Demo Update 10

Backend/server.py

@@ -93,15 +93,49 @@ def load_speech_models():
     # Load Whisper model for speech recognition
     try:
         logger.info(f"Loading speech recognition model on {device}...")
-        speech_recognizer = pipeline("automatic-speech-recognition", 
+        
-                                    model="openai/whisper-small", 
+        # Try with newer API first
-                                    device=device)
+        try:
+            from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline
+            
+            model_id = "openai/whisper-small"
+            
+            # Load model and processor
+            model = AutoModelForSpeechSeq2Seq.from_pretrained(
+                model_id,
+                torch_dtype=torch.float16 if device == "cuda" else torch.float32,
+                device_map=device,
+            )
+            processor = AutoProcessor.from_pretrained(model_id)
+            
+            # Create pipeline with specific parameters
+            speech_recognizer = pipeline(
+                "automatic-speech-recognition",
+                model=model,
+                tokenizer=processor.tokenizer,
+                feature_extractor=processor.feature_extractor,
+                max_new_tokens=128,
+                chunk_length_s=30,
+                batch_size=16,
+                device=device,
+            )
+            
+        except Exception as api_error:
+            logger.warning(f"Newer API loading failed: {api_error}, trying simpler approach")
+            
+            # Fallback to simpler API
+            speech_recognizer = pipeline(
+                "automatic-speech-recognition", 
+                model="openai/whisper-small", 
+                device=device
+            )
+        
         logger.info("Speech recognition model loaded successfully")
+        return generator, speech_recognizer
+        
     except Exception as e:
         logger.error(f"Error loading speech recognition model: {e}")
-        speech_recognizer = None
+        return generator, None
-    
-    return generator, speech_recognizer
 
 # Unpack both models
 generator, speech_recognizer = load_speech_models()
@@ -308,9 +342,28 @@ def process_speech(audio_tensor: torch.Tensor, client_id: str) -> str:
         temp_path = os.path.join(base_dir, f"temp_{time.time()}.wav")
         torchaudio.save(temp_path, audio_tensor.unsqueeze(0).cpu(), generator.sample_rate)
         
-        # Perform speech recognition - using input_features instead of inputs
+        # Perform speech recognition - handle the warning differently
-        result = speech_recognizer(temp_path, input_features=None)  # input_features=None forces use of the correct parameter name
+        # Just pass the path without any additional parameters
-        transcription = result["text"]
+        try:
+            # First try - use default parameters
+            result = speech_recognizer(temp_path)
+            transcription = result["text"]
+        except Exception as whisper_error:
+            logger.warning(f"First transcription attempt failed: {whisper_error}")
+            # Try with explicit parameters for older versions of transformers
+            import numpy as np
+            import soundfile as sf
+            
+            # Load audio as numpy array
+            audio_np, sr = sf.read(temp_path)
+            if sr != 16000:
+                # Whisper expects 16kHz audio
+                from scipy import signal
+                audio_np = signal.resample(audio_np, int(len(audio_np) * 16000 / sr))
+            
+            # Try with numpy array directly
+            result = speech_recognizer(audio_np)
+            transcription = result["text"]
         
         # Clean up temp file
         if os.path.exists(temp_path):
@@ -320,6 +373,7 @@ def process_speech(audio_tensor: torch.Tensor, client_id: str) -> str:
         if not transcription or transcription.isspace():
             return "I didn't detect any speech. Could you please try again?"
         
+        logger.info(f"Transcription successful: '{transcription}'")
         return transcription
         
     except Exception as e: