Update README.md

README.md

@@ -46,7 +46,7 @@ from transformers import pipeline
 import librosa
 
 # Initialize the ASR pipeline
-asr = pipeline("automatic-speech-recognition", model="NCAIR-NG/Hausa")
+asr = pipeline("automatic-speech-recognition", model="NCAIR1/Hausa-ASR")
 
 # Load audio file (16kHz recommended)
 audio, sr = librosa.load("your_hausa_audio.wav", sr=16000)
@@ -65,7 +65,7 @@ import librosa
 
 # Load model and processor
 processor = WhisperProcessor.from_pretrained("NCAIR-NG/Hausa")
-model = WhisperForConditionalGeneration.from_pretrained("NCAIR-NG/Hausa")
+model = WhisperForConditionalGeneration.from_pretrained("NCAIR1/Hausa-ASR")
 
 # Process audio
 audio, sr = librosa.load("audio_file.wav", sr=16000)
@@ -126,7 +126,7 @@ For domain-specific applications, this model can be further fine-tuned:
 from transformers import WhisperForConditionalGeneration, Seq2SeqTrainer
 
 # Load base model
-model = WhisperForConditionalGeneration.from_pretrained("NCAIR-NG/Hausa")
+model = WhisperForConditionalGeneration.from_pretrained("NCAIR1/Hausa-ASR")
 
 # Fine-tune with your domain-specific Hausa data
 # Recommended: 10-20 hours of high-quality domain audio
@@ -147,7 +147,7 @@ model = WhisperForConditionalGeneration.from_pretrained("NCAIR-NG/Hausa")
   author={Awarri Technologies},
   year={2025},
   howpublished={Hugging Face Model Hub},
-  url={https://huggingface.co/NCAIR-NG/Hausa}
+  url={https://huggingface.co/NCAIR1/Hausa-ASR}
 }
 ```