voirs-cloning

Advanced Voice Cloning and Speaker Adaptation System

This crate provides comprehensive voice cloning capabilities including few-shot speaker adaptation, speaker verification, voice similarity measurement, and cross-language cloning.

🎭 Features

Core Voice Cloning

• Few-shot Cloning - Clone voices with as little as 30 seconds of audio
• Speaker Adaptation - Adapt existing models to new speakers
• Cross-lingual Cloning - Clone voices across different languages
• Real-time Adaptation - Live voice adaptation during synthesis

Speaker Analysis

• Speaker Embeddings - Deep neural speaker representations
• Voice Similarity - Perceptual and embedding-based similarity metrics
• Speaker Verification - Identity verification for cloned voices
• Voice Characteristics - Analysis of pitch, timbre, and prosody

Quality Control

• Cloning Quality Assessment - Automated quality metrics
• Similarity Scoring - Multi-dimensional similarity evaluation
• Authenticity Verification - Detection of cloned vs. original voices
• Ethical Safeguards - Built-in protections against misuse

Advanced Features

• Voice Morphing - Blend characteristics from multiple speakers
• Age/Gender Adaptation - Modify apparent age and gender
• Emotion Transfer - Transfer emotional characteristics between speakers
• Style Preservation - Maintain speaking style across adaptations

🚀 Quick Start

Basic Voice Cloning

use voirs_cloning::prelude::*;

#[tokio::main]
async fn main() -> Result<()> {
    // Load audio samples for cloning
    let voice_samples = vec![
        VoiceSample::from_file("speaker1_sample1.wav").await?,
        VoiceSample::from_file("speaker1_sample2.wav").await?,
        VoiceSample::from_file("speaker1_sample3.wav").await?,
    ];

    // Create voice cloner
    let cloner = VoiceCloner::builder()
        .with_method(CloningMethod::FewShot)
        .with_quality_threshold(0.8)
        .build().await?;

    // Clone the voice
    let request = VoiceCloneRequest::new(voice_samples)
        .with_target_text("Hello, this is a test of voice cloning.")
        .with_adaptation_steps(100);

    let result = cloner.clone_voice(request).await?;
    
    // Save cloned voice model
    result.save_model("cloned_voice.bin").await?;
    
    // Evaluate cloning quality
    let quality = result.quality_metrics();
    println!("Cloning quality: {:.2}", quality.overall_score);
    
    Ok(())
}

Speaker Embedding Extraction

use voirs_cloning::prelude::*;

// Create embedding extractor
let extractor = SpeakerEmbeddingExtractor::new().await?;

// Extract embeddings from audio
let audio_data = load_audio("speaker.wav").await?;
let embedding = extractor.extract_embedding(&audio_data).await?;

// Compare with another speaker
let other_embedding = extractor.extract_embedding(&other_audio).await?;
let similarity = embedding.cosine_similarity(&other_embedding);

println!("Speaker similarity: {:.3}", similarity);

Real-time Voice Adaptation

use voirs_cloning::prelude::*;
use tokio::time::{interval, Duration};

#[tokio::main]
async fn main() -> Result<()> {
    let cloner = VoiceCloner::builder()
        .with_real_time_adaptation()
        .build().await?;

    let mut adaptation_buffer = Vec::new();
    let mut timer = interval(Duration::from_secs(5));

    loop {
        timer.tick().await;
        
        // Collect new audio samples
        if let Ok(new_sample) = collect_audio_sample().await {
            adaptation_buffer.push(new_sample);
            
            // Adapt voice model with new data
            if adaptation_buffer.len() >= 3 {
                let adapted_model = cloner
                    .adapt_voice(&adaptation_buffer)
                    .await?;
                
                update_synthesis_model(adapted_model).await?;
                adaptation_buffer.clear();
            }
        }
    }
}

🔍 Voice Analysis

Speaker Characteristics

use voirs_cloning::prelude::*;

// Analyze voice characteristics
let analyzer = VoiceAnalyzer::new().await?;
let audio = load_audio("speaker.wav").await?;

let characteristics = analyzer.analyze_voice(&audio).await?;

println!("Fundamental frequency: {:.1} Hz", characteristics.f0_mean);
println!("Pitch range: {:.1} semitones", characteristics.pitch_range);
println!("Speaking rate: {:.1} words/min", characteristics.speaking_rate);
println!("Voice quality: {:?}", characteristics.voice_quality);

Voice Similarity Measurement

use voirs_cloning::similarity::*;

// Create similarity measurer
let measurer = SimilarityMeasurer::new()
    .with_perceptual_weighting(0.6)
    .with_embedding_weighting(0.4)
    .build()?;

// Measure similarity between voices
let similarity_score = measurer.measure_similarity(
    &original_voice,
    &cloned_voice,
).await?;

println!("Perceptual similarity: {:.3}", similarity_score.perceptual);
println!("Embedding similarity: {:.3}", similarity_score.embedding);
println!("Overall similarity: {:.3}", similarity_score.overall);

🔧 Configuration

Cloning Methods

use voirs_cloning::types::*;

// Configure different cloning approaches
let few_shot_config = CloningConfig::builder()
    .method(CloningMethod::FewShot {
        min_samples: 3,
        max_samples: 10,
        adaptation_steps: 200,
    })
    .quality_threshold(0.85)
    .build()?;

let zero_shot_config = CloningConfig::builder()
    .method(CloningMethod::ZeroShot {
        embedding_similarity_threshold: 0.9,
    })
    .enable_cross_lingual(true)
    .build()?;

Speaker Profile Creation

use voirs_cloning::types::*;

// Create comprehensive speaker profile
let profile = SpeakerProfile::builder()
    .with_name("Speaker 1")
    .with_samples(voice_samples)
    .with_metadata(SpeakerMetadata {
        age_range: Some(25..35),
        gender: Some(Gender::Female),
        accent: Some("American English".to_string()),
        languages: vec!["en-US".to_string()],
    })
    .with_embedding(speaker_embedding)
    .build()?;

🎪 Advanced Features

Cross-lingual Voice Cloning

use voirs_cloning::prelude::*;

// Clone voice across languages
let cloner = VoiceCloner::builder()
    .with_cross_lingual_support()
    .with_phoneme_mapping("en-US", "ja-JP")
    .build().await?;

// English source, Japanese target
let english_samples = load_english_samples().await?;
let japanese_text = "こんにちは、これは音声クローンのテストです。";

let result = cloner.clone_cross_lingual(
    english_samples,
    japanese_text,
    "ja-JP"
).await?;

Voice Morphing

use voirs_cloning::morphing::*;

// Morph between multiple speakers
let morpher = VoiceMorpher::new();

let speaker_a = load_speaker_profile("speaker_a.json").await?;
let speaker_b = load_speaker_profile("speaker_b.json").await?;

// Create morphed voice (30% A, 70% B)
let morphed_profile = morpher.morph_voices(
    &[(speaker_a, 0.3), (speaker_b, 0.7)]
).await?;

Quality Assessment

use voirs_cloning::quality::*;

// Assess cloning quality
let assessor = CloningQualityAssessor::new().await?;

let metrics = assessor.assess_quality(
    &original_audio,
    &cloned_audio,
    &reference_text
).await?;

println!("Naturalness: {:.2}", metrics.naturalness);
println!("Speaker similarity: {:.2}", metrics.speaker_similarity);
println!("Intelligibility: {:.2}", metrics.intelligibility);
println!("Audio quality: {:.2}", metrics.audio_quality);

🔒 Ethical Safeguards

Consent Verification

use voirs_cloning::ethics::*;

// Require explicit consent for voice cloning
let consent_manager = ConsentManager::new();

// Verify consent before cloning
let consent_token = consent_manager.verify_consent(
    &speaker_identity,
    &voice_samples,
    ConsentLevel::FullCloning
).await?;

let cloner = VoiceCloner::builder()
    .with_consent_requirement(consent_token)
    .with_usage_tracking(true)
    .build().await?;

Usage Monitoring

use voirs_cloning::monitoring::*;

// Track voice cloning usage
let monitor = UsageMonitor::new()
    .with_audit_logging(true)
    .with_anomaly_detection(true)
    .build()?;

// Log cloning activity
monitor.log_cloning_activity(CloningActivity {
    speaker_id: "speaker_123".to_string(),
    target_text: text.clone(),
    quality_score: result.quality_score,
    timestamp: Utc::now(),
    usage_purpose: UsagePurpose::PersonalAssistant,
}).await?;

🔍 Performance

Benchmarks

Optimization Settings

use voirs_cloning::config::*;

// Performance-optimized configuration
let config = CloningConfig::builder()
    .with_performance_mode(PerformanceMode::Fast)
    .with_batch_size(16)
    .with_gpu_optimization(true)
    .with_memory_limit(4_000_000_000) // 4GB
    .build()?;

// Quality-optimized configuration
let config = CloningConfig::builder()
    .with_performance_mode(PerformanceMode::HighQuality)
    .with_adaptation_steps(500)
    .with_quality_threshold(0.95)
    .build()?;

🧪 Testing

# Run voice cloning tests
cargo test --package voirs-cloning

# Run similarity measurement tests
cargo test --package voirs-cloning similarity

# Run quality assessment tests
cargo test --package voirs-cloning quality

# Run cross-lingual cloning tests
cargo test --package voirs-cloning cross_lingual

# Run performance benchmarks
cargo bench --package voirs-cloning

🔗 Integration

With Acoustic Models

use voirs_cloning::acoustic::*;

// Integrate with acoustic models
let acoustic_adapter = AcousticModelAdapter::new();
let adapted_model = acoustic_adapter
    .adapt_model(&base_model, &speaker_embedding)
    .await?;

With Other VoiRS Crates

• voirs-acoustic - Speaker adaptation for acoustic models
• voirs-vocoder - Speaker-conditioned vocoding
• voirs-emotion - Emotion transfer between speakers
• voirs-evaluation - Cloning quality metrics
• voirs-sdk - High-level cloning API

🎓 Examples

See the examples/ directory for comprehensive usage examples:

• voice_cloning_example.rs - Basic voice cloning
• speaker_similarity.rs - Similarity measurement
• real_time_adaptation.rs - Live adaptation
• cross_lingual_cloning.rs - Multi-language cloning

⚠️ Ethical Guidelines

1. Explicit Consent - Always obtain explicit consent before cloning someone's voice
2. Clear Disclosure - Clearly indicate when synthesized voice is used
3. Legitimate Use - Only use for legitimate, legal purposes
4. Privacy Protection - Protect speaker identity and voice data
5. Misuse Prevention - Implement safeguards against malicious use

📝 License

Licensed under either of Apache License 2.0 or MIT License at your option.

Part of the VoiRS neural speech synthesis ecosystem.