Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
Turkish
whisper
whisper-event
Generated from Trainer
Eval Results (legacy)
Instructions to use sgangireddy/whisper-medium-tr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sgangireddy/whisper-medium-tr with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="sgangireddy/whisper-medium-tr")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("sgangireddy/whisper-medium-tr") model = AutoModelForSpeechSeq2Seq.from_pretrained("sgangireddy/whisper-medium-tr") - Notebooks
- Google Colab
- Kaggle
whisper-medium-tr / runs /Dec07_17-25-52_129-154-231-61 /events.out.tfevents.1670433999.129-154-231-61.428224.0
- Xet hash:
- efd168034fb76461fe7e3401ab2994a9302e71721b5f647f63ccc33e9ed136eb
- Size of remote file:
- 11.1 kB
- SHA256:
- 760584eb1c4333cc95cbe615b69d5e660c8bcf06e209628f2491654949bb34e4
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.