Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
Tamil
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use deepdml/whisper-medium-ta-mix-norm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use deepdml/whisper-medium-ta-mix-norm with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="deepdml/whisper-medium-ta-mix-norm")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("deepdml/whisper-medium-ta-mix-norm") model = AutoModelForSpeechSeq2Seq.from_pretrained("deepdml/whisper-medium-ta-mix-norm", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 851ae59a207600c44f30c0203cc570dcd92ed7a73c8795c6f857c365a0a58fd2
- Size of remote file:
- 5.24 kB
- SHA256:
- dcea8fd056e7e2c3cc7d48101519fa86166995234810499d83d216cad19f2d8c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.