# Audiobook generation pipeline
import asyncio
from pathlib import Path

async def generate_audiobook(text_path, chapter_length=2000):
    """Split long text into chapters for consistent quality"""

    # Clone narrator voice from 3-second sample
    narrator_voice = model.clone_voice(
        reference_audio="narrator_sample.wav",
        speaker="narrator_professional"
    )

    # Split text into chapters (2000 words each)
    chapters = split_into_chapters(text_path, chapter_length)

    # Generate audio in parallel (4 concurrent workers)
    tasks = [
        model.generate_async(
            text=chapter,
            voice=narrator_voice,
            emotion="neutral"  # Or vary per scene
        )
        for chapter in chapters
    ]

    audio_files = await asyncio.gather(*tasks)

    # Stitch together with silence between chapters
    final_audio = stitch_audio(audio_files, silence_ms=1500)

    return final_audio

# Real-time assistant pipeline
from fastapi import FastAPI
from fastapi.websockets import WebSocket
import asyncio

app = FastAPI()

@app.websocket("/ws/voice-assistant")
async def voice_assistant(websocket: WebSocket):
    await websocket.accept()

    # Load model once (shared across sessions)
    voice_profile = model.load_voice("customer_service_friendly")

    try:
        while True:
            # Receive user audio (streaming ASR)
            user_audio = await websocket.recv_bytes()

            # Transcribe (using Whisper or Qwen-ASR)
            user_text = asr_model.transcribe(user_audio)

            # Generate LLM response
            llm_response = llm_model.generate(
                user_text,
                context=conversation_history
            )

            # Generate speech (streaming)
            audio_stream = model.generate_streaming(
                text=llm_response,
                voice=voice_profile,
                chunk_size=512  # 42ms chunks
            )

            # Send audio back immediately
            async for chunk in audio_stream:
                await websocket.send_bytes(chunk)

    except WebSocketDisconnect:
        pass

# Accessibility-focused screen reader
class AccessiScreenReader:
    def __init__(self):
        # Load fast model for low-latency
        self.model = load_model("Qwen3-TTS-12Hz-0.6B-CustomVoice")

        # User preference persistence
        self.user_voices = {}

    def speak(self, text, user_id):
        """Generate speech with user's preferred voice"""

        # Get user's voice profile
        voice = self.user_voices.get(user_id, "default_neutral")

        # Adjust speaking rate based on user preference
        speed = self.get_user_preference(user_id, "speaking_speed")

        # Generate audio
        audio = self.model.generate(
            text=text,
            voice=voice,
            speed=speed,
            emotion="neutral"  # Screen readers should be neutral
        )

        # Play immediately
        self.play_audio(audio)

    def set_voice_from_sample(self, user_id, sample_audio):
        """Let users clone their own voice or a familiar voice"""

        cloned_voice = self.model.clone_voice(
            reference_audio=sample_audio,
            language="en"
        )

        self.user_voices[user_id] = cloned_voice

# Dynamic NPC voice generation
class NPCVoiceManager:
    def __init__(self):
        # Create 50 unique voice profiles for NPCs
        self.npc_voices = self.generate_npc_voices()

    def generate_npc_voices(self):
        """Generate diverse NPC voices using VoiceDesign"""

        voices = {}

        # Generate voice profiles based on character traits
        archetypes = [
            "grumpy blacksmith, deep gravelly voice",
            "energetic shopkeeper, high-pitched cheerful",
            "wise old wizard, slow deliberate speech",
            "young adventurer, eager and enthusiastic",
            # ... 45 more archetypes
        ]

        for archetype in archetypes:
            voice_name = archetype.split(",")[0].replace(" ", "_")

            # Generate voice using VoiceDesign
            voice = self.model.design_voice(
                description=archetype,
                language="en"
            )

            voices[voice_name] = voice

        return voices

    def generate_dialogue(self, npc_name, dialogue_text, emotion):
        """Generate NPC dialogue with emotion"""

        voice = self.npc_voices[npc_name]

        audio = self.model.generate(
            text=dialogue_text,
            voice=voice,
            emotion=emotion,  # "angry", "happy", "sad", etc.
            speaking_rate=self.get_npc_rate(npc_name)
        )

        return audio

# E-learning course narration
class CourseNarrator:
    def __init__(self):
        # Different voices for different course types
        self.instructors = {
            "technical": self.clone_instructor("technical_professor.wav"),
            "creative": self.clone_instructor("creative_mentor.wav"),
            "business": self.clone_instructor("business_executive.wav")
        }

    def generate_lesson_audio(self, lesson_content, course_type):
        """Generate lesson narration with appropriate voice"""

        instructor = self.instructors[course_type]

        # Split content into paragraphs
        paragraphs = lesson_content.split("\n\n")

        audio_segments = []

        for i, para in enumerate(paragraphs):
            # Add variation based on content type
            if self.is_code_example(para):
                # Slower, more deliberate for code
                audio = self.model.generate(
                    text=para,
                    voice=instructor,
                    speaking_rate=0.85,
                    emotion="neutral"
                )
            elif self.is_key_concept(para):
                # More emphasis for key concepts
                audio = self.model.generate(
                    text=para,
                    voice=instructor,
                    speaking_rate=0.95,
                    emotion="enthusiastic"
                )
            else:
                # Normal narration
                audio = self.model.generate(
                    text=para,
                    voice=instructor,
                    speaking_rate=1.0,
                    emotion="neutral"
                )

            audio_segments.append(audio)

        # Combine segments
        full_lesson = self.combine_audio(audio_segments)

        return full_lesson

# Automated podcast generation pipeline
class PodcastGenerator:
    def __init__(self):
        # Two-host conversation format
        self.host_a = model.clone_voice("host_a_sample.wav")
        self.host_b = model.clone_voice("host_b_sample.wav")

    def generate_episode(self, blog_post_url):
        """Generate full podcast episode from blog post"""

        # 1. Extract content from blog post
        content = self.scrape_blog_post(blog_post_url)

        # 2. Generate dialogue script (using LLM)
        script = self.llm.generate_dialogue(
            content=content,
            format="conversation",
            length="15_minutes"
        )

        # 3. Generate audio (alternating hosts)
        audio_segments = []

        for line in script.dialogue:
            speaker = self.host_a if line.speaker == "A" else self.host_b

            # Add natural conversational elements
            if line.emotion == "excited":
                emotion = "enthusiastic"
            elif line.emotion == "thoughtful":
                emotion = "contemplative"
            else:
                emotion = "neutral"

            audio = self.model.generate(
                text=line.text,
                voice=speaker,
                emotion=emotion,
                speaking_rate=1.0  # Natural conversational pace
            )

            audio_segments.append(audio)

            # Add small pauses between speakers (natural rhythm)
            audio_segments.append(self.silence(300))  # 300ms pause

        # 4. Add intro/outro music
        final_episode = self.add_music(
            self.concatenate(audio_segments),
            intro_music="intro.mp3",
            outro_music="outro.mp3"
        )

        return final_episode

# Voice preservation service
class VoiceKeeper:
    def preserve_voice(self, user_id, reference_audio):
        """Clone and permanently store user's voice"""

        # Clone voice from 3-second sample
        cloned_voice = model.clone_voice(
            reference_audio=reference_audio,
            language="auto"
        )

        # Store voice profile (encrypted)
        self.save_voice_profile(
            user_id=user_id,
            voice_profile=cloned_voice,
            encryption_key=user_encryption_key
        )

        # Generate test samples for user to verify
        test_phrases = [
            "Hello, this is my preserved voice.",
            "I am grateful for this technology.",
            "Thank you for listening."
        ]

        samples = []
        for phrase in test_phrases:
            audio = model.generate(
                text=phrase,
                voice=cloned_voice
            )
            samples.append(audio)

        return samples

    def generate_speech(self, user_id, text, decryption_key):
        """Generate speech in user's preserved voice"""

        # Retrieve encrypted voice profile
        voice_profile = self.load_voice_profile(
            user_id=user_id,
            decryption_key=decryption_key
        )

        # Generate speech
        audio = model.generate(
            text=text,
            voice=voice_profile
        )

        return audio