Automatic Speech Recognition
Transformers
Safetensors
Fon
wav2vec2
mms
fongbe
african-languages
low-resource-languages
tone-preserved
audio
Eval Results (legacy)
Instructions to use Professor/mms-300m-fongbe with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Professor/mms-300m-fongbe with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Professor/mms-300m-fongbe")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("Professor/mms-300m-fongbe") model = AutoModelForCTC.from_pretrained("Professor/mms-300m-fongbe", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Built a platform to deploy AI models instantly. Looking for honest feedback
#1
by jensenjesper - opened
I built a platform called Quantlix because deploying models often felt more complex than training them.
The goal is simple:
upload model → get endpoint → done.
Right now it runs CPU inference by default for portability, with GPU support planned via dedicated nodes.
It’s still early and I’m mainly looking for feedback from people who’ve deployed models before.
If you’ve worked with model deployment, I’d really like to know:
what’s the most painful part today?
Site: https://quantlix.ai