Add Chaka-ASR model card, inference example, and upstream credits

README.md

@@ -25,8 +25,8 @@ It is intended for transcription, voice interfaces, and speech applications in E
 
 | Property | Details |
 |---|---|
-| Model ID | `abrilo/Chaka-ASR` |
-| Maintainer | [abrilo](https://huggingface.co/abrilo) |
+| Model ID | `caesar-abrham/Chaka-ASR` |
+| Maintainer | [caesar-abrham](https://huggingface.co/caesar-abrham) |
 | Language | Amharic |
 | Task | Automatic speech recognition |
 | Architecture | Wav2Vec2-BERT with a CTC output head |
@@ -102,15 +102,15 @@ A release license for Chaka-ASR has not yet been declared in this repository. Th
 
 ## Feedback
 
-Report transcription issues and technical questions in the [Community tab](https://huggingface.co/abrilo/Chaka-ASR/discussions). Include the model revision and runtime versions. Share recordings only when you have permission and remove personal or confidential information.
+Report transcription issues and technical questions in the [Community tab](https://huggingface.co/caesar-abrham/Chaka-ASR/discussions). Include the model revision and runtime versions. Share recordings only when you have permission and remove personal or confidential information.
 
 ## Citation
 
 ```bibtex
 @misc{chaka_asr_2026,
-  author = {abrilo},
+  author = {caesar-abrham},
   title = {Chaka-ASR: Amharic Automatic Speech Recognition},
   year = {2026},
-  url = {https://huggingface.co/abrilo/Chaka-ASR}
+  url = {https://huggingface.co/caesar-abrham/Chaka-ASR}
 }
 ```

examples/transcribe.py

@@ -19,8 +19,8 @@ def main():
         raise ValueError("Audio recording is empty")
     if not np.isfinite(audio).all():
         raise ValueError("Audio contains non-finite samples")
-    processor = AutoProcessor.from_pretrained("abrilo/Chaka-ASR", revision=args.revision)
-    model = AutoModelForCTC.from_pretrained("abrilo/Chaka-ASR", revision=args.revision)
+    processor = AutoProcessor.from_pretrained("caesar-abrham/Chaka-ASR", revision=args.revision)
+    model = AutoModelForCTC.from_pretrained("caesar-abrham/Chaka-ASR", revision=args.revision)
     target_rate = processor.feature_extractor.sampling_rate
     if sample_rate != target_rate:
         divisor = gcd(sample_rate, target_rate)