Update app.py

app.py

@@ -1,93 +1,47 @@
 import gradio as gr
 from transformers import pipeline
-import traceback
-import os
 
-print("🚀 Starting Kurmanji ASR application...")
+# IMPORTANT: Replace this with the exact ID of your uploaded model 
+MODEL_ID = "amedcj/kurmanji-asr-model" # Assuming your model ID uses your Space's username
 
-# Define MODEL_ID: Set to '.' because model files are in the same Space repository.
-MODEL_ID = "." 
+# Load the ASR model pipeline
+# The pipeline handles downloading the weights and configuration.
+try:
+    transcriber = pipeline(
+        "automatic-speech-recognition", 
+        model=MODEL_ID,
+        # device=0 # Uncomment this if you upgrade your Space to a GPU
+    )
+except Exception as e:
+    # Fallback for error handling if the model fails to load
+    gr.Warning(f"Failed to load model: {e}")
+    transcriber = None
 
-# --- MODEL LOADING FUNCTION ---
-asr = None
-def load_asr_model():
-    global asr
-    try:
-        print("📥 Loading ASR pipeline...")
-        
-        # Use the pipeline's auto-loading feature
-        asr = pipeline(
-            "automatic-speech-recognition",
-            model=MODEL_ID,
-            # Optimized for limited resources/speed—you may need to adjust these values
-            chunk_length_s=5,          # Smaller chunks require less memory
-            stride_length_s=(1, 1),    # Reduced stride for less overlap
-            device="cpu",              # Explicitly use CPU (important for free tier)
-            low_cpu_mem_usage=True,    # Saves RAM during model initialization
-            # VITAL FOR QUALITY: Force the model to use the Kurdish language (ku)
-            generate_kwargs={"language": "ku", "task": "transcribe"} 
-        )
-        print("✅ ASR pipeline created successfully!")
-    except Exception as e:
-        print(f"❌ Error loading ASR model: {e}")
-        traceback.print_exc()
-        asr = None
-        
-load_asr_model() # Load the model at start
 
-# --- TRANSCRIPTION FUNCTION ---
-def transcribe(audio_file):
-    print("=== ASR Function Called ===")
-    
-    try:
-        if audio_file is None:
-            return "Ji kerema xwe dosyeyek deng bar bike. / Please upload an audio file."
-        
-        if asr is None:
-            return "Model nehatiye barkirin. / ASR model not loaded properly."
-        
-        print(f"🎵 Processing audio file: {audio_file}")
-        
-        # Transcribe the audio
-        result = asr(audio_file)
-        
-        # Post-processing: clean output and remove leading/trailing spaces
-        transcription = result["text"].strip() 
+# Define the prediction function
+def transcribe_audio(audio_file_path):
+    if audio_file_path is None:
+        return "Please provide an audio input."
 
-        print(f"✅ Transcription completed: {transcription}")
-        return transcription
-        
-    except Exception as e:
-        error_msg = f"Çewtî: {str(e)} / Error: {str(e)}"
-        print(f"❌ Error in transcription: {e}")
-        traceback.print_exc()
-        return error_msg
+    if transcriber is None:
+         return "Error: Model failed to initialize."
 
-# --- GRADIO INTERFACE ---
+    # Pass the local file path provided by Gradio to the pipeline
+    result = transcriber(audio_file_path)
+    return result["text"]
+
+# Create the Gradio interface
 demo = gr.Interface(
-    fn=transcribe,
+    fn=transcribe_audio,
     inputs=gr.Audio(
-        sources=["microphone", "upload"],
+        sources=["microphone", "upload"], 
         type="filepath",
-        label="🎤 Deng Tomar Bike an Dosye Bar Bike / Record Voice or Upload File"
-    ),
-    outputs=gr.Textbox(
-        label="📝 Encam / Result",
-        placeholder="Li vir nivîsa wergerandî dê xuya bibe... / Transcribed text will appear here...",
-        lines=5,
-        show_copy_button=True
+        label="Kurmanji Audio Input"
     ),
-    title="🗣️ Kurmancî ASR - Kurdish Speech Recognition",
-    description="""
-    **Deng bo Nivîs / Speech to Text**
-    **Formatên çêdibin:** WAV, MP3, M4A, FLAC
-    """,
-    submit_btn="Wergerîne / Transcribe",
-    clear_btn="Paqij Bike / Clear",
-    cache_examples=False
+    outputs=gr.Textbox(label="Kurmanji Transcription Result"),
+    title="Kurmanji ASR Demo",
+    description="Automatic Speech Recognition for Kurmanji using a fine-tuned Hugging Face Transformer model."
 )
 
-# --- LAUNCH ---
-print("🚀 Launching Gradio app...")
-if __name__ == "__main__":
-    demo.launch()
+# Launch the application
+demo.launch()