Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
Turkish
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use Sercan/whisper-small-cv-tr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sercan/whisper-small-cv-tr with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Sercan/whisper-small-cv-tr")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("Sercan/whisper-small-cv-tr") model = AutoModelForSpeechSeq2Seq.from_pretrained("Sercan/whisper-small-cv-tr") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b32f288403ecdc9c444b7a842533668bcecba093114a5e7c9a0d31d0810eb963
- Size of remote file:
- 3.58 kB
- SHA256:
- 1a2ff2f1c5c4f8ee03736b9fee9682b62b390a836d73f8c13226e80e86a35a2b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.