Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
Turkish
whisper
hf-asr-leaderboard
Generated from Trainer
Instructions to use Mehtap/base_04 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Mehtap/base_04 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Mehtap/base_04")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("Mehtap/base_04") model = AutoModelForSpeechSeq2Seq.from_pretrained("Mehtap/base_04", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Mehtap/base_04: direct link, hf CLI and curl.
- Browser
- Download file 3.63 kB
-
https://huggingface.co/Mehtap/base_04/resolve/main/training_args.bin
- Command line
-
hf download hf://Mehtap/base_04/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Mehtap/base_04/resolve/main/training_args.bin
3.63 kB
- Xet hash:
- 3737afdf8b2a4d1358f18e2f51025407c2e66fe1e45c9263e729841ee6580e2b
- Size of remote file:
- 3.63 kB
- SHA256:
- a3640640e8470c91917b6443f325e7e78fc0e6d6139480dee3833b862a37906b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.