Update app.py

app.py

@@ -1,21 +1,30 @@
 import gradio as gr
 from transformers import pipeline
 
-# Load Whisper model
-asr_pipeline = pipeline(model="openai/whisper-small", task="automatic-speech-recognition")
+# Whisper pipeline (supports Hindi, Punjabi, etc.)
+asr_pipeline = pipeline(
+    task="automatic-speech-recognition",
+    model="openai/whisper-small",
+)
 
-# Define transcription function
-def transcribe(audio):
-    result = asr_pipeline(audio)
-    return result["text"]
+# Function to transcribe and detect language
+def transcribe(audio_np, sample_rate):
+    result = asr_pipeline({
+        "array": audio_np,
+        "sampling_rate": sample_rate
+    })
+    text = result["text"]
+    lang = result.get("language", "unknown")
+    return f"Detected Language: {lang}\n\nTranscription:\n{text}"
 
-# Gradio Interface (v4.x style)
+# Gradio Interface
 interface = gr.Interface(
     fn=transcribe,
-    inputs=gr.Audio(type="filepath", label="Upload or Record Audio"),  # No 'source'
-    outputs=gr.Textbox(label="Transcription"),
-    title="Hindi Speech-to-Text",
-    description="Speak or upload audio in Hindi to get transcription using Whisper."
+    inputs=gr.Audio(type="numpy", label="Upload or Record Audio"),  # FIXED
+    outputs=gr.Textbox(label="Detected Language & Transcription"),
+    title="Auto Language Detection - Whisper",
+    description="Upload or record Hindi or Punjabi audio. Whisper will auto-detect language and transcribe."
 )
 
 interface.launch()
+