Update app.py

app.py

@@ -3,14 +3,50 @@ import whisper
 import os
 from pydub import AudioSegment
 
-# Mapping of model names to Whisper model sizes
-MODELS = {
-    "Tiny (Fastest)": "tiny",
-    "Base (Faster)": "base",
-    "Small (Balanced)": "small",
-    "Medium (Accurate)": "medium",
-    "Large (Most Accurate)": "large"
-}
+# Load the base Whisper model
+base_model = whisper.load_model("base")  # Default model for non-Sinhala languages
+
+# Load the fine-tuned Sinhala model (if available)
+sinhala_model = None
+try:
+    from transformers import WhisperForConditionalGeneration, WhisperProcessor
+    sinhala_model = WhisperForConditionalGeneration.from_pretrained("Subhaka/whisper-small-Sinhala-Fine_Tune")
+    sinhala_processor = WhisperProcessor.from_pretrained("Subhaka/whisper-small-Sinhala-Fine_Tune")
+except Exception as e:
+    print("Failed to load fine-tuned Sinhala model. Falling back to the base model.")
+    print(f"Error: {e}")
+
+def transcribe_audio(audio_file, language="Auto Detect"):
+    # Convert audio to 16kHz mono for better compatibility with Whisper
+    audio = AudioSegment.from_file(audio_file)
+    audio = audio.set_frame_rate(16000).set_channels(1)
+    processed_audio_path = "processed_audio.wav"
+    audio.export(processed_audio_path, format="wav")
+    
+    # Load the appropriate model based on the selected language
+    if language == "Sinhala" and sinhala_model is not None:
+        print("Using fine-tuned Sinhala model.")
+        model = sinhala_model
+        processor = sinhala_processor
+    else:
+        print("Using base Whisper model.")
+        model = base_model
+        processor = None
+    
+    # Transcribe the audio
+    if language == "Auto Detect":
+        result = model.transcribe(processed_audio_path, fp16=False)  # Auto-detect language
+        detected_language = result.get("language", "unknown")
+    else:
+        language_code = LANGUAGE_NAME_TO_CODE.get(language, "en")  # Default to English if not found
+        result = model.transcribe(processed_audio_path, language=language_code, fp16=False)
+        detected_language = language_code
+    
+    # Clean up processed audio file
+    os.remove(processed_audio_path)
+    
+    # Return transcription and detected language
+    return f"Detected Language: {detected_language}\n\nTranscription:\n{result['text']}"
 
 # Mapping of full language names to language codes
 LANGUAGE_NAME_TO_CODE = {
@@ -116,31 +152,6 @@ LANGUAGE_NAME_TO_CODE = {
     "Sundanese": "su",
 }
 
-def transcribe_audio(audio_file, language="Auto Detect", model_size="Base (Faster)"):
-    # Load the selected Whisper model
-    model = whisper.load_model(MODELS[model_size])
-    
-    # Convert audio to 16kHz mono for better compatibility with Whisper
-    audio = AudioSegment.from_file(audio_file)
-    audio = audio.set_frame_rate(16000).set_channels(1)
-    processed_audio_path = "processed_audio.wav"
-    audio.export(processed_audio_path, format="wav")
-    
-    # Transcribe the audio
-    if language == "Auto Detect":
-        result = model.transcribe(processed_audio_path, fp16=False)  # Auto-detect language
-        detected_language = result.get("language", "unknown")
-    else:
-        language_code = LANGUAGE_NAME_TO_CODE.get(language, "en")  # Default to English if not found
-        result = model.transcribe(processed_audio_path, language=language_code, fp16=False)
-        detected_language = language_code
-    
-    # Clean up processed audio file
-    os.remove(processed_audio_path)
-    
-    # Return transcription and detected language
-    return f"Detected Language: {detected_language}\n\nTranscription:\n{result['text']}"
-
 # Define the Gradio interface
 iface = gr.Interface(
     fn=transcribe_audio,
@@ -150,16 +161,11 @@ iface = gr.Interface(
             choices=list(LANGUAGE_NAME_TO_CODE.keys()),  # Full language names
             label="Select Language",
             value="Auto Detect"
-        ),
-        gr.Dropdown(
-            choices=list(MODELS.keys()),  # Model options
-            label="Select Model",
-            value="Base (Faster)"  # Default to "Base" model
         )
     ],
     outputs=gr.Textbox(label="Transcription and Detected Language"),
-    title="Audio Transcription with Language and Model Selection",
-    description="Upload an audio file, select a language (or choose 'Auto Detect'), and choose a model for transcription."
+    title="Audio Transcription with Language Selection",
+    description="Upload an audio file and select a language (or choose 'Auto Detect'). For Sinhala, a fine-tuned model will be used automatically."
 )
 
 # Launch the Gradio interface