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 pytorch_model.bin from Mehtap/base_04: direct link, hf CLI and curl.
- Browser
- Download file 290 MB
-
https://huggingface.co/Mehtap/base_04/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Mehtap/base_04/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Mehtap/base_04/resolve/main/pytorch_model.bin
290 MB
- Xet hash:
- 665a7b1d71b5059fb2db20c2e4d8f3587e3b302066894ed19f45b2c6a8412d27
- Size of remote file:
- 290 MB
- SHA256:
- 0cbc41e112f116fd9c0089b79b52bc616181906bcf1c00cba3e8d272413082ed
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.