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")# pip install -U transformers accelerate # 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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from gingercake01/STT_15000_4method_audio_basev2_0607: direct link, hf CLI and curl.
- Browser
- Download file 5.3 kB
-
https://huggingface.co/gingercake01/STT_15000_4method_audio_basev2_0607/resolve/main/training_args.bin
- Command line
-
hf download hf://gingercake01/STT_15000_4method_audio_basev2_0607/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/gingercake01/STT_15000_4method_audio_basev2_0607/resolve/main/training_args.bin
5.3 kB
- Xet hash:
- e42008854ec18ff371ae837671ba107caefd69170a0deb13bb0f0839655afb7d
- Size of remote file:
- 5.3 kB
- SHA256:
- 4a88f90122561f8bb5f8929048f3a6ec850161ffd681ca04e6210df5095595f8
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.