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.1670452253.129-154-231-61.428224.2
- Xet hash:
- b672598d24f7afa9663a6c4862f32f22c89eb0b7b156f9c44d27ad5e2b91dbaf
- Size of remote file:
- 358 Bytes
- SHA256:
- e51721345e2bded455bbff4ca1b6b39601325324c122355479fa515e86657049
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.