Update app.py

app.py

@@ -1,70 +1,41 @@
 import transformers
-
 import gradio as gr
+import librosa
 import torch
-import numpy as np
-from typing import Dict, List
 import spaces
 
-# Constants
-MODEL_NAME = 'sarvamai/shuka_v1'
-SAMPLE_RATE = 16000
-MAX_NEW_TOKENS = 256
-
-# Load the ShukaPipeline
-def load_pipeline():
-    model = transformers.AutoModel.from_pretrained(MODEL_NAME, trust_remote_code=True)
-    pipeline = transformers.pipeline(
-        "shuka-pipeline",
-        model=model,
-        torch_dtype=torch.float16,
-        device=0 if torch.cuda.is_available() else -1,
-    )
-    return pipeline
-
-pipe = load_pipeline()
-
-def create_conversation_turns(prompt: str) -> List[Dict[str, str]]:
-    return [
-        {'role': 'system', 'content': 'Respond naturally and informatively.'},
-        {'role': 'user', 'content': prompt}
-    ]
-
 @spaces.GPU(duration=120)
-def transcribe_and_respond(audio: np.ndarray) -> str:
+def transcribe_and_respond(audio_file):
     try:
-        # Ensure audio is float32
-        if audio.dtype != np.float32:
-            audio = audio.astype(np.float32)
+        pipe = transformers.pipeline(
+     model='sarvamai/shuka_v1',
+     trust_remote_code=True,
+     device=0,
+     torch_dtype=torch.bfloat16
+     )
+
+        audio, sr = librosa.load(audio_file, sr=16000)
+
+        turns = [
+            {'role': 'system', 'content': 'Respond naturally and informatively.'},
+            {'role': 'user', 'content': ''}
+        ]
 
+        output = pipe({'audio': audio, 'turns': turns, 'sampling_rate': sr}, max_new_tokens=512)
 
+        return output
 
-        
-        # Create input for the pipeline
-        turns = create_conversation_turns("<|audio|>")
-        inputs = {
-            'audio': audio,
-            'turns': turns,
-            'sampling_rate': SAMPLE_RATE
-        }
-        
-        # Generate response
-        response = pipe(inputs, max_new_tokens=MAX_NEW_TOKENS, temperature=0.7, repetition_penalty=1.1)
-        
-        return response
     except Exception as e:
-        return f"Error processing audio: {str(e)}"
+        return f"Error: {str(e)}"
 
-# Create the Gradio interface
 iface = gr.Interface(
     fn=transcribe_and_respond,
-    inputs=gr.Audio(sources="microphone", type="numpy", sampling_rate=SAMPLE_RATE),
-    outputs="text",
-    title="Live Voice Input for Transcription and Response",
+    inputs=gr.Audio(sources="microphone", type="filepath"),  # Accept audio input from microphone
+    outputs="text",  # Output as text
+    title="Live Transcription and Response",
     description="Speak into your microphone, and the model will respond naturally and informatively.",
-    live=True
+    live=True  # Enable live processing
 )
 
-# Launch the app
 if __name__ == "__main__":
     iface.launch()