Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
Malayalam
whisper
whisper-event
Generated from Trainer
Eval Results (legacy)
Instructions to use thennal/whisper-medium-ml with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use thennal/whisper-medium-ml with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="thennal/whisper-medium-ml")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("thennal/whisper-medium-ml") model = AutoModelForSpeechSeq2Seq.from_pretrained("thennal/whisper-medium-ml", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5f73ac261edd836c240d13ea81e160f9667d75a72f63b9b6d666ad4732e33872
- Size of remote file:
- 3.06 GB
- SHA256:
- 7938f5404adf4a79a9d0f4f8463e9f6f7a2f3db20a94073d5d0a25109aa60a94
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.