google/WaxalNLP
Preview • Updated • 70.1k • 270
How to use teckedd/gha-dondo-w2v-bert-twi-v1 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("automatic-speech-recognition", model="teckedd/gha-dondo-w2v-bert-twi-v1") # Load model directly
from transformers import AutoProcessor, AutoModelForCTC
processor = AutoProcessor.from_pretrained("teckedd/gha-dondo-w2v-bert-twi-v1")
model = AutoModelForCTC.from_pretrained("teckedd/gha-dondo-w2v-bert-twi-v1", device_map="auto")DONDO w2v-BERT CTC fine-tune trial for Ghana Health AI (dondo-waxal-twi-v1). Promotion candidate: False.
KhayaAI/w2v-bert-ada_ewe_fat_fra_gaa_nzi_twi_enautomatic-speech-recognitiontw, akNot a medical device. Outputs support community health guidance only.
| Metric | Value |
|---|---|
val_wer |
0.3577 |
val_cer |
0.1219 |
google/WaxalNLPfrom transformers import pipeline # or AutoModel + processor per task
# repo: teckedd/gha-dondo-w2v-bert-twi-v1
If you use this checkpoint, please credit Ghana Health AI / Serendepify and the upstream base model authors plus any listed datasets.
This checkpoint follows DONDO's language-conditioned CTC setup. For Asante Twi, prepend
language id 2 to acoustic features before decoding.
0.30440.3577304964539007