Update app.py

app.py

@@ -4,11 +4,14 @@ from transformers import pipeline
 from sentence_transformers import SentenceTransformer
 from sklearn.metrics.pairwise import cosine_similarity
 import PyPDF2
+from fairseq.checkpoint_utils import load_model_ensemble_and_task_from_hf_hub
+
+# Load models for TTS from Hugging Face Hub
+models, cfg, task = load_model_ensemble_and_task_from_hf_hub("facebook/fastspeech2-en-ljspeech")
 
 # Load local models for inference
 stt_model = pipeline("automatic-speech-recognition", model="openai/whisper-base")
 conversation_model = pipeline("text-generation", model="facebook/blenderbot-400M-distill")
-tts_model = pipeline("text-to-speech", model="espnet/fastspeech2-en-ljspeech")
 
 # Load a pre-trained model for vector embeddings
 embedding_model = SentenceTransformer('all-MiniLM-L6-v2')
@@ -47,9 +50,10 @@ def generate_question(user_input, resume_embeddings):
 
 # Generate TTS output
 def generate_audio(text):
-    """Convert text to audio using TTS model."""
-    audio_data = tts_model(text, return_tensors=True)["waveform"]
-    return audio_data
+    """Convert text to audio using Fairseq TTS model."""
+    # Here you can integrate model-specific logic to produce audio from text
+    # Placeholder return until Fairseq TTS model logic is implemented
+    return text  # Replace with actual waveform generation
 
 # Gradio interface
 class MockInterview: