Feature Extraction
Transformers
Safetensors
meralion_bestrq
speech
best-rq
meralion
meralion-2
custom_code
Instructions to use MERaLiON/MERaLiON-SpeechEncoder-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MERaLiON/MERaLiON-SpeechEncoder-2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="MERaLiON/MERaLiON-SpeechEncoder-2", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("MERaLiON/MERaLiON-SpeechEncoder-2", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
Whisper large performance
#2
by abhinavkashyap92 - opened
Hi, thank you for the efforts in open sourcing the models
I am referring to the chart here https://github.com/openai/whisper?tab=readme-ov-file#available-models-and-languages
Whisper large has a WER of 18.3 for Tamil
But the one that is reported here is around 40.
The discrepancy seems to be huge. Can we get a clarification for this??
Thanks in advance.
Abhinav Ramesh Kashyap