d6507d4d69d923fbcb109c2e1733f3db9
- Size of remote file:
- 290 MB
- SHA256:
- aafbec584807f7973b62376ad9a2c04fcde315fbbdc53886e35406267cc32645
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.