SITA: Learning Speaker-Invariant and Tone-Aware Speech Representations for Low-Resource Tonal Languages
Paper β’ 2601.09050 β’ Published
How to use twangodev/hmong-screener with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("automatic-speech-recognition", model="twangodev/hmong-screener") # pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForCTC
processor = AutoProcessor.from_pretrained("twangodev/hmong-screener")
model = AutoModelForCTC.from_pretrained("twangodev/hmong-screener", device_map="auto")XLS-R 300M fine-tuned for Hmong CTC speech recognition.
from transformers import AutoModelForCTC, AutoProcessor
processor = AutoProcessor.from_pretrained("twangodev/hmong-screener")
model = AutoModelForCTC.from_pretrained("twangodev/hmong-screener").eval()
Use mono audio at 16 kHz. Word and tone screening requires the upstream scoring code and assets.
Base model
facebook/wav2vec2-xls-r-300m