import sagea
 
client = sagea.VoraClient(api_key="your-api-key")
 
# Create a custom voice with audio samples
voice_id = client.clone_voice(
    name="my_custom_voice",
    audio_samples=[
        "sample1.wav",
        "sample2.wav", 
        "sample3.wav"
    ],
    text_transcripts=[
        "This is the first training sample.",
        "Here's another sample for the voice model.",
        "And one more sample for better quality."
    ]
)
 
# Use the cloned voice
audio = client.synthesize(
    text="Hello! This is my cloned voice speaking.",
    voice_id=voice_id,
    model="vora-v1"
)

# Enhanced voice cloning with options
voice_id = client.clone_voice(
    name="professional_narrator",
    audio_samples=audio_files,
    text_transcripts=transcripts,
    options={
        "quality": "premium",  # standard, high, premium
        "training_steps": 2000,
        "preserve_accent": True,
        "emotional_range": "full"
    }
)
 
# Monitor training progress
status = client.get_cloning_status(voice_id)
print(f"Training progress: {status.progress}%")

# Test the cloned voice quality
test_audio = client.synthesize(
    text="This is a test of the cloned voice quality.",
    voice_id=voice_id,
    model="vora-v1"
)
 
# Get similarity score
similarity = client.verify_voice_similarity(
    original_sample="original.wav",
    generated_audio=test_audio
)
print(f"Voice similarity: {similarity.score}%")

# Clone voice with multilingual support
multilingual_voice = client.clone_voice(
    name="global_narrator",
    audio_samples=samples,
    text_transcripts=transcripts,
    options={
        "multilingual": True,
        "target_languages": ["en-US", "es-ES", "fr-FR", "hi-IN"]
    }
)
 
# Use across different languages
english_audio = client.synthesize(
    text="Hello, welcome to our service.",
    voice_id=multilingual_voice,
    language="en-US"
)
 
spanish_audio = client.synthesize(
    text="Hola, bienvenido a nuestro servicio.",
    voice_id=multilingual_voice,
    language="es-ES"
)

# Test emotional range
emotions = ["neutral", "happy", "professional", "excited", "calm"]
 
for emotion in emotions:
    audio = client.synthesize(
        text=f"This is the {emotion} emotion demonstration.",
        voice_id=voice_id,
        emotion=emotion,
        emotion_intensity=0.7
    )
    audio.save(f"test_{emotion}.wav")

# Adapt voice for different contexts
business_voice = client.adapt_voice(
    base_voice_id=voice_id,
    adaptation_type="professional",
    sample_texts=[
        "Welcome to our quarterly business review.",
        "The financial projections show positive growth.",
        "Thank you for your attention to this matter."
    ]
)
 
# Use adapted voice
formal_audio = client.synthesize(
    text="Good morning, let's begin today's presentation.",
    voice_id=business_voice,
    emotion="professional"
)

# Enterprise voice cloning with brand guidelines
brand_voice = client.clone_voice(
    name="company_brand_voice",
    audio_samples=brand_samples,
    text_transcripts=brand_scripts,
    options={
        "brand_guidelines": {
            "tone": "friendly_professional",
            "pace": "moderate",
            "emphasis_style": "subtle",
            "cultural_adaptation": True
        },
        "quality": "premium",
        "watermark": True
    }
)

# Licensed celebrity voice cloning
celebrity_voice = client.clone_voice(
    name="licensed_celebrity",
    audio_samples=licensed_samples,
    options={
        "licensing": {
            "agreement_id": "celeb_license_001",
            "usage_rights": ["commercial", "advertising"],
            "content_filtering": True,
            "watermark_required": True
        }
    }
)

# Secure voice deployment
secure_voice = client.deploy_voice(
    voice_id=voice_id,
    security_options={
        "authentication_required": True,
        "usage_logging": True,
        "content_approval": True,
        "geographic_restrictions": ["US", "EU"],
        "blacklist_terms": ["inappropriate", "content"]
    }
)

# Analyze sample quality before training
quality_report = client.analyze_samples(
    audio_samples=samples,
    text_transcripts=transcripts
)
 
print("Quality Analysis:")
print(f"Overall Score: {quality_report.overall_score}/10")
print(f"Audio Quality: {quality_report.audio_quality}")
print(f"Content Diversity: {quality_report.content_diversity}")
print(f"Recommendations: {quality_report.recommendations}")

# Improve voice quality with additional samples
improved_voice = client.enhance_voice(
    base_voice_id=voice_id,
    additional_samples=new_samples,
    enhancement_type="quality_boost"
)
 
# A/B test voice versions
comparison = client.compare_voices(
    voice_a=voice_id,
    voice_b=improved_voice,
    test_texts=test_scripts
)

# Clone podcast host voice
podcast_voice = client.clone_voice(
    name="podcast_host",
    audio_samples=host_samples,
    options={"content_type": "podcast"}
)
 
# Generate episode content
episode_audio = client.synthesize(
    text=episode_script,
    voice_id=podcast_voice,
    emotion="conversational"
)

# Professional narrator cloning
narrator_voice = client.clone_voice(
    name="audiobook_narrator",
    options={
        "narration_style": "literary",
        "character_voices": True,
        "emotional_range": "full"
    }
)

# Consistent customer service voice
service_voice = client.clone_voice(
    name="customer_service",
    options={
        "tone": "helpful_professional",
        "multilingual": True,
        "emotion_adaptation": True
    }
)

# Restore voice for accessibility needs
restored_voice = client.clone_voice(
    name="personal_voice_backup",
    audio_samples=historical_recordings,
    options={
        "preservation_mode": True,
        "quality": "premium"
    }
)

# Implement ethical safeguards
ethical_voice = client.clone_voice(
    name="ethical_deployment",
    options={
        "consent_verification": True,
        "content_filtering": True,
        "usage_monitoring": True,
        "owner_controls": True
    }
)

# Diagnostic voice analysis
diagnostics = client.diagnose_voice(voice_id)
 
for issue in diagnostics.issues:
    print(f"Issue: {issue.type}")
    print(f"Recommendation: {issue.solution}")
    print(f"Priority: {issue.priority}")