Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use rdomanski/training with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rdomanski/training with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="rdomanski/training")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("rdomanski/training") model = AutoModelForSpeechSeq2Seq.from_pretrained("rdomanski/training", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from rdomanski/training: direct link, hf CLI and curl.
- Browser
- Download file 5.24 kB
-
https://huggingface.co/rdomanski/training/resolve/main/training_args.bin
- Command line
-
hf download hf://rdomanski/training/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/rdomanski/training/resolve/main/training_args.bin
5.24 kB
- Xet hash:
- a3db7b8d16cc46b0d98c24956cc93220292dce64f920b512dd1c6f95f7194751
- Size of remote file:
- 5.24 kB
- SHA256:
- 4e45774f209c0596a5eac532409c8541bb7dce275bbdbfd0c867b91def5d16c6
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.