Update tts.py

tts.py

@@ -1,42 +1,19 @@
-import torch
-from transformers import SpeechT5ForTextToSpeech, SpeechT5Processor
-import logging
 import numpy as np
-import soundfile as sf
 import torchaudio
-
-# Set up logging
-logging.basicConfig(level=logging.DEBUG)
-
-MODEL_ID = "microsoft/speecht5_tts"
-
-# Try to load the model and processor
-try:
-    processor = SpeechT5Processor.from_pretrained(MODEL_ID)
-    model = SpeechT5ForTextToSpeech.from_pretrained(MODEL_ID)
-    logging.info("Model and processor loaded successfully.")
-except Exception as e:
-    logging.error(f"Error loading model or processor: {e}")
+import logging
 
 def synthesize_speech(text):
     try:
-        inputs = processor(text, return_tensors="pt")
-        device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-        model.to(device)
-        inputs = inputs.to(device)
-
-        with torch.no_grad():
-            speech = model.generate(**inputs)
-
-        logging.info("Speech generated successfully.")
+        # Generate a simple sine wave for testing
+        sr = 16000
+        t = np.linspace(0, 1, sr)
+        waveform = 0.5 * np.sin(2 * np.pi * 440 * t).astype(np.float32)
 
-        # Decode the generated speech and save to an audio file
-        waveform = speech.cpu().numpy().flatten()
-        # Use torchaudio to save the waveform
-        file_path = "/tmp/output.wav"  # Use a temporary directory
-        torchaudio.save(file_path, torch.tensor(waveform).unsqueeze(0), 16000)
-        logging.info(f"Audio file saved successfully at {file_path}.")
+        # Save the sine wave to a file
+        file_path = "/tmp/output.wav"
+        torchaudio.save(file_path, torch.tensor(waveform).unsqueeze(0), sr)
+        logging.info(f"Test audio file saved successfully at {file_path}.")
         return file_path
     except Exception as e:
-        logging.error(f"Error during speech synthesis: {e}")
+        logging.error(f"Error during test audio generation: {e}")
         return None