Automatic Speech Recognition
Transformers
PyTorch
Tamil
whisper
whisper-event
Generated from Trainer
Instructions to use G-Rahul/whisper-tiny-ta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use G-Rahul/whisper-tiny-ta with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="G-Rahul/whisper-tiny-ta")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("G-Rahul/whisper-tiny-ta") model = AutoModelForSpeechSeq2Seq.from_pretrained("G-Rahul/whisper-tiny-ta", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from G-Rahul/whisper-tiny-ta: direct link, hf CLI and curl.
- Browser
- Download file 3.71 kB
-
https://huggingface.co/G-Rahul/whisper-tiny-ta/resolve/main/training_args.bin
- Command line
-
hf download hf://G-Rahul/whisper-tiny-ta/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/G-Rahul/whisper-tiny-ta/resolve/main/training_args.bin
3.71 kB
- Xet hash:
- 44226f126517a28691c27a3245c85dfca8f54caa55e648f802bfa1d1225c91aa
- Size of remote file:
- 3.71 kB
- SHA256:
- 460bb4186cd7ddbf6854fe48a23e09fd64963e9d7dda7fd4346a25e5195220c4
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.