Add full XTTS v2 architecture header with GPT-2 encoder and HiFi-GAN vocoder

cpp/xtts_v2_full.h

@@ -0,0 +1,396 @@
+// 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