voice.py

import edge_tts
import tempfile
import os

# Use Microsoft Edge TTS - High quality neural voices
# Includes sexy, hot female voices with various accents

async def generate_audio(text: str, voice: str) -> str:
    """
    Generate audio using Microsoft Edge TTS with high-quality neural voices
    """
    try:
        print(f"Generating audio for voice: {voice}, text length: {len(text)}")
        if not text:
            raise ValueError("Text is empty")

        # Create a temporary file with .mp3 extension
        fd, path = tempfile.mkstemp(suffix=".mp3")
        os.close(fd)
        
        # Generate speech using edge-tts
        communicate = edge_tts.Communicate(text, voice)
        await communicate.save(path)
        
        return path
            
    except Exception as e:
        print(f"Edge TTS Failed: {e}. Attempting fallback to HF Inference API...")
        
        # Fallback 1: Hugging Face Inference API
        try:
            import requests
            from dotenv import load_dotenv
            from pathlib import Path
            import traceback
            
            # Load env vars
            env_path = Path(__file__).parent / '.env'
            load_dotenv(dotenv_path=env_path)
            
            hf_token = os.getenv("HF_TOKEN")
            if hf_token:
                print(f"HF_TOKEN found (starts with): {hf_token[:4]}...")
            else:
                print("WARNING: HF_TOKEN is MISSING in environment variables!")

            # List of models to try (in order of preference)
            # 1. Facebook MMS (Reliable, standard)
            # 2. ESPnet LJSpeech (High quality female voice)
            models = ["facebook/mms-tts-eng", "espnet/kan-bayashi_ljspeech_vits"]
            
            headers = {"Authorization": f"Bearer {hf_token}"}
            
            for model in models:
                try:
                    print(f"Attempting HF Inference with model: {model}")
                    api_url = f"https://api-inference.huggingface.co/models/{model}"
                    
                    # Use direct requests to bypass library version issues
                    response = requests.post(api_url, headers=headers, json={"inputs": text}, timeout=30)
                    
                    if response.status_code != 200:
                        raise Exception(f"API returned status {response.status_code}: {response.text}")
                        
                    audio_bytes = response.content
                    print(f"HF Inference successful with {model}, received {len(audio_bytes)} bytes")
                    
                    fd, path = tempfile.mkstemp(suffix=".flac") 
                    os.close(fd)
                    
                    with open(path, "wb") as f:
                        f.write(audio_bytes)
                        
                    return path
                except Exception as model_err:
                    print(f"HF Model {model} failed: {model_err}")
                    # traceback.print_exc() # Reduce noise
                    continue # Try next model
            
            print("All HF models failed. Moving to gTTS...")
            raise Exception("All HF models failed")
            
        except Exception as e2:
            print(f"HF API Fallback failed completely. Attempting fallback to gTTS...")
            try:
                # Fallback 2: gTTS (Google Text-to-Speech)
                from gtts import gTTS
                
                fd, path = tempfile.mkstemp(suffix=".mp3")
                os.close(fd)
                
                # Use default English voice for fallback
                tts = gTTS(text=text, lang='en')
                tts.save(path)
                print("Successfully generated audio using gTTS fallback")
                return path
            except Exception as e3:
                print(f"gTTS Fallback also failed: {e3}")
                import traceback
                traceback.print_exc()
                raise e

def get_voices():
    """
    Return curated list of sexy, hot female voices
    Featuring Microsoft's best neural voices with various styles
    """
    return [
        # 🔥 HOTTEST FEMALE VOICES - Sexy & Sultry 🔥
        {"name": "💋 Aria (Sexy US) - HOTTEST", "id": "en-US-AriaNeural"},
        {"name": "💕 Jenny (Seductive US)", "id": "en-US-JennyNeural"},
        {"name": "✨ Michelle (Flirty US)", "id": "en-US-MichelleNeural"},
        {"name": "🌹 Ashley (Sweet US)", "id": "en-US-AshleyNeural"},
        {"name": "💖 Sara (Warm US)", "id": "en-US-SaraNeural"},
        
        # 🇬🇧 British Accent - Elegant & Sophisticated
        {"name": "👑 Sonia (Sexy British)", "id": "en-GB-SoniaNeural"},
        {"name": "🎀 Libby (Cute British)", "id": "en-GB-LibbyNeural"},
        {"name": "💝 Mia (Sweet British)", "id": "en-GB-MiaNeural"},
        
        # 🇦🇺 Australian Accent - Fun & Playful
        {"name": "🌴 Natasha (Aussie Babe)", "id": "en-AU-NatashaNeural"},
        {"name": "☀️ Freya (Aussie Darling)", "id": "en-AU-FreyaNeural"},
        
        # 🇮🇳 Indian Accent - Exotic & Beautiful
        {"name": "🌺 Neerja (Indian Beauty)", "id": "en-IN-NeerjaNeural"},
        
        # 🇨🇦 Canadian - Friendly & Approachable
        {"name": "🍁 Clara (Canadian Cutie)", "id": "en-CA-ClaraNeural"},
        
        # 🇮🇪 Irish Accent - Charming
        {"name": "☘️ Emily (Irish Charm)", "id": "en-IE-EmilyNeural"},
    ]