Automatic Speech Recognition
Transformers
Safetensors
English
whisper
text-generation-inference
unsloth
Instructions to use Kibalama/lg_stt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Kibalama/lg_stt with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Kibalama/lg_stt")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("Kibalama/lg_stt") model = AutoModelForSpeechSeq2Seq.from_pretrained("Kibalama/lg_stt", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Studio
How to use Kibalama/lg_stt with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for Kibalama/lg_stt to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for Kibalama/lg_stt to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Kibalama/lg_stt to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="Kibalama/lg_stt", max_seq_length=2048, )
- Xet hash:
- 4cac76ab91340653fac8fac9f5924799ccbba1e12e7076dffdf6e09dad4567c4
- Size of remote file:
- 882 MB
- SHA256:
- e93ce9dc142ce329f6c0c77d0b83460514242ebc5e051a26a8e9103899650bba
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