cpp/xtts_v2_full.h

// xtts_v2_full.h - Full XTTS v2 GGUF Implementation
#ifndef XTTS_V2_FULL_H
#define XTTS_V2_FULL_H

#include <ggml.h>
#include <ggml-alloc.h>
#include <ggml-backend.h>
#include <cstdint>
#include <string>
#include <vector>
#include <memory>
#include <unordered_map>

namespace xtts_v2 {

// XTTS v2 Architecture Constants
struct XTTSConfig {
    // GPT-2 Text Encoder
    static constexpr int GPT_N_VOCAB = 6681;        // BPE vocab size
    static constexpr int GPT_N_CTX = 402;           // Max context length
    static constexpr int GPT_N_EMBD = 1024;         // Hidden dimension
    static constexpr int GPT_N_HEAD = 16;           // Attention heads
    static constexpr int GPT_N_LAYER = 30;          // Transformer layers
    static constexpr int GPT_INTERMEDIATE = 4096;   // FFN intermediate size

    // Latent Diffusion Decoder
    static constexpr int LATENT_DIM = 1024;         // Latent vector dimension
    static constexpr int MEL_CHANNELS = 80;         // Mel spectrogram bins
    static constexpr int DECODER_LAYERS = 12;        // Decoder depth
    static constexpr int REF_ENCODER_LAYERS = 6;     // Reference encoder layers

    // HiFi-GAN Vocoder
    static constexpr int HIFIGAN_UPSAMPLE_RATES[4] = {8, 8, 2, 2};
    static constexpr int HIFIGAN_KERNEL_SIZES[4] = {16, 16, 4, 4};
    static constexpr int HIFIGAN_CHANNELS = 512;
    static constexpr int HIFIGAN_RESBLOCK_KERNELS[3] = {3, 7, 11};
    static constexpr int HIFIGAN_RESBLOCK_DILATIONS[3][3] = {
        {1, 3, 5}, {1, 3, 5}, {1, 3, 5}
    };

    // Audio settings
    static constexpr int SAMPLE_RATE = 24000;
    static constexpr int HOP_LENGTH = 256;
    static constexpr int WIN_LENGTH = 1024;

    // Languages (17 supported)
    static constexpr int N_LANGUAGES = 17;
    static constexpr int SPEAKER_EMBEDDING_DIM = 512;

    // Conditioning
    static constexpr int COND_LATENT_DIM = 1024;
    static constexpr int MAX_MEL_LENGTH = 605;
    static constexpr int MAX_AUDIO_LENGTH = 155520;  // ~6.5 seconds @ 24kHz
};

// Full XTTS v2 Model Components
struct XTTSv2Model {
    // Text Encoder (GPT-2 style)
    struct TextEncoder {
        ggml_tensor* wte;           // Token embeddings [n_vocab, n_embd]
        ggml_tensor* wpe;           // Position embeddings [n_ctx, n_embd]

        // Per-layer components
        struct Layer {
            // Attention
            ggml_tensor* ln1_g;     // LayerNorm gain
            ggml_tensor* ln1_b;     // LayerNorm bias
            ggml_tensor* attn_qkv;  // Combined QKV projection
            ggml_tensor* attn_proj; // Output projection

            // FFN
            ggml_tensor* ln2_g;     // LayerNorm gain
            ggml_tensor* ln2_b;     // LayerNorm bias
            ggml_tensor* ffn_fc1;   // FFN first layer
            ggml_tensor* ffn_fc2;   // FFN second layer
        };
        std::vector<Layer> layers;

        ggml_tensor* ln_f_g;        // Final LayerNorm gain
        ggml_tensor* ln_f_b;        // Final LayerNorm bias
    } text_encoder;

    // Reference Encoder (for voice cloning)
    struct ReferenceEncoder {
        ggml_tensor* mel_conv1;      // Initial mel convolution

        struct ConvBlock {
            ggml_tensor* conv;
            ggml_tensor* norm_g;
            ggml_tensor* norm_b;
        };
        std::vector<ConvBlock> conv_blocks;

        ggml_tensor* gru_ih;         // GRU input-hidden weights
        ggml_tensor* gru_hh;         // GRU hidden-hidden weights
        ggml_tensor* gru_bias;       // GRU bias

        ggml_tensor* speaker_proj;   // Project to speaker embedding
    } ref_encoder;

    // Latent Diffusion Decoder
    struct LatentDecoder {
        ggml_tensor* latent_proj;    // Project latents to hidden

        struct DecoderLayer {
            // Self-attention
            ggml_tensor* sa_ln_g;
            ggml_tensor* sa_ln_b;
            ggml_tensor* sa_qkv;
            ggml_tensor* sa_proj;

            // Cross-attention (to text)
            ggml_tensor* ca_ln_g;
            ggml_tensor* ca_ln_b;
            ggml_tensor* ca_q;
            ggml_tensor* ca_kv;
            ggml_tensor* ca_proj;

            // FFN
            ggml_tensor* ffn_ln_g;
            ggml_tensor* ffn_ln_b;
            ggml_tensor* ffn_fc1;
            ggml_tensor* ffn_fc2;
        };
        std::vector<DecoderLayer> layers;

        ggml_tensor* mel_head;       // Project to mel spectrogram
        ggml_tensor* stop_head;      // Predict stop token
    } decoder;

    // HiFi-GAN Vocoder
    struct Vocoder {
        ggml_tensor* conv_pre;       // Pre-conv [80, 512, 7]

        struct UpsampleBlock {
            ggml_tensor* conv_transpose;  // Transposed convolution

            struct ResBlock {
                ggml_tensor* conv1;
                ggml_tensor* conv2;
            };
            std::vector<ResBlock> res_blocks;
        };
        std::vector<UpsampleBlock> upsample_blocks;

        ggml_tensor* conv_post;      // Post-conv [512, 1, 7]
    } vocoder;

    // Conditioning layers
    struct Conditioning {
        ggml_tensor* speaker_embedding;  // Speaker lookup table
        ggml_tensor* language_embedding; // Language embeddings
        ggml_tensor* style_embedding;    // Style tokens (optional)
    } conditioning;

    // Model context
    ggml_context* ctx = nullptr;
    ggml_backend_t backend = nullptr;
    ggml_backend_buffer_t buffer = nullptr;
    size_t buffer_size = 0;
};

// KV Cache for autoregressive generation
struct XTTSKVCache {
    // Text encoder cache
    struct {
        ggml_tensor* k[30];  // K cache per layer
        ggml_tensor* v[30];  // V cache per layer
        int n_cached = 0;
    } text_cache;

    // Decoder cache
    struct {
        ggml_tensor* k[12];  // K cache per layer
        ggml_tensor* v[12];  // V cache per layer
        ggml_tensor* cross_k[12];  // Cross-attention K cache
        ggml_tensor* cross_v[12];  // Cross-attention V cache
        int n_cached = 0;
    } decoder_cache;
};

// Main XTTS v2 Inference Engine
class XTTSv2Inference {
public:
    XTTSv2Inference();
    ~XTTSv2Inference();

    // Load model from GGUF file
    bool load_model(const std::string& model_path, bool use_mmap = true);

    // High-level TTS interface
    std::vector<float> synthesize(
        const std::string& text,
        const std::string& language = "en",
        const std::vector<float>& speaker_wav = {},  // Optional reference audio
        float temperature = 0.65f,
        float length_penalty = 1.0f,
        float repetition_penalty = 2.0f,
        float top_k = 50,
        float top_p = 0.85f,
        float speed = 1.0f
    );

    // Component-wise inference (for debugging/testing)
    struct InferenceComponents {
        std::vector<int32_t> tokens;           // BPE tokens
        ggml_tensor* text_embeddings;          // Text encoder output
        ggml_tensor* speaker_embedding;        // Speaker embedding
        ggml_tensor* latents;                  // Decoder latents
        ggml_tensor* mel_spectrogram;         // Generated mel
        std::vector<float> audio;             // Final audio
    };

    InferenceComponents synthesize_components(
        const std::string& text,
        const std::string& language = "en",
        const std::vector<float>& speaker_wav = {}
    );

    // Streaming interface
    class Stream {
    public:
        Stream(XTTSv2Inference* parent, const std::string& text,
               const std::string& language, const std::vector<float>& speaker_wav);
        ~Stream();

        std::vector<float> get_chunk(size_t max_samples = 4096);
        bool is_done() const { return done; }

    private:
        XTTSv2Inference* parent;
        InferenceComponents components;
        size_t mel_offset = 0;
        size_t audio_offset = 0;
        bool done = false;

        void generate_next_mel_chunk();
        std::vector<float> vocoder_chunk(ggml_tensor* mel_chunk);
    };

    std::unique_ptr<Stream> create_stream(
        const std::string& text,
        const std::string& language = "en",
        const std::vector<float>& speaker_wav = {}
    );

private:
    XTTSConfig config;
    XTTSv2Model model;
    XTTSKVCache kv_cache;

    // GGUF file handling
    struct gguf_context* gguf_ctx = nullptr;
    void* mapped_memory = nullptr;
    size_t mapped_size = 0;

    // Computation graph
    ggml_cgraph* gf = nullptr;
    ggml_gallocr* allocr = nullptr;

    // Tokenizer
    std::unordered_map<std::string, int32_t> bpe_vocab;
    std::vector<std::pair<std::string, std::string>> bpe_merges;

    // Internal methods
    bool load_gguf_weights(const std::string& path, bool use_mmap);
    void init_model_architecture();

    // Text processing
    std::vector<int32_t> tokenize(const std::string& text);
    std::vector<std::string> bpe_encode(const std::string& text);

    // Model forward passes
    ggml_tensor* text_encoder_forward(
        const std::vector<int32_t>& tokens,
        const std::string& language
    );

    ggml_tensor* reference_encoder_forward(
        const std::vector<float>& audio_wav
    );

    ggml_tensor* decoder_forward(
        ggml_tensor* text_embeddings,
        ggml_tensor* speaker_embedding,
        float temperature,
        float length_penalty
    );

    std::vector<float> vocoder_forward(
        ggml_tensor* mel_spectrogram
    );

    // Attention mechanisms
    ggml_tensor* multi_head_attention(
        ggml_tensor* q, ggml_tensor* k, ggml_tensor* v,
        int n_heads, bool use_cache = true
    );

    ggml_tensor* cross_attention(
        ggml_tensor* queries,
        ggml_tensor* keys,
        ggml_tensor* values,
        int n_heads
    );

    // Helper functions
    ggml_tensor* layer_norm(ggml_tensor* x, ggml_tensor* g, ggml_tensor* b, float eps = 1e-5f);
    ggml_tensor* gelu(ggml_tensor* x);
    ggml_tensor* conv1d(ggml_tensor* x, ggml_tensor* w, ggml_tensor* b, int stride, int padding);
    ggml_tensor* conv_transpose1d(ggml_tensor* x, ggml_tensor* w, ggml_tensor* b, int stride, int padding);

    // Sampling
    std::vector<int32_t> sample_latents(
        ggml_tensor* logits,
        float temperature,
        float top_k,
        float top_p,
        float repetition_penalty
    );
};

// NEON-optimized kernels for ARM
namespace kernels {
#ifdef __ARM_NEON

void gemm_q4_neon(
    const uint8_t* a_q4,
    const float* b,
    float* c,
    int m, int k, int n,
    const float* scales
);

void conv1d_q8_neon(
    const uint8_t* input_q8,
    const uint8_t* kernel_q8,
    float* output,
    int batch, int in_c, int out_c,
    int length, int kernel_size,
    int stride, int padding,
    const float* input_scale,
    const float* kernel_scale
);

void attention_q4_neon(
    const uint8_t* q_q4,
    const uint8_t* k_q4,
    const uint8_t* v_q4,
    float* output,
    int seq_len, int n_heads, int head_dim,
    const float* q_scale,
    const float* k_scale,
    const float* v_scale
);

#endif // __ARM_NEON
} // namespace kernels

// C API for React Native / FFI
extern "C" {
    void* xtts_v2_init(const char* model_path, bool use_mmap);

    float* xtts_v2_synthesize(
        void* model,
        const char* text,
        const char* language,
        const float* speaker_wav,
        size_t speaker_wav_len,
        float temperature,
        float speed,
        size_t* out_len
    );

    void* xtts_v2_stream_init(
        void* model,
        const char* text,
        const char* language,
        const float* speaker_wav,
        size_t speaker_wav_len
    );

    float* xtts_v2_stream_chunk(
        void* stream,
        size_t chunk_size,
        size_t* out_len
    );

    void xtts_v2_stream_free(void* stream);
    void xtts_v2_free(void* model);
    void xtts_v2_free_audio(float* audio);
}

} // namespace xtts_v2

#endif // XTTS_V2_FULL_H