voirs_conversion/style_transfer/

system.rs

//! Main style transfer system and repository

use crate::zero_shot::SpeakerEmbedding;
use crate::Result;
use serde::{Deserialize, Serialize};
use std::collections::HashMap;
use std::sync::{Arc, RwLock};
use std::time::{Duration, Instant};

use super::characteristics::*;
use super::components::*;
use super::config::*;
use super::models::*;

/// Main style transfer system that orchestrates the style transfer pipeline.
///
/// This system coordinates content-style decomposition, style encoding/decoding,
/// quality assessment, and caching for efficient style transfer operations.
pub struct StyleTransferSystem {
    /// Configuration for style transfer
    config: StyleTransferConfig,

    /// Style model repository
    style_models: Arc<RwLock<StyleModelRepository>>,

    /// Content-style decomposer
    decomposer: ContentStyleDecomposer,

    /// Style encoder
    style_encoder: StyleEncoder,

    /// Style decoder
    style_decoder: StyleDecoder,

    /// Quality assessor
    quality_assessor: StyleQualityAssessor,

    /// Performance metrics
    metrics: StyleTransferMetrics,

    /// Transfer cache
    transfer_cache: Arc<RwLock<HashMap<String, CachedStyleTransfer>>>,
}

/// Repository for managing style transfer models and their metadata.
///
/// Maintains a collection of style models with associated metadata, performance metrics,
/// and usage statistics for efficient model selection and management.
pub struct StyleModelRepository {
    /// Style models indexed by style ID
    models: HashMap<String, StyleModel>,

    /// Model metadata
    metadata: HashMap<String, StyleModelMetadata>,

    /// Model performance metrics
    performance_metrics: HashMap<String, ModelPerformanceMetrics>,

    /// Model usage statistics
    usage_statistics: HashMap<String, ModelUsageStatistics>,

    /// Repository configuration
    config: RepositoryConfig,
}

/// Metrics tracking the performance and quality of style transfer operations.
///
/// Records statistics about successful/failed transfers, processing times,
/// quality scores, cache efficiency, and resource utilization.
pub struct StyleTransferMetrics {
    /// Number of successful transfers
    pub successful_transfers: u64,

    /// Number of failed transfers
    pub failed_transfers: u64,

    /// Average processing time (ms)
    pub avg_processing_time: f32,

    /// Average quality score
    pub avg_quality_score: f32,

    /// Cache hit rate
    pub cache_hit_rate: f32,

    /// Style model utilization
    pub model_utilization: HashMap<String, f32>,

    /// Performance statistics
    pub performance_stats: StylePerformanceStats,
}

/// Style performance statistics
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct StylePerformanceStats {
    /// CPU usage (%)
    pub cpu_usage: f32,

    /// Memory usage (MB)
    pub memory_usage: f32,

    /// GPU usage (%)
    pub gpu_usage: Option<f32>,

    /// I/O throughput (MB/s)
    pub io_throughput: f32,

    /// Network usage (MB/s)
    pub network_usage: f32,
}

impl Default for StyleTransferMetrics {
    fn default() -> Self {
        Self {
            successful_transfers: 0,
            failed_transfers: 0,
            avg_processing_time: 0.0,
            avg_quality_score: 0.0,
            cache_hit_rate: 0.0,
            model_utilization: HashMap::new(),
            performance_stats: StylePerformanceStats {
                cpu_usage: 0.0,
                memory_usage: 0.0,
                gpu_usage: None,
                io_throughput: 0.0,
                network_usage: 0.0,
            },
        }
    }
}

/// Cached style transfer
#[derive(Debug, Clone)]
pub struct CachedStyleTransfer {
    /// Transfer result
    pub result: Vec<f32>,

    /// Transfer quality
    pub quality: f32,

    /// Processing time
    pub processing_time: Duration,

    /// Cache timestamp
    pub timestamp: Instant,

    /// Usage count
    pub usage_count: u32,

    /// Transfer metadata
    pub metadata: TransferMetadata,
}

/// Transfer metadata
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TransferMetadata {
    /// Source style ID
    pub source_style_id: String,

    /// Target style ID
    pub target_style_id: String,

    /// Transfer method used
    pub method: StyleTransferMethod,

    /// Configuration hash
    pub config_hash: String,
}

// Main implementation

// Implementations

impl StyleTransferSystem {
    /// Create new style transfer system
    pub fn new(config: StyleTransferConfig) -> Self {
        Self {
            config,
            style_models: Arc::new(RwLock::new(StyleModelRepository::new())),
            decomposer: ContentStyleDecomposer::new(),
            style_encoder: StyleEncoder::new(),
            style_decoder: StyleDecoder::new(),
            quality_assessor: StyleQualityAssessor::new(),
            metrics: StyleTransferMetrics::default(),
            transfer_cache: Arc::new(RwLock::new(HashMap::new())),
        }
    }

    /// Transfer style from source to target
    pub fn transfer_style(
        &mut self,
        source_audio: &[f32],
        target_style_id: &str,
        sample_rate: u32,
    ) -> Result<Vec<f32>> {
        let start_time = Instant::now();

        // Generate cache key
        let cache_key = self.generate_transfer_cache_key(source_audio, target_style_id);

        // Check cache
        if let Some(cached) = self.check_transfer_cache(&cache_key)? {
            self.update_cache_metrics();
            return Ok(cached.result);
        }

        // Get target style model
        let transferred_audio = {
            let style_models = self
                .style_models
                .read()
                .expect("lock should not be poisoned");
            let target_model = style_models.get_model(target_style_id)?;

            // Perform style transfer based on method
            match self.config.transfer_method {
                StyleTransferMethod::ContentStyleDecomposition => {
                    self.transfer_via_decomposition(source_audio, target_model, sample_rate)?
                }
                StyleTransferMethod::AdversarialTransfer => {
                    self.transfer_via_adversarial(source_audio, target_model, sample_rate)?
                }
                StyleTransferMethod::CycleConsistentTransfer => {
                    self.transfer_via_cycle_consistent(source_audio, target_model, sample_rate)?
                }
                StyleTransferMethod::NeuralStyleTransfer => {
                    self.transfer_via_neural(source_audio, target_model, sample_rate)?
                }
                StyleTransferMethod::SemanticStyleTransfer => {
                    self.transfer_via_semantic(source_audio, target_model, sample_rate)?
                }
                StyleTransferMethod::HierarchicalTransfer => {
                    self.transfer_via_hierarchical(source_audio, target_model, sample_rate)?
                }
            }
        };

        // Assess transfer quality
        let target_style_rep = self.style_encoder.encode_style(source_audio, sample_rate)?;
        let quality_score = self.quality_assessor.assess_transfer_quality(
            source_audio,
            &transferred_audio,
            &target_style_rep,
            sample_rate,
        )?;

        // Update metrics
        let processing_time = start_time.elapsed();
        self.update_transfer_metrics(processing_time, quality_score, true);

        // Cache result
        self.cache_transfer_result(
            cache_key,
            transferred_audio.clone(),
            quality_score,
            processing_time,
            target_style_id.to_string(),
        )?;

        Ok(transferred_audio)
    }

    /// Add style model to repository
    pub fn add_style_model(&mut self, model: StyleModel) -> Result<()> {
        let mut repo = self
            .style_models
            .write()
            .expect("lock should not be poisoned");
        repo.add_model(model)
    }

    /// Remove style model from repository
    pub fn remove_style_model(&mut self, model_id: &str) -> Result<()> {
        let mut repo = self
            .style_models
            .write()
            .expect("lock should not be poisoned");
        repo.remove_model(model_id)
    }

    /// Get style transfer metrics
    pub fn metrics(&self) -> &StyleTransferMetrics {
        &self.metrics
    }

    /// Update configuration
    pub fn update_config(&mut self, config: StyleTransferConfig) {
        self.config = config;
    }

    // Private implementation methods

    fn generate_transfer_cache_key(&self, source_audio: &[f32], target_style_id: &str) -> String {
        format!(
            "style_transfer_{}_{}_{}",
            source_audio.len(),
            target_style_id,
            self.config.transfer_method as u8
        )
    }

    fn check_transfer_cache(&self, cache_key: &str) -> Result<Option<CachedStyleTransfer>> {
        let cache = self
            .transfer_cache
            .read()
            .expect("lock should not be poisoned");
        Ok(cache.get(cache_key).cloned())
    }

    fn cache_transfer_result(
        &mut self,
        cache_key: String,
        result: Vec<f32>,
        quality: f32,
        processing_time: Duration,
        target_style_id: String,
    ) -> Result<()> {
        let mut cache = self
            .transfer_cache
            .write()
            .expect("lock should not be poisoned");
        cache.insert(
            cache_key,
            CachedStyleTransfer {
                result,
                quality,
                processing_time,
                timestamp: Instant::now(),
                usage_count: 1,
                metadata: TransferMetadata {
                    source_style_id: "source".to_string(),
                    target_style_id,
                    method: self.config.transfer_method,
                    config_hash: "config_hash".to_string(),
                },
            },
        );
        Ok(())
    }

    fn transfer_via_decomposition(
        &self,
        source_audio: &[f32],
        target_model: &StyleModel,
        sample_rate: u32,
    ) -> Result<Vec<f32>> {
        // Decompose source audio
        let decomposition = self.decomposer.decompose(source_audio, sample_rate)?;

        // Extract target style
        let target_style = self.extract_target_style_from_model(target_model)?;

        // Combine content with target style
        self.style_decoder
            .decode_and_synthesize(&decomposition.content, &target_style, sample_rate)
    }

    fn transfer_via_adversarial(
        &self,
        source_audio: &[f32],
        target_model: &StyleModel,
        sample_rate: u32,
    ) -> Result<Vec<f32>> {
        // Placeholder for adversarial transfer
        Ok(source_audio.to_vec())
    }

    fn transfer_via_cycle_consistent(
        &self,
        source_audio: &[f32],
        target_model: &StyleModel,
        sample_rate: u32,
    ) -> Result<Vec<f32>> {
        // Placeholder for cycle-consistent transfer
        Ok(source_audio.to_vec())
    }

    fn transfer_via_neural(
        &self,
        source_audio: &[f32],
        target_model: &StyleModel,
        sample_rate: u32,
    ) -> Result<Vec<f32>> {
        // Placeholder for neural style transfer
        Ok(source_audio.to_vec())
    }

    fn transfer_via_semantic(
        &self,
        source_audio: &[f32],
        target_model: &StyleModel,
        sample_rate: u32,
    ) -> Result<Vec<f32>> {
        // Placeholder for semantic style transfer
        Ok(source_audio.to_vec())
    }

    fn transfer_via_hierarchical(
        &self,
        source_audio: &[f32],
        target_model: &StyleModel,
        sample_rate: u32,
    ) -> Result<Vec<f32>> {
        // Placeholder for hierarchical transfer
        Ok(source_audio.to_vec())
    }

    fn extract_target_style_from_model(&self, model: &StyleModel) -> Result<StyleRepresentation> {
        // Placeholder implementation
        Ok(StyleRepresentation {
            features: vec![0.0; 256],
            embedding: vec![0.0; 128],
            confidence: 0.8,
        })
    }

    fn update_cache_metrics(&mut self) {
        self.metrics.cache_hit_rate += 1.0;
    }

    fn update_transfer_metrics(
        &mut self,
        processing_time: Duration,
        quality_score: f32,
        success: bool,
    ) {
        if success {
            self.metrics.successful_transfers += 1;
        } else {
            self.metrics.failed_transfers += 1;
        }

        let processing_time_ms = processing_time.as_millis() as f32;
        self.metrics.avg_processing_time =
            (self.metrics.avg_processing_time + processing_time_ms) / 2.0;

        self.metrics.avg_quality_score = (self.metrics.avg_quality_score + quality_score) / 2.0;
    }
}

// Implementation of supporting structures

impl StyleModelRepository {
    fn new() -> Self {
        Self {
            models: HashMap::new(),
            metadata: HashMap::new(),
            performance_metrics: HashMap::new(),
            usage_statistics: HashMap::new(),
            config: RepositoryConfig {
                max_models: 100,
                cache_size_limit: 1024,
                auto_cleanup: true,
                cleanup_threshold: 0.1,
                versioning_enabled: true,
            },
        }
    }

    fn add_model(&mut self, model: StyleModel) -> Result<()> {
        let model_id = model.id.clone();
        self.models.insert(model_id.clone(), model);
        Ok(())
    }

    fn remove_model(&mut self, model_id: &str) -> Result<()> {
        self.models.remove(model_id);
        self.metadata.remove(model_id);
        self.performance_metrics.remove(model_id);
        self.usage_statistics.remove(model_id);
        Ok(())
    }

    fn get_model(&self, model_id: &str) -> Result<&StyleModel> {
        self.models
            .get(model_id)
            .ok_or_else(|| crate::Error::processing(format!("Style model not found: {}", model_id)))
    }
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn test_style_transfer_config_creation() {
        let config = StyleTransferConfig::default();
        assert!(config.enabled);
        assert_eq!(config.content_preservation_weight, 0.7);
        assert_eq!(config.style_transfer_strength, 0.8);
    }

    #[test]
    fn test_style_transfer_system_creation() {
        let config = StyleTransferConfig::default();
        let system = StyleTransferSystem::new(config);
        assert_eq!(system.metrics().successful_transfers, 0);
    }

    #[test]
    fn test_style_characteristics() {
        let characteristics = StyleCharacteristics {
            speaking_style: SpeakingStyleCategory::Conversational,
            emotional_characteristics: EmotionalCharacteristics {
                primary_emotion: EmotionType::Neutral,
                intensity: 0.5,
                stability: 0.8,
                emotional_range: vec![EmotionType::Neutral, EmotionType::Happy],
                transition_patterns: Vec::new(),
            },
            prosodic_characteristics: ProsodicCharacteristics {
                f0_characteristics: F0Characteristics {
                    mean_f0: 150.0,
                    f0_range: (80.0, 300.0),
                    f0_variability: 0.3,
                    contour_patterns: Vec::new(),
                    pitch_accent_patterns: Vec::new(),
                },
                rhythm_characteristics: RhythmCharacteristics {
                    speaking_rate: 4.5,
                    rate_variability: 0.2,
                    pause_patterns: Vec::new(),
                    rhythmic_patterns: Vec::new(),
                    tempo_characteristics: TempoCharacteristics {
                        base_tempo: 120.0,
                        tempo_variations: Vec::new(),
                        acceleration_patterns: Vec::new(),
                        rubato_characteristics: RubatoCharacteristics {
                            strength: 0.5,
                            patterns: Vec::new(),
                            context_sensitivity: 0.7,
                        },
                    },
                },
                stress_characteristics: StressCharacteristics {
                    stress_patterns: Vec::new(),
                    stress_marking: Vec::new(),
                    stress_hierarchy: StressHierarchy {
                        levels: Vec::new(),
                        interaction_patterns: Vec::new(),
                    },
                },
                intonation_patterns: Vec::new(),
            },
            articulation_characteristics: ArticulationCharacteristics {
                consonant_articulation: ConsonantArticulation {
                    place_preferences: HashMap::new(),
                    manner_preferences: HashMap::new(),
                    voicing_characteristics: VoicingCharacteristics {
                        vot_patterns: HashMap::new(),
                        assimilation_patterns: Vec::new(),
                        devoicing_patterns: Vec::new(),
                    },
                    cluster_handling: ConsonantClusterHandling {
                        simplification_patterns: Vec::new(),
                        epenthesis_patterns: Vec::new(),
                        deletion_patterns: Vec::new(),
                    },
                },
                vowel_articulation: VowelArticulation {
                    vowel_space: VowelSpaceCharacteristics {
                        formant_space: HashMap::new(),
                        dispersion: 0.8,
                        centralization_tendency: 0.3,
                        dynamic_range: 0.9,
                    },
                    reduction_patterns: Vec::new(),
                    harmony_patterns: Vec::new(),
                    diphthongization_patterns: Vec::new(),
                },
                coarticulation_patterns: Vec::new(),
                articulatory_precision: ArticulatoryPrecision {
                    overall_precision: 0.8,
                    consonant_precision: 0.85,
                    vowel_precision: 0.75,
                    precision_variability: 0.1,
                    context_effects: Vec::new(),
                },
            },
            voice_quality_characteristics: VoiceQualityCharacteristics {
                phonation_type: PhonationType::Modal,
                breathiness: BreathinessCharacteristics {
                    level: 0.3,
                    variability: 0.1,
                    context_dependencies: Vec::new(),
                    acoustic_correlates: BreathinessAcousticCorrelates {
                        hnr: 15.0,
                        spectral_tilt: -10.0,
                        f1_bandwidth: 80.0,
                        aspiration_noise: 0.2,
                    },
                },
                roughness: RoughnessCharacteristics {
                    level: 0.2,
                    variability: 0.05,
                    roughness_type: RoughnessType::Periodic,
                    acoustic_correlates: RoughnessAcousticCorrelates {
                        jitter: 0.5,
                        shimmer: 3.0,
                        nhr: 0.1,
                        f0_irregularity: 0.02,
                    },
                },
                creakiness: CreakynessCharacteristics {
                    level: 0.1,
                    variability: 0.02,
                    distribution: CreakDistribution {
                        phrase_initial: 0.05,
                        phrase_final: 0.3,
                        stressed_syllable: 0.1,
                        vowel_specific: HashMap::new(),
                    },
                    acoustic_correlates: CreakyAcousticCorrelates {
                        f0_characteristics: CreakyF0Characteristics {
                            mean_f0: 70.0,
                            f0_irregularity: 0.1,
                            subharmonics: 0.2,
                        },
                        spectral_characteristics: CreakySpectralCharacteristics {
                            spectral_tilt: -15.0,
                            high_frequency_energy: 0.3,
                            formant_damping: 1.2,
                        },
                        temporal_characteristics: CreakyTemporalCharacteristics {
                            pulse_irregularity: 0.15,
                            inter_pulse_intervals: vec![10.0, 12.0, 11.5],
                            duration_patterns: vec![50.0, 60.0, 55.0],
                        },
                    },
                },
                tenseness: TensenessCharacteristics {
                    level: 0.4,
                    variability: 0.08,
                    distribution: TensenessDistribution {
                        context_tenseness: HashMap::new(),
                        emotion_tenseness: HashMap::new(),
                        stress_tenseness: HashMap::new(),
                    },
                    acoustic_correlates: TensenessAcousticCorrelates {
                        f0_elevation: 10.0,
                        formant_shifts: HashMap::new(),
                        spectral_energy: 0.7,
                        voice_source: VoiceSourceCharacteristics {
                            open_quotient: 0.6,
                            closing_quotient: 0.3,
                            spectral_tilt: -12.0,
                            flow_derivative: 0.8,
                        },
                    },
                },
                resonance: ResonanceCharacteristics {
                    vocal_tract_length: 17.5,
                    formant_frequencies: HashMap::new(),
                    formant_bandwidths: HashMap::new(),
                    resonance_coupling: ResonanceCoupling {
                        oral_nasal_coupling: 0.2,
                        pharyngeal_coupling: 0.3,
                        coupling_variability: 0.1,
                    },
                    nasality: NasalityCharacteristics {
                        level: 0.15,
                        variability: 0.05,
                        distribution: NasalityDistribution {
                            consonant_nasality: HashMap::new(),
                            vowel_nasality: HashMap::new(),
                            context_effects: Vec::new(),
                        },
                        acoustic_correlates: NasalityAcousticCorrelates {
                            nasal_formants: vec![250.0, 1000.0, 2500.0],
                            anti_formants: vec![500.0, 1500.0],
                            coupling_bandwidth: 100.0,
                            spectral_zeros: vec![800.0, 1200.0],
                        },
                    },
                },
            },
            cultural_characteristics: CulturalCharacteristics {
                regional_features: Vec::new(),
                sociolinguistic_markers: Vec::new(),
                speaking_norms: SpeakingNorms {
                    turn_taking: TurnTakingPatterns {
                        overlap_tolerance: 0.3,
                        pause_expectations: Vec::new(),
                        interruption_patterns: Vec::new(),
                    },
                    politeness_strategies: Vec::new(),
                    discourse_markers: Vec::new(),
                    cultural_taboos: Vec::new(),
                },
                code_switching: CodeSwitchingPatterns {
                    languages: vec!["en".to_string()],
                    triggers: Vec::new(),
                    switching_points: Vec::new(),
                    strategies: Vec::new(),
                },
            },
        };

        assert_eq!(
            characteristics.speaking_style,
            SpeakingStyleCategory::Conversational
        );
        assert_eq!(
            characteristics.emotional_characteristics.primary_emotion,
            EmotionType::Neutral
        );
    }

    #[test]
    fn test_style_model_creation() {
        let model = StyleModel {
            id: "conversational_style".to_string(),
            name: "Conversational Speaking Style".to_string(),
            style_characteristics: StyleCharacteristics {
                speaking_style: SpeakingStyleCategory::Conversational,
                emotional_characteristics: EmotionalCharacteristics {
                    primary_emotion: EmotionType::Neutral,
                    intensity: 0.5,
                    stability: 0.8,
                    emotional_range: vec![EmotionType::Neutral],
                    transition_patterns: Vec::new(),
                },
                prosodic_characteristics: ProsodicCharacteristics {
                    f0_characteristics: F0Characteristics {
                        mean_f0: 150.0,
                        f0_range: (80.0, 300.0),
                        f0_variability: 0.3,
                        contour_patterns: Vec::new(),
                        pitch_accent_patterns: Vec::new(),
                    },
                    rhythm_characteristics: RhythmCharacteristics {
                        speaking_rate: 4.5,
                        rate_variability: 0.2,
                        pause_patterns: Vec::new(),
                        rhythmic_patterns: Vec::new(),
                        tempo_characteristics: TempoCharacteristics {
                            base_tempo: 120.0,
                            tempo_variations: Vec::new(),
                            acceleration_patterns: Vec::new(),
                            rubato_characteristics: RubatoCharacteristics {
                                strength: 0.5,
                                patterns: Vec::new(),
                                context_sensitivity: 0.7,
                            },
                        },
                    },
                    stress_characteristics: StressCharacteristics {
                        stress_patterns: Vec::new(),
                        stress_marking: Vec::new(),
                        stress_hierarchy: StressHierarchy {
                            levels: Vec::new(),
                            interaction_patterns: Vec::new(),
                        },
                    },
                    intonation_patterns: Vec::new(),
                },
                articulation_characteristics: ArticulationCharacteristics {
                    consonant_articulation: ConsonantArticulation {
                        place_preferences: HashMap::new(),
                        manner_preferences: HashMap::new(),
                        voicing_characteristics: VoicingCharacteristics {
                            vot_patterns: HashMap::new(),
                            assimilation_patterns: Vec::new(),
                            devoicing_patterns: Vec::new(),
                        },
                        cluster_handling: ConsonantClusterHandling {
                            simplification_patterns: Vec::new(),
                            epenthesis_patterns: Vec::new(),
                            deletion_patterns: Vec::new(),
                        },
                    },
                    vowel_articulation: VowelArticulation {
                        vowel_space: VowelSpaceCharacteristics {
                            formant_space: HashMap::new(),
                            dispersion: 0.8,
                            centralization_tendency: 0.3,
                            dynamic_range: 0.9,
                        },
                        reduction_patterns: Vec::new(),
                        harmony_patterns: Vec::new(),
                        diphthongization_patterns: Vec::new(),
                    },
                    coarticulation_patterns: Vec::new(),
                    articulatory_precision: ArticulatoryPrecision {
                        overall_precision: 0.8,
                        consonant_precision: 0.85,
                        vowel_precision: 0.75,
                        precision_variability: 0.1,
                        context_effects: Vec::new(),
                    },
                },
                voice_quality_characteristics: VoiceQualityCharacteristics {
                    phonation_type: PhonationType::Modal,
                    breathiness: BreathinessCharacteristics {
                        level: 0.3,
                        variability: 0.1,
                        context_dependencies: Vec::new(),
                        acoustic_correlates: BreathinessAcousticCorrelates {
                            hnr: 15.0,
                            spectral_tilt: -10.0,
                            f1_bandwidth: 80.0,
                            aspiration_noise: 0.2,
                        },
                    },
                    roughness: RoughnessCharacteristics {
                        level: 0.2,
                        variability: 0.05,
                        roughness_type: RoughnessType::Periodic,
                        acoustic_correlates: RoughnessAcousticCorrelates {
                            jitter: 0.5,
                            shimmer: 3.0,
                            nhr: 0.1,
                            f0_irregularity: 0.02,
                        },
                    },
                    creakiness: CreakynessCharacteristics {
                        level: 0.1,
                        variability: 0.02,
                        distribution: CreakDistribution {
                            phrase_initial: 0.05,
                            phrase_final: 0.3,
                            stressed_syllable: 0.1,
                            vowel_specific: HashMap::new(),
                        },
                        acoustic_correlates: CreakyAcousticCorrelates {
                            f0_characteristics: CreakyF0Characteristics {
                                mean_f0: 70.0,
                                f0_irregularity: 0.1,
                                subharmonics: 0.2,
                            },
                            spectral_characteristics: CreakySpectralCharacteristics {
                                spectral_tilt: -15.0,
                                high_frequency_energy: 0.3,
                                formant_damping: 1.2,
                            },
                            temporal_characteristics: CreakyTemporalCharacteristics {
                                pulse_irregularity: 0.15,
                                inter_pulse_intervals: vec![10.0, 12.0, 11.5],
                                duration_patterns: vec![50.0, 60.0, 55.0],
                            },
                        },
                    },
                    tenseness: TensenessCharacteristics {
                        level: 0.4,
                        variability: 0.08,
                        distribution: TensenessDistribution {
                            context_tenseness: HashMap::new(),
                            emotion_tenseness: HashMap::new(),
                            stress_tenseness: HashMap::new(),
                        },
                        acoustic_correlates: TensenessAcousticCorrelates {
                            f0_elevation: 10.0,
                            formant_shifts: HashMap::new(),
                            spectral_energy: 0.7,
                            voice_source: VoiceSourceCharacteristics {
                                open_quotient: 0.6,
                                closing_quotient: 0.3,
                                spectral_tilt: -12.0,
                                flow_derivative: 0.8,
                            },
                        },
                    },
                    resonance: ResonanceCharacteristics {
                        vocal_tract_length: 17.5,
                        formant_frequencies: HashMap::new(),
                        formant_bandwidths: HashMap::new(),
                        resonance_coupling: ResonanceCoupling {
                            oral_nasal_coupling: 0.2,
                            pharyngeal_coupling: 0.3,
                            coupling_variability: 0.1,
                        },
                        nasality: NasalityCharacteristics {
                            level: 0.15,
                            variability: 0.05,
                            distribution: NasalityDistribution {
                                consonant_nasality: HashMap::new(),
                                vowel_nasality: HashMap::new(),
                                context_effects: Vec::new(),
                            },
                            acoustic_correlates: NasalityAcousticCorrelates {
                                nasal_formants: vec![250.0, 1000.0, 2500.0],
                                anti_formants: vec![500.0, 1500.0],
                                coupling_bandwidth: 100.0,
                                spectral_zeros: vec![800.0, 1200.0],
                            },
                        },
                    },
                },
                cultural_characteristics: CulturalCharacteristics {
                    regional_features: Vec::new(),
                    sociolinguistic_markers: Vec::new(),
                    speaking_norms: SpeakingNorms {
                        turn_taking: TurnTakingPatterns {
                            overlap_tolerance: 0.3,
                            pause_expectations: Vec::new(),
                            interruption_patterns: Vec::new(),
                        },
                        politeness_strategies: Vec::new(),
                        discourse_markers: Vec::new(),
                        cultural_taboos: Vec::new(),
                    },
                    code_switching: CodeSwitchingPatterns {
                        languages: vec!["en".to_string()],
                        triggers: Vec::new(),
                        switching_points: Vec::new(),
                        strategies: Vec::new(),
                    },
                },
            },
            parameters: StyleModelParameters {
                encoder_params: EncoderParameters {
                    input_dim: 80,
                    hidden_dims: vec![256, 128],
                    output_dim: 64,
                    layer_types: vec![LayerType::Linear, LayerType::Linear],
                    activations: vec![ActivationType::ReLU, ActivationType::Tanh],
                },
                decoder_params: DecoderParameters {
                    input_dim: 64,
                    hidden_dims: vec![128, 256],
                    output_dim: 80,
                    layer_types: vec![LayerType::Linear, LayerType::Linear],
                    activations: vec![ActivationType::ReLU, ActivationType::Tanh],
                },
                discriminator_params: None,
                architecture: ModelArchitecture {
                    name: "Autoencoder".to_string(),
                    architecture_type: ArchitectureType::Autoencoder,
                    components: Vec::new(),
                    connections: Vec::new(),
                },
            },
            training_info: StyleTrainingInfo {
                dataset_info: DatasetInfo {
                    name: "ConversationalDataset".to_string(),
                    size: 1000,
                    num_speakers: 50,
                    total_duration: 10.0,
                    languages: vec!["en".to_string()],
                    speaking_styles: vec!["conversational".to_string()],
                },
                hyperparameters: TrainingHyperparameters {
                    learning_rate: 0.001,
                    batch_size: 32,
                    num_epochs: 100,
                    optimizer: OptimizerType::Adam,
                    loss_weights: HashMap::new(),
                    regularization: RegularizationParameters {
                        l1_weight: 0.0,
                        l2_weight: 0.01,
                        dropout_rate: 0.1,
                        batch_norm: true,
                        layer_norm: false,
                    },
                },
                training_metrics: TrainingMetrics {
                    loss_history: vec![1.0, 0.8, 0.6, 0.4, 0.2],
                    accuracy_history: vec![0.6, 0.7, 0.8, 0.85, 0.9],
                    time_per_epoch: vec![60.0, 58.0, 56.0, 55.0, 54.0],
                    convergence_info: ConvergenceInfo {
                        converged: true,
                        convergence_epoch: Some(80),
                        criteria: ConvergenceCriteria {
                            loss_tolerance: 0.01,
                            patience: 10,
                            min_improvement: 0.001,
                        },
                    },
                },
                validation_metrics: ValidationMetrics {
                    loss_history: vec![1.1, 0.85, 0.65, 0.45, 0.25],
                    accuracy_history: vec![0.55, 0.65, 0.75, 0.8, 0.85],
                    best_score: 0.85,
                    early_stopping: EarlyStoppingInfo {
                        early_stopped: false,
                        stopping_epoch: None,
                        stopping_reason: None,
                    },
                },
            },
            quality_metrics: StyleModelQualityMetrics {
                overall_quality: 0.85,
                transfer_accuracy: 0.8,
                content_preservation: 0.9,
                style_consistency: 0.85,
                perceptual_scores: PerceptualQualityScores {
                    naturalness: 0.8,
                    style_similarity: 0.85,
                    intelligibility: 0.9,
                    preference: 0.75,
                    confidence_intervals: HashMap::new(),
                },
                objective_metrics: ObjectiveQualityMetrics {
                    mcd: 6.5,
                    f0_rmse: 15.0,
                    voicing_error: 0.05,
                    spectral_distortion: 0.8,
                    prosodic_correlation: 0.7,
                },
            },
            created: Some(Instant::now()),
            last_updated: None,
        };

        assert_eq!(model.id, "conversational_style");
        assert_eq!(model.name, "Conversational Speaking Style");
    }

    #[test]
    fn test_style_model_repository() {
        let mut repo = StyleModelRepository::new();
        assert_eq!(repo.models.len(), 0);

        let model = StyleModel {
            id: "test_style".to_string(),
            name: "Test Style".to_string(),
            style_characteristics: StyleCharacteristics {
                speaking_style: SpeakingStyleCategory::Formal,
                emotional_characteristics: EmotionalCharacteristics {
                    primary_emotion: EmotionType::Neutral,
                    intensity: 0.5,
                    stability: 0.8,
                    emotional_range: vec![EmotionType::Neutral],
                    transition_patterns: Vec::new(),
                },
                prosodic_characteristics: ProsodicCharacteristics {
                    f0_characteristics: F0Characteristics {
                        mean_f0: 120.0,
                        f0_range: (80.0, 250.0),
                        f0_variability: 0.2,
                        contour_patterns: Vec::new(),
                        pitch_accent_patterns: Vec::new(),
                    },
                    rhythm_characteristics: RhythmCharacteristics {
                        speaking_rate: 3.5,
                        rate_variability: 0.1,
                        pause_patterns: Vec::new(),
                        rhythmic_patterns: Vec::new(),
                        tempo_characteristics: TempoCharacteristics {
                            base_tempo: 100.0,
                            tempo_variations: Vec::new(),
                            acceleration_patterns: Vec::new(),
                            rubato_characteristics: RubatoCharacteristics {
                                strength: 0.3,
                                patterns: Vec::new(),
                                context_sensitivity: 0.8,
                            },
                        },
                    },
                    stress_characteristics: StressCharacteristics {
                        stress_patterns: Vec::new(),
                        stress_marking: Vec::new(),
                        stress_hierarchy: StressHierarchy {
                            levels: Vec::new(),
                            interaction_patterns: Vec::new(),
                        },
                    },
                    intonation_patterns: Vec::new(),
                },
                articulation_characteristics: ArticulationCharacteristics {
                    consonant_articulation: ConsonantArticulation {
                        place_preferences: HashMap::new(),
                        manner_preferences: HashMap::new(),
                        voicing_characteristics: VoicingCharacteristics {
                            vot_patterns: HashMap::new(),
                            assimilation_patterns: Vec::new(),
                            devoicing_patterns: Vec::new(),
                        },
                        cluster_handling: ConsonantClusterHandling {
                            simplification_patterns: Vec::new(),
                            epenthesis_patterns: Vec::new(),
                            deletion_patterns: Vec::new(),
                        },
                    },
                    vowel_articulation: VowelArticulation {
                        vowel_space: VowelSpaceCharacteristics {
                            formant_space: HashMap::new(),
                            dispersion: 0.9,
                            centralization_tendency: 0.2,
                            dynamic_range: 0.95,
                        },
                        reduction_patterns: Vec::new(),
                        harmony_patterns: Vec::new(),
                        diphthongization_patterns: Vec::new(),
                    },
                    coarticulation_patterns: Vec::new(),
                    articulatory_precision: ArticulatoryPrecision {
                        overall_precision: 0.9,
                        consonant_precision: 0.92,
                        vowel_precision: 0.88,
                        precision_variability: 0.05,
                        context_effects: Vec::new(),
                    },
                },
                voice_quality_characteristics: VoiceQualityCharacteristics {
                    phonation_type: PhonationType::Modal,
                    breathiness: BreathinessCharacteristics {
                        level: 0.1,
                        variability: 0.05,
                        context_dependencies: Vec::new(),
                        acoustic_correlates: BreathinessAcousticCorrelates {
                            hnr: 20.0,
                            spectral_tilt: -8.0,
                            f1_bandwidth: 60.0,
                            aspiration_noise: 0.1,
                        },
                    },
                    roughness: RoughnessCharacteristics {
                        level: 0.1,
                        variability: 0.02,
                        roughness_type: RoughnessType::Periodic,
                        acoustic_correlates: RoughnessAcousticCorrelates {
                            jitter: 0.3,
                            shimmer: 2.0,
                            nhr: 0.05,
                            f0_irregularity: 0.01,
                        },
                    },
                    creakiness: CreakynessCharacteristics {
                        level: 0.05,
                        variability: 0.01,
                        distribution: CreakDistribution {
                            phrase_initial: 0.02,
                            phrase_final: 0.1,
                            stressed_syllable: 0.05,
                            vowel_specific: HashMap::new(),
                        },
                        acoustic_correlates: CreakyAcousticCorrelates {
                            f0_characteristics: CreakyF0Characteristics {
                                mean_f0: 80.0,
                                f0_irregularity: 0.05,
                                subharmonics: 0.1,
                            },
                            spectral_characteristics: CreakySpectralCharacteristics {
                                spectral_tilt: -12.0,
                                high_frequency_energy: 0.4,
                                formant_damping: 1.0,
                            },
                            temporal_characteristics: CreakyTemporalCharacteristics {
                                pulse_irregularity: 0.08,
                                inter_pulse_intervals: vec![12.0, 13.0, 12.5],
                                duration_patterns: vec![40.0, 45.0, 42.0],
                            },
                        },
                    },
                    tenseness: TensenessCharacteristics {
                        level: 0.6,
                        variability: 0.1,
                        distribution: TensenessDistribution {
                            context_tenseness: HashMap::new(),
                            emotion_tenseness: HashMap::new(),
                            stress_tenseness: HashMap::new(),
                        },
                        acoustic_correlates: TensenessAcousticCorrelates {
                            f0_elevation: 15.0,
                            formant_shifts: HashMap::new(),
                            spectral_energy: 0.8,
                            voice_source: VoiceSourceCharacteristics {
                                open_quotient: 0.5,
                                closing_quotient: 0.4,
                                spectral_tilt: -10.0,
                                flow_derivative: 0.9,
                            },
                        },
                    },
                    resonance: ResonanceCharacteristics {
                        vocal_tract_length: 18.0,
                        formant_frequencies: HashMap::new(),
                        formant_bandwidths: HashMap::new(),
                        resonance_coupling: ResonanceCoupling {
                            oral_nasal_coupling: 0.1,
                            pharyngeal_coupling: 0.2,
                            coupling_variability: 0.05,
                        },
                        nasality: NasalityCharacteristics {
                            level: 0.1,
                            variability: 0.02,
                            distribution: NasalityDistribution {
                                consonant_nasality: HashMap::new(),
                                vowel_nasality: HashMap::new(),
                                context_effects: Vec::new(),
                            },
                            acoustic_correlates: NasalityAcousticCorrelates {
                                nasal_formants: vec![280.0, 1100.0, 2600.0],
                                anti_formants: vec![600.0, 1600.0],
                                coupling_bandwidth: 80.0,
                                spectral_zeros: vec![900.0, 1300.0],
                            },
                        },
                    },
                },
                cultural_characteristics: CulturalCharacteristics {
                    regional_features: Vec::new(),
                    sociolinguistic_markers: Vec::new(),
                    speaking_norms: SpeakingNorms {
                        turn_taking: TurnTakingPatterns {
                            overlap_tolerance: 0.2,
                            pause_expectations: Vec::new(),
                            interruption_patterns: Vec::new(),
                        },
                        politeness_strategies: Vec::new(),
                        discourse_markers: Vec::new(),
                        cultural_taboos: Vec::new(),
                    },
                    code_switching: CodeSwitchingPatterns {
                        languages: vec!["en".to_string()],
                        triggers: Vec::new(),
                        switching_points: Vec::new(),
                        strategies: Vec::new(),
                    },
                },
            },
            parameters: StyleModelParameters {
                encoder_params: EncoderParameters {
                    input_dim: 80,
                    hidden_dims: vec![128, 64],
                    output_dim: 32,
                    layer_types: vec![LayerType::Linear, LayerType::Linear],
                    activations: vec![ActivationType::ReLU, ActivationType::Tanh],
                },
                decoder_params: DecoderParameters {
                    input_dim: 32,
                    hidden_dims: vec![64, 128],
                    output_dim: 80,
                    layer_types: vec![LayerType::Linear, LayerType::Linear],
                    activations: vec![ActivationType::ReLU, ActivationType::Tanh],
                },
                discriminator_params: None,
                architecture: ModelArchitecture {
                    name: "SimpleAutoencoder".to_string(),
                    architecture_type: ArchitectureType::Autoencoder,
                    components: Vec::new(),
                    connections: Vec::new(),
                },
            },
            training_info: StyleTrainingInfo {
                dataset_info: DatasetInfo {
                    name: "FormalDataset".to_string(),
                    size: 500,
                    num_speakers: 25,
                    total_duration: 5.0,
                    languages: vec!["en".to_string()],
                    speaking_styles: vec!["formal".to_string()],
                },
                hyperparameters: TrainingHyperparameters {
                    learning_rate: 0.0001,
                    batch_size: 16,
                    num_epochs: 50,
                    optimizer: OptimizerType::Adam,
                    loss_weights: HashMap::new(),
                    regularization: RegularizationParameters {
                        l1_weight: 0.0,
                        l2_weight: 0.001,
                        dropout_rate: 0.05,
                        batch_norm: true,
                        layer_norm: false,
                    },
                },
                training_metrics: TrainingMetrics {
                    loss_history: vec![0.8, 0.6, 0.4, 0.3, 0.25],
                    accuracy_history: vec![0.7, 0.75, 0.8, 0.85, 0.87],
                    time_per_epoch: vec![30.0, 28.0, 26.0, 25.0, 24.0],
                    convergence_info: ConvergenceInfo {
                        converged: true,
                        convergence_epoch: Some(40),
                        criteria: ConvergenceCriteria {
                            loss_tolerance: 0.005,
                            patience: 5,
                            min_improvement: 0.0005,
                        },
                    },
                },
                validation_metrics: ValidationMetrics {
                    loss_history: vec![0.85, 0.65, 0.45, 0.35, 0.3],
                    accuracy_history: vec![0.65, 0.7, 0.75, 0.8, 0.82],
                    best_score: 0.82,
                    early_stopping: EarlyStoppingInfo {
                        early_stopped: false,
                        stopping_epoch: None,
                        stopping_reason: None,
                    },
                },
            },
            quality_metrics: StyleModelQualityMetrics {
                overall_quality: 0.82,
                transfer_accuracy: 0.8,
                content_preservation: 0.85,
                style_consistency: 0.8,
                perceptual_scores: PerceptualQualityScores {
                    naturalness: 0.75,
                    style_similarity: 0.8,
                    intelligibility: 0.85,
                    preference: 0.7,
                    confidence_intervals: HashMap::new(),
                },
                objective_metrics: ObjectiveQualityMetrics {
                    mcd: 7.0,
                    f0_rmse: 18.0,
                    voicing_error: 0.06,
                    spectral_distortion: 0.9,
                    prosodic_correlation: 0.65,
                },
            },
            created: Some(Instant::now()),
            last_updated: None,
        };

        repo.add_model(model).unwrap();
        assert_eq!(repo.models.len(), 1);

        let retrieved_model = repo.get_model("test_style").unwrap();
        assert_eq!(retrieved_model.name, "Test Style");
    }

    #[test]
    fn test_style_transfer_method_enum() {
        let method = StyleTransferMethod::ContentStyleDecomposition;
        assert_eq!(method, StyleTransferMethod::ContentStyleDecomposition);
        assert_ne!(method, StyleTransferMethod::AdversarialTransfer);
    }

    #[test]
    fn test_emotion_type_enum() {
        let emotion = EmotionType::Happy;
        assert_eq!(emotion, EmotionType::Happy);
        assert_ne!(emotion, EmotionType::Sad);
    }
}