Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
Tamil
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use MCDHANUSH/whisper-tiny-ta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MCDHANUSH/whisper-tiny-ta with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="MCDHANUSH/whisper-tiny-ta")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("MCDHANUSH/whisper-tiny-ta") model = AutoModelForSpeechSeq2Seq.from_pretrained("MCDHANUSH/whisper-tiny-ta", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from MCDHANUSH/whisper-tiny-ta: direct link, hf CLI and curl.
- Browser
- Download file 5.5 kB
-
https://huggingface.co/MCDHANUSH/whisper-tiny-ta/resolve/main/training_args.bin
- Command line
-
hf download hf://MCDHANUSH/whisper-tiny-ta/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/MCDHANUSH/whisper-tiny-ta/resolve/main/training_args.bin
5.5 kB
- Xet hash:
- 1c80fa364696b6b9a284f5c4fbede78852766782e325e611e0ea4d6a64f55987
- Size of remote file:
- 5.5 kB
- SHA256:
- 62960e13a2111b6497cc7b831cb5c5085174fd8d327530913d03044a758170e5
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.