Update app.py

app.py

@@ -28,7 +28,7 @@ DESCRIPTION = """\
 
 This Gradio demo showcases **IndicSeamless**, a fine-tuned **SeamlessM4T-v2-large** model for **speech-to-text translation** across **13 Indian languages and English**. Trained on **BhasaAnuvaad**, the largest open-source speech translation dataset for Indian languages, it delivers **accurate and robust translations** across diverse linguistic and acoustic conditions.  
 
-🔗 **Model Checkpoint:** [ai4bharat/seamless-m4t-v2-large-stt](https://huggingface.co/ai4bharat/seamless-m4t-v2-large-stt)  
+🔗 **Model Checkpoint:** [ai4bharat/indic-seamless](https://huggingface.co/ai4bharat/indic-seamless)  
 
 #### **How to Use:**  
 1. **Upload or record** an audio clip in any supported Indian language.  
@@ -42,9 +42,9 @@ hf_token = os.getenv("HF_TOKEN")
 device = "cuda:0" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu"
 torch_dtype = torch.bfloat16 if device != "cpu" else torch.float32
 
-model = SeamlessM4Tv2ForSpeechToText.from_pretrained("ai4bharat/seamless-m4t-v2-large-stt", torch_dtype=torch_dtype, token=hf_token).to(device)
-processor = SeamlessM4TFeatureExtractor.from_pretrained("ai4bharat/seamless-m4t-v2-large-stt", token=hf_token)
-tokenizer = SeamlessM4TTokenizer.from_pretrained("ai4bharat/seamless-m4t-v2-large-stt", token=hf_token)
+model = SeamlessM4Tv2ForSpeechToText.from_pretrained("ai4bharat/indic-seamless", torch_dtype=torch_dtype, token=hf_token).to(device)
+processor = SeamlessM4TFeatureExtractor.from_pretrained("ai4bharat/indic-seamless", token=hf_token)
+tokenizer = SeamlessM4TTokenizer.from_pretrained("ai4bharat/indic-seamless", token=hf_token)
 
 CACHE_EXAMPLES = os.getenv("CACHE_EXAMPLES") == "1" and torch.cuda.is_available()