Automatic Speech Recognition
Transformers
Safetensors
English
whisper
text-generation-inference
unsloth
Instructions to use Kibalama/lugSTT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Kibalama/lugSTT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Kibalama/lugSTT")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("Kibalama/lugSTT") model = AutoModelForSpeechSeq2Seq.from_pretrained("Kibalama/lugSTT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Desktop
# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
processor = AutoProcessor.from_pretrained("Kibalama/lugSTT")
model = AutoModelForSpeechSeq2Seq.from_pretrained("Kibalama/lugSTT", device_map="auto")Quick Links
Uploaded finetuned model
- Developed by: Kibalama
- License: apache-2.0
- Finetuned from model : unsloth/whisper-small
This whisper model was trained 2x faster with Unsloth and Huggingface's TRL library.
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Kibalama/lugSTT")